IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Multi-Objective Evolutionary Algorithms: Application in Designing Particle Reinforced Mould Materials

Multi-Objective Evolutionary Algorithms: Application in Designing Particle Reinforced Mould Materials
View Sample PDF
Author(s): A. K. Nandi (CSIR – Central Mechanical Engineering Research Institute, India)and K. Deb (Michigan State University, USA)
Copyright: 2017
Pages: 45
Source title: Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1798-6.ch008

Purchase

View Multi-Objective Evolutionary Algorithms: Application in Designing Particle Reinforced Mould Materials on the publisher's website for pricing and purchasing information.

Abstract

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Related Content

Erfan Nouri, Alireza Kardan, Vahid Mottaghitalab. © 2024. 33 pages.
Mudassar Shahzad, Noor-ul-Huda Altaf, Muhammad Ayyaz, Sehrish Maqsood, Tayyba Shoukat, Mumtaz Ali, Muhammad Yasin Naz, Shazia Shukrullah. © 2024. 31 pages.
Erfan Nouri, Alireza Kardan, Vahid Mottaghitalab. © 2024. 32 pages.
Davronjon Abduvokhidov, Zhitong Chen, Jamoliddin Razzokov. © 2024. 16 pages.
Shahid Ali. © 2024. 25 pages.
Aamir Shahzad, Rabia Waris, Muhammad Kashif, Alina Manzoor, Maogang He. © 2024. 13 pages.
Soraya Trabelsi, Ezeddine Sediki. © 2024. 23 pages.
Body Bottom