Design Optimization of a Wafer Dough Blade Using Artificial Neural Network and Monte-Carlo Simulation

IF 0.6 4区 工程技术 Q4 MECHANICS
Mechanika Pub Date : 2023-10-18 DOI:10.5755/j02.mech.32249
Murat MAYDA, Mesut BİTKİN
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引用次数: 0

Abstract

In this work, a systematic method to conduct the surrogate-based design optimization is proposed by utilizing Artificial Neural Network and Monte Carlo Simulation. To show its applicability, the design optimization of a wafer dough blade that is an important component in the food industry is carried out. In the optimization problem, design variables or inputs are totally six variables including distances, diameter and thickness, and design responses or outputs are the blade mass, the maximum stress occurred on it, and its surface area. When the results of the initial and optimum designs are compared, there is a significant decrease in the maximum stress (nearly 66%) whereas there was a reasonable low difference in both the mass and surface area. Thanks to the proposed method, it can be possible to take into account the experimental data instead of analytical data in a design problem. Moreover, the followed method provides engineers with a practical and systematic way to find the optimum solution for even nonlinear problems needs to be solved during engineering design process.
基于人工神经网络和蒙特卡罗仿真的圆面片叶片设计优化
本文提出了一种基于人工神经网络和蒙特卡罗仿真的系统设计优化方法。为证明其适用性,对食品工业中重要部件——晶圆面团刀片进行了设计优化。在优化问题中,设计变量或输入为距离、直径、厚度共6个变量,设计响应或输出为叶片质量、叶片所受最大应力、叶片表面积。当初始设计和优化设计的结果进行比较时,最大应力显著降低(近66%),而质量和表面积的差异都很低。由于提出的方法,在设计问题中可以考虑实验数据而不是分析数据。该方法为工程设计过程中需要求解的非线性问题的最优解提供了一种实用而系统的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mechanika
Mechanika 物理-力学
CiteScore
1.30
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: The journal is publishing scientific papers dealing with the following problems: Mechanics of Solid Bodies; Mechanics of Fluids and Gases; Dynamics of Mechanical Systems; Design and Optimization of Mechanical Systems; Mechanical Technologies.
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