Improving a soldering process applying the dual response approach to a Taguchi's orthogonal array

Híjar-Rivera, Humberto; Sánchez-Leal, Jaime; Valles-Chavez, Adan

doi:10.1109/iccie.2009.5223949

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…However, for robustness optimization, Taguchi's formulas, also in combination with other methods, still offer a great way to explore, visualize, and optimize the systems robustness by dividing the model's factors into decision and noise [38,41]. This is why they still today are the subject of current research [19,28], especially in the simulation community [12,22,44]. The strength of simulation-based robustness optimization using Taguchi's formulas is that a broad range of possible noise and decision configurations can be evaluated when large-scale experiment plans are used.…”

Section: Introductionmentioning

confidence: 99%

A Method Using Generative Adversarial Networks for Robustness Optimization

Feldkamp

Bergmann

Conrad

et al. 2022

ACM Trans. Model. Comput. Simul.

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The evaluation of robustness is an important goal within simulation-based analysis, especially in production and logistics systems. Robustness refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has minimal effect on a given output. In this paper, we present an approach for optimizing robustness based on deep generative models, a special method of deep learning. We propose a method consisting of two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors in a competitive, turn-based game. In a case study, the proposed method is tested and compared to traditional methods for robustness analysis including Taguchi method and Response Surface Method.

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