Experimental analysis and optimization of coated and uncoated carbide tool in drilling aisi 316 L austenite stainless steel using minimum quantity lubrication technique - Mukhtar, Malik (2018)
Modern machining processes face continuous cost pressures and high quality expectations. To remain competitive a company must continually identify cost reduction opportunities in production, exploit economic opportunities, and continuously improve production processes. Minimum Quantity Lubricants (MQL) eliminates large quantities of water and oil-based coolants and replaces them with a small quantity of lubricant mixed with air delivered to the cutting tool’s edge. In this study, performance analysis of solid and TiAIN coated carbide tool in drilling AISI 316L Austenite Stainless Steel using MQL based on surface roughness, tool life and dimensional error as the responses. This project focuses on the drilling small hole on the austenite stainless steel by using high speed milling machine. This research was carried out to analyse and optimize machining parameters of cutting speed, feed rate and tool geometry of point of angle using coated and uncoated tools. Design of Experiment was applied to identify 12 number experiment settings based on three levels for each input parameters. Two analyses of Main Effect Analysis and then verified by Analysis of Variance (ANOVA) were employed to compare the performance between coated and uncoated tools. Before optimize the 3 input parameters, first order and second order regression were applied to determine the highest accuracy prediction can be used for optimization stage. Both single and multiple optimization were conducted to search the optimal cutting speed, feed rate and tool geometry of point based on based on surface roughness, tool wear and dimensional error. According to Main Effect Analysis and ANOVA results, it is showed that point angle is the most influence input parameters for TiAIN coated tool, however, feed rate is the most influence input parameters for uncoated tool. Second order regression was used for high accuracy prediction compared to first order. Coated tools was used for optimization showed that multi optimum cutting parameters are 110 m/min of cutting velocity, 0.150 mm/rev of feed rate and 110° to achieve 35.5 tool life, 1.062 um surface roughness and 6.051 hole accuracy. Confirmation showed that less error from prediction and actual experimental to confirm coated tool with certain point, feed rate and cutting speed can produce the optimal machining performance. Under MQL condition, coated carbide tool can be optimized for drilling AISI 316L Austenite Stainless Steels to satisfy the product quality and productivity.