Minimization of machining process sequence based on ant colony algorithm and conventional method

• Main Authors: Abdullah, Haslina, Law, Boon Hui C., Zakaria, Mohamad Shukri
• Format: Article
• Language: English
• Published: Taylor's University 2023
• Online Access: http://eprints.utem.edu.my/id/eprint/27334/2/0196026122023.PDF; http://eprints.utem.edu.my/id/eprint/27334/; https://jestec.taylors.edu.my/Vol%2018%20Issue%202%20April%202023/18_2_8.pdf

Machining airtime or non-productive time or airtime is a process of movement of the tool before shaping the workpiece. One of the methods to decrease the total machining time is by reducing airtime. Thus, in this study, an optimization of the sequence operation in machining was conducted using an Artificial Intelligence method, which is the Ant Colony algorithm. This algorithm was employed to decrease the machining airtime to enhance the effectiveness of the machining process. A three-dimensional model consisting of the drilling process and pocket milling process was developed using Solidworks software. Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. Hence, the results of the optimization were implemented in MasterCAM software to run the machining simulation. Then, the results of machining time that used the tool path generated by the Ant Colony algorithm method was compared with the machining time that used tool paths generated by conventional methods. Based on the simulation, the Ant Colony algorithm method is, on average, 10.8% better than conventional methods in reducing machining time. It can be concluded that the Ant Colony algorithm is capable of reducing airtime machining and enhancing the machining process's performance.