An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Azfanizam²,

et al. 2019

10.5267/j.dsl

Self Cite

In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model's output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

Section: Continues Design Variables Direct Gradient Methodsmentioning

confidence: 99%

Recent developments in metamodel based robust black-box simulation optimization: An overview

Azfanizam²,

et al. 2019

10.5267/j.dsl

Self Cite

“…Investigate the literature shows interesting issues in application of robust design optimization in production and manufacturing processes (e.g. Parnianifard et al, 2018).…”

Section: Expected Quality Loss Functions Based Onmentioning

confidence: 99%

Trade-off in robustness, cost and performance by a multi-objective robust production optimization method

Azfanizam²,

et al. 2019

10.5267/j.ijiec

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Designing a production process normally is involved with some important constraints such as uncertainty, trade-off between production costs and quality, customer's expectations and production tolerances. In this paper, a novel multi-objective robust optimization model is introduced to investigate the best levels of design variables. The primary objective is to minimize the production cost while increasing robustness and performance. The response surface methodology is utilized as a common approximation model to fit the relationship between responses and design variables in the worst-case scenario of uncertainties. The target mean ratio is applied to ensure the quality of the process by providing the robustness for all types of quality characteristics and with a trade-off between variability and deviance from the ideal point. The Lp metric method is used to integrate all objectives in one overall function. In order to estimate target value of the quality loss by considering production tolerances, the process capability ratio ( ) is applied. At the end, a numerical chemical mixture problem is served to show the applicability of the proposed method.

“…In robust design approach, two types of factors can treat in experiments, fixed and random types, as depicted in Figure 4 [13].…”

Section: Types Of Factors and Data In Rdmentioning

confidence: 99%

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

AHMAD²,

et al. 2019

Preprint

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One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.