Sensor fusion often uses multiple sensors to evaluate a single quantity. The work presented in this paper attempts to use information from a single sensor to estimate overall machining performance (characterized by cutting forces, chip breakability, surface roughness, and dimensional deviation due to tool wear). In particular, the performance is aimed at reflecting the in-process changes of the above-named quantities with respect to tool wear progression (major flank, crater and minor flank wear). 3-D cutting force measured by a tool dynamometer is fully utilized by aggregating multivariate time series models and neural network techniques. Dispersion analysis is used to extract signal features which correlate well with progressive tool wear. The results have shown the effectiveness of the proposed method which also has the obvious merit of simplicity.
Skip Nav Destination
Article navigation
August 1997
Technical Briefs
In-process Evaluation of the Overall Machining Performance in Finish-Turning via Single Data Source
X. D. Fang,
X. D. Fang
Department of Mechanical Engineering, University of Wollongong, NSW 2500, Australia
Search for other works by this author on:
Y. L. Yao
Y. L. Yao
School of Mechanical and Manufacturing Engineering, University of New South Wales, NSW 2033, Australia
Search for other works by this author on:
X. D. Fang
Department of Mechanical Engineering, University of Wollongong, NSW 2500, Australia
Y. L. Yao
School of Mechanical and Manufacturing Engineering, University of New South Wales, NSW 2033, Australia
J. Manuf. Sci. Eng. Aug 1997, 119(3): 444-447 (4 pages)
Published Online: August 1, 1997
Article history
Received:
October 1, 1993
Revised:
June 1, 1996
Online:
January 17, 2008
Citation
Fang, X. D., and Yao, Y. L. (August 1, 1997). "In-process Evaluation of the Overall Machining Performance in Finish-Turning via Single Data Source." ASME. J. Manuf. Sci. Eng. August 1997; 119(3): 444–447. https://doi.org/10.1115/1.2831127
Download citation file:
Get Email Alerts
Cited By
Multi-pass laser polishing of as-built DED surfaces
J. Manuf. Sci. Eng
Classification of Chip-Level Defect Types in Wafer Bin Maps Using Only Wafer-Level Labels
J. Manuf. Sci. Eng (July 2024)
Few-Shot Classification of Wafer Bin Maps Using Transfer Learning and Ensemble Learning
J. Manuf. Sci. Eng (July 2024)
Effects of Antifoaming Agents on Manufacturing Silver Dendrites Through Fluoride-Assisted Galvanic Replacement Reaction
J. Manuf. Sci. Eng (June 2024)
Related Articles
Directionally Independent Failure Prediction of End-Milling Tools During Pocketing Maneuvers
J. Manuf. Sci. Eng (August,2007)
Fractal Estimation of Flank Wear in Turning
J. Dyn. Sys., Meas., Control (March,2000)
Hidden Markov Model-based Tool Wear Monitoring in Turning
J. Manuf. Sci. Eng (August,2002)
A Neuro-Fuzzy System for Tool Condition Monitoring in Metal Cutting
J. Manuf. Sci. Eng (May,2001)
Related Proceedings Papers
Related Chapters
On-Line a Predictive Model of Cutting Force in Turning with 3 Axis Acceleration Transducer Using Neural Network
International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)
GA Based Multi Objective Optimization of the Predicted Models of Cutting Temperature, Chip Reduction Co-Efficient and Surface Roughness in Turning AISI 4320 Steel by Uncoated Carbide Insert under HPC Condition
Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)
Effectiveness of Minimum Quantity Lubrication (MQL) for Different Work Materials When Turning by Uncoated Carbide (SNMM and SNMG) Inserts
Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)