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首页> 外文期刊>International journal of quality, statistics, & reliability >A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method
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A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

机译：基于人工神经网络和Taguchi方法的多反应统计优化问题的鲁棒智能框架。

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摘要

An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.
机译：产品或过程设计中遇到的一个重要问题是设置过程变量以满足所需的质量特征规范（响应变量），这被称为多响应优化（MRO）问题。常见的优化方法通常始于估计响应变量与过程变量之间的关系。在这些方法中，响应面方法（RSM）由于简单性，近年来引起了最多的关注。但是，在许多制造情况下，一方面，响应变量与过程变量之间的关系过于复杂，无法有效地估算；另一方面，它们之间的关系非常复杂。另一方面，用精确的技术解决这种优化问题与问题相关。本文提出的替代方法是使用人工神经网络来估计响应函数，并在过程优化中满足启发式算法。另外，所提出的方法使用田口鲁棒参数设计来克服现有多重响应方法的共同局限性，该局限性通常忽略了响应的分散效应。本文提供了一个案例研究，以说明所提出的智能框架解决多重响应优化问题的有效性。

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    Ali Salmasnia; Mahdi Bastan; Asghar Moeini;

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    Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran;

    Department of Industrial Engineering, Eyvanekey University, Semnan, Iran;

    Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran;

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