Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.

[1]  Htay Naing. Than.  Tool condition monitoring using vibration signal , 2012 .

[2]  X. Wang,et al.  Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect , 2005 .

[3]  P. S. Heyns,et al.  An industrial tool wear monitoring system for interrupted turning , 2004 .

[4]  Lesley F. Wright,et al.  Information Gap Decision Theory: Decisions under Severe Uncertainty , 2004 .

[5]  Fritz Keinert,et al.  Wavelets and Multiwavelets , 2003 .

[6]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[7]  Jongwon Kim,et al.  Real-Time Tool Breakage Monitoring for NC Milling Process , 1995 .

[8]  Purushothaman Srinivasan,et al.  Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns , 2010, J. Intell. Manuf..

[9]  Alan F. Murray,et al.  Confidence estimation methods for neural networks : a practical comparison , 2001, ESANN.

[10]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[11]  Jaromir Audy,et al.  An appraisal of techniques and equipment for cutting force measurement , 2006 .

[12]  Adam G. Rehorn,et al.  State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[13]  Ping Yi Chao,et al.  An improved neural network model for the prediction of cutting tool life , 1997, J. Intell. Manuf..

[14]  Ioannis Minis,et al.  WAVELET BASED CUTTING STATE IDENTIFICATION , 1998 .

[15]  I. Jolliffe.  Principal Component Analysis , 2002 .

[16]  Zhao ShuKun,et al.  플라즈마 식각공정에서 Radial Basis Function Neural Network Model를 이용한 식각 종료점 검출 , 2010 .

[17]  M. A. Mannan,et al.  Monitoring and Adaptive Control of Cutting Process by Means of Motor Power and Current Measurements , 1989 .

[18]  M. F. DeVries,et al.  Neural Network Sensor Fusion for Tool Condition Monitoring , 1990 .

[19]  Wen Wang,et al.  Design of neural network-based estimator for tool wear modeling in hard turning , 2008, J. Intell. Manuf..

[20]  Yakov Ben-Haim,et al.  Information-gap decision theory , 2001 .

[21]  Toshiyuki Obikawa,et al.  Tool wear monitoring for optimizing cutting conditions , 1996 .

[22]  Ruxu Du,et al.  FEATURE EXTRACTION AND ASSESSMENT USING WAVELET PACKETS FOR MONITORING OF MACHINING PROCESSES , 1996 .

[23]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[24]  Krzysztof Jemielniak,et al.  Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network , 1998, J. Intell. Manuf..

[25]  P. S. Heyns.  Tool condition monitoring using vibration measurements a review , 2007 .

[26]  Rene de Jesus Romero-Troncoso,et al.  Sensorless tool failure monitoring system for drilling machines , 2006 .

[27]  M. Grasserbauer,et al.  Wavelet denoising of Gaussian peaks: A comparative study , 1996 .

[28]  Jin Jiang,et al.  Erratum to: State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[29]  A. Velayudhama,et al.  Acoustic emission based drill condition monitoring during drilling of glass / phenolic polymeric composite using wavelet packet transform , 2005 .