Optimization of Laser Welding Parameters of Aluminium Alloy 2024 using Particle Swarm Optimization Technique
- Authors: Aparna Duggirala1, Upama Dey2, Souradip Paul3, Bappa Acherjee4, Souren Mitra5
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View Affiliations Hide AffiliationsAffiliations: 1 School of Laser Science and Engineering, Jadavpur University, Kolkata, 700032, India 2 School of Laser Science and Engineering, Jadavpur University, Kolkata, 700032, India 3 School of Laser Science and Engineering, Jadavpur University, Kolkata, 700032, India 4 Department of Production and Industrial Engineering, Birla Institute of Technology: Mesra, Ranchi, 835215, India 5 Department of Production Engineering, Jadavpur University, Kolkata, 700032, India
- Source: Manufacturing and Processing of Advanced Materials , pp 58-68
- Publication Date: December 2023
- Language: English
Optimization of Laser Welding Parameters of Aluminium Alloy 2024 using Particle Swarm Optimization Technique, Page 1 of 1
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Laser welding is a viable method of joining aluminium alloys. The input parameters employed in the welding process have a significant impact on the weld quality. There are several parameters that influence weld quality, however, describing their relationship with weld seam characteristics is challenging. This study uses the Taguchi approach and particle swarm optimization (PSO) techniques for improving the weld quality in an Al 2024 lap joint to achieve a consistent and reliable joint. The experiments are performed on a laser welding machine following an L9 orthogonal array experimental design with peak power, scanning speed, and frequency as input parameters. Here, breaking load, bond width and throat length are considered as the responses. Experimentally a maximum breaking load of 1233 N and a minimum bond width of 398.81 µm is achieved. The throat length ranged from 340.72 µm to 983.94 µm. Regression analysis is used to establish the relationship between the input and the responses. The regression equations are utilized as the objective function in an optimization problem. The crowding distance PSO is used to acquire the global optima. Finally, the optimal process parameters for achieving the desired goals are presented.<br>
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