基于仿生人工智能算法的搅拌摩擦焊接热处理铝合金力学性能优化

IF 1.2 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY

Frattura ed Integrita Strutturale Pub Date : 2022-09-22 DOI:10.3221/igf-esis.62.31

Akshansh Mishra, Anish Dasgupta

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引用次数: 0

摘要

自然界中生物进化的概念和灵感被用来在新兴的生物启发计算优化算法领域创造新的有效竞争策略。在本工作中，对9个类似合金的试样，即AA6262进行了搅拌摩擦焊接。主轴速度（RPM）、横向速度（mm/min）和柱塞深度（mm）是输入参数，而极限拉伸强度（MPa）是输出参数。该工作的主要目标是通过使用生物启发的人工智能算法，即微分进化和Max Lipschitz优化（Max LIPO）算法，获得最大优化极限抗拉强度（MPa）。结果表明，与Max LIPO算法相比，差分进化算法导致极限抗拉强度的值略高。

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Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms

The concepts and inspiration of biological evolution in nature are used to create new and effective competing tactics in the burgeoning field of bio-inspired computing optimization algorithms. In the present work, nine specimens of similar alloys i.e., AA6262 were Friction Stir Welded. Spindle Speed (RPM), Traverse Speed (mm/min), and Plunge Depth (mm) were the input parameters while the Ultimate Tensile Strength (MPa) was an output parameter. The main objective of the work is to obtain the maximum optimized Ultimate Tensile Strength (MPa) by using Bio-Inspired Artificial Intelligence Algorithms i.e., Differential Evolution and Max Lipschitz optimization (Max LIPO) Algorithm. The results showed that the Differential Evolution algorithm resulted in a slightly higher value of the Ultimate Tensile Strength in comparison to the Max LIPO algorithm.

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