The Multiple Principal Component (MPC) Fuzzy Neural Network for tool condition monitoring in machining under varying cutting conditions is proposed. This approach is based on three major components of “soft computation,” namely fuzzy logic, neural network, and probability reasoning. The MPC classification fuzzy neural networks were built through training with learning data obtained from cutting tests performed in a reasonable range of cutting conditions. Several sensors were used for monitoring feature selection. Force, vibration, and spindle motor power signals were fused in multiple principal component directions to give a highly sensitive feature space. The tool conditions considered in the monitoring tests included sharp tool, tool breakage, slight wear, medium wear, and severe wear. The results showed success rates of approximate 94 percent in self-classification tests (i.e., the same data samples were used for both learning and classification), 84 percent in tests performed using different records for classification than those used for learning under the same cutting conditions, and about 80 percent in tests performed using samples obtained at different cutting conditions for classification than those used for learning within the same range of cutting conditions. The MPC fuzzy neural network classification strategy performed better than back-propagation trained feed-forward neural networks in these tests.
Skip Nav Destination
Article navigation
December 1996
Technical Papers
Tool Condition Monitoring in Machining by Fuzzy Neural Networks
S. Li,
S. Li
Currently with IRDI, Midland, Ontario, Canada
Search for other works by this author on:
M. A. Elbestawi
M. A. Elbestawi
Department of Mechanical Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L7
Search for other works by this author on:
S. Li
Currently with IRDI, Midland, Ontario, Canada
M. A. Elbestawi
Department of Mechanical Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L7
J. Dyn. Sys., Meas., Control. Dec 1996, 118(4): 665-672 (8 pages)
Published Online: December 1, 1996
Article history
Received:
July 11, 1994
Online:
December 3, 2007
Citation
Li, S., and Elbestawi, M. A. (December 1, 1996). "Tool Condition Monitoring in Machining by Fuzzy Neural Networks." ASME. J. Dyn. Sys., Meas., Control. December 1996; 118(4): 665–672. https://doi.org/10.1115/1.2802341
Download citation file:
Get Email Alerts
Data-Driven Tracking Control of a Cushion Robot With Safe Autonomous Motion Considering Human-Machine Interaction Environment
J. Dyn. Sys., Meas., Control (July 2025)
Dynamic Obstacle Avoidance Strategy for High-Speed Vehicles Via Constrained Model Predictive Control and Improved Artificial Potential Field
J. Dyn. Sys., Meas., Control (July 2025)
An Adaptive Sliding-Mode Observer-Based Fuzzy PI Control Method for Temperature Control of Laser Soldering Process
J. Dyn. Sys., Meas., Control
Related Articles
A Neuro-Fuzzy System for Tool Condition Monitoring in Metal Cutting
J. Manuf. Sci. Eng (May,2001)
Heuristic Feature Selection for Shaving Tool Wear Classification
J. Manuf. Sci. Eng (April,2017)
Quality and Inspection of Machining Operations: Tool Condition Monitoring
J. Manuf. Sci. Eng (August,2010)
Fractal Estimation of Flank Wear in Turning
J. Dyn. Sys., Meas., Control (March,2000)
Related Proceedings Papers
Related Chapters
Computation of Gradient and Hessian in Feed-Forward Neural Networks: A Variational Approach
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Tool Condition Monitoring in Metal Cutting Processes - a Systematic Approach Using ANN Based Multiple Sensor Fusion Strategy
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
Drill Wear Prediction Using Fuzzy Neural Network
International Conference on Computer and Computer Intelligence (ICCCI 2011)