Kurumun Atıf Alan Makalesi
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A review of indirect tool condition monitoring systems and decision‐making methods in turning: Critical analysis and trends
Scopus
Open Access Toplam 267 atıf DOI
The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In‐direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emis-sion, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without in-tervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration re-quire a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.
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Atıf Yapan Yayın
Monitoring Built-Up Edge, Chipping, Thermal Cracking, and Plastic Deformation of Milling Cutter Inserts through Spindle Vibration Signals
Scopus
Havuzumuzda Open Access 8 atıf almış
Condition monitoring provides insights into the type of damage occurring in the cutting tool during machining to facilitate its timely maintenance or replacement. By detecting and analyzing machining consequences (vibrations, chatter, noise, power consumption, spindle load, etc.), correlating them with different tool conditions enables real-time monitoring and the automated detection of tool failures. Machine learning (ML) plays a vital role in making tool condition monitoring (TCM) frameworks intelligent, and most research is geared toward classifying various types of tool wear. However, monitoring built-up edges, chipping, thermal cracking, and plastic deformation of milling cutter inserts are challenging and need careful consideration. To effectively monitor these phenomena, spindle vibrations can narrate the corresponding dynamic behavior of tool conditions and therefore have been investigated in this research. The acquired vibration data are then analyzed using histogram features and trained through the Partial C4.5 (PART) classifier to extract meaningful recommendations related to the milling cutter inserts condition.
Atıf Yapan Makale Bilgileri
Kurumlar (5)
Bydgoszcz University of Science and Technology
Bydgoszcz, Poland
COEP Technological University, Pune
Pune, India
MKSSS’s Cummins College of Engineering for Women
Pune, India
Rizvi College of Engineering
Mumbai, India
Selçuk Üniversitesi
Selçuklu, Turkey