Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Investigation of progressive tool wear for determining of optimized machining parameters in turning
Scopus
Toplam 177 atıf DOI
On-line monitoring of tool wear and tool breakage are very important to reduce production costs through the optimization of machining parameters. Increasing cutting forces affect workpiece quality and tool condition that is the ultimate aim of production line and progressive tool wear which can trigger the tool breakage. Taguchi method is extensively used for determining number of experiment while variance analysis (ANOVA) deals with which parameter/s is/are effective on output. This study contains experiments and optimization processes during turning of AISI 1050 material with 3 input parameters (cutting speed, feed rate, tool tip) using Taguchi method. In order to determine the condition of the cutting tool, measurement of tangential cutting force and acoustic emission (AE) were carried out during metal removing. ANOVA results showed that cutting speed is the most effective about %45 and tool tip is the second about %35 on tool wear. On the other hand, the effect of feed rate on tangential cutting force (%88) and cutting speed on AE (%80) is remarkably higher than the other two parameters. In order to obtain the minimum tool wear value, the optimum cutting parameters have been selected as v 1 = 135 m/min, f 2 = 0,214 mm/rev, T 2 = P25. By implemented sensor system tool breakage can be successfully detected and used for producing high quality materials with low costs.
Atıf Kaynağı
Atıf Yapan Yayın
Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via tool condition monitoring system in turning of aisi 5140
Scopus
Havuzumuzda Open Access 108 atıf almış
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%).
Atıf Yapan Makale Bilgileri
Kurumlar (4)
Bydgoszcz University of Science and Technology
Bydgoszcz, Poland
Selçuk Üniversitesi
Selçuklu, Turkey
South Ural State University
Chelyabinsk, Russian Federation
University of Portsmouth
Portsmouth, United Kingdom