Genetic Algorithm-Based Design and Development of Particle-Reinforced Silicone Rubber for Soft Tooling Process

被引：12

作者：

Nandi, A. K. ^{[1
]}

Deb, K. ^{[2
]}

Datta, S. ^{[3
]}

机构：

[1] Cent Mech Engn Res Inst CSIR CMERI, Durgapur, W Bengal, India

[2] Indian Inst Technol, Dept Mech Engn, Kanpur 208016, Uttar Pradesh, India

[3] Bengal Engn & Sci Univ, Sch Mat Sci & Engn, Sibpur 711103, Howrah, India

来源：

MATERIALS AND MANUFACTURING PROCESSES | 2013年 / 28卷 / 07期

关键词：

Composites; Evolutionary algorithm; Multiobjective optimization; Silicone rubber; Soft tooling; Solidification time; EFFECTIVE THERMAL-CONDUCTIVITY; MULTIOBJECTIVE OPTIMIZATION; COMPOSITES; FILLERS;

D O I：

10.1080/10426914.2013.773022

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In order to enhance the solidification rate of soft tooling process, design of a silicone rubber composite mold material is carried out based on multiobjective optimization (MOO) of conflicting objectives. The elitist nondominated sorting genetic algorithm (NSGA-II), a genetic algorithm-based MOO tool, is used to find the optimum parameters first by obtaining the Pareto-optimal front and then selecting a single solution or a small set of solutions for manufacturing applications using a suitable multi-criterion decision making technique. Based on the optimal design parameters, an experimental study in soft tooling process is carried out in particle-reinforced silicone, and it is observed that the solidification time is minimized appreciably keeping the same advantages of soft tooling process.

引用

页码：753 / 760

页数：8

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