Investigation on the correlation between surface roughness and acoustic emission characteristics in turning process

Abstract

One of the most significant feature for monitoring in machining processes is tool wear. It has a direct influence on the quality of machined surfaces. In-order to maintain the product quality and to reduce material wastage, online tool wear monitoring has become a regular practice. With the progress in tool wear, surface roughness changes accordingly and this change can be used to assess the tool condition. However, it is difficult to measure the surface roughness online. It is well known that interaction of tool and workpiece results in high frequency stress waves known as Acoustic Emissions (AE), which can be used as an indirect online method to monitor the surface roughness and in-turn tool wear. The analysis of AE signals which received significant attention in structural and machine health monitoring opens wide opportunities to monitor the machining process. Hence, in the present work an attempt has been made to explore the correlation between the acoustic emission characteristics and workpiece surface roughness during the high speed turning operation using AISI 4340 alloy steel workpiece with the help of Zirconia Toughened Alumina (ZTA) tool on a lathe machine. Experiments has been designed as per Central Composite Design (CCD) of Response Surface Methodology (RSM) with varying 3 levels of 3 parameters such as cutting speed, feed rate and depth of cut. For each experiment, AE signals are acquired and surface roughness is measured using Surtronic 25 portable surface roughness meter. Analysis of variance (ANOVA) is used to study the effect of control parameters on output responses and a model is prepared using regression analysis. It is observed from the ANOVA analysis that feed rate and cutting speed have profound influence on surface roughness and RMS respectively. The optimum condition is found at Cutting speed of 300 m/min with feed rate of 0.12 mm/rev and depth of cut of 1.5 mm with 97.15% desirability for minimum surface roughness and nominal RMS value. From the parametric study, it is observed that AE characteristic (RMS) shows good correlation with surface roughness which can be used for further analysis in online monitoring of tool wear.