1. VOLUME 21, ISSUE 5
SEPTEMBER | OCTOBER 2015
ASS E T I N T E G R I T Y I N T E L L I G E N C E
SAFER INSPECTIONEERING
Featured Article
PAUL J. RAMIREZ, Quality Assurance Engineer at NASA - Jet Propulsion Laboratory (JPL)

2. 2 Inspectioneering Journal SEPTEMBER | OCTOBER 2015
INTRODUCTION
Whether a hardware assembly is being constructed for use nearby
on the ground or is to be launched into deep space, the Quality
Assurance Engineer (QAE) provides a safety net for mission-criti-
cal hardware. The QAE inspects structural welds, micro-electronic
installations on antennas, parachute soft goods, and a myriad of
other assemblies in order to identify risks and avert hardware
failures that may occur millions of miles away or thousands of
hours after startup. The variety and complexity of hardware, and
the many dimensions of the QA inspection process, combine in
such a manner that even the most experienced QAE may miss
problems. Even worse—and a major theme of this article—the
QAE's very experience and skill set may
contribute in unexpected ways to the risk
of error. There are no absolute guarantees
of successful quality assurance.
For example, cross-training QAEs in mul-
tiple facets of QA, or deploying QAEs
who are Subject Matter Experts (SMEs)
reduces, but does not eliminate the risk of
faulty QA. Although it does not offer a fool-
proof method of QA, this article proposes
a new perspective on the methodology of
QA that may afford modest reductions in
the risk of error.
BACKGROUND
An internet search of "Quality Assurance
Algorithms" discloses the surprising result
that there currently exist no widely used algorithmic approaches
guiding the process of inspection. Inspectors and QAEs have
undoubtedly built their own informal and undocumented proto-
cols over time, likely based on their field experience, prior knowl-
edge, and disciplinary backgrounds.This informal approach to the
inspection process can promote efficient and reliable inspections
as long as QAEs encounter essentially familiar challenges in their
work. But this very familiarity can impair the QAE’s ability to per-
ceive new issues in the structure or assembly under inspection.
QAEs, like everyone else, often see what they expect to see. As the
cliché goes: “if all you have is a hammer, everything looks like a
nail.” For example, the QAE with a materials science background
observes small flaws in an assembly and sees evidence of corro-
sion. Another inspector, trained differently, attributes the same
flaws to the effects of mechanical stress. This article aims to
make the inspection process less familiar to the QAE and thereby
mitigate the risk of errors stemming from familiarity, habit, and
unconscious discipline-linked bias.
The QA algorithm set out below focuses on visual inspection
of typical hardware assemblies and structures, although occa-
sions arise when auditory, tactile, or even olfactory cues may be
significant.
Inspection of an assembly or system component proceeds
through three phases:
1. Detection: identification of visible or concealed defects;
2. Recognition: recalling elements from the QAE's disciplinary
and experiential knowledge base that validate attribution of
a visible defect to an underlying cause or source;
3. Interpretation: explaining the meaning of the identified
defect and causal nexus in such a way as to ensure appropri-
ate remediation.
These processes usually occur sequen-
tially, although interdependent and recur-
sive pathways inevitably emerge among
their elements. For example, attribution of
causality often points up possible remedia-
tion, but failed efforts at remediation may
counter previous attributions of causality.
If the fix doesn't work, the bug or defect
may have arisen from a different unidenti-
fied source.
Emphasis in this discussion centers on
visual inspection. Human vision and cog-
nition remain an enigma: how the QAE
sees, what they see, and what they infer
from what they see can challenge even
the most experienced inspector. As noted,
previous experience can often serve as an obstacle to successful
on-going QA.
The challenge addressed in this article is not to develop new ways
to document or communicate what the QAE sees; it is instead to
reflect on how visual inputs are processed upstream in order to
help the QAE see things in new ways. Given that hardware sys-
tems vary in size and complexity, the problem of conducting a
thorough and accurate visual inspection to identify noticeable
risks/defects is non-trivial. An assurance algorithm that reflects
the three phases of the inspection process (detection, recognition,
and interpretation), and thereby encourages awareness of inspec-
tion alternatives, promises to deepen the inspection process and
improve its outcomes.
THE SAFER FLOWCHART
Visual inspection performed by any QAE on any structure or
assembly should detect and determine all risks and defects exhib-
ited by the hardware assembly. Successful visual inspection can
be hindered by ill-posed problems created by assumptions (often
from the QAE’s own discipline background) about what is and
isn’t seen. The diagram below posits three (3) phases of the QA
process:
SAFER INSPECTIONEERING
BY: PAUL J. RAMIREZ, Quality Assurance Engineer at NASA - Jet Propulsion Laboratory (JPL)
...the QAE's very experience
and skill set may contribute
in unexpected ways to
the risk of error. There are
no absolute guarantees
of successful quality
assurance.

3. SEPTEMBER | OCTOBER 2015 Inspectioneering Journal 3
1. Triangles O – D - I = Observation/Detection,
2. Diagnosis /Recognition, and
3. Interpretation/Inspection Report
and five (5) foci of QA:
1. Structure
2. Assembly
3. Function
4. Energy
5. Risk
The proposed algorithm is divided into five elements: Structure,
Assembly, Function, Energy, and Risk. Structure addresses
the building, framework, and the way parts of an assembly are
arranged. Assembly addresses the collection, putting together,
and manufacturing processes of the structure which are intended
to allow it to perform its intended purpose or function. Function
refers to the specific duty of a single component in the assembly.
Energy can refer to the energy that is required for the hardware
to function, as well as any harnessed, transferred, or converted
energy made by the hardware or its working environment. Finally,
Risk targets the possible conflicts that each of these elements may
have with one another.
• Structure – (The building, framework, and the way parts
of an assembly are arranged).
• Assembly – (The collection, putting together, and manu-
facturing processes of the structure to perform its special
purpose and or function).
• Function – (The special, specific job, use, or duty of
the hardware).
• Energy – (The type energy used to operate the hardware).
• Risk – (Recognizing the summation of risk by revising
in reverse).
PERMUTATION OF SAFER COMPONENTS
The entirety of the hardware assembly can be addressed diagnos-
tically from the point of view of each of the SAFER components.
For example, Structure guides inspection of the assembly in terms
of how appropriate its dimensions and its potential are to encom-
pass the assembly's operations. Assembly itself focuses atten-
tion on the interrelations of components and examines issues
of efficiency and economy of operation. Function introduces a
time dimension. For example, functional QA would attempt to
envisage the dynamics of the working assembly over time (i.e.,
does the currently inspected packaging of a parachute make it
feasible for successful deployment?). Energy addresses the impact
of energy types, quantities, and circuits as the hardware assembly
is powered during operation. Risk poses more abstract and, again,
time-dependent issues relating to the projected lifetime oper-
ation of the assembly within different environments and when
exposed to different stresses.
SAFER INVENTORY OF QA SUB-ELEMENTS
LATERAL THINKING AND THE DOWNSIDE OF SUBJECT
MATTER EXPERTISE
MostQAEspossesssubjectmatterexpertiseinoneareaoranother.
Such expertise allows the QAE to perform a more efficient, and
often more effective inspection. But the area of expertise itself
also handicaps the QAE. Their background can sometimes predis-
pose them to look at the assembly from a distinctive and limited
point of view. Questions that might arise to someone with a dif-
ferent discipline background might elude them.
Although it is theoretically possible to imagine a given assembly
being inspected by a team of QAEs from different SME back-
grounds, this is clearly impractical. The alternative is to utilize
lateral thinking approaches. Just as effective proofreading of a
written text is facilitated by reading the text from right to left,
the SAFER algorithm forces the QAE to grapple with each SAFER
element and retrieve the assumptions underlying that element
to aid in the formation of a distinct perspective for viewing the
assembly. The purpose of right to left proofreading is to slow
the proofreader down and to interfere with their expectations
concerning what will arise from the text next. In the same way,
the SAFER algorithm is intended to slow the QAE down by con-
sistently reminding them of discipline perspectives that are not
naturally their own.
ALGORITHMIC MODELING OF SAFER
SAFER can be represented in the form of a transitive reduction
closure algorithm. This makes it possible to solve reachability

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questions efficiently by creating a binary relation between two
elements. These QA elements are then allowed to interrogate one
another, thereby creating more risk queries to be answered.
THE PURPOSES OF THE SAFER ALGORITHM
The narrow purpose of the SAFER algorithm is to standardize the
qualitative assessment process of highly technical, one off, or low
production rate products. But there are larger benefits that stem
from such standardization. For example, QA inevitably works in
partial opposition to other parties within the engineering orga-
nization. The priorities of hard charging engineers may include
a stepped up time schedule and expedited development of proj-
ects. Therefore, the QAE may often be seen as someone who slows
down work and imposes needless constraints on developers' free
flowing ideas.
Sometimes that perspective on QA is undoubtedly accurate. At
other times, however, it may very well be the case that effective
QA greatly benefits project development and ultimately short-
ens times to completion of a successful project. One way to
improve the contribution of QA to overall project success is to
make the purposes and methods of QA clearer to other stake-
holders. Sharing the SAFER algorithm with engineers could
facilitate better communication by illustrating the QAEs com-
mon goal in ensuring the successful production and operation of
quality hardware. n
For more information on this subject or the author, please email
us at inquiries@inspectioneering.com.

5. SEPTEMBER | OCTOBER 2015 Inspectioneering Journal 5
PAUL RAMIREZ
Paul J. Ramirez holds a degree in Materials Science and Engineering Technology from Don
Bosco College, Rosemead CA, and a Self-Designed B.A. degree in Chinese from Chapman
University, Orange CA. He is a recipient of the prestigious George Kehl Award from the
American Society of Materials and the International Metallographic Society for research on
explosively formed projectiles. Paul is also a recipient of the Horatio Alger Military Scholar
Award. He is a former Combat Infantryman, Paratrooper, and Unexploded Ordnance
Technician. Currently, Paul serves as a Quality Assurance Engineer at NASA/JPL in Pasadena
California assisting the Deep Space Network, and project INSIGHT.
CONTRIBUTING AUTHORS