A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture

Zhong Ren Wang; Yu Feng Zou; Fan Zhang

doi:10.4028/www.scientific.net/AMR.154-155.412

Paper Titles

A Machining Error Compensation Method Using on-Machine Profile Measurement in 3-Axis Aspheric Grinding
p.390

Self-Adaptive Compensation Method on the Tool Position of Roller Gear Cam Surface
p.396

Phase-Field Modeling of Solute Trapping in Single-Phase Alloys during Directional Solidification
p.401

Study of Nickel-Based Composite Coatings on H13 Steel Surface by Laser
p.407

A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture
p.412

Numerical Simulation on Quenching Temperature Field of 40Cr Steel
p.417

Influence of Weld Heat Input on Weld Joint between B610CF and 16MnR Steel
p.421

Repairing Surface Fatigue Damage of the Metal Material by Heat Treatment
p.425

Discussion on the Polarity Effect of EDM
p.429

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 154-155A Machine Vision Approach to Tool Wear Monitoring...

A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture

Abstract:

Machine vision technique is an advanced method for tool wear monitoring. In this article, a holding system has been designed and fabricated to realize the combination of machine tools and machine vision system. On-machine experiments were carried out to test the effect of this method. Experimental results indicate that tool condition monitoring can be successfully accomplished by analyzing texture feature information extracted from the machined surface.