Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Nonlinear and Evolutionary Optimization in Materials and Engineering
share announcement

Nonlinear and Evolutionary Optimization in Materials and Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 500

Special Issue Editor

Dr. Amit Banerjee

E-Mail Website
Guest Editor
Mechanical Engineering, School of Science Engineering and Technology, Pennsylvania State University Harrisburg, Middletown, PA 17057, USA
Interests: nonlinear optimization; evolutionary computation; automation for manufacturing; product design

Special Issue Information

Dear Colleagues,

This Special Issue "Nonlinear and Evolutionary Optimization in Materials and Engineering" delves into the intricate fusion of evolutionary algorithms in the realm of materials science and engineering. This Special Issue aims to provide an in-depth examination of the symbiotic relationship between evolutionary optimization techniques and the optimization challenges inherent in material properties, processing methodologies, and engineering structures. Artificial evolutionary methods such as genetic algorithms and other population-based nonlinear optimization techniques, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than via physical experiments alone. Machine learning models can augment experimental measurements of material fitness to accelerate the identification of useful and novel materials in vast material compositions or property spaces.

This Special Issue welcomes novel research and applications where evolutionary algorithms, nonlinear optimization, artificial intelligence, and machine learning are employed to optimize material properties, manufacturing processes, and engineering designs. All the works involved in the application of evolutionary algorithms, artificial intelligence, and machine learning to material science and engineering are welcome.

Dr. Amit Banerjee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolutionary algorithms
  • evolutionary optimization
  • nonlinear optimization
  • artificial intelligence
  • machine learning
  • evolutionary computation
  • deep learning
  • material science
  • computational materials design
  • smart materials
  • additive manufacturing
  • smart manufacturing processes

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 9122 KiB  
Article
Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis
by Zhongyuan Che and Chong Peng
Mathematics 2024, 12(8), 1153; https://doi.org/10.3390/math12081153 - 11 Apr 2024
Viewed by 307
Abstract
Low-alloy steel is widely employed in the aviation industry for its exceptional mechanical properties. These materials are frequently used in critical structural components such as aircraft landing gear and engine mounts, where a high strength-to-weight ratio is crucial for optimal performance. However, the [...] Read more.
Low-alloy steel is widely employed in the aviation industry for its exceptional mechanical properties. These materials are frequently used in critical structural components such as aircraft landing gear and engine mounts, where a high strength-to-weight ratio is crucial for optimal performance. However, the mechanical properties of low-alloy steel are influenced by various components and their compositions, making identification and prediction challenging. Accurately predicting these mechanical properties can significantly reduce the development time of new alloy steel, lower production costs, and offer valuable insights for design analysis. support vector regression (SVR) is known for its superior learning and generalization capabilities. However, optimizing SVR performance can be challenging due to the significant impact of the penalty factor and kernel parameters. To address this issue, a hybrid method called SMA-SVR is proposed, which combines the Slime Mould Algorithm (SMA) with SVR. This hybrid approach aims to efficiently and accurately predict two crucial mechanical parameters of low-alloy steel: tensile strength and 0.2% proof stress. Detailed descriptions of the modeling processes and principles that are involved in the hybrid method are provided. Furthermore, three other popular hybrid models for comparison are introduced. To evaluate the performance of these models, four statistical measures are utilized: Mean Absolute Error, Root Mean Square Error, R-Squared, and computational time. Using data from the NIMS database and from material tests conducted on a universal testing machine, experiments were carried out to compare the performance of these models. The results indicate that SMA-SVR outperforms the other methods in terms of accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Nonlinear and Evolutionary Optimization in Materials and Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop