Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning
Scopus
Toplam 185 atıf DOI
Monitoring of the cutting area with different type of sensors requires confirmation for composing sensor fusion to obtain longer tool life and high-quality product. The complex structure of machining and interaction between variables affect the influence of parameters on quality indicators. Using multiple sensors provide comparison of information acquired from different resources and make easier to decide about tool and workpiece condition. In this experimental research for the first time, five different sensors were adopted to a lathe for collecting data to measure the capability of each sensor in reflecting the tool wear. Cutting forces, vibration, acoustic emission, temperature and current measurements were carried out during turning of AISI 5140 with coated carbide tools. Considering the graphical investigation, the successes of sensors on detection of progressive flank wear and tool breakage were investigated. Besides, the effects of cutting parameters on measured variables were interpreted considering graphs. According to results, temperature and acoustic emission signals seem to be effective about 74% for flank wear. In addition, fuzzy logic based prediction of flank wear was performed with the assistance of temperature and acoustic emission sensors with high accuracy which demonstrates their availability for sensor fusion. Tool breakage occurs instantly which can prevent with the assistance of sensor signals and tangential and feed cutting forces, acoustic emission and vibration signals seem as reliable indicators for approaching major breakage. Sensor fusion based turning provides confirmed information which enables more reliable, robust and consistent machining.
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Atıf Yapan Yayın
Indirect monitoring of machining characteristics via advanced sensor systems: a critical review
Scopus
Havuzumuzda 83 atıf almış
On-line monitoring of the machining processes provides to detect the amount and type of tool wear which is critical for the determination of remaining useful lifetime of cutting tool. According to Industry 4.0 revolution, the machining performance in terms of cutting forces, surface roughness, power consumptions, tool wear, tool life, etc. needs to be automatically monitored because the unfavorable conditions in machining cause chatter vibrations, tool breakage, and dimensional accuracy. Therefore, the usage of advanced sensor systems plays a key role in achieving the improved machining characteristics in terms of less human effort, errors, production time, etc. and fulfills the requirement of Industry 4.0. Hence, this review presents the holistic knowledge of online detection systems including sensors and signal processing software preferred in mechanical machining operations. Initially, this paper is starting with the up-to-date literature introduction section followed by type of sensors used in machining, online detection methods in machining, challenges and suggestions, etc. Eventually, the article concluded the findings and future remarks especially focused on the theme of Industry 4.0. In the end, it is worthy to mention that this review paper is very helpful for researchers and academicians working in the industrial sectors.
Atıf Yapan Makale Bilgileri
Kurumlar (4)
Karabük Üniversitesi
Karabuk, Turkey
Opole University of Technology
Opole, Poland
Selçuk Üniversitesi
Selçuklu, Turkey
South Ural State University
Chelyabinsk, Russian Federation