Online monitoring and measurements of tool wear for precision turning of stainless steel parts

Online monitoring and measurements of tool wear were carried out using cutting forces for precision turning of stainless steel parts. The best combination of features was selected from 14 features extracted from force signals by using a Sequential Forward Search algorithm. Back-propagation neural networks (BPNs) used two features for online classification. When the adaptive neuro-fuzzy inference system (ANFIS) was applied, seven features were needed for the classification. For online measurements, only one feature is needed for BPN. Three features are needed for ANFIS for online measurements. For online classification of turning tool conditions, a 2 × 20 × 1 BPN can achieve a success rate of higher than 86% while a 7 × 2 ANFIS can reach a success rate of higher than 96%. For online measurements of tool wear, the estimation error can be as low as 1.37% when a 1 × 20 × 1 BPN was used while the error can be as low as 0.56% using a 3 × 3 ANFIS. Therefore, the 3 × 3 ANFIS can be used first to predict the degradation of tool conditions during the turning process. It can also be used to measure the tool wear online so as to take feedback control action to enhance accuracy of the process. Once the detected tool wear is close to the worn-out threshold, the 7 × 2 ANFIS will be then applied to classify the tool conditions in order to stop the turning operation on time automatically so as to assure the quality of products and to avoid catastrophic failure.

[1]  D. Dudzinski,et al.  Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers , 2007 .

[2]  Amiya R Mohanty,et al.  Estimation of tool wear during CNC milling using neural network-based sensor fusion , 2007 .

[3]  Tien-I Liu,et al.  Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing , 2005, Int. J. Knowl. Based Intell. Eng. Syst..

[4]  Jay Lee,et al.  On-line monitoring of boring tools for control of boring operations , 2010 .

[5]  Tien-I Liu,et al.  Enhancement of Drilling Safety and Quality Using Online Sensors and Artificial Neural Networks , 2003, International journal of occupational safety and ergonomics : JOSE.

[6]  Christopher A. Suprock,et al.  Endmill Condition Monitoring and Failure Forecasting Method for Curvilinear Cuts of Nonconstant Radii , 2009 .

[7]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[8]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[9]  Fusaomi Nagata,et al.  Intelligent machining system for the artistic design of wooden paint rollers , 2009 .

[10]  Yean-Ren Hwang,et al.  A cutter tool monitoring in machining process using Hilbert–Huang transform , 2010 .

[11]  Arthur D. Friedman,et al.  Systems of Asynchronously Operating Modules , 1971, IEEE Transactions on Computers.

[12]  Hervé Pingaud,et al.  Knowledge-based system for collaborative process specification , 2010, Comput. Ind..

[13]  Xiaoli Li,et al.  Real-time tool wear condition monitoring in turning , 2001 .

[14]  Joel Martins Crichigno Filho.  Prediction of cutting forces in mill turning through process simulation using a five-axis machining center , 2012 .

[15]  Wei He,et al.  Development of a Fuzzy-Neuro System for Parameter Resetting of Injection Molding , 2001 .

[16]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[17]  Samuel H. Huang,et al.  A smart machine supervisory system framework , 2012 .

[18]  Zhi-Hai Zhang,et al.  A web-based machining parameter selection system for life cycle cost reduction and product quality enhancement , 2008, Comput. Ind..

[19]  Tomasz Urbański,et al.  Tool condition monitoring based on numerous signal features , 2012 .

[20]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[21]  T. I. Liu,et al.  Intelligent control of dynamic systems , 1993 .

[22]  Tien-I Liu,et al.  Control of Shape Memory Alloy Actuators with a Neuro-fuzzy Feedforward Model Element , 2006, J. Intell. Manuf..

[23]  Ibrahim N. Tansel,et al.  Detecting chatter and estimating wear from the torque of end milling signals by using Index Based Reasoner (IBR) , 2012 .

[24]  Ming Liang,et al.  Chatter detection based on probability distribution of wavelet modulus maxima , 2009 .

[25]  Geok Soon Hong,et al.  Multi-Category Classification of Tool Conditions Using Wavelet Packets and ART2 Network , 1998 .

[26]  T. I. Liu,et al.  INTELLIGENT DETECTION OF DRILL WEAR , 1998 .

[27]  Gary W. Johnson,et al.  LabVIEW Graphical Programming: Practical Applications in Instrumentation and Control , 1997 .

[28]  James Nga-Kwok Liu,et al.  Design and Implement a Web News Retrieval System , 2005, KES.

[29]  Seungchul Lee,et al.  Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model , 2010 .

[30]  Chong Nam Chu,et al.  Prediction of drill failure using features extraction in time and frequency domains of feed motor current , 2008 .