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Orthogonal Array Experiments and Response Surface Methodology

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Orthogonal Array Experiments and Response Surface Methodology Unit 7: Orthogonal Array Experiments and Response Surface Methodology • A modern system of experimental design. • Orthogonal arrays (sections 8.1-8.2; appendix 8A and 8C). • Analysis of experiments with complex aliasing (part of sections 9.1-9.4). • Brief response surface methodology, central composite designs (sections 10.1-10.2). 1 Two Types of Fractional Factorial Designs • Regular (2n−k, 3n−k designs): columns of the design matrix form a group over a finite field; the interaction between any two columns is among the columns, ⇒ any two factorial effects are either orthogonal or fully aliased. • Nonregular (mixed-level designs, orthogonal arrays) some pairs of factorial effects can be partially aliased ⇒ more complex aliasing pattern. This includes 3n−k designs with linear-quadratic system. 2 A Modern System of Experimental Design It has four branches: • Regular orthogonal arrays (Fisher, Yates, Finney, ...): 2n−k, 3n−k designs, using minimum aberration criterion. • Nonregular orthogonal designs (Plackett-Burman, Rao, Bose): Plackett-Burman designs, orthogonal arrays. • Response surface designs (Box): fitting a parametric response surface. • Optimal designs (Kiefer): optimality driven by specific model/criterion. 3 Orthogonal Arrays • In Tables 1 and 2, the design used does not belong to the 2k−p series (Chapter 5) or the 3k−p series (Chapter 6), because the latter would require run size as a power of 2 or 3. These designs belong to the class of orthogonal arrays. m1 mγ • An orthogonal array array OA(N,s1 ...sγ ,t) of strength t is an N × m matrix, m = m1 + ... + mγ, in which mi columns have si(≥ 2) symbols or levels such that, for any t columns, all possible combinations of symbols appear equally often in the matrix. • For OA of strength two, the index t = 2 is dropped for simplicity. • An OA(12,211) is used in Table 1 and an OA(18,2137) is used in Table 2. 4 Example : OA(12,211) Table 1: Design Matrix and Lifetime Data, Cast Fatigue Experiment Factor Logged Run ABCDEFG 8 9 10 11 Lifetime 1 + + − + + + − − − + − 6.058 2 + − + + + − − − + − + 4.733 3 − + + + − − − + − + + 4.625 4 + + + − − − + − + + − 5.899 5 + + − − − + − + + − + 7.000 6 + − − − + − + + − + + 5.752 7 − − − + − + + − + + + 5.682 8 − − + − + + − + + + − 6.607 9 − + − + + − + + + − − 5.818 10 + − + + − + + + − − − 5.917 11 − + + − + + + − − − + 5.863 12 −−−−−−−−−−− 4.809 5 Example : OA(18,2137) Table 2: Design Matrix and Response Data, Blood Glucose Experiment Factor Mean Run AGBCDEFH Reading 1 00000000 97.94 2 00111111 83.40 3 00222222 95.88 4 01001122 88.86 5 01112200 106.58 6 01220011 89.57 7 02010212 91.98 8 02121020 98.41 9 02202101 87.56 10 10022110 88.11 11 10100221 83.81 12 10211002 98.27 13 11012021 115.52 14 11120102 94.89 15 11201210 94.70 16 12021201 121.62 17 12102012 93.86 18 12210120 96.10 6 Why Using Orthogonal Array • Run size economy. Suppose 8-11 factors at two levels are to be studied. Using an OA(12,211) will save 4 runs over a 16-run 2k−p design. Similarly, suppose 5-7 factors at three levels are to be studied. Using an OA(18,37) will save 9 runs over a 27-run 3k−p design. • Flexibility. Many OA’s exist for flexible combinations of factor levels. See the collection on next page. 7 A Result on Run Size of OA m1 mγ • Lemma. For an orthogonal array OA(N, s1 ···sγ , t), its run size N must γ ∏ ki be divisible by the least common multiple of i=1 si for all possible ∑γ γ combinations of ki with ki ≤ mi and i=1 ki = t, i = 1,..., . Its proof is given on p. 375 of WH. • Example of its use. Suppose an experimenter needs to find an OA(N, 2233) k k of strength two. Among 2 1 3 2 with k1 + k2 = 2, there are three possibilities: 22, 32, and 2 · 3. Its least common multiple is 4 × 9 = 36. Thus the minimal N value is 36. 8 Useful Orthogonal Arrays • Collection in Appendix 8A and 8C of WH: *OA(12,211) OA(12,3124) *OA(18,2137) OA(18,6136) OA(20,219) OA(24,31216) OA(24,61214) *OA(36,211312) OA(36,3763) OA(36,2863) OA(48,211412) OA(50,21511) OA(54,21325) * especially useful • Learn to choose and use the design tables in the collection. 9 OA(18,2137) and OA(18,6136) Table 3: OA(18,2137) (columns 1–8) and OA(18,6136) (columns 1′ and 3–8) Run 1′ 12345678 1 000000000 2 000111111 3 000222222 4 101001122 5 101112200 6 101220011 7 202010212 8 202121020 9 202202101 10 310022110 11 310100221 12 310211002 13 411012021 14 411120102 15 411201210 16 512021201 17 512102012 18 512210120 10 OA(36,211312) Table 4: OA(36,211312) Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 00000000000000000000000 2 00000000000111111111111 3 00000000000222222222222 4 00000111111000011112222 5 00000111111111122220000 6 00000111111222200001111 7 00111000111001201220112 8 00111000111112012001220 9 00111000111220120112001 10 01011011001002102121021 11 01011011001110210202102 12 01011011001221021010210 13 01101101010012021022101 14 01101101010120102100212 15 01101101010201210211020 16 01110110100012100212210 17 01110110100120211020021 18 01110110100201022101102 11 OA(36,211312) Table 4: Continued Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 19 10110011010010222011012 20 10110011010121000122120 21 10110011010202111200201 22 10101110001011220100221 23 10101110001122001211002 24 10101110001200112022110 25 10011101100021012202011 26 10011101100102120010122 27 10011101100210201121200 28 11100001101021110021202 29 11100001101102221102010 30 11100001101210002210121 31 11010100011022212110100 32 11010100011100020221211 33 11010100011211101002022 34 11001010110020121201120 35 11001010110101202012201 36 11001010110212010120012 12 Partial and Complex Aliasing • For the 12-run Plackett-Burman design OA(12,211) ˆ 1 E(βi)= βi + ∑ ±β jk 3 j,k= i 1 partial aliasing: coefficient ± 3 10 complex aliasing: 45(= 2 ) partial aliases • Traditionally complex aliasing was considered to be a disadvantage because it leads to many possible models which can be hard to discriminate. 13 Analysis Strategies • Traditionally experiments with complex aliasing were used for screening purpose, i.e., estimating main effects only • A paradigm shift: using effect sparsity/heredity, Hamada-Wu (1992) recognized that complex aliasing can be turned into an advantage for studying interactions • Analysis methods allow two-factor interactions to be entertained (in addition to main effects). Effective if the number of significant interactions is small. Frequentist method in Section 9.4 and Bayesian method in Section 9.5. 14 Traditional Approach: Main Effect Analysis · F · D · · absolute constrasts · A · B · · C · G · E · 0 1 2 3 4 5 6 0.0 0.5 1.0 1.5 2.0 2.5 half-normal quantiles Figure 1: Half-normal plot, cast fatigue experiment. • Only factor F is significant (R2 = 0.45). Adding the next effect D gives R2 = 0.59, a marginal improvement. 15 Complex Aliasing • Consider the cast fatigue experiment. Let the main effect estimate of factor A be Aˆ = y¯(A = +) − y¯(A = −), etc. • Assuming no 3fi’s or higher, it can be shown that 1 1 1 1 1 E(Aˆ)= A − BC − BD − BE + BF − BG 3 3 3 3 3 1 1 1 1 1 + CD − CE − CF + CG + DE 3 3 3 3 3 1 1 1 1 1 + DF − DG − EF − EG − FG, 3 3 3 3 3 1 1 1 1 1 E(Bˆ)= B − AC − AD − AE + AF − AG 3 3 3 3 3 1 1 1 1 1 − CD − CE − CF + CG + DE 3 3 3 3 3 1 1 1 1 1 − DF − DG + EF + EG − FG, 3 3 3 3 3 • Similar complex equations for Cˆ, Dˆ , Eˆ, Fˆ, Gˆ. This relationship is called complex aliasing. 16 Complex Aliasing Simplified by Effect Sparsity • For the cast fatigue experiment, suppose the true (but unknown) significant effects are A, F, G, AB and FG, which can be justified by effect sparsity. Then the relationships are simplified: 1 E(Aˆ) = A − FG, 3 1 E(Bˆ) = − FG, 3 1 1 E(Cˆ) = − AB + FG, 3 3 1 1 E(Dˆ ) = − AB + FG, 3 3 1 1 E(Eˆ) = − AB − FG, 3 3 1 E(Fˆ) = F + AB, 3 1 E(Gˆ) = G − AB, 3 suggesting the viability of estimating significant main effects and 2fi’s. 17 Hamada-Wu Analysis Strategy 1. For each factor X, consider X and all 2fi’s XY involving X. Use stepwise regression to identify significant effects. Repeat this for each X and keep the best model. 2. Use stepwise regression to identify significant effects among effects identified in 1 and the main effects. 3. Using effect heredity, consider (i) effects identified in 2 and (ii) 2fi’s with at least one parent factor appearing in the main effects in (i). Use stepwise regression to identify significant effects. 4. Iterate between 2 and 3 until model stops changing. 18 Analysis of Cast Fatigue Experiment • Since F is significant in the main effect analysis, consider 2fi’s involving F. This leads to FG and R2 is increased to 0.89, almost doubled! • A better prediction based on yˆ = 5.7 + 0.458F − 0.459FG. By choosing G− and F+, predicted life is 5.7 + 0.458 − 0.459(−1)= 5.7 + 0.92 = 6.62, 0.92 a 16% increase (= 5.7 ) over the main effect model with F only. 19 Summary of Analysis Results • Cast Fatigue Experiment: Main effect analysis : F (R2 = 0.45) F, D (R2 = 0.59) HW analysis : F, FG (R2 = 0.89) F, FG, D (R2 = 0.92) • Blood Glucose Experiment: 2 Main effect analysis : Eq, Fq (R = 0.36) 2 HW analysis : Bl, (BH)lq, (BH)qq (R = 0.89) 20 Poorman’s Response Surface Methodology • Consider an experiment to study three quantitative factors with up to 5 levels.
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