Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—In this research, a novel constrained response surface optimisation model (CRSOM) with the incremental solution constructions via the metaheuristic of ant colony optimisation is introduced to determine the preferable levels of industrial process parameters. They are developed in two forms including both linear (LCRSOM) and nonlinear (NLCRSOM) regression models for estimation of influential parameter coefficients. Then, in order to compare the accuracy of the proposed algorithm, a comparison is made on a laser welding process of the electronic industry. On the current situation of the head support and suspension, assembly it has been found that shear strength is quite higher than customers' specification. During an inspection, the sample size and frequency are set at high levels. Other quality characteristics include welding diameter and depth as well. The proposed method is having a provision to include both explicit constraints of influential process parameters as well as implicit constraints of customer specifications. From experimental results, the mean absolute errors of the CRSOM on ACO are better and the best so far solutions are provided by nonlinear form (NLCRSOM) due to fluctuations of responses affected by the influential parameters. The selected levels of influential process parameters have been successfully implemented for all three responses. The advantage of the incremental solution constructions via both metaheuristics in each type of CRSOM is that all the experimental data are simultaneously collected and analysed to obtain a final operating condition. When industrial problems are large and complicated, finite instructions from the proposed approach are effective. Setting industrial parameters is more useful, systematical and practical.
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