Genetic Algorithm (GA)-Based Single and Multi-Objective Optimisation for the Laser Cladding Process N/A
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Genetic Algorithim (GA), Hardness, Laser Cladding Process, Layer Thickness, Radial Basis Function Neural -Network (RBFNN)
Abstract
In the manufacturing field it’s very important to find the optimum operation conditions of the operational process as well as the best values of the input system parameters. In other words, find the best possible solutions for the system variables to reach the minimum cost of the operation process and maximum quality of the product at the same time. In order to address these challenges, Radial Basis Function Neural Network (RBFNN) was used to model the laser cladding process and to predict the hardness and layer thickness of the laser cladding operation. The operation data set was collected from Talleres Mecanicos Comas (TMC). Also, the Genetic Algorithm (GA) for a single and multi-objective optimisation framework for the laser cladding process is presented in this paper. The main objective of this technique is to find optimal solutions for three different input variables, which are Travel Speed (TS), Powder Fed Rate (PFR) and Laser Power (LP), that can help the user with the optimal design of the laser cladding process. The simulation results showed that very good optimisation solutions were obtained for the three process parameters (TS, PER and LP).
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