We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multi-Point Tool Condition Monitoring System - A Comparative Study.
- Authors
Kumar, D. Pradeep; Muralidharan, V.; Hameed, Syed Shaul
- Abstract
In the metal removal process, the condition of the tool plays a vital role to achieve maximum productivity. Hence, monitoring the tool condition becomes inevitable. The multipoint cutting tool used in the face milling process is taken up for the study. Cutting inserts made up of carbide with different conditions such as fault-free tool (G), flank wear (FW), wear on rake face (C) and tool with broken tip (B) are considered. During machining of mild steel, vibration signals are acquired for different conditions of the tool using a tri-axial accelerometer, and statistical features are extracted. Then, the significant features are selected using the decision tree algorithm. Support Vector Machine(SVM) algorithm is applied to classify the conditions of the tool. The results are compared with the performance of the K-Star algorithm. The classification accuracy obtained is encouraging hence, the study is recommended for real-time application.
- Subjects
CUTTING tools; FEATURE extraction; MILD steel; SUPPORT vector machines; DECISION trees; COMPARATIVE studies
- Publication
FME Transactions, 2022, Vol 50, Issue 1, p193
- ISSN
1451-2092
- Publication type
Article
- DOI
10.5937/fme2201193K