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Donald Jasurda

CMSC

Applying Measurement Data to Engineering Process Improvement

Published: Monday, November 7, 2011 - 13:42

Manufacturers face many obstacles across the life cycle of delivering a product to market.

They often find themselves:
• Spending a lot of time reworking or repairing parts
• With parts that fail inspection, but fit and function properly when assembled
• With parts that pass inspection, but do not fit with other parts and assemblies
• Disagreeing with vendors about whether parts are made dimensionally correct
• With different understandings about design intent—in regards to form, fit, and function—by various groups within their organizations
• Dealing with team members who are intimidated when they see GD&T symbols on drawings
• With high manufacturing costs due to tight tolerances on noncritical features

Many of these problems are the result of an unclear definition of product and process requirements and an under-utilization of existing measurement data.

The case has never been greater for manufacturers to have a well-defined dimensional engineering (DE) process that enables the collection and analysis of relevant, meaningful variation measurements at every stage, from design through production.

Let’s look more closely at an ideal DE process, shown graphically in figure 1 below:

https://lh3.googleusercontent.com/FRDNXwyqeGvLxoCF3VtnJMoBmLt2PVRpzUE5v2UkZh1R9kZJLQ7vQXgHUEY6sN9JScTyjKgoDdG_pEYuk43xS4nyG2-N5lcOOLZbT1JSaznX5tPZvyg
Figure 1: An ideal dimensional engineering process

Management commitment, build objectives and process strategies

A comprehensive DE process begins with setting directives at the start of a program and is integrated into a company’s quality program throughout engineering and manufacturing. The process starts with the upfront identification of deliverables and a shared management commitment to them.

In establishing clear build objectives, whether they relate to fit and finish (such as an airplane fuselage gap and step), or function (such as a cockpit door-closing efforts), the entire engineering and design community focuses on the characteristics of the design that are most critical to overall quality. The development of “robust” build strategies also helps keep the engineering community focused on defining a build process that will achieve the desired quality defined in the build objectives.

GD&T

Through geometric dimensioning and tolerance (GD&T), the build objectives are translated into a common and meaningful language for communicating part and assembly specifications to manufacturing.

Tolerance analysis

Another key element of the DE process is tolerance analysis, which is also known as variation analysis. Tolerance analysis is used to predict dimensional variability of the assembly and pinpoint the source of variation. Tolerance analysis “drives” the design (both geometry and tolerances) and assembly process to achieve the defined quality (build objectives) and cost requirements, while meeting the component manufacturer’s capabilities. Tolerance analysis offers engineering the opportunity to optimize component tolerances to maximize quality while minimizing costs.

Measurement plans

As a product moves into production, the results of the tolerance analysis identify critical features, also known as key characteristic controls (KCCs). These KCCs define what is critical to quality and must therefore be measured. Measurement plans are developed based on these key features, and are communicated to the manufacturing floor. Once the measurements are made, the data are loaded into standardized reports to monitor build quality and support root-cause analysis for problem identification and resolution during the build process.

Dimensional data reports and root cause analysis

Graphical reporting systems enable a template-based approach to quality management. CAD model data and measurement data from plant-floor gauges provide the basis for reporting, analyzing, and problem-solving using a standardized easy-to-interpret format that is visible across the corporation.

Figure 2 shows an example of a measurement plan given to gauge operators to provide a clear set of instructions on what critical features need to be measured on a part.


Figure 2: Measurement plan

From the same template, figure 3 is an example of a quality report that provides information on the build status and dimensional consistency of the manufacturing process to the engineers responsible for the program.


Figure 3: Quality report

Maximizing the use of measurement data

One of the key advantages of this “closed loop” DE process is that the entire organization can consider the impact of what was found and solved on the plant floor, feed the pilot or prototype data back into the variation analysis model, and adjust for any issues within a given product program.

A closed loop process enables the reporting and problem-solving process from the plant floor to be combined with the simulation analysis model used in the development stages. This allows the valuable inspection data to also be used for virtual assembly fitting and process optimization throughout the DE process.

As finite element analysis (FEA) became an industry standard within engineering, variation analysis has also become commonplace. However, what is not yet a common practice is using these same analysis models for fit validation with actual measured data.

Figure 3 is a view of the fit conditions of panels on a fuselage section.

Looking at this in more detail in figure 4, we can see the fit condition analysis of the critical features of the top panel to the right-hand wall panel, based on the actual measurement data taken from each. This is especially useful, given that these components may have been manufactured in different plants, in different countries, and involving different suppliers.


Figure 4: Fit condition analysis of critical features

Based on the fit conditions and the assembly process issues identified during virtual assembly of this fuselage section, engineering has the ability to optimize the design based on foresight into the manufacturing requirements.

Robustness and timing

The Defense Advanced Research Projects Agency (DARPA) considers U.S. manufacturing capability a matter of national security, and “robust design” functionality is a key element of this capability. Timing is a critical element to design robustness.

A well-defined DE process allows users to use simulation and analysis to evaluate design and assembly concepts up front, where problems can be identified early in the product development life cycle, when the cost of quality is virtually free (figure 5).


Figure 5: Efficient problem mitigation

It has been shown that nearly 70 percent of a product’s cost commitment occurs in the earliest 5 to 10 percent of the development cycle. At this stage, the downstream impact areas are all affected by the specifications set during initial design. “Runaway costs,” scrapped parts, and reworks often result when specifications are not validated until after manufacturing is well underway.

Today’s solutions
Many leading OEMs use dimensional engineering and supporting software to maximize their use of measurement data by driving model-based root cause analysis. The software provided by Dimensional Control Systems, for example, enables users to validate manufactured components, regardless of where or how they are manufactured. With the use of 3-D geometry, plant floor measurements, visual simulation and testing, engineers can quickly pinpoint issues and perform corrective actions—avoiding the need to chase problems through their build process steps by trial and error.

In summary, existing solutions support the upfront engineering process and downstream quality management that maximize the use of measurement data through a closed-loop DE process. Issues can be analyzed and multiple solutions considered through simulation, and validated through automated reporting and analysis.

Given their highly competitive industry, aerospace and defense manufacturers must consider the tools available to maximize their use of measurement data and ultimately reduce costs, improve quality, and shorten their time to market.

Discuss

About The Author

Donald Jasurda’s picture

Donald Jasurda

Donald Jasurda has more than 30 years of engineering process improvement experience in the automotive, aerospace, medical device, and machinery industries where he worked on leading-edge projects ranging from mechanical artificial heart valves to composite commercial aircrafts, wind power generation and most recently, electric vehicles. Jasurda is the vice president of sales at Dimensional Control Systems Inc. (DCS), where he leads the sales and process transformation teams. DCS is a global provider of dimensional engineering consulting services and 3-D tolerance analysis and quality assurance software solutions that fully support the entire product life cycle.