Robust Design Technique

Your first objective with axiomatic design is to make your design as independent as possible with reference to functionai requirements. After this you wouid make it as robust as possible, as per the information axiom, using such techniques as Robust Design (Technique 38), Design FMEA (Technique 40), and Mistake Proofing (Technique 49). 

Are there any design corrections that can be made to improve performance (See Robust Design, Technique 38, and Process Capability, Technique 37.)... 

Kapur, K. 1993 Quality Engineering and Robust Design. In Kusiak, A. (ed.). Concurrent Engineering Automation, Tools and Techniques. NY Wiley. 

The development of "internal model control," a design technique that bridges traditional and robust techniques for designing control systems, has provided the framework for unifying and extending these advances. It is now available in commercially available design software. 

The strategy for robust design experiments that will be considered in Section 2.3 is based on the statistical techniques associated with response surface methodology. This section will give an overview of response surface methodology, presenting some of the more common experimental designs that have been developed in this area. 

A design that meets the information axiom is called a robust design (see Technique 38) because it maximizes the probability that it will meet its specihcations (process variables, or PVs) on an ongoing basis. For example, a process variable like tensile strength or data-entry errors can function as intended along a spectrum from zero to absolute perfection but in reality it always functions somewhere in between the two extremes. 

To undertake robust design, you ll definitely need help from an experienced engineer or statistician familiar with this approach to testing and analysis. You ll also need to know how to apply several other techniques in this book including Performance and Perception Expectations (Technique 30), Axiomatic Design (Technique 31), Design FMEA (Technique 40), and Design of Experiments (Technique 50). 

Robust design starts with conceptual system, design, during which you define the ideal performance for your innovation, and make a list of measurable system features that are critical to the customer. You may have already done this using Performance and Perception Expectations (Technique 30). For our skin patch example, the ideal design will consistently dispense a dose of 1.0 mg/hr 0.2, regardless of patient fat content, skin condition, or other environmental factors. 

Credible evaluation uses robust statistical techniques to construct the counterfactual. These techniques include randomization or experimental design quasi-experiments such as matching, regression discontinuity design, or double differences and nonexperimental or instrumental variable methods (annex 6.3). All these techniques try to guarantee the comparability of the treatment and control groups by avoiding what statisticians refer to as bias (box 6.7). 

Process analyzers based on a no-moving-parts axial transmissive spectrograph [4,175] design have demonstrated excellent calibration stability when operated in a reasonably stable environment. Analyzers of this type have yielded consistent chemical model results for up to several years without recalibration using robust modeling techniques such as... 

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