Titlebook: Quality Improvement with Design of Experiments; A Response Surface A Ivan N. Vuchkov,Lidia N. Boyadjieva Book 2001 Springer Science+Busines

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Optimization Procedures for Robust Design of Products and Processes with Errors in the Factors,om the product parameters to the response are taken into account. Using these models one can find the optimal parameter values. In this chapter we consider optimization procedures that provide robustness of product or process performance characteristics against errors in factor levels. In the next c

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Quality Improvement through Mechanistic Models,ne before making a prototype of the product. This way the ambiguity about the initial choice of the parameter settings can be avoided and the product development stage can be shortened. It also gives better understanding of the product behaviour under raw material or component variations, manufactur

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Quality Improvement of Products Depending on Both Qualitative and Quantitative Factors,lly in a physical measuring scale. Such factors are temperature, pressure, length, resistance, etc. Qualitative (or .) factors’ levels can be expressed in categories and can only be named or numbered. Examples of qualitative factors are type of raw material, method of treatment or measurement, type

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Other Methods for Model Based Quality Improvement,ons are heteroscedastic, i.e. their variance varies with the factor levels. In this situation once again we come across the problem of variance minimization, while keeping the mean value on a target. A model-based solution is readily obtainable on the basis of repeated observations. They make possib

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Topics in Safety, Risk, Reliability and Qualityhttp://image.papertrans.cn/q/image/780273.jpg

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https://doi.org/10.1007/978-94-009-0009-7calculus; data analysis; design; engineering design; model; optimization; process design; quality; regressio

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978-1-4020-0392-9Springer Science+Business Media Dordrecht 2001

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