Post Weld Heat Treatment Optimization of Dissimilar Friction Stir Welded AA2024-T3 and AA7075-T651 Using Machine Learning and Metaheuristics

被引:12
作者
Insua, Pinmanee [1 ,2 ]
Nakkiew, Wasawat [2 ,3 ]
Wisittipanich, Warisa [2 ]
机构
[1] Chiang Mai Univ, Fac Engn, Grad Program Ind Engn, Chiang Mai 50200, Thailand
[2] Chiang Mai Univ, Fac Engn, Dept Ind Engn, Chiang Mai 50200, Thailand
[3] Chiang Mai Univ, Fac Engn, Adv Mfg & Management Technol Res Ctr AM2Tech, Dept Ind Engn, Chiang Mai 50200, Thailand
关键词
dissimilar friction stir welding; post weld heat treatment; machine learning; metaheuristics; process optimization; ultimate tensile strength; elongation percentage; MECHANICAL-PROPERTIES; ALUMINUM-ALLOY; MICROSTRUCTURE; JOINTS; STRENGTH; 2024-T4;
D O I
10.3390/ma16052081
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Post weld heat treatment, or PWHT, is often used to improve the mechanical properties of materials that have been welded. Several publications have investigated the effects of the PWHT process using experimental designs. However, the modeling and optimization using the integration of machine learning (ML) and metaheuristics have yet to be reported, which are fundamental steps toward intelligent manufacturing applications. This research proposes a novel approach using ML techniques and metaheuristics to optimize PWHT process parameters. The goal is to determine the optimal PWHT parameters for both single and multiple objective perspectives. In this research, support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) were ML techniques employed to obtain a relationship model between PWHT parameters and mechanical properties: ultimate tensile strength (UTS) and elongation percentage (EL). The results show that the SVR demonstrated superior performance among ML techniques for both UTS and EL models. Then, SVR is used with metaheuristics such as differential evolution (DE), particle swarm optimization (PSO), and genetic algorithms (GA). SVR-PSO shows the fastest convergence among other combinations. The final solutions of single-objective and Pareto solutions were also suggested in this research.
引用
收藏
页数:17
相关论文
共 50 条
[41]   Effect of thermo-mechanical processing on quench-induced precipitates morphology and mechanical properties in high strength AA7075 aluminum alloy [J].
Scharifi, E. ;
Savaci, U. ;
Kavaklioglu, Z. B. ;
Weidig, U. ;
Turan, S. ;
Steinhoff, K. .
MATERIALS CHARACTERIZATION, 2021, 174
[42]   Effect of post weld heat treatments on microstructure and mechanical properties of friction stir welded joints of Al-Zn-Mg alloy AA7039 [J].
Sharma, Chaitanya ;
Dwivedi, Dheerendra Kumar ;
Kumar, Pradeep .
MATERIALS & DESIGN, 2013, 43 :134-143
[43]   Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm [J].
Shojaeefard, Mohammad Hasan ;
Behnagh, Reza Abdi ;
Akbari, Mostafa ;
Givi, Mohammad Kazem Besharati ;
Farhani, Foad .
MATERIALS & DESIGN, 2013, 44 :190-198
[44]   Effect of post weld heat treatment on tensile properties and microstructure characteristics of friction stir welded armour grade AA7075-T651 aluminium alloy [J].
Sivaraj, P. ;
Kanagarajan, D. ;
Balasubramanian, V. .
DEFENCE TECHNOLOGY, 2014, 10 (01) :1-8
[45]   Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm [J].
Tamjidy, Mehran ;
Baharudin, B. T. Hang Tuah ;
Paslar, Shahla ;
Matori, Khamirul Amin ;
Sulaiman, Shamsuddin ;
Fadaeifard, Firouz .
MATERIALS, 2017, 10 (05)
[46]   A survey on metaheuristics for optimization in food manufacturing industry [J].
Wari, Ezra ;
Zhu, Weihang .
APPLIED SOFT COMPUTING, 2016, 46 :328-343
[47]   Machine learning in manufacturing: advantages, challenges, and applications [J].
Wuest, Thorsten ;
Weimer, Daniel ;
Irgens, Christopher ;
Thoben, Klaus-Dieter .
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL, 2016, 4 (01) :23-45
[48]   Effect of post-weld heat treatment on mechanical properties and fatigue crack growth rate in welded AA-2024 [J].
Yadav, Vinay Kumar ;
Gaur, Vidit ;
Singh, I. V. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2020, 779
[49]   An experimental investigation on the deformation and post-formed strength of heat-treatable aluminium alloys using different elevated temperature forming processes [J].
Zheng, Kailun ;
Dong, Yangchun ;
Zheng, Dengqi ;
Lin, Jianguo ;
Dean, Trevor A. .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2019, 268 :87-96
[50]  
Zitzler E, 1998, LECT NOTES COMPUT SC, V1498, P292, DOI 10.1007/BFb0056872