Tool positioning and tool wear or breakage is an integral part of the development of a micromachining center. The nature of worn tools, producing deficiencies in good surface finish and dimension control, is a major concern in machining operations. Current techniques in place for tool location depend on the encoder feedback of the CNC system, with the assumption of accurate tool radius compensation. Additionally, in order to maintain machining quality and to prevent damage to the work-piece, accurate monitoring or early prediction of tool condition is important. These techniques however are insufficient for micro-machining where the tool itself is usually invisible to the human eye. Because the diameter is so small, accuracy is inherently compromised. Furthermore, due to the micron scales of micro-machining, detection as well as determination of tool wear or breakage is quite challenging. This paper reports on the results of an ongoing research project to investigate and develop machine vision applications for micro machining tool location and tool wear monitoring. The determination of an optimal optical setup is reported together with some algorithms for image processing and feature classification. The optical setup utilizes a 3 mega pixel CMOS capturing device mounted on 12X ultra zoom lens. Lighting is achieved by direct, indirect and backlighting. Initial image analysis, utilizing basic Gaussian filters and histogram equalization indicates that lighting is a critical factor in this application. Wear determination is performed by a comparison of the image of an unused tool with that of a used tool using exclusive operators. Although the results seem promising, there is need for finer enhancements on images prior to the application of classification algorithms.
[1]
David Kerr,et al.
Assessment and visualisation of machine tool wear using computer vision
,
2006
.
[2]
Hideki Aoyama,et al.
Prediction of tool wear and tool failure in milling by utilizing magnetostrictive torque sensor
,
1998
.
[3]
Colin Bradley,et al.
A review of machine vision sensors for tool condition monitoring
,
1997
.
[4]
Elisabetta Ceretti,et al.
A Neural Network Architecture for Tool Wear Detection through Digital Camera Observations
,
1996
.
[5]
Tilo Pfeifer,et al.
Reliable tool wear monitoring by optimized image and illumination control in machine vision
,
2000
.
[6]
Kwang Ho Kim,et al.
Tool wear measuring technique on the machine using CCD and exclusive jig
,
2002
.
[7]
M. Sortino,et al.
Application of statistical filtering for optical detection of tool wear
,
2003
.
[8]
Y. G. Srinivasa,et al.
In-process tool wear monitoring through time series modelling and pattern recognition
,
1997
.
[9]
Ulf Engel,et al.
Microforming—from basic research to its realization
,
2002
.
[10]
D. E. Dimla,et al.
Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods
,
2000
.
[11]
N. Constantinides,et al.
An investigation of methods for the on-line estimation of tool wear
,
1987
.