This document summarizes an article from the International Journal of Mechanical Engineering and Technology that discusses optimizing critical processing parameters for plastic injection molding. It begins by stating that determining optimal initial process parameters is important for productivity, quality and costs. It then outlines the objectives of identifying optimized levels for factors like temperature and pressure. The methodology proposed uses Taguchi design of experiments and Moldflow simulation software to optimize parameters for different materials like polypropylene. The scope is to study injection molding parameters for polypropylene and provide recommendations for parameter settings based on material and geometry.

1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 6, November - December (2013), pp. 223-226
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IJMET
©IAEME
OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS FOR
PLASTIC INJECTION MOLDING FOR ENHANCED PRODUCTIVITY AND
REDUCED TIME FOR DEVELOPMENT
Mr. Anandrao B. Humbe1,
Dr. M.S. Kadam2
1
2
M.E. Manufacturing MGM’S Jawaharlal Nehru Engineering College Aurangabad
Mechanical Engineering Department, Jawaharlal Nehru Engineering College, Aurangabad
ABSTRACT
The injection molding process itself is a complex mix of time, temperature and pressure
variables with a multitude of manufacturing defects that can occur without the right combination of
processing parameters and design components. Determining optimal initial process parameter
settings critically influences productivity, quality, and costs of production in the plastic injection
molding (PIM) industry. Up to now, most production engineers have used trial-and-error method to
determine initial settings for a number of parameters, including melt temperature, injection pressure,
injection velocity, injection time, packing pressure, packing time, cooling temperature, and cooling
time which depend on the engineers’ experience and intuition to determine initial process parameter
settings. However, the trial-and-error process is costly and time consuming.
The method for finding the optimized solution would involve use of Flow analysis software
like Mold-Flow. The use of this software would highlight the recommended values of the parameters
for Injection Molding for the specified component design. Defect prevention coupled with minimum
cycle time results in optimization of the process.
Mold-flow software offers simulation of the melt during passage through the Mold. The
design features like the gate, vent holes, draft, etc too can be improved through the interface. A real
time analysis of the flow helps to identify the problem areas and depicts the results while the
parameters are manipulated for effecting better results.
• Set the parameters based on historical data and experience.
• Fine tune the process parameters for the component which can be considered for evaluation and
observe the trend while setting each process.
• Document the data for research and analysis further using DOE (Taguchi Method/Minitab).
• Derive a standard based on the material and the configuration.
• Optimize the setting time and validate the process Injection Molding Machine Sectional view.
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
• Document the data for research and analysis further using DOE (Taguchi Method/Minitab).
• Derive a standard based on the material and the configuration.
1. INTRODUCTION
Injection molding has been a challenging process for many manufacturers and researchers to
produce products meeting requirements at the lowest cost. Faced with global competition in injection
molding industry, using the trial and-error approach to determine the process parameters for injection
molding is no longer good enough. Factors that affect the quality of a molded part can be classified
into four categories: part design, mold design, machine performance and processing conditions. The
part and mold design are assumed as established and fixed. During production, quality characteristics
may deviate due to drifting or shifting of processing conditions caused by machine wear,
environmental change or operator fatigue.
Determining optimal process parameter settings critically influences productivity, quality,
and cost of production in the plastic injection molding (PIM) industry. Previously, production
engineers used either trial-and-error method to determine optimal process parameter settings for
PIM. However, this method is unsuitable in present PIM because of the increasing complexity of
product design and the requirement of multi-response quality characteristics.
OBJECTIVES OF THE WORK
•
•
•
Identify optimized levels for each factor for the processing
Provide for a reference chart for thermoplastic material – Poly Propylene (PP) for setting the
levels for each Factor
Reduce the development time for the current process while reducing the number of defects
PROPOSED WORK
•
•
•
Study the injection molding process parameters for three different thermoplastic materials
with at least two varieties in each.
Develop a methodology to produce defects free parts by controlling the initial process
parameters settings (like melt temperature, injection pressure, injection velocity, injection
time, packing pressure, packing time, cooling temperature, cooling time, etc). Identify the
critical parameters that need the longest time for iteration for study.
Optimize the setting time as a result.
2. LITERATURE REVIEW
1. Author name is Tuncay Erzurumlu, Babur Ozcelik, paper name is “Minimization of warpage and
sink index in injection-molded thermoplastic parts using Taguchi optimization method.” The
objective of this paper consist of minimization of the warpage and sink index in terms of process
parameters of the plastic parts have different rib cross-section types, and rib layout angle using
Taguchi optimization method. A number of mold analyses consisting of 243 trails based on a full
factorial, three level, design were conducted to collect the warpage and sink index data obtained
from mold analyses.
2. Author Name L.M. Galantucci, R. Spina paper name is Evaluation of filling conditions of
injection moulding by integrating numerical simulations and experimental tests. Through data
integration between FEA and the Design of experiment approach, the parts with complex geometries
was studied to optimize injection process parameters improve the product quality. Using the FE
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3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
system, a deeper investigation of stress and strain distributions can be performed to predict defect
presence in the final product.
3. Author name Keun Park a,∗, Jong-Ho Ahnb paper name is “Design of experiment considering
two-way interactions and its application to injection molding processes with numerical analysis” The
optimal design for injection molding processes is sought by using the design of experiment (DOE)
and numerical analysis.The proposed approach has been applied to the injection molding process of a
deflection yoke, a precision electrical part with high geometric complexity.
4. Author name Kevin Alam, Musa R. Kamal paper name is” A robust optimization of injection
molding runner balancing A robust optimization of injection molding runner balancing” A general
methodology to robust process optimization that incorporated several innovative strategies was
developed and applied to the injection molding runner balancing problem.The key parameter was the
diameter ratio between the secondary runners furthest from the injection point. Optimizations that
varied the runner lengths in addition to their diameters significantly reduced the volume needed to
balance a robust runner system, when compared to the results of optimizations.
5. V.N. Gaitondea,∗, S.R. Karnikb, J. Paulo Davimc paper name is “Taguchi multiple∗
performance characteristics optimization in drilling of medium density fiberboard (MDF) to
minimize delimitation using utility concept” This paper presents the methodology of Taguchi
optimization method for simultaneous minimization of delimitation factor at entry and exit of the
holes in drilling of SUPERPANMDF panel. In this study, the effects of feed rate and cutting speed
on multiple-performance characteristics were investigated statistically.
6 A.G. Gerber *, R. Dubay, A. Healy CFD-based predictive control of melt temperature in plastic
injection molding. The methodology is based on using CFD to generate, via open-loop testing, a
temperature and input dependent system model for multi-variable control of a three-heater barrel on
an injection molding machine. This paper demonstrates the use of transient CFD simulations to
develop a dynamic process model required in model predictive control based on expected process
set-point changes in melt temperature.
4. SCOPE OF THE WORK
•
•
•
•
Study the injection molding process parameters for thermoplastic material Poly Propylene
Develop a methodology to produce defects free parts by controlling the initial process
parameters settings (namely - melt temperature, injection pressure, packing pressure).
Identify the critical parameters that need the longest time for iteration for study.
Provide a common ground for recommendation of processing parameters settings according
to geometrical and the material characteristics of the injection molded component.
METHODOLOGY
•
•
•
•
•
•
Select a popular variety of Poly Propylene (PP) for injection molded parts for
studying the process parameters.
Classify the parts according to size and shape
Conduct trial while setting the process parameters with levels determined through
historic data
Using Taguchi method for DOE, document and analyze the data for experimentation
Optimize the levels for each factor
Apply the optimized levels to a new component for validation
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4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
EXPERIMENTATION
L&T Demag Plastic Injection Molding machine with a capacity of 40Ton to be used for the
experimentation. Upon completion of mold development, the mold to be installed over the machine
for trials. Suitable setting values (levels) to be selected for the parameters of processing based on the
DOE result. Optimization to be carried out till the defects are minimized. Visual inspection of the
components can be done during the settings.
VALIDATION
Comparison over the optimized levels achieved through DOE (Taguchi Methods) with the
samples achieved during the physical experiments shall be done using visual aids (Magnifier/
Microscope). A good match over the results by each methodology would suggest the thesis work as
validated. Typically, an error or difference of about 15~20% for such studies can be accommodated.
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