Moayyedian, Mehdi, Chalak Qazani, Mohammad Reza, Cvorovic, Vuk, Asi, Fahad, Mussin, Askhat, Hedayati-Dezfooli, Mohsen, and Dinc, Ali (2023) Tensile Test Optimization Using the Design of Experiment and Soft Computing. Processes, 11 (11). 3106.

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Abstract

The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator on the tensile test of brass samples. This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). By combining these techniques, the study aims to leverage their individual strengths and achieve superior results. The results reveal that the area of the contact point exerts the most significant influence on the tensile test, followed by the operator and temperature. The optimal levels of these parameters are determined to be a level of two for the operator, a level of three for the area of the contact point, and a level of one for the temperature. The study demonstrated that the hybrid soft computing method outperformed the traditional DOE method, achieving a substantial improvement in elongation of 32.9%. The optimized combination of machining parameters led to a notable enhancement in the brass samples’ tensile properties, highlighting the effectiveness of the applied methodology. The marginal error of only 0.72% in the hybrid approach showcases its high precision and reliability in determining the optimal levels of machining parameters. These findings underscore the potential of the Taguchi optimization method, ANFIS, and GA in achieving superior results in the tensile testing of materials, particularly in cases where multiple parameters are involved. The research results provide valuable insights for industries relying on precise material characterization, offering a robust methodology for optimizing tensile testing procedures. The study’s success in leveraging a hybrid soft computing approach serves as a promising avenue for future research in the field of material testing and optimization techniques.

Item ID:	86724
Item Type:	Article (Research - C1)
ISSN:	2227-9717
Copyright Information:	© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited:	10 Sep 2025 01:12
FoR Codes:	40 ENGINEERING > 4016 Materials engineering > 401605 Functional materials @ 15% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460199 Applied computing not elsewhere classified @ 25%
SEO Codes:	24 MANUFACTURING > 2412 Machinery and equipment > 241204 Industrial machinery and equipment @ 60% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 40%
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