Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140.

Mustafa Kuntoğlu,Hacı Sağlam,Khaled Giasin,Abdullah Aslan,Danil Yurievich Pimenov,Tadeusz Mikolajczyk

doi:10.3390/s20164377

Abstract

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

Highlights

Sensors are devices that measure a certain value and convert them into electrical signals
ANOVA results for the response parameters, namely VB and Ra, and Table 6 shows ANOVA results for sensor data, namely cutting force, AE, temperature, vibration and motor current
This study shows the statistical analysis and optimization of experimental results for the turning of AISI 5140 steel

Summary

Introduction

Sensors are devices that measure a certain value and convert them into electrical signals. The main working principle of sensors is to convert physical changes into electrical quantities, and match mechanical systems with electrical systems. They ensure that a physical change in a system can be detected, measured and transferred to the environment, where it will be evaluated by the Sensors 2020, 20, 4377; doi:10.3390/s20164377 www.mdpi.com/journal/sensors. The aim is to obtain a stable and reliable production using various sensors. It is desired to prevent unexpected losses by monitoring the manufacturing process with high sensitivity sensors. Various breakdowns and accidents cause the halting of the production, leading to increased costs and lower efficiency [1]

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