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Chapter 12. Design for Six Sigma. DFSS Activities. Concept development , determining product functionality based upon customer requirements, technological capabilities, and economic realities
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Chapter 12 Design for Six Sigma
DFSS Activities • Concept development, determining product functionality based upon customer requirements, technological capabilities, and economic realities • Design development, focusing on product and process performance issues necessary to fulfill the product and service requirements in manufacturing or delivery • Design optimization, seeking to minimize the impact of variation in production and use, creating a “robust” design • Design verification, ensuring that the capability of the production system meets the appropriate sigma level
Key Idea Like Six Sigma itself, most tools for DFSS have been around for some time; its uniqueness lies in the manner in which they are integrated into a formal methodology, driven by the Six Sigma philosophy, with clear business objectives in mind.
Tools for Concept Development • Concept development– the process of applying scientific, engineering, and business knowledge to produce a basic functional design that meets both customer needs and manufacturing or service delivery requirements. • Quality function deployment (QFD) • Concept engineering
Key Idea Developing a basic functional design involves translating customer requirements into measurable technical requirements and, subsequently, into detailed design specifications.
technical requirements component characteristics process operations quality plan Quality Function Deployment
Key Idea QFD benefits companies through improved communication and teamwork between all constituencies in the value chain, such as between marketing and design, between design and manufacturing, and between purchasing and suppliers.
Interrelationships Customer requirement priorities Technical requirements Voice of the customer Relationship matrix Technical requirement priorities Competitive evaluation House of Quality
Building the House of Quality • Identify customer requirements. • Identify technical requirements. • Relate the customer requirements to the technical requirements. • Conduct an evaluation of competing products or services. • Evaluate technical requirements and develop targets. • Determine which technical requirements to deploy in the remainder of the production/delivery process.
Concept Engineering • Understanding the customer’s environment. • Converting understanding into requirements. • Operationalizing what has been learned. • Concept generation. • Concept selection.
Tools for Design Development • Tolerance design • Design failure mode and effects analysis • Reliability prediction
Key Idea Manufacturing specifications consist of nominal dimensions and tolerances. Nominalrefers to the ideal dimension or the target value that manufacturing seeks to meet; toleranceis the permissible variation, recognizing the difficulty of meeting a target consistently.
Tolerance Design • Determining permissible variation in a dimension • Understand tradeoffs between costs and performance
Key Idea Tolerances are necessary because not all parts can be produced exactly to nominal specifications because of natural variations (common causes) in production processes due to the “5 Ms”: men and women, materials, machines, methods, and measurement.
DFMEA • Design failure mode and effects analysis (DFMEA)– identification of all the ways in which a failure can occur, to estimate the effect and seriousness of the failure, and to recommend corrective design actions.
Reliability Prediction • Reliability • Generally defined as the ability of a product to perform as expected over time • Formally defined as the probability that a product, piece of equipment, or system performs its intended function for a stated period of time under specified operating conditions
Types of Failures • Functional failure – failure that occurs at the start of product life due to manufacturing or material detects • Reliability failure – failure after some period of use
Types of Reliability • Inherent reliability – predicted by product design • Achieved reliability – observed during use
Reliability Measurement • Failure rate (l) – number of failures per unit time • Alternative measures • Mean time to failure • Mean time between failures
Key Idea Many electronic components commonly exhibit a high, but decreasing, failure rate early in their lives (as evidenced by the steep slope of the curve), followed by a period of a relatively constant failure rate, and ending with an increasing failure rate.
Failure Rate Curve “Infant mortality period”
Reliability Function • Probability density function of failures f(t) = le-lt for t > 0 • Probability of failure from (0, T) F(t) = 1 – e-lT • Reliability function R(T) = 1 – F(T) = e-lT
1 2 n Series Systems RS = R1 R2 ... Rn
1 2 n Parallel Systems RS = 1 - (1 - R1) (1 - R2)... (1 - Rn)
Series-Parallel Systems C • Convert to equivalent series system RA RB RD RC A B D C RC RA RB RD A B C’ D RC’ = 1 – (1-RC)(1-RC)
Tools for Design Optimization • Taguchi loss function • Optimizing reliability
Key Idea Design optimization includes setting proper tolerances to ensure maximum product performance and making designs robust, that is, insensitive to variations in manufacturing or the use environment.
loss no loss loss nominal tolerance loss loss Loss Functions Traditional View Taguchi’s View
Taguchi Loss Function Calculations Loss function: L(x) = k(x - T)2 Example: Specification = .500 .020. Failure outside of the tolerance range costs $50 to repair. Thus, 50 = k(.020)2. Solving for k yields k = 125,000. The loss function is: L(x) = 125,000(x - .500)2 Expected loss = k(2 + D2) where D is the deviation from the target.
Optimizing Reliability • Standardization • Redundancy • Physics of failure
Tools for Design Verification • Reliability testing • Measurement systems evaluation • Process capability evaluation
Key Idea Design verification is necessary to ensure that designs will meet customer requirements and can be produced to specifications.
Reliability testing • Life testing • Accelerated life testing • Environmental testing • Vibration and shock testing • Burn-in (component stress testing)
Measurement System Evaluation • Whenever variation is observed in measurements, some portion is due to measurement system error. Some errors are systematic (called bias); others are random. The size of the errors relative to the measurement value can significantly affect the quality of the data and resulting decisions.
Metrology - Science of Measurement • Accuracy - closeness of agreement between an observed value and a standard • Precision - closeness of agreement between randomly selected individual measurements
Repeatability and Reproducibility • Repeatability (equipment variation)– variation in multiple measurements by an individual using the same instrument. • Reproducibility (operator variation) - variation in the same measuring instrument used by different individuals
Repeatability & Reproducibility Studies • Quantify and evaluate the capability of a measurement system • Select m operators and n parts • Calibrate the measuring instrument • Randomly measure each part by each operator for r trials • Compute key statistics to quantify repeatability and reproducibility
R&R Evaluation • Under 10% error - OK • 10-30% error - may be OK • over 30% error - unacceptable
Key Idea One of the most important functions of metrology is calibration—the comparison of a measurement device or system having a known relation-ship to national standards against another device or system whose relationship to national standards is unknown.
Process Capability • The range over which the natural variation of a process occurs as determined by the system of common causes • Measured by the proportion of output that can be produced within design specifications
Types of Capability Studies • Peak performance study- how a process performs under ideal conditions • Process characterization study- how a process performs under actual operating conditions • Component variability study- relative contribution of different sources of variation (e.g., process factors, measurement system)
Process Capability Study • Choose a representative machine or process • Define the process conditions • Select a representative operator • Provide the right materials • Specify the gauging or measurement method • Record the measurements • Construct a histogram and compute descriptive statistics: mean and standard deviation • Compare results with specified tolerances
(a) (b) specification specification natural variation natural variation (c) (d) specification specification natural variation natural variation Process Capability
Key Idea The process capability index, Cp (sometimes called the process potential index), is defined as the ratio of the specification width to the natural tolerance of the process. Cp relates the natural variation of the process with the design specifications in a single, quantitative measure.
Process Capability Index UTL - LTL 6s Cp = UTL - m 3s Cpu = m - LTL 3s Cpl = Cpk = min{ Cpl, Cpu }
