Design for Six Sigma for Aerospace Applications

                                                   Adi Choudri*
         ...
•   Lean Manufacturing – Arranging production and
                                                         business proces...
Operational six sigma’s measure, analyze, improve and control, MAIC approach as shown in Figure 3, is tried and
true, and ...
MAIC approach requires data driven decisions and existence of process and non-conformance data naturally evolves
transitio...
Customers
                  Customers                                                           Cr
                   Need...
Monte Carlo simulation or Multi-vari analysis. Control plans and mistake proofing is done for critical process
parameters....
• Reliability analysis to minimize premature failures at the customer
   • Tolerance design through analytical, Monte Carl...
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Preparation of Papers for AIAA Technical Conferences

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Preparation of Papers for AIAA Technical Conferences

  1. 1. Design for Six Sigma for Aerospace Applications Adi Choudri* Aerojet, Sacramento, CA, 95813 At the heart of Design for Six Sigma (DFSS) is the emphasis to design in quality and producibility and design out waste/cost in the front end of the design process. To achieve these objectives the foundation of DFSS is critical parameter management. Critical Parameter Management (CPM) is the practice of identifying the critical factors of the design that are required for the product to function successfully. This essentially is the 80/20 principle, where design parameters can be grouped into 20 percent as critical and 80 percent as important. By identifying and managing these Critical to Customer (CTQ) Quality factors starting with Voice of the customer to Process capability data in the manufacturing process, we focus our resources on characterizing, validating and monitoring these factors throughout the product life cycle. Design Sigma Scorecard metrics is used to monitor the performance of the critical parameters and ensure that the resulting design is robust to variations in the manufacturing processes and materials. Nomenclature CP = Critical Parameter CPM = Critical Parameter Management DFSS = Design for Six Sigma CDOC = Concept Development, Design Development, Optimization and Capability Verification DMAIC = Define & Measure, Analyze, Improve and Control FMEA = Failure Mode and Effect Analysis KJ = Kawakita Jiro RFP = Request for Proposal QFD = Quality Function Deployment VOC = Voice of the Customer I. Introduction Aerojet has been at the leading edge of lean and Six-sigma effort since GenCorp adopted the Operational Excellence paradigm since mid nineties. Aerojet’s DFSS program is a customer focused structured approach that systematically strives for design and development of affordable robust products that enables use of Lean Manufacturing Methodologies in Production. Aerojet’s DFSS program enhances our existing Design and Development process with “Best Practice” tools and Methods to establish a Robust Design Approach to develop affordable and producible products for our customers. Lean manufacturing cells for Titan IV chamber was first developed at Aerojet in early nineties. Several operational blackbelts, greenbelts and master blackbelts were trained and deployed at various locations and functions. However, it was soon realized that Aerojet must create an umbrella corporate level initiative to harness all of theses competing initiatives. This was simply called operational excellence because it tries to streamline and optimize all processes regardless of the type of tools used (Lean, Six sigma, DFSS and high performance supplier teams). In 1999, Aerojet was awarded an airforce contract to develop lean tools for Product Development and with the realization that “leanness” and “quality” must be built into the design, operational excellence evolved into the four initiatives as shown in Figure 1. * Manager, Engineering Six Sigma and Lean 1 American Institute of Aeronautics and Astronautics
  2. 2. • Lean Manufacturing – Arranging production and business processes to minimize waste, focus on value, and make value flow smoothly at the pull of the customer in the pursuit of perfection Lean Lean • Six Sigma – Eliminating defects, reducing variability, and minimizing cycle time through the application of scientific methods and tools Supplier DFSS DFSS Supplier Development Development • Design for Six Sigma – Designing and developing robust products that meet all customer needs through a systematic methodology that Six Six Sigma optimizes value with statistical tools Sigma • Supplier Development – Developing strategic suppliers who will employ Operational Excellence to improve quality, delivery, and cost performance Operational Excellence – The Right Tool for the Right Problem Operational Excellence – The Right Tool for the Right Problem Figure 1. The Components of Operational Excellence The vision of a lean system is shown in Figure 2. Lean starts with the basic discipline of “5S” (5S stands for Sort, Set In Order, Shine, Standardize, Sustain) effort to keep the working environment clean and organized and forms the basis for the visual factory. Once order is brought to workplace work can be standardized and work can pulled to meet customer’s demand based on a predetermined takt time. Various Kaizen (process improvement) events need to be performed to work our way all the way to a single piece flow vision in our manufacturing and non-manufacturing processes. We are striving to do that to the extent possible in aerospace and defense environment. Many kaizen events are routinely conducted at Aerojet encompassing a variety of problem and scope. Interestingly, some of the same lean tools can be utilized to improve the product development process. The Continuous Goal: The Continuous Goal: Elimination of Waste Elimination of Waste SPF Kaizen Standardized Work Visual Factory 5S Workplace Organization Lean Manufacturing will provide the discipline required to sustain the gains realized through Operational Excellence projects. Figure 2. Vision of Lean System 2 American Institute of Aeronautics and Astronautics
  3. 3. Operational six sigma’s measure, analyze, improve and control, MAIC approach as shown in Figure 3, is tried and true, and has been applied to many key projects and problems at Aerojet including many transactional process problems. Once the problem is well defined and can be measured, a variety of statistical tools can applied to data to arrive at permanent solutions and then controls must be set in place so that the process improvements can be monitored and sustained long term. These have been in practice at Aerojet for several years now. As many MAIC projects are completed, the enterprise starts to build a database of process capability, product lessons learned and history and soon realizes that without incorporating that corporate knowledge early into design elimination of defect is not possible. DFSS starts with VOC and translates them into designs that are optimized around internal and supplier processes. MEASURE MEASURE ANALYZE ANALYZE IMPROVE IMPROVE CONTROL CONTROL Project Definition Project Definition Knowledge Sharing Knowledge Sharing Failure Modes and Effects Analysis (FMEA) Failure Modes and Effects Analysis (FMEA) Measurement System Capability Measurement System Capability Process Capability Process Capability Design of Experiments Design of Experiments Cause and Effects Analysis Cause and Effects Analysis Multi-variable Analysis Multi-variable Analysis Hypothesis Testing FMEA (Sustain) Hypothesis Testing FMEA (Sustain) Mistake Proofing and Control Plans Mistake Proofing and Control Plans Figure 3. Six Sigma Problem Solving Approach Evolutionary Optimization and Lean Mfg Evolutionary Optimization and Lean Mfg DFSS seeks to incorporate all the process knowledge gained from Six Sigma projects into new designs so that defects and production problems are avoided before they occur as shown in Figure 4. DFSS focus is robust designs that are optimized around internal and supplier processes. Six Sigma and DFSS employ a common language and set of goals, encouraging and strengthening inter-organizational cooperation. LSL USL 6σ Cp = 2.0 QFD Y = f (x1, x2…Xn) Six Sigma Process Design for Process Knowledge Six Sigma Improvement Figure 4. Evolution of DFSS from Six Sigma 3 American Institute of Aeronautics and Astronautics
  4. 4. MAIC approach requires data driven decisions and existence of process and non-conformance data naturally evolves transition into a DFSS. It is impossible to achieve a six-sigma product without early incorporation of lean and six- sigma thinking into the product design. This is well illustrated in Figure 5 and shows how DFSS can be leveraged to move the new development knowledge to the left and reduce both product life cycle and development costs. This phenomenon has been well documented from several industries as well studies completed at Lean Aerospace Initiative at Massachusetts Institute of Technology. As a matter of fact it is estimated that design concepts are generated within few hour of receiving the RFP and that concept is a function of the design engineer’s domain knowledge. Formalized DFSS tools must be employed from the beginning to insure that point designs are not derived too early in the product development phase. DFSS tries to strike an optimum balance between customer expectation and requirements and manufacturing capabilities. As shown in the figure the idea is to push the product knowledge curve to the left when more design flexibility can be accommodated because changes in design gets expensive exponentially as it moves towards production. Design Flexibility Knowledge DFSS Concept Development Production DFSS seeks to maximize knowledge available early in development cycle • Customer expectations and requirements • Critical input, output and noise parameters • Manufacturing process capabilities Figure 5. 75% of Product’s Life Cycle Cost is committed by the PDR DFSS consist of three components. Best practice tools are proven tools and methods to insure design robustness and compatibility with selected manufacturing processes. Critical Parameter Management (CPM) is at the core of DFSS. This process is based on the 80/20 principle and is utilized to define critical parameters that affect customer requirements and must be managed throughout the development process into production. As depicted in Figure 6 customer requirements must be collected and translated into system level product requirements into design parameters and they in turn must be translated into process parameters. We must also insure that at each system/sub system level these critical parameters are compliant and producible and result into robust designs. This can accomplished through data based DFSS scorecards and a disciplined Stage Gate process of the Corporate Product Development process. Several six sigma tools such as QFD, Image/Requirements KJ, FMEA etc are employed to complete these tasks. Stage-gate process provides structure and risk management as well as opportunity for management intervention and support. CPM focuses the development team on those critical parameters (features, requirements, etc.) that satisfy the customer needs. Criticality begins with technology development or proposal phase and continues through production and is based on and traces to customer needs. DFSS tools to perform CPM include CP maps, Quality Function Deployment (QFD) matrices, Design Failure Modes and Effects Analyses (FMEA), and Design Scorecards. 4 American Institute of Aeronautics and Astronautics
  5. 5. Customers Customers Cr Needs Needs Voice of the Customer i ti c al Fu n ct i Compliance Metrics on System System Requirements al Re q uir Compliance Metrics e me Subsystem nt Subsystem Requirements s (C FR Cri ) tica Compliance Metrics l to Subassembly Subassembly Requirements Fu n cti o n( CT Compliance Metrics F) Component Sp Component Requirements e ci f ic a ti o ns Compliance Metrics Manufacturing Manufacturing Processes Processes Figure 6. DFSS tools used to perform critical parameter management include CP Maps, Quality Function Deployment (QFD) matrices, Design Failure Modes and Effects Analyses (FMEA), and Scorecards, Process capability data, Regression modeling, Response Surface Methods, Monte Carlo Simulations, DOE etc. Product development involves staging the activities as shown in Figure 7. Each stage is where the actual development work takes place and DFSS tools are used. Gates are phased critical program decision points with specific deliverables such as PDR, CDR etc. Gates incrementalize risk. Gate scorecards are used to manage deliverables. Preliminary Development CDR Qualification PRR LRIP Proposal SRR PDR Design Capture Definition Development Pilot Production GATE 1 GATE 2 GATE 3 Pilot GATE 4 Capture GATE 1 Definition GATE 2 Development GATE 3 Production GATE 4 Production Production Concept Defined Product Defined Optimize Design Capability Concept Defined Product Defined Optimize Design Capability (Value Proposition, VOC) (FMEA, Mod Design) (DOE, Robust Design, Gage) (Multi-vari, Control Plan, Tolerance) (Value Proposition, VOC) (FMEA, Mod Design) (DOE, Robust Design, Gage) (Multi-vari, Control Plan, Tolerance) Figure 7. Stage-gate development process provides structure and risk management II. DFSS Approach to Product Development DFSS affects all phases of product development as shown in Figure 8. Concept phase Without the proper input at the concept phase, the product is destined for failure from the very beginning. At the Concept stage of product development DFSS starts with the customer value proposition and captures the VOC utilizing a variety of tools. These then translate into product functional requirements and eventually critical design parameters are developed that are critical to Customer satisfaction. These activities help to identify critical functions where we can apply six sigma tools. Once the product functions have been identified, DFSS tools such as Pugh selection method, FMEA and Design for Manufacturing and Assembly can be applied to create a robust modular design. During the optimization phase critical parameters are characterized through advanced DOE and Taguchi methods. With a nominally robust design we can now create statistically based tolerances for critical functions and determine appropriate controls to optimize performance and cost. At this Capability assessment phase designs are evaluated to insure features and functions are producible with sufficient margins utilizing existing process data, 5 American Institute of Aeronautics and Astronautics
  6. 6. Monte Carlo simulation or Multi-vari analysis. Control plans and mistake proofing is done for critical process parameters. • Value Proposition • Business Plan • Critical Design Parameters CONCEPT CONCEPT Y = f(X1, X2, X3 … Xn) • Voice of Customer • Product Req’ts PRODUCT LAUNCH • Minimize Complexity (DFMA / Modular Design) PRODUCTION • Minimize Risk (FMEA) DESIGN DESIGN • Design Critical Parameter Flow • Mfg Process Maps • Minimize Measurement Systems Noise • Optimize Design (DOE / RSM) OPTIMIZE OPTIMIZE • Robustness of Design (Operations / Customer Noise • Robustness Verification (Multi-Vari) CAPABILITY CAPABILITY • Statistical Tolerancing • Product Launch (Control Plans) Figure 8. DFSS Approach to Product Development Design Phase Once the functions have been identified we can begin utilizing statistical methods for assessing these functions. This phase will focus on • Critical parameter management and concept selection • Statistical tools for assessment and comparison of concepts and functionality • Risk reduction through Failure Modes and Effects Analysis of the design and features • Design for Manufacturing and Assembly taking into account Lean process layouts and capabilities • Modular design to find opportunities of standardizing for quality and costs These activities will aid in reduction of risk for critical features. Optimize Phase With critical functions identified and initial reliability assessed, the product model can be optimized and refined. This phase will focus on • Characterization of critical measurement systems • Defining the empirical model of the product through Design of Experiments(DOE) • Refining the model through advanced DOE methods • Making the design more robust through Taguchi techniques The output of this phase is a design whose critical functions are “nominally” robust. Capability Phase With a nominally robust design, we can now create statistically based tolerances for critical functions and determine appropriate controls to optimize performances and cost. In this phase we will focus on: • Capability assessment of similar features and functions 6 American Institute of Aeronautics and Astronautics
  7. 7. • Reliability analysis to minimize premature failures at the customer • Tolerance design through analytical, Monte Carlo, and Empirical methods • Design capability assessment through pilot runs( Multi vari analysis) • Control plans and Mistake-proofing for critical parameters The output of this phase is product design that has known capabilities for critical parameters in manufacturing. III. But How to Put It All Together? It is important for the top executives and Engineering management to establish a clear DFSS vision and objective statement so that with consistent message is communicated to everyone. Resources must be spent to train engineers in DFSS tools and enhance the current design methodology. These are Aerojet approach to DFSS and they will need to be customized for each company. Acknowledgments The author recognizes the contribution from, Michael Allen, Aerojet, DFSS Master Blackbelt and Marvin Young, VP of Engineering for his guidance. References 1 Crevling, Slutsky, and Antis, Jr. Design For Six Sigma, 1st ed., Prentice Hall, New York, 2003, Chaps. 1, 2, 3. 2 Lean Aerospace Archives, MIT 7 American Institute of Aeronautics and Astronautics

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