Optimization machining parameters in pocket milling using genetic algorithm and mastercam

Selection of the optimum machining parameters is essential to enhance the performance of the machining process. These parameters will determine the quality of surface finish, rate of tool wear, material removal rate and production cycle time. This research focused on obtaining the optimum machinin...

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Bibliographic Details
Main Authors: Abdullah, Haslina, Isa, Nurshafinaz, Zakaria, Mohamad Shukri
Format: Conference or Workshop Item
Language:en
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/28087/1/Optimization%20machining%20parameters%20in%20pocket%20milling%20using%20genetic%20algorithm%20and%20mastercam.pdf
http://eprints.utem.edu.my/id/eprint/28087/
https://pubs.aip.org/aip/acp/article-abstract/2955/1/020024/2930463/Optimization-machining-parameters-in-pocket?redirectedFrom=fulltext
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Summary:Selection of the optimum machining parameters is essential to enhance the performance of the machining process. These parameters will determine the quality of surface finish, rate of tool wear, material removal rate and production cycle time. This research focused on obtaining the optimum machining parameters by minimizing the machining time in pocket milling operations. The parameter such as feed per tooth, cutting speed,and depth of cut are considered the important variables to minimize the machining time in milling process since it has produced a significant consequence on the machining process. Genetic Algorithm (GA) has been implemented to obtain the optimum parameters. A fitness function of production time incorporated of roughing and finishing process has been developed in Matlab Software. Then, Genetic Algorithm Toolbox has been employed to conduct the computational experiment. Mastercam software has been used to verify the algorithm's results by applying the optimum parameter generated by GA in the Mastercam. Based on the comparison, it has been found that the difference in machining for both methods is 3.48%. It shows that optimization based on GA is capable of reducing production time in the machining process.