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基于元启发式算法的高导电性和硬度铜镍硅合金优化

Metaheuristics Algorithm-Based Optimization for High Conductivity and Hardness CuNiSi Alloy.

作者信息

Konieczny Jarosław, Labisz Krzysztof, Ürgün Satılmış, Yiğit Halil, Fidan Sinan, Bora Mustafa Özgür, Atapek Ş Hakan

机构信息

Department of Railway Transport, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland.

Faculty of Aviation and Astronautics, Aviation Electrical Electronics, Kocaeli University, Kocaeli 41001, Türkiye.

出版信息

Materials (Basel). 2025 Feb 27;18(5):1060. doi: 10.3390/ma18051060.

DOI:10.3390/ma18051060
PMID:40077285
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11901186/
Abstract

The optimization of CuNiSi alloy's mechanical and electrical properties was achieved through a combination of experimental approaches and metaheuristic algorithms. Optimizing hardness and electrical conductivity through a variation in aging temperature (450-600 °C) and aging duration (1-420 min) was taken under consideration in the present work. Cold rolling with 50% strain after solution annealing aided in microstructure refinement and accelerated NiSi precipitates' development, and property improvement increased. Optimum temperature and holding period were 450 °C and 30 min, respectively, with 266 HV and 13 MS/m and 167 HV and 11.2 MS/m for non-deformed samples, respectively. SPBO, genetic algorithm (GA), and particle swarm optimization (PSO) metaheuristic algorithms were considered, and SPBO exhibited the best prediction accuracy. SPBO predicted 450 °C for 61.75 min, and experimental testing exhibited 267 HV and 14 MS/m, respectively. Polynomial regressions with 0.98 and 0.96 values for R confirmed these values' accuracy. According to this work, computational optimization proves effective in optimizing development and property tailoring for application in industries including aerospace and electrical engineering.

摘要

通过实验方法和元启发式算法相结合,实现了CuNiSi合金力学性能和电学性能的优化。本研究考虑了通过改变时效温度(450-600°C)和时效时间(1-420分钟)来优化硬度和电导率。固溶退火后进行50%应变的冷轧有助于细化微观结构,并加速NiSi析出相的形成,性能得到进一步改善。对于未变形的样品,最佳温度和保温时间分别为450°C和30分钟,硬度分别为266 HV和167 HV,电导率分别为13 MS/m和11.2 MS/m。考虑了SPBO、遗传算法(GA)和粒子群优化(PSO)等元启发式算法,其中SPBO表现出最佳的预测精度。SPBO预测的温度为450°C,时间为61.75分钟,实验测试得到的硬度和电导率分别为267 HV和14 MS/m。R值分别为0.98和0.96的多项式回归证实了这些值的准确性。根据这项工作,计算优化在优化开发和定制性能以应用于航空航天和电气工程等行业方面被证明是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/aa6dfc1af0b0/materials-18-01060-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/a8cdb5e750c0/materials-18-01060-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/8aedbc6baadf/materials-18-01060-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/aa6dfc1af0b0/materials-18-01060-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/a8cdb5e750c0/materials-18-01060-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/8aedbc6baadf/materials-18-01060-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd5/11901186/aa6dfc1af0b0/materials-18-01060-g003.jpg

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