Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via tool condition monitoring system in turning of aisi 5140
Sensors (Switzerland) · Ağustos 2020
YÖKSİS Kayıtları
Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140
Sensors · 2020 SCI-Expanded
DOÇENT MUSTAFA KUNTOĞLU →
Makale Bilgileri
DergiSensors (Switzerland)
Yayın TarihiAğustos 2020
Cilt / Sayfa20 · 1-22
Scopus ID2-s2.0-85089301234
Erişim🔓 Açık Erişim
Özet
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%).
Yazarlar (6)
1
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
2
Abdullah Aslan
3
Haci Sağlam
ORCID: 0000-0002-6598-8262
4
Danil Yurievich Pimenov
5
Khaled Giasin
6
Tadeusz Mikolajczyk
ORCID: 0000-0002-5253-590X
Anahtar Kelimeler
Acoustic emission
Cutting force
Flank wear
Motor current
Surface roughness
Temperature
Tool Condition Monitoring
Vibration
Kurumlar
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
Metrikler
83
Atıf
6
Yazar
8
Anahtar Kelime