Surface roughness optimization in end milling using the multi objective genetic algorithm approach

• Main Authors: Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Riza, Muhammad, Mohammad Yuhan, Suprianto
• Format: Article
• Language: English; English
• Published: Trans Tech Publications Inc. 2012
• Subjects: T Technology (General)
• Online Access: http://irep.iium.edu.my/55357/1/10.1.1.971.7225.pdf; http://irep.iium.edu.my/55357/7/55357_Surface%20Roughness%20Optimization_SCOPUS.pdf; http://irep.iium.edu.my/55357/; https://www.scientific.net/AMR.576.103
• Institution: Universiti Islam Antarabangsa Malaysia

In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.