Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms
American Journal of Neural Networks and Applications
Volume 3, Issue 6, December 2017, Pages: 56-62
Received: Oct. 30, 2017;
Accepted: Nov. 22, 2017;
Published: Jan. 11, 2018
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Vijay Nagandran, Department of Mechanical Engineering, Universiti Tenaga Nasional, Selangor, Malaysia
Tiagrajah V. Janahiraman, Department of Electronics and Communication Engineering, Universiti Tenaga Nasional, Selangor, Malaysia
Nooraziah Ahmad, Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Kelantan, Malaysia
Surface roughness or surface quality is considered to be one of the most crucial requirement of a machined part since it directly influences the mechanical properties of the part. However, the traditional method of choosing cutting parameters’ values to obtain a good surface finish has its own disadvantages. Therefore, an experimental study has been conducted to develop a suitable mathematical model and pair it with an optimization technique that able to produce low surface roughness of carbon steel AISI 1045. Response surface methodology (RSM) is used to develop the mathematical model whereas three types of heuristic optimization methods namely Genetics Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) employed to optimize the model and find the optimal cutting parameters’ values. A brief comparison of the three optimization methods has been made to study their performance to the developed model. Experimental results indicate that the proposed modeling technique and PSO are quite efficient in determining optimal cutting parameters for CNC turning of carbon steel AISI 1045.
Tiagrajah V. Janahiraman,
Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms, American Journal of Neural Networks and Applications.
Vol. 3, No. 6,
2017, pp. 56-62.
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