Tool breakage detection from 2D workpiece profile using vision method

2nd International Manufacturing Engineering Conference; 3rd Asia-Pacific Conference on Manufacturing Systems

Lee, W.K.^1 ; Ratnam, M.M.^1 ; Ahmad, Z.A.^2

School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang

14300, Malaysia^1

School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang

14300, Malaysia^2

关键词: Aluminium oxide;    Autocorrelation functions;    Continuous fractures;    High resolution;    Invariant moment;    Subpixel accuracy;    Surface profiles;    Tool breakage detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/114/1/012132/pdf
DOI  :  10.1088/1757-899X/114/1/012132

来源: IOP

【 摘 要 】

In-process tool breakage monitoring can significantly save cost and prevent damages to machine tool. In this paper, a machine vision approach was employed to detect the tool fracture in commercial aluminium oxide ceramic cutting tool during turning of AISI 52100 hardened steel. The contour of the workpiece profile was captured with the aid of backlighting during turning using a high-resolution DSLR camera with a shutter speed of 1/4000 s. The surface profile of the workpiece was extracted to sub-pixel accuracy using the invariant moment method. The effect of fracture in ceramic cutting tools on the surface profile signature of the machined workpiece using autocorrelation was studied. Fracture in the aluminum oxide ceramic tool was found to cause the peaks of autocorrelation function of the workpiece profile to decrease rapidly as the lag distance increased. The envelope of the peaks of the autocorrelation function was observed to deviate significantly from one another at different workpiece angles when the tool has fractured due to the continuous fracture of ceramic cutting insert during machining.