SlideShare uma empresa Scribd logo
1 de 5
Baixar para ler offline
ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012



  Machine Vision System for Inspecting Flank Wear on
                    Cutting Tools
                                                R. Schmitt1, Y. Cai1 and A. Pavim1,2
         1
             Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany
                                    Email: {R.Schmitt, Y.Cai, A.Pavim}@ wzl.rwth-aachen.de
                                           2
                                             Scholarship holder of the Brazilian CNPq

Abstract—This paper describes the development of a machine            wear. In ISO 3685 the measurement parameters (Fig. 1) for
vision system for automated tool wear inspection. The proposed
                                                                      flank wear are defined [3]. The maximum flank wear value
approach measures the tool wear region based on the active
contour algorithm and classifies the wear type by means of
                                                                      (VBmax) indicates the maximum appeared value for the flank
neural networks. Test results show that prevalent tool wears          wear – the maximum distance between the unworn cutting
can be checked robustly in a real production environment and          edge on the top of the tool to the bottom end of the flank
therefore the manufacturing automation can be improved.               wear land area. The flank wear value (VB) is defined as the
                                                                      average width of the flank wear land area. Additional to this,
Index Terms—industrial image processing, machine vision,              AVB describes the whole area of the flank wear land.
neural networks, tool wear, measurement
                                                                                            III. RELATED WORK
                         I. INTRODUCTION
                                                                          Generally, the tool wear inspection is performed in three
    The current production tendency is the improvement of             different ways [2] [4]. First, a statistical evaluation, based on
both production performance and quality levels in order to            estimated or FEM (Finite Element Method) simulated lifetime
reduce costs and to avoid scrap. In the industrial manufacture        intervals is possible [2]. Second, process signals like the
flexible production systems with high performance and quality         cutting forces or the acoustic emission can be used for a
characteristics are required. Hence, the antiquated quality           wear analysis [2] [5]. These indirect techniques try to evaluate
assurance method by measuring the specification conformity            the tool wear by inspecting the process data, which have a
of a product at the end of the production line is replaced by         tight relationship with the tool wear. Third, a direct
a preventive quality strategy with inline-metrology [1]. Milling      measurement on the cutting edge can be performed by using
and turning are very common processes in industry today.              optical sensors [4] [6]. In [7] a strategy is developed for
Therefore, process monitoring of these machining processes            identifying cutting tool wear by automatically recognizing
has become of crucial importance to optimise production in            wear patterns in the cutting force signal. The strategy uses a
view of quality and costs [1, 2]. Monitoring methods focus            mechanistic model to predict cutting forces under conditions
on the inspection of important process parameters, such as            of tool wear. This model is also extended to account for the
cutting forces, temperature and tool wear. Tool wear is usually       multiple inserts. On the basis of predicted force signals linear
the most relevant parameter inspected, as it has direct               discriminant functions are trained to identify the wear state
influence on the final product quality, the machine tool              of the process. In [8] [9] model-based approaches for tool
performance and the tool lifetime.                                    wear monitoring on the basis of neuro-fuzzy techniques are
                                                                      presented. A model with four inputs (time, cutting forces,
               II. CUTTING TOOLS AND TOOL WEAR                        vibrations and acoustic emissions signals) and one output
                                                                      (tool wear rate) is designed and implemented on the basis of
                                                                      three neuro-fuzzy approaches (inductive, transductive and
                                                                      evolving neuro-fuzzy systems). The tool wear model is used
                                                                      for monitoring only the turning process. For the indirect
                                                                      techniques a precise and also computation-efficient model
                                                                      for predicting tool wear is essential. Machining processes
                                                                      (turning, drilling, milling, and grinding) performed by different
                                                                      machine tools are extremely complex and fraught with
                                                                      uncertainty. Therefore their behavior is practically difficult to
                                                                      describe exactly by modeling tools, even though approaches
                                                                      based on artificial intelligence techniques like fuzzy logic and
             Figure 1. Flank tool wear and its parameters             neural networks are used. The direct methods deal directly
The flank wear is the most referred tool wear parameter in the        with the measurement of the desired variable, thus usually
monitoring of machining processes – it allows to estimate             providing a more precise result of the acquired signal.
the cutting tool’s lifetime and to control the production             Currently, the first two values (VBmax and VB) are manually
process [2]. Fig. 1 shows the characteristics of flank tool           measured with microscope in industry [1] [2]. The top and
© 2012 ACEEE                                                   27
DOI: 01.IJCSI.03.01.13
ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012


bottom references for the flank wear region are determined by                      Z-axis: positioning the camera for tools with different
the worker subjectively. Hence, the measurement of the flank                        heights;
wear requires expert knowledge, is relatively time-consuming                       X-axis: driving the cutting tool head in camera focus
and its result is also strongly user-dependent [6]. Furthermore,                   for tools with different diameters;
it is not possible to quantitatively determine the area of the                     C-axis: rotating the tool for the positioning of all
wear region (AVB) with microscope. In this context, the main                       cutting inserts in the focus of camera.
objective of this research work is to develop a machine vision            After the placing the tool in a HSK 63A receiver in the
system for tool wear inspection on cutting tools. This                    reference position, the tool and camera are positioned
automated system should realize a robust and fast flank wear              adaptively to the tool geometry. During the rotation of C-
measurement next to the production line. Based on the                     axis, each cutting insert is detected automatically and finely
measurement, a reproducible wear type classification should               tuned to a well-focused orientation. After successive image
also be realized.                                                         acquisition of all inserts, the tool is driven back to the
                                                                          reference position.
                IV. MACHINE VISION PROTOTYPE
                                                                                           V. IMAGE PROCESSING CHAIN
   The developed prototype consists of the following hard-
ware modules: illumination unit, camera/optic-system and                     To achieve the goal of an automated measurement of flank
mechanical system.                                                        wear, the machine vision prototype was built with an image
                                                                          processing chain, which is developed to determine the VBmax,
A. Illumination Unit
                                                                          VB and AVB values for different kinds of tools (Fig. 4). The
    Due to the different geometries and surface properties of             basic image processing tasks of the chain are the following:
cutting tools, a flexible illumination unit is required. To record        image acquisition, tool edge detection, highlighting wear
all necessary information for the measurement and                         region, feature extraction, wear type classification and finally
classification task, the illumination concept of the cutting              wear measurement. Each of them consists of a certain number
tool inspection system employs a combination of three                     of image processing steps with special configuration
different lighting types: top light, half-ring light and side             parameters (Fig. 4).
lights (Fig. 2). A dual-image acquisition under different
illumination conditions enables an optimal detection and
measurement of the flank wear.




              Figure 2. Developed illumination unit
   First, a full-illuminated image of the worn cutting insert is             Figure 3. Machine vision prototype for tool wear inspection.
taken for the detection of the tool area in image with all three          A. Tool Edge Detection
lightings. Second, a side-illuminated image is acquired for
                                                                              In order to measure the tool wear, it is only necessary to
the feature extraction of wear region and the final wear
                                                                          proceed tool area within the acquired image. The tool area
measurement solely with side lights (Fig. 2).
                                                                          could be separated from image background by finding the
B. Camera/Optic-System                                                    top and side tool edges in the full-illuminated image (Fig. 4, 2.
   For the camera/optic-system, a monochrome CCD-camera                   column). To detect both edges, two ROIs (regions of interest)
with an effective sensor size of 752x582 pixels is used. In               on each edge are predefined. The locations of the ROIs in the
combination with an optical lens with a fixed focus length of             image are calculated based on tool type information (e.g.
42 mm, a resolution of 4.4 µm is realized.                                radius, length). After applying the Canny edge detector [10]
                                                                          to the image areas defined by ROIs, image pixels on tool
C. Mechanical System                                                      borders are extracted. By fitting the sequence of detected
   For a flexible measurement of the flank wear, three                    points along the tool borders with a line function, the top
motorized axes are required (Fig. 3):                                     and side tool edges are determined.
© 2012 ACEEE                                                         28
DOI: 01.IJCSI.03.01.13
ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012




                                 Figure 4. Image processing chain for the tool wear inspection system.

B. Highlighting Wear Region                                             contour point pi and its two neighboring points pi 1 , pi 1
    First, automatic histogram equalization [11] is performed           must be kept as straight as possible. This leads to a smooth-
on the side-illuminated image, which results in the contrast            ing of the contour.
enhancement of the wear region. A histogram is a graphical              For the binary image, the image energy is set equal to the
diagram that provides information about the frequency in                gradient of the image
which each brightness level appears in the whole image. A
linear transformation in its brightness histogram achieves a                           Eimg   grad (I ) .                          (3)
better distribution of the brightness spectrum of the image,
                                                                        where I is the pixel intensity. For the minimization of this
resulting in the enhancement of the wear area and in the
improvement of the visual information of the tool. Subsequent           energy, the contour must be attracted to such locations in
to image thresholding, morphological filtering and blob                 the image, where the gray value varies strongly, such as
analysis are applied to binarized image and used to eliminate           region edges. For the active contour algorithm, the system
the remaining noises in the image, leaving the wear area (Fig.          energy is defined as weighted sum of above introduced
4, 3. column).                                                          energies

C. Feature Extraction                                                             E    Econt    Ecurv    Eimg ,             (4)
    Depending on cutting conditions and duration, the tool              where  ,  and  are weights for the individual energies.
wear could appear in different forms. In order to achieve a             With optimally adjusted weighting, the active contour
reliable tool wear inspection, the active contour algorithm [12]        algorithm tracks the edge of tool wear region dynamically
is used to extract the wear region, which detects the edges of          and adaptively, considering its shape should be as regular
regions with inconsistent shapes robustly. By iteratively               and smooth as possible. A convex polygon, surrounding the
minimizing system energy, this algorithm converges a chain              wear region, is used as the initial estimation to start searching
to surround the whole tool wear region as closely as possible.          the wear contour by means of the active contour algorithm.
Similar to [12], the following three part energy functions are          At the end of the iteration, it provides the best set of points
used. The continuity energy is defined as                               that represents the tool wear area contour (Fig. 4, 4. column).
           Econt    pi  pi 1 ,                         (1)         D. Wear Type Classification
where   is the mean distance between two points on the                     On the base of the extracted outer contour of the wear
                                                                        region, the classification of tool wear type is performed. A
con-tour and pi is the contour point. By a minimized energy             neural network based method is developed because of its
function (1) equidistance between the contour points should             ability to solve and generalize non-linear classification
be obtained.                                                            problems [13]. This work currently focuses on the two most
The curvature energy is modeled by                                      important cases: flank wear and tool breakage (Fig. 4, 5.
                                         2                              column). To build a distinctive description of the tool wear,
          Ecurv  pi 1  2  pi  pi 1 .                  (2)         the following features are extracted from the segmented wear
To minimize the curvature energy in (2), the angle between a            region, which are tested as inputs for the neural network.
© 2012 ACEEE                                                       29
DOI: 01.IJCSI.03.01.13
ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012


image statistics: average, maximum, minimum, standard                 checkerboard squares, the calibration factor of the designed
deviation                                                               prototype results in a value of 4.44 µm for each pixel in the
 surface texture: average variance of gray values of the              image.
segmented area, which analyzes image textures of wear region
Canny analysis: Canny filtering, which characterizes the                                VI. EXPERIMENTAL RESULTS
high-frequency image details, such as edges and wear
                                                                            The measurement method is evaluated on real cutting
textures
                                                                        tools, which have different geometries from different
 histogram: which describes the brightness of the wear
                                                                        manufacturers. An example test set is given in Fig. 6. In order
region
                                                                        to evaluate the system, a set of five worn cutting inserts is
Fourier coefficients: the normalized 10 lowest coefficients
                                                                        used. For each cutting insert 10 test images are acquired.
without the constant component calculated from the outer
                                                                        With this insert set a test for the tool wear measurement can
contour, which give a translation and rotation decorrelated
                                                                        be estimated from a sample of 5x10=50 test images of real
description of the contour of wear region
                                                                        cutting tools. In Fig. 7 the results of the example test set of
    To find a good input-output function for the neural net-
                                                                        cutting tools are presented. They are validated against the
work, different feed-forward network topologies are tested.
                                                                        manual measurement with microscope, which is performed
The best structure uses neurons with sigmoid output
                                                                        by tool specialists and used as reference. This comparison
response [13], 14 inputs combined from Canny analysis and
                                                                        shows that similar measurement accuracy is achieved by the
image statistics, a hidden layer with 10 neurons and an out-
                                                                        auto-mated machine vision system as expert measurement.
put layer with 2 neurons, each one for a specific wear type.
                                                                        Our analysis indicates that the deviation is caused by the
                                                                        dirt on the cutting. Based on a validation of inspection
                                                                        equipment applicability of this machine vision system
                                                                        according to the guideline QS 9000 (measurement system
                                                                        analysis, MSA) [15], the repeatability of the automated tool
                                                                        wear measurement is determined to 7.5 µm. Compared to the
                                                                        uncertainty of wear measurement with microscope (39.5 µm),
                                                                        the developed machine vision system can be classified after
                                                                        MSA as suitable for the inspection task.
         Figure 5. Results of tool wear type classification




                   Figure 6. Test cutting tools
This network achieves an accuracy rate of 96% in classifying
a set of 25 worn tools (Fig. 5). A back-propagation algorithm
is used to train 15 sample pairs. The validation of the results
is confirmed with a microscope, based on expert knowledge.
E. Tool Wear Measurement
                                                                         Figure 7. Comparison of test results achieved by microscope and
    After flank wear is detected, the measurement of its                             the developed machine vision system
parameters is performed. According the ISO 3685 [3], the flank
wear parameters VB and VBmax are evaluated as the                                                CONCLUSIONS
perpendicular distances from the lower contour points to the
top cutting edge. As introduced in section 1, AVB is the sum                This paper describes the development of a machine vision
of all pixels in the wear area (Fig. 4, 6. column). These values        system for an automated tool wear inspection. The hardware
are firstly calculated in number of pixels. To represent this           requirements and software solutions are characterized. The
parameter in the standard measurement unit, a calibration               experiments on real cutting tools show that the proposed
method based on [14] is applied to the image processing                 prototype can achieve both accuracy and robustness for tool
system. Using a checkerboard pattern, the transformation                wear measurement and wear type classification.
for correcting perspective is computed and radial lens
distortions are compensated. With the accurate size of the

© 2012 ACEEE                                                       30
DOI: 01.IJCSI.03.01.13
ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012


                           REFERENCES                                       [8] A. Gajate, R. e: Haber, J. R. Alique and P. I. Vega, “Weighted
                                                                            Neuro-Fuzzy Inference System for Tool Wear Prediction in a
[1] T. Pfeifer, Production metrology, Oldenbourg, 2002.                     Turning Process”, Proceedings of the 4th International Conference
[2] F. Klocke and A. Kuchle, Manufacturing Processes 1: Cutting:            on Hybrid Artificial Intelligence Systems, pp. 113-120, 2009.
Lathing, Milling, Drilling, Springer, 2011.                                 [9] A. Gajate, R. Haber, R. del Toro, P. Vega and A. Bustillo,
[3] ISO 3685, “Tool-life testing with single-point turning tools”,          “Tool wear monitoring using neuro-fuzzy techniques: a comparative
1993.                                                                       study in a turning process”, Journal of Intelligent Manufacturing (2
[4] T. Pfeifer, D. Sack, A. Orth, M. R. Stemmer, M. L. Roloff,              August 2010), pp. 1-14, 2010.
“Measuring flank tool wear on cutting tools with machine vision –           [10] B. Jaehne, Digital image processing, Springer, 2002.
a case solution”, Proceedings of IEEE conference on mechatronics            [11] A. Hornberg, Handbook of machine vision, Wiley-VCH, 2006.
and machine vision in practice, pp. 169-175, 2002.                          [12] M. Kass, A. Witkin, D. Terzopoulos, “Snakes: Active Contour
[5] S. Kurada and C. Bradley, “A review of machine vision sensors           Models”, International Journal of Computer Vision, Vol. 1, No. 4,
for tool condition monitoring”, Computers in Industry, vol. 34, pp.         pp. 321-331, 1988.
55–72, 1997.                                                                [13] E. Davies, Machine vision - theory algorithms practicalities,
[6] M. Lanzetta, “A new flexible high-resolution sensor for tool            Elsevier, 2005.
condition monitoring”, Journal of Materials Processing Technology,          [14] Z. Zhang, “Flexible Camera Calibration by Viewing a Plane
vol. 119, pp. 73–82, 2001.                                                  From Unknown Orientations”, Proceedings of International
[7] D. J. Waldorf, S. G. Kapoor, R. E. DeVor, “Automatic                    Conference on Computer Vision, pp. 666-673, 1999.
recognition of tool wear on a face mill using a mechanistic modeling        [15] N.N., “measurement system analysis, (MSA) QS 9000 /
approach”, International Journal of Wear, Vo. 157, Iss. 2, pp. 305-         A.I.A.G.- Chrysler Corp., Ford Motor Co., General Motors Corp”,
323, 1992.                                                                  3. Edition, Grays/Essex, 2002.




© 2012 ACEEE                                                           31
DOI: 01.IJCSI.03.01.13

Mais conteúdo relacionado

Mais procurados

Intro to CAD/CAM/CIM
Intro to CAD/CAM/CIMIntro to CAD/CAM/CIM
Intro to CAD/CAM/CIMAbhay Gore
 
8 automated assembly-systems
8 automated assembly-systems8 automated assembly-systems
8 automated assembly-systemslizard_mn
 
Surface finish Metrology
Surface finish MetrologySurface finish Metrology
Surface finish MetrologyUDITMODI5
 
Unit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURING
Unit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURINGUnit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURING
Unit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURINGMohanumar S
 
Advances in cutting tool technology
Advances in cutting tool technologyAdvances in cutting tool technology
Advances in cutting tool technologyNandu Sonmankar
 
LASER BEAM MACHINING - NON TRADITIONAL MACHINING
LASER BEAM MACHINING - NON TRADITIONAL MACHININGLASER BEAM MACHINING - NON TRADITIONAL MACHINING
LASER BEAM MACHINING - NON TRADITIONAL MACHININGSajal Tiwari
 
Additive Manufacturing
Additive ManufacturingAdditive Manufacturing
Additive ManufacturingamarRVIT
 
Cold isostatic pressing
Cold isostatic pressingCold isostatic pressing
Cold isostatic pressingLahiru Dilshan
 
Design for Additive Manufacturing Essentials
Design for Additive Manufacturing EssentialsDesign for Additive Manufacturing Essentials
Design for Additive Manufacturing EssentialsRising Media, Inc.
 
Reverse engineering & its application
Reverse engineering & its applicationReverse engineering & its application
Reverse engineering & its applicationmapqrs
 
COMPUTER AIDED PROCESS PLANNING (CAPP)
COMPUTER AIDED PROCESS PLANNING (CAPP)COMPUTER AIDED PROCESS PLANNING (CAPP)
COMPUTER AIDED PROCESS PLANNING (CAPP)KRUNAL RAVAL
 
Cad lecture-5
Cad lecture-5Cad lecture-5
Cad lecture-527273737
 
Unit 3-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURING
Unit 3-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURINGUnit 3-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURING
Unit 3-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURINGMohanumar S
 
Chapter 7 measurement of surface finish
Chapter 7 measurement of surface finishChapter 7 measurement of surface finish
Chapter 7 measurement of surface finishVISHALM580
 
Product Development & Design for Additive Manufacturing (DfAM)
Product Development & Design for Additive Manufacturing (DfAM)Product Development & Design for Additive Manufacturing (DfAM)
Product Development & Design for Additive Manufacturing (DfAM)Katie Marzocchi
 
CELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & M
CELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & MCELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & M
CELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & MBalamurugan Subburaj
 

Mais procurados (20)

Form measurement
Form measurementForm measurement
Form measurement
 
Intro to CAD/CAM/CIM
Intro to CAD/CAM/CIMIntro to CAD/CAM/CIM
Intro to CAD/CAM/CIM
 
8 automated assembly-systems
8 automated assembly-systems8 automated assembly-systems
8 automated assembly-systems
 
Surface finish Metrology
Surface finish MetrologySurface finish Metrology
Surface finish Metrology
 
Unit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURING
Unit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURINGUnit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURING
Unit 5 2nd-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURING
 
Advances in cutting tool technology
Advances in cutting tool technologyAdvances in cutting tool technology
Advances in cutting tool technology
 
LASER BEAM MACHINING - NON TRADITIONAL MACHINING
LASER BEAM MACHINING - NON TRADITIONAL MACHININGLASER BEAM MACHINING - NON TRADITIONAL MACHINING
LASER BEAM MACHINING - NON TRADITIONAL MACHINING
 
Powder metallurgy
Powder metallurgyPowder metallurgy
Powder metallurgy
 
Additive Manufacturing
Additive ManufacturingAdditive Manufacturing
Additive Manufacturing
 
Laser beam welding
Laser beam weldingLaser beam welding
Laser beam welding
 
Cold isostatic pressing
Cold isostatic pressingCold isostatic pressing
Cold isostatic pressing
 
Me6703 cim systems
Me6703 cim systemsMe6703 cim systems
Me6703 cim systems
 
Design for Additive Manufacturing Essentials
Design for Additive Manufacturing EssentialsDesign for Additive Manufacturing Essentials
Design for Additive Manufacturing Essentials
 
Reverse engineering & its application
Reverse engineering & its applicationReverse engineering & its application
Reverse engineering & its application
 
COMPUTER AIDED PROCESS PLANNING (CAPP)
COMPUTER AIDED PROCESS PLANNING (CAPP)COMPUTER AIDED PROCESS PLANNING (CAPP)
COMPUTER AIDED PROCESS PLANNING (CAPP)
 
Cad lecture-5
Cad lecture-5Cad lecture-5
Cad lecture-5
 
Unit 3-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURING
Unit 3-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURINGUnit 3-ME8691 & COMPUTER AIDED DESIGN AND    MANUFACTURING
Unit 3-ME8691 & COMPUTER AIDED DESIGN AND MANUFACTURING
 
Chapter 7 measurement of surface finish
Chapter 7 measurement of surface finishChapter 7 measurement of surface finish
Chapter 7 measurement of surface finish
 
Product Development & Design for Additive Manufacturing (DfAM)
Product Development & Design for Additive Manufacturing (DfAM)Product Development & Design for Additive Manufacturing (DfAM)
Product Development & Design for Additive Manufacturing (DfAM)
 
CELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & M
CELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & MCELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & M
CELLULAR MANUFACTURING & FLEXIBLE MANUFACTURING SYSTEM - UNIT 5 - CAD & M
 

Semelhante a Machine Vision System for Inspecting Flank Wear on Cutting Tools

PC-based Vision System for Operating Parameter Identification on a CNC Machine
PC-based Vision System for Operating Parameter Identification on a CNC MachinePC-based Vision System for Operating Parameter Identification on a CNC Machine
PC-based Vision System for Operating Parameter Identification on a CNC MachineIDES Editor
 
VISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEM
VISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEMVISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEM
VISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEMijmech
 
COMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptx
COMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptxCOMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptx
COMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptxDr.M BALA THEJA
 
ANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUE
ANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUEANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUE
ANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUEIRJET Journal
 
Inspection Principles and practices, Inspection technologies.pptx
Inspection Principles and practices, Inspection technologies.pptxInspection Principles and practices, Inspection technologies.pptx
Inspection Principles and practices, Inspection technologies.pptxSonuSteephen
 
IRJET- Advanced Control Strategies for Mold Level Process
IRJET- Advanced Control Strategies for Mold Level ProcessIRJET- Advanced Control Strategies for Mold Level Process
IRJET- Advanced Control Strategies for Mold Level ProcessIRJET Journal
 
Monitor and Quality Control for Automatic Production Line System
Monitor and Quality Control for Automatic Production Line SystemMonitor and Quality Control for Automatic Production Line System
Monitor and Quality Control for Automatic Production Line SystemIRJET Journal
 
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...Innerspec Technologies
 
A condition monitoring system
A condition monitoring systemA condition monitoring system
A condition monitoring systemprj_publication
 
I0333043049
I0333043049I0333043049
I0333043049theijes
 
Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...
Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...
Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...theijes
 
A Fusion of Statistical Distance and Signature Length Based Approach for Offl...
A Fusion of Statistical Distance and Signature Length Based Approach for Offl...A Fusion of Statistical Distance and Signature Length Based Approach for Offl...
A Fusion of Statistical Distance and Signature Length Based Approach for Offl...IRJET Journal
 
IRJET- Border Security using Computer Vision
IRJET- Border Security using Computer VisionIRJET- Border Security using Computer Vision
IRJET- Border Security using Computer VisionIRJET Journal
 
Crack Detection using Deep Learning
Crack Detection using Deep LearningCrack Detection using Deep Learning
Crack Detection using Deep LearningIRJET Journal
 
APPLICATION OF MECHATRONICS IN CMM
APPLICATION OF MECHATRONICS IN CMMAPPLICATION OF MECHATRONICS IN CMM
APPLICATION OF MECHATRONICS IN CMMSajid Sheikh
 
Quality Improvement and Automation of a Flywheel Engraving Machine
Quality Improvement and Automation of  a Flywheel Engraving MachineQuality Improvement and Automation of  a Flywheel Engraving Machine
Quality Improvement and Automation of a Flywheel Engraving MachineIRJET Journal
 
Effect Of Frequency On Cantilever Beam with Different Locations of Smart Patch
Effect Of Frequency On Cantilever Beam with Different Locations of Smart PatchEffect Of Frequency On Cantilever Beam with Different Locations of Smart Patch
Effect Of Frequency On Cantilever Beam with Different Locations of Smart PatchIRJET Journal
 

Semelhante a Machine Vision System for Inspecting Flank Wear on Cutting Tools (20)

30320140501003 2
30320140501003 230320140501003 2
30320140501003 2
 
PC-based Vision System for Operating Parameter Identification on a CNC Machine
PC-based Vision System for Operating Parameter Identification on a CNC MachinePC-based Vision System for Operating Parameter Identification on a CNC Machine
PC-based Vision System for Operating Parameter Identification on a CNC Machine
 
VISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEM
VISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEMVISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEM
VISION ALGORITHM FOR SEAM TRACKING IN AUTOMATIC WELDING SYSTEM
 
COMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptx
COMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptxCOMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptx
COMPUTER AIDED QUALITY CONTROL AND HANDLING SYSTEMS.pptx
 
ANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUE
ANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUEANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUE
ANALYSIS OF TOOL WEAR IN MILLING USING MACHINE LEARNING TECHNIQUE
 
Inspection Principles and practices, Inspection technologies.pptx
Inspection Principles and practices, Inspection technologies.pptxInspection Principles and practices, Inspection technologies.pptx
Inspection Principles and practices, Inspection technologies.pptx
 
IRJET- Advanced Control Strategies for Mold Level Process
IRJET- Advanced Control Strategies for Mold Level ProcessIRJET- Advanced Control Strategies for Mold Level Process
IRJET- Advanced Control Strategies for Mold Level Process
 
Monitor and Quality Control for Automatic Production Line System
Monitor and Quality Control for Automatic Production Line SystemMonitor and Quality Control for Automatic Production Line System
Monitor and Quality Control for Automatic Production Line System
 
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...
Camera Encoded Phased Array for Semi-Automated Inspection of Complex Composit...
 
A condition monitoring system
A condition monitoring systemA condition monitoring system
A condition monitoring system
 
I0333043049
I0333043049I0333043049
I0333043049
 
Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...
Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...
Disturbance Observer And Optimal Fuzzy Controllers Used In Controlling Force ...
 
A Fusion of Statistical Distance and Signature Length Based Approach for Offl...
A Fusion of Statistical Distance and Signature Length Based Approach for Offl...A Fusion of Statistical Distance and Signature Length Based Approach for Offl...
A Fusion of Statistical Distance and Signature Length Based Approach for Offl...
 
IRJET- Border Security using Computer Vision
IRJET- Border Security using Computer VisionIRJET- Border Security using Computer Vision
IRJET- Border Security using Computer Vision
 
Crack Detection using Deep Learning
Crack Detection using Deep LearningCrack Detection using Deep Learning
Crack Detection using Deep Learning
 
REVERSE ENGINEERING
REVERSE ENGINEERING REVERSE ENGINEERING
REVERSE ENGINEERING
 
T03301030107
T03301030107T03301030107
T03301030107
 
APPLICATION OF MECHATRONICS IN CMM
APPLICATION OF MECHATRONICS IN CMMAPPLICATION OF MECHATRONICS IN CMM
APPLICATION OF MECHATRONICS IN CMM
 
Quality Improvement and Automation of a Flywheel Engraving Machine
Quality Improvement and Automation of  a Flywheel Engraving MachineQuality Improvement and Automation of  a Flywheel Engraving Machine
Quality Improvement and Automation of a Flywheel Engraving Machine
 
Effect Of Frequency On Cantilever Beam with Different Locations of Smart Patch
Effect Of Frequency On Cantilever Beam with Different Locations of Smart PatchEffect Of Frequency On Cantilever Beam with Different Locations of Smart Patch
Effect Of Frequency On Cantilever Beam with Different Locations of Smart Patch
 

Mais de IDES Editor

Power System State Estimation - A Review
Power System State Estimation - A ReviewPower System State Estimation - A Review
Power System State Estimation - A ReviewIDES Editor
 
Artificial Intelligence Technique based Reactive Power Planning Incorporating...
Artificial Intelligence Technique based Reactive Power Planning Incorporating...Artificial Intelligence Technique based Reactive Power Planning Incorporating...
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
 
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
 
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
 
Line Losses in the 14-Bus Power System Network using UPFC
Line Losses in the 14-Bus Power System Network using UPFCLine Losses in the 14-Bus Power System Network using UPFC
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
 
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
 
Assessing Uncertainty of Pushover Analysis to Geometric Modeling
Assessing Uncertainty of Pushover Analysis to Geometric ModelingAssessing Uncertainty of Pushover Analysis to Geometric Modeling
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
 
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
 
Selfish Node Isolation & Incentivation using Progressive Thresholds
Selfish Node Isolation & Incentivation using Progressive ThresholdsSelfish Node Isolation & Incentivation using Progressive Thresholds
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
 
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
 
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
 
Cloud Security and Data Integrity with Client Accountability Framework
Cloud Security and Data Integrity with Client Accountability FrameworkCloud Security and Data Integrity with Client Accountability Framework
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
 
Genetic Algorithm based Layered Detection and Defense of HTTP Botnet
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetGenetic Algorithm based Layered Detection and Defense of HTTP Botnet
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
 
Enhancing Data Storage Security in Cloud Computing Through Steganography
Enhancing Data Storage Security in Cloud Computing Through SteganographyEnhancing Data Storage Security in Cloud Computing Through Steganography
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
 
Low Energy Routing for WSN’s
Low Energy Routing for WSN’sLow Energy Routing for WSN’s
Low Energy Routing for WSN’sIDES Editor
 
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
 
Rotman Lens Performance Analysis
Rotman Lens Performance AnalysisRotman Lens Performance Analysis
Rotman Lens Performance AnalysisIDES Editor
 
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral Images
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesBand Clustering for the Lossless Compression of AVIRIS Hyperspectral Images
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
 
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
 
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...Texture Unit based Monocular Real-world Scene Classification using SOM and KN...
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
 

Mais de IDES Editor (20)

Power System State Estimation - A Review
Power System State Estimation - A ReviewPower System State Estimation - A Review
Power System State Estimation - A Review
 
Artificial Intelligence Technique based Reactive Power Planning Incorporating...
Artificial Intelligence Technique based Reactive Power Planning Incorporating...Artificial Intelligence Technique based Reactive Power Planning Incorporating...
Artificial Intelligence Technique based Reactive Power Planning Incorporating...
 
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...
 
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...
 
Line Losses in the 14-Bus Power System Network using UPFC
Line Losses in the 14-Bus Power System Network using UPFCLine Losses in the 14-Bus Power System Network using UPFC
Line Losses in the 14-Bus Power System Network using UPFC
 
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...
 
Assessing Uncertainty of Pushover Analysis to Geometric Modeling
Assessing Uncertainty of Pushover Analysis to Geometric ModelingAssessing Uncertainty of Pushover Analysis to Geometric Modeling
Assessing Uncertainty of Pushover Analysis to Geometric Modeling
 
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...
 
Selfish Node Isolation & Incentivation using Progressive Thresholds
Selfish Node Isolation & Incentivation using Progressive ThresholdsSelfish Node Isolation & Incentivation using Progressive Thresholds
Selfish Node Isolation & Incentivation using Progressive Thresholds
 
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...
 
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...
 
Cloud Security and Data Integrity with Client Accountability Framework
Cloud Security and Data Integrity with Client Accountability FrameworkCloud Security and Data Integrity with Client Accountability Framework
Cloud Security and Data Integrity with Client Accountability Framework
 
Genetic Algorithm based Layered Detection and Defense of HTTP Botnet
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetGenetic Algorithm based Layered Detection and Defense of HTTP Botnet
Genetic Algorithm based Layered Detection and Defense of HTTP Botnet
 
Enhancing Data Storage Security in Cloud Computing Through Steganography
Enhancing Data Storage Security in Cloud Computing Through SteganographyEnhancing Data Storage Security in Cloud Computing Through Steganography
Enhancing Data Storage Security in Cloud Computing Through Steganography
 
Low Energy Routing for WSN’s
Low Energy Routing for WSN’sLow Energy Routing for WSN’s
Low Energy Routing for WSN’s
 
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...
 
Rotman Lens Performance Analysis
Rotman Lens Performance AnalysisRotman Lens Performance Analysis
Rotman Lens Performance Analysis
 
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral Images
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesBand Clustering for the Lossless Compression of AVIRIS Hyperspectral Images
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral Images
 
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...
 
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...Texture Unit based Monocular Real-world Scene Classification using SOM and KN...
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...
 

Último

Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...
Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...
Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...Precisely
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
What Developers Need to Unlearn for High Performance NoSQL
What Developers Need to Unlearn for High Performance NoSQLWhat Developers Need to Unlearn for High Performance NoSQL
What Developers Need to Unlearn for High Performance NoSQLScyllaDB
 
AI-based audio transcription solutions (IDP)
AI-based audio transcription solutions (IDP)AI-based audio transcription solutions (IDP)
AI-based audio transcription solutions (IDP)KapilVaidya4
 
HHUG-03-2024-Impactful-Reporting-in-HubSpot.pptx
HHUG-03-2024-Impactful-Reporting-in-HubSpot.pptxHHUG-03-2024-Impactful-Reporting-in-HubSpot.pptx
HHUG-03-2024-Impactful-Reporting-in-HubSpot.pptxHampshireHUG
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
Deliver Latency Free Customer Experience
Deliver Latency Free Customer ExperienceDeliver Latency Free Customer Experience
Deliver Latency Free Customer ExperienceOpsTree solutions
 
Does AI(Artificial intelligence) need a Working Memory??
Does AI(Artificial intelligence) need a Working Memory??Does AI(Artificial intelligence) need a Working Memory??
Does AI(Artificial intelligence) need a Working Memory??N.K KooZN
 
Checklist to troubleshoot CD moisture profiles.docx
Checklist to troubleshoot CD moisture profiles.docxChecklist to troubleshoot CD moisture profiles.docx
Checklist to troubleshoot CD moisture profiles.docxNoman khan
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
IEEE Computer Society 2024 Technology Predictions Update
IEEE Computer Society 2024 Technology Predictions UpdateIEEE Computer Society 2024 Technology Predictions Update
IEEE Computer Society 2024 Technology Predictions UpdateHironori Washizaki
 
Reference Domain Ontologies and Large Medical Language Models.pptx
Reference Domain Ontologies and Large Medical Language Models.pptxReference Domain Ontologies and Large Medical Language Models.pptx
Reference Domain Ontologies and Large Medical Language Models.pptxChimezie Ogbuji
 
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServicePicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServiceRenan Moreira de Oliveira
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 
Tracking license compliance made easy - intro to Grant (OSS)
Tracking license compliance made easy - intro to Grant (OSS)Tracking license compliance made easy - intro to Grant (OSS)
Tracking license compliance made easy - intro to Grant (OSS)Anchore
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxYounusS2
 
Unleashing the power of AI in UiPath Studio with UiPath Autopilot.
Unleashing the power of AI in UiPath Studio with UiPath Autopilot.Unleashing the power of AI in UiPath Studio with UiPath Autopilot.
Unleashing the power of AI in UiPath Studio with UiPath Autopilot.DianaGray10
 
Retrofitting for the Built Environment - IES
Retrofitting for the Built Environment - IESRetrofitting for the Built Environment - IES
Retrofitting for the Built Environment - IESIES VE
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 

Último (20)

Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...
Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...
Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
What Developers Need to Unlearn for High Performance NoSQL
What Developers Need to Unlearn for High Performance NoSQLWhat Developers Need to Unlearn for High Performance NoSQL
What Developers Need to Unlearn for High Performance NoSQL
 
AI-based audio transcription solutions (IDP)
AI-based audio transcription solutions (IDP)AI-based audio transcription solutions (IDP)
AI-based audio transcription solutions (IDP)
 
HHUG-03-2024-Impactful-Reporting-in-HubSpot.pptx
HHUG-03-2024-Impactful-Reporting-in-HubSpot.pptxHHUG-03-2024-Impactful-Reporting-in-HubSpot.pptx
HHUG-03-2024-Impactful-Reporting-in-HubSpot.pptx
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
Deliver Latency Free Customer Experience
Deliver Latency Free Customer ExperienceDeliver Latency Free Customer Experience
Deliver Latency Free Customer Experience
 
Does AI(Artificial intelligence) need a Working Memory??
Does AI(Artificial intelligence) need a Working Memory??Does AI(Artificial intelligence) need a Working Memory??
Does AI(Artificial intelligence) need a Working Memory??
 
Checklist to troubleshoot CD moisture profiles.docx
Checklist to troubleshoot CD moisture profiles.docxChecklist to troubleshoot CD moisture profiles.docx
Checklist to troubleshoot CD moisture profiles.docx
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
IEEE Computer Society 2024 Technology Predictions Update
IEEE Computer Society 2024 Technology Predictions UpdateIEEE Computer Society 2024 Technology Predictions Update
IEEE Computer Society 2024 Technology Predictions Update
 
Reference Domain Ontologies and Large Medical Language Models.pptx
Reference Domain Ontologies and Large Medical Language Models.pptxReference Domain Ontologies and Large Medical Language Models.pptx
Reference Domain Ontologies and Large Medical Language Models.pptx
 
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServicePicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 
Tracking license compliance made easy - intro to Grant (OSS)
Tracking license compliance made easy - intro to Grant (OSS)Tracking license compliance made easy - intro to Grant (OSS)
Tracking license compliance made easy - intro to Grant (OSS)
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptx
 
Unleashing the power of AI in UiPath Studio with UiPath Autopilot.
Unleashing the power of AI in UiPath Studio with UiPath Autopilot.Unleashing the power of AI in UiPath Studio with UiPath Autopilot.
Unleashing the power of AI in UiPath Studio with UiPath Autopilot.
 
Retrofitting for the Built Environment - IES
Retrofitting for the Built Environment - IESRetrofitting for the Built Environment - IES
Retrofitting for the Built Environment - IES
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 

Machine Vision System for Inspecting Flank Wear on Cutting Tools

  • 1. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012 Machine Vision System for Inspecting Flank Wear on Cutting Tools R. Schmitt1, Y. Cai1 and A. Pavim1,2 1 Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany Email: {R.Schmitt, Y.Cai, A.Pavim}@ wzl.rwth-aachen.de 2 Scholarship holder of the Brazilian CNPq Abstract—This paper describes the development of a machine wear. In ISO 3685 the measurement parameters (Fig. 1) for vision system for automated tool wear inspection. The proposed flank wear are defined [3]. The maximum flank wear value approach measures the tool wear region based on the active contour algorithm and classifies the wear type by means of (VBmax) indicates the maximum appeared value for the flank neural networks. Test results show that prevalent tool wears wear – the maximum distance between the unworn cutting can be checked robustly in a real production environment and edge on the top of the tool to the bottom end of the flank therefore the manufacturing automation can be improved. wear land area. The flank wear value (VB) is defined as the average width of the flank wear land area. Additional to this, Index Terms—industrial image processing, machine vision, AVB describes the whole area of the flank wear land. neural networks, tool wear, measurement III. RELATED WORK I. INTRODUCTION Generally, the tool wear inspection is performed in three The current production tendency is the improvement of different ways [2] [4]. First, a statistical evaluation, based on both production performance and quality levels in order to estimated or FEM (Finite Element Method) simulated lifetime reduce costs and to avoid scrap. In the industrial manufacture intervals is possible [2]. Second, process signals like the flexible production systems with high performance and quality cutting forces or the acoustic emission can be used for a characteristics are required. Hence, the antiquated quality wear analysis [2] [5]. These indirect techniques try to evaluate assurance method by measuring the specification conformity the tool wear by inspecting the process data, which have a of a product at the end of the production line is replaced by tight relationship with the tool wear. Third, a direct a preventive quality strategy with inline-metrology [1]. Milling measurement on the cutting edge can be performed by using and turning are very common processes in industry today. optical sensors [4] [6]. In [7] a strategy is developed for Therefore, process monitoring of these machining processes identifying cutting tool wear by automatically recognizing has become of crucial importance to optimise production in wear patterns in the cutting force signal. The strategy uses a view of quality and costs [1, 2]. Monitoring methods focus mechanistic model to predict cutting forces under conditions on the inspection of important process parameters, such as of tool wear. This model is also extended to account for the cutting forces, temperature and tool wear. Tool wear is usually multiple inserts. On the basis of predicted force signals linear the most relevant parameter inspected, as it has direct discriminant functions are trained to identify the wear state influence on the final product quality, the machine tool of the process. In [8] [9] model-based approaches for tool performance and the tool lifetime. wear monitoring on the basis of neuro-fuzzy techniques are presented. A model with four inputs (time, cutting forces, II. CUTTING TOOLS AND TOOL WEAR vibrations and acoustic emissions signals) and one output (tool wear rate) is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is used for monitoring only the turning process. For the indirect techniques a precise and also computation-efficient model for predicting tool wear is essential. Machining processes (turning, drilling, milling, and grinding) performed by different machine tools are extremely complex and fraught with uncertainty. Therefore their behavior is practically difficult to describe exactly by modeling tools, even though approaches based on artificial intelligence techniques like fuzzy logic and Figure 1. Flank tool wear and its parameters neural networks are used. The direct methods deal directly The flank wear is the most referred tool wear parameter in the with the measurement of the desired variable, thus usually monitoring of machining processes – it allows to estimate providing a more precise result of the acquired signal. the cutting tool’s lifetime and to control the production Currently, the first two values (VBmax and VB) are manually process [2]. Fig. 1 shows the characteristics of flank tool measured with microscope in industry [1] [2]. The top and © 2012 ACEEE 27 DOI: 01.IJCSI.03.01.13
  • 2. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012 bottom references for the flank wear region are determined by  Z-axis: positioning the camera for tools with different the worker subjectively. Hence, the measurement of the flank heights; wear requires expert knowledge, is relatively time-consuming  X-axis: driving the cutting tool head in camera focus and its result is also strongly user-dependent [6]. Furthermore, for tools with different diameters; it is not possible to quantitatively determine the area of the  C-axis: rotating the tool for the positioning of all wear region (AVB) with microscope. In this context, the main cutting inserts in the focus of camera. objective of this research work is to develop a machine vision After the placing the tool in a HSK 63A receiver in the system for tool wear inspection on cutting tools. This reference position, the tool and camera are positioned automated system should realize a robust and fast flank wear adaptively to the tool geometry. During the rotation of C- measurement next to the production line. Based on the axis, each cutting insert is detected automatically and finely measurement, a reproducible wear type classification should tuned to a well-focused orientation. After successive image also be realized. acquisition of all inserts, the tool is driven back to the reference position. IV. MACHINE VISION PROTOTYPE V. IMAGE PROCESSING CHAIN The developed prototype consists of the following hard- ware modules: illumination unit, camera/optic-system and To achieve the goal of an automated measurement of flank mechanical system. wear, the machine vision prototype was built with an image processing chain, which is developed to determine the VBmax, A. Illumination Unit VB and AVB values for different kinds of tools (Fig. 4). The Due to the different geometries and surface properties of basic image processing tasks of the chain are the following: cutting tools, a flexible illumination unit is required. To record image acquisition, tool edge detection, highlighting wear all necessary information for the measurement and region, feature extraction, wear type classification and finally classification task, the illumination concept of the cutting wear measurement. Each of them consists of a certain number tool inspection system employs a combination of three of image processing steps with special configuration different lighting types: top light, half-ring light and side parameters (Fig. 4). lights (Fig. 2). A dual-image acquisition under different illumination conditions enables an optimal detection and measurement of the flank wear. Figure 2. Developed illumination unit First, a full-illuminated image of the worn cutting insert is Figure 3. Machine vision prototype for tool wear inspection. taken for the detection of the tool area in image with all three A. Tool Edge Detection lightings. Second, a side-illuminated image is acquired for In order to measure the tool wear, it is only necessary to the feature extraction of wear region and the final wear proceed tool area within the acquired image. The tool area measurement solely with side lights (Fig. 2). could be separated from image background by finding the B. Camera/Optic-System top and side tool edges in the full-illuminated image (Fig. 4, 2. For the camera/optic-system, a monochrome CCD-camera column). To detect both edges, two ROIs (regions of interest) with an effective sensor size of 752x582 pixels is used. In on each edge are predefined. The locations of the ROIs in the combination with an optical lens with a fixed focus length of image are calculated based on tool type information (e.g. 42 mm, a resolution of 4.4 µm is realized. radius, length). After applying the Canny edge detector [10] to the image areas defined by ROIs, image pixels on tool C. Mechanical System borders are extracted. By fitting the sequence of detected For a flexible measurement of the flank wear, three points along the tool borders with a line function, the top motorized axes are required (Fig. 3): and side tool edges are determined. © 2012 ACEEE 28 DOI: 01.IJCSI.03.01.13
  • 3. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012 Figure 4. Image processing chain for the tool wear inspection system. B. Highlighting Wear Region contour point pi and its two neighboring points pi 1 , pi 1 First, automatic histogram equalization [11] is performed must be kept as straight as possible. This leads to a smooth- on the side-illuminated image, which results in the contrast ing of the contour. enhancement of the wear region. A histogram is a graphical For the binary image, the image energy is set equal to the diagram that provides information about the frequency in gradient of the image which each brightness level appears in the whole image. A linear transformation in its brightness histogram achieves a Eimg   grad (I ) . (3) better distribution of the brightness spectrum of the image, where I is the pixel intensity. For the minimization of this resulting in the enhancement of the wear area and in the improvement of the visual information of the tool. Subsequent energy, the contour must be attracted to such locations in to image thresholding, morphological filtering and blob the image, where the gray value varies strongly, such as analysis are applied to binarized image and used to eliminate region edges. For the active contour algorithm, the system the remaining noises in the image, leaving the wear area (Fig. energy is defined as weighted sum of above introduced 4, 3. column). energies C. Feature Extraction E    Econt    Ecurv    Eimg , (4) Depending on cutting conditions and duration, the tool where  ,  and  are weights for the individual energies. wear could appear in different forms. In order to achieve a With optimally adjusted weighting, the active contour reliable tool wear inspection, the active contour algorithm [12] algorithm tracks the edge of tool wear region dynamically is used to extract the wear region, which detects the edges of and adaptively, considering its shape should be as regular regions with inconsistent shapes robustly. By iteratively and smooth as possible. A convex polygon, surrounding the minimizing system energy, this algorithm converges a chain wear region, is used as the initial estimation to start searching to surround the whole tool wear region as closely as possible. the wear contour by means of the active contour algorithm. Similar to [12], the following three part energy functions are At the end of the iteration, it provides the best set of points used. The continuity energy is defined as that represents the tool wear area contour (Fig. 4, 4. column). Econt    pi  pi 1 , (1) D. Wear Type Classification where  is the mean distance between two points on the On the base of the extracted outer contour of the wear region, the classification of tool wear type is performed. A con-tour and pi is the contour point. By a minimized energy neural network based method is developed because of its function (1) equidistance between the contour points should ability to solve and generalize non-linear classification be obtained. problems [13]. This work currently focuses on the two most The curvature energy is modeled by important cases: flank wear and tool breakage (Fig. 4, 5. 2 column). To build a distinctive description of the tool wear, Ecurv  pi 1  2  pi  pi 1 . (2) the following features are extracted from the segmented wear To minimize the curvature energy in (2), the angle between a region, which are tested as inputs for the neural network. © 2012 ACEEE 29 DOI: 01.IJCSI.03.01.13
  • 4. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012 image statistics: average, maximum, minimum, standard checkerboard squares, the calibration factor of the designed deviation prototype results in a value of 4.44 µm for each pixel in the  surface texture: average variance of gray values of the image. segmented area, which analyzes image textures of wear region Canny analysis: Canny filtering, which characterizes the VI. EXPERIMENTAL RESULTS high-frequency image details, such as edges and wear The measurement method is evaluated on real cutting textures tools, which have different geometries from different  histogram: which describes the brightness of the wear manufacturers. An example test set is given in Fig. 6. In order region to evaluate the system, a set of five worn cutting inserts is Fourier coefficients: the normalized 10 lowest coefficients used. For each cutting insert 10 test images are acquired. without the constant component calculated from the outer With this insert set a test for the tool wear measurement can contour, which give a translation and rotation decorrelated be estimated from a sample of 5x10=50 test images of real description of the contour of wear region cutting tools. In Fig. 7 the results of the example test set of To find a good input-output function for the neural net- cutting tools are presented. They are validated against the work, different feed-forward network topologies are tested. manual measurement with microscope, which is performed The best structure uses neurons with sigmoid output by tool specialists and used as reference. This comparison response [13], 14 inputs combined from Canny analysis and shows that similar measurement accuracy is achieved by the image statistics, a hidden layer with 10 neurons and an out- auto-mated machine vision system as expert measurement. put layer with 2 neurons, each one for a specific wear type. Our analysis indicates that the deviation is caused by the dirt on the cutting. Based on a validation of inspection equipment applicability of this machine vision system according to the guideline QS 9000 (measurement system analysis, MSA) [15], the repeatability of the automated tool wear measurement is determined to 7.5 µm. Compared to the uncertainty of wear measurement with microscope (39.5 µm), the developed machine vision system can be classified after MSA as suitable for the inspection task. Figure 5. Results of tool wear type classification Figure 6. Test cutting tools This network achieves an accuracy rate of 96% in classifying a set of 25 worn tools (Fig. 5). A back-propagation algorithm is used to train 15 sample pairs. The validation of the results is confirmed with a microscope, based on expert knowledge. E. Tool Wear Measurement Figure 7. Comparison of test results achieved by microscope and After flank wear is detected, the measurement of its the developed machine vision system parameters is performed. According the ISO 3685 [3], the flank wear parameters VB and VBmax are evaluated as the CONCLUSIONS perpendicular distances from the lower contour points to the top cutting edge. As introduced in section 1, AVB is the sum This paper describes the development of a machine vision of all pixels in the wear area (Fig. 4, 6. column). These values system for an automated tool wear inspection. The hardware are firstly calculated in number of pixels. To represent this requirements and software solutions are characterized. The parameter in the standard measurement unit, a calibration experiments on real cutting tools show that the proposed method based on [14] is applied to the image processing prototype can achieve both accuracy and robustness for tool system. Using a checkerboard pattern, the transformation wear measurement and wear type classification. for correcting perspective is computed and radial lens distortions are compensated. With the accurate size of the © 2012 ACEEE 30 DOI: 01.IJCSI.03.01.13
  • 5. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012 REFERENCES [8] A. Gajate, R. e: Haber, J. R. Alique and P. I. Vega, “Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a [1] T. Pfeifer, Production metrology, Oldenbourg, 2002. Turning Process”, Proceedings of the 4th International Conference [2] F. Klocke and A. Kuchle, Manufacturing Processes 1: Cutting: on Hybrid Artificial Intelligence Systems, pp. 113-120, 2009. Lathing, Milling, Drilling, Springer, 2011. [9] A. Gajate, R. Haber, R. del Toro, P. Vega and A. Bustillo, [3] ISO 3685, “Tool-life testing with single-point turning tools”, “Tool wear monitoring using neuro-fuzzy techniques: a comparative 1993. study in a turning process”, Journal of Intelligent Manufacturing (2 [4] T. Pfeifer, D. Sack, A. Orth, M. R. Stemmer, M. L. Roloff, August 2010), pp. 1-14, 2010. “Measuring flank tool wear on cutting tools with machine vision – [10] B. Jaehne, Digital image processing, Springer, 2002. a case solution”, Proceedings of IEEE conference on mechatronics [11] A. Hornberg, Handbook of machine vision, Wiley-VCH, 2006. and machine vision in practice, pp. 169-175, 2002. [12] M. Kass, A. Witkin, D. Terzopoulos, “Snakes: Active Contour [5] S. Kurada and C. Bradley, “A review of machine vision sensors Models”, International Journal of Computer Vision, Vol. 1, No. 4, for tool condition monitoring”, Computers in Industry, vol. 34, pp. pp. 321-331, 1988. 55–72, 1997. [13] E. Davies, Machine vision - theory algorithms practicalities, [6] M. Lanzetta, “A new flexible high-resolution sensor for tool Elsevier, 2005. condition monitoring”, Journal of Materials Processing Technology, [14] Z. Zhang, “Flexible Camera Calibration by Viewing a Plane vol. 119, pp. 73–82, 2001. From Unknown Orientations”, Proceedings of International [7] D. J. Waldorf, S. G. Kapoor, R. E. DeVor, “Automatic Conference on Computer Vision, pp. 666-673, 1999. recognition of tool wear on a face mill using a mechanistic modeling [15] N.N., “measurement system analysis, (MSA) QS 9000 / approach”, International Journal of Wear, Vo. 157, Iss. 2, pp. 305- A.I.A.G.- Chrysler Corp., Ford Motor Co., General Motors Corp”, 323, 1992. 3. Edition, Grays/Essex, 2002. © 2012 ACEEE 31 DOI: 01.IJCSI.03.01.13