Experimental investigation of MQL Optimum Parameters In End Milling of AA6061-T6 using Taguchi Method

• Main Authors: Wahaizad, Safiei, M. M., Rahman, Siti Aishah, Rusdan
• Format: Article
• Language: English
• Published: Science Publishing Corporation 2018
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/22363/3/Experimental%20Investigation%20of%20MQL%20Optimum%20Parameters.pdf; http://umpir.ump.edu.my/id/eprint/22363/; https://www.sciencepubco.com/index.php/ijet/article/view/22360/10963
• Institution: Universiti Malaysia Pahang

Minimum Quantity Lubricants is a technique in supplying small quantity of lubricant into machining area which also part of green manufac-turing approach that receive wide attention globally. The main driven of introducing MQL method was due to negative environmental impact which leads to safety and health issues of conventional coolant among workers especially in tool and mould industries. Besides, based on research findings, the MQL system has the capability for lubricating and cooling both work piece and cutting tool. In order to find the best solution for machining and also to enhance machining performance, first and foremost the MQL parameters must be controlled wisely as it has remarkable effects on lubricant coverage, droplets size and subsequently influence the machining performance. Nozzle angle, nozzle distance and MQL flow rate are the important parameters studied and surface roughness is the response parameter. Therefore, in this study, MQL optimum parameters were explored by minimizing surface roughness in end milling process using Taguchi L9 orthogonal method. Aluminum Alloy 6061-T6 was selected as work piece material. The results show that the best combination of MQL parameters in minimizing surface roughness was obtained at 30mm nozzle distance, 30 degree nozzle angle and 1.98 mL/min MQL flow rate. Hence, based on this optimal condition, three confirmation runs were conducted. The margin error is acceptable which less than 10% and within prediction interval. This results can work as a base line guidance for any experimental that employ MQL system