PROMISE: Our kitties will never sit on top of content. Please turn off your ad blocker for our site.
puuuuuuurrrrrrrrrrrr
Ken Vakil
Published: Monday, August 22, 2011 - 12:41 The purpose of this article is to discuss automated analysis and report generation of
key characteristics measurement data. Key
characteristics (KCs) are those features of a
part whose measurements must be kept to
the nominal values through process control
to minimize the "Taguchi Loss." KC measurements are taken using different
types of metrology heads, such as laser
trackers, or other types of scanners. The
actual process discussed in this article
requires gathering KC data using laser trackers. The metrology data are imported
into software to compare the actual values
with their nominal values. Thereafter, the
data go through several file format
transformations before the reports are
created in the format required by the
customer.
This article examines current manual
processes and proposes an automated
solution that can be implemented throughout
the entire assembly line. Key characteristics are features of a process,
tool, part, or assembly that can have a
negative impact on product performance
(e.g., form, fit, and function) when it varies from
its nominal dimension. To be useful, KCs
must be quantifiable and measurable. Figure 1 shows the parabolic relationship
between the variable value and the cost of
quality (Y axis), or Y = AX2. As the
variable moves away from its nominal
value, the resulting loss is referred to as the "Taguchi Loss." Both the KC and non-KC functions are also displayed.
KC identification and control has four
distinct steps during the design and
development stage (figure 2):
1. The KC identification process begins
during an early design stage, with the
flow moving down from top-level customer
requirements. These requirements
are converted into measurable KCs,
i.e., outer mold line features such as
step, gap, or fastener flushness,
which are directly derived from the
customer requirements.
4. Predicted performances are then
compared, and risk is mitigated.
Based on the variation simulation
study, a final set of assembly KCs
are identified and flowed down to
manufacturing for monitoring and
control. During the production stage, KCs are
monitored and feedback is sent to
the engineering department to validate the design.
KCs are measured and controlled using
statistical process-controlled techniques to
ensure that each KC feature is held as close
to its nominal value as possible to reduce or
eliminate the cost of quality.
An ultimate goal of the enterprise should be
to reduce or eliminate KCs. In moving toward
the elimination of KCs, continuous
process improvement efforts must be
undertaken on the factory floor to improve
KC performance.
As the underlying process capability
approaches Cpk >= 1.33, consideration may
be given to drop this feature from the active
KC list.
At Northrop Grumman Aerospace Systems
(NGAS), we saw an opportunity to reduce
labor cost by using minimal capital
investment to automate the report-generation
process. Analysis of time spent on these
activities pointed to the fact that the manual
report-generation process took just as much
time as was spent acquiring the metrology
data. The new process automatically uploads
the KC part-acquired data and then performs
the deviation analysis, using the already
embedded tolerance values for the
associated part. The CATIA model is
imported into the application software, a
small section at a time, to mitigate any
computer memory issues. The current process, described in figure 3,
involves the use of laser trackers operated
by two mechanics. The scanning of the KC
surfaces begins with the laser tracker
alignment.
Once the scanned data are acquired in comma separated value (CSV) format, it is
converted into data template (DAT) format
before importing into the Metrolog software.
Nominal values for each KC are manually
entered into the system to conduct deviation
analysis for each of the 65 characteristics. Additional file format transformations are
required (PDF and TXT) before the file is
ready to generate Excel or PowerPoint
charts required by the customer. All of these
activities are manual and increase both labor
costs and product cycle time. The improved process eliminates the need
for manual file format transformations
(see figure 4). Once scanned measurement data are available, they are imported into the
application software along with the
respective portion of the aircraft. A script
is then written that loads the respective
tolerance values for the KC deviation
analysis. The resulting data can generate the
required customer reports with no file
format transformations required. In 2010, NGAS initiated a system
development project for automated data
analysis and report generation using
commercial-off-the-shelf (COTS)
application software. The cost center
selected for testing this software was an
ideal candidate for initial implementation because it was at the end of the assembly line
and prior to the aircraft shipment to the
customer. The project goals were: The following requirements were identified
before development work began. System requirements: Report requirements: The new report-generation software,
developed in less than three months, has
Report Plan and Execution as two major
components. The Report Plan function is basically a menu of all
parts and KC features that are required in the
data analysis and report generation. The
menu can be revised as changes occur in the
KC list.
As shown in figure 5, the green area indicates
that the KC is within tolerance, yellow
indicates it is approaching the limits, and red
indicates it is out of tolerance. Those areas
that remain white indicate the fact the
scanned data are missing for analysis. The scanned files, in CSV format from laser
trackers, are directly imported into the
application software, eliminating several of
the file transformations required in the old
process (see figure 6). Each KC datum, i.e., part
number with its corresponding surface
relationship, is defined. The tolerance
values for each KC area are already loaded
into the system for deviation analysis, which
saves a considerable amount of time. The operator creates the PowerPoint report
with a single mouse click. The process
opens the NGAS PowerPoint template and
uploads screenshots and other graphic-rich
data (see figure 7). Screenshots are grayed out
to indicate missing data. The floor team then
analyzes the results and takes corrective
action. Figure 8 provides a graphic of the KC
performance summary. Deviation data are
presented in balloon and bar formats for
ease of reading and understanding. The KC
status to the right shows which KCs are in or
out of tolerance. The white space shows the
missing data. 1. The previous manual method of
report generation was time-consuming, requiring several file
transformations. In some cases, time
spent for report generation was as
lengthy as it was for data acquisition.
Substantial savings can result from
implementation of an automated
report-generation system. 4. The automated report generation can
be leveraged across the entire
production line. For migration, all
that is required is to create a
measurement plan for each cost
center and identify its specific
reporting needs, parts used, and associated tolerance requirements. The author wishes to thank the following
persons who participated in the development
of the automated report analysis and
generation solution: Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types. However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. So please consider turning off your ad blocker for our site. Thanks, Ken Vakil, manufacturing technology engineer at Northrop Grumman Aerospace Systems, has more than 45 years experience in manufacturing engineering, industrial engineering, and financial systems. Some of his current projects at Northrop include development, justification, and integration of advanced metrology systems that are focused on meeting engineering requirements, making the company products affordable while ensuring that newly implemented manufacturing operations are user-friendly and value-added. Automating Data Analysis and Report Generation of Key Characteristics Measurements
Key characteristics: definition
Fig. 1: KC and non-KC functionKC identification process
2. A review of selected assemblies,
including part location and assembly
sequencing methods, is then
performed.
3. In the variation and risk analysis
step, KC performance is predicted
for each assembly strategy.
Fig. 2: KC identification and control processKC monitoring and control
Problem statement and hypothesis
Current process
Fig. 3: File transformations: current processImprovement opportunity
Fig. 4: Improvement opportunity
Project objectives
1. Develop an automated system that
can eliminate file format conversions
of the scanned data
2. Perform deviation analysis against the model
3. Capture screenshots required to illustrate KC performance
4. Create report generation in both PowerPoint and Excel formats. System design
• Be affordable in terms of initial
software development and periodic
maintenance cost
• Be user-friendly and easy to learn
• Provide a work-around for those
KCs that cannot be measured
because a part is missing or is being
reworked
• Issue an error message if the file
containing certain KC data is
missing
• Provide graphically rich reports for
easy analysis and corrective action
• Guarantee data integrity from data
importation through the report
generation process.
• Flag alarm conditions using visual
and audible alarms during the
measurement process
• Perform archiving of all pertinent
data by each part, version, and serial
number of the aircraft
• Create reports using the same
formats as the manual process
• Provide capability to print the report
or view online
• Provide reports on quality inspection "buy-off" of the features per
inspection requirementsNew automated process
Fig. 5: KC performance
Fig. 6: Automated process
Fig. 7: KC areas
Fig. 8: KC performance summary
Conclusions
2. Software developed for this project
can be used for applications in other
cost centers by examining data flows
specific to that cost center and by
creating measurement maps to guide
the mechanics for report creation.
3. The desirable software strategy is to
implement a common software data
analysis and report-generation
platform throughout the assembly
line (see figure 9). The results of such a strategy are reduced software
development, implementation, and
training costs.
Fig. 9: Software strategy
5. The NGAS KC analysis and automated report-generation system
is transparent to the metrology head
used for data collection. In the
future, if the data acquisition system
is changed, there will be practically
no impact in the way the data are
analyzed and reports are generated.
This approach gives the company
desired flexibility to change the
method of data acquisition as
advanced metrology solutions
become available.Acknowledgement
• Ernie Huston, president, Verisurf
• James Edwards, sales manager, Verisurf
Our PROMISE: Quality Digest only displays static ads that never overlay or cover up content. They never get in your way. They are there for you to read, or not.
Quality Digest Discuss
About The Author
Ken Vakil
© 2023 Quality Digest. Copyright on content held by Quality Digest or by individual authors. Contact Quality Digest for reprint information.
“Quality Digest" is a trademark owned by Quality Circle Institute, Inc.