On-line Cutting Tool Condition Monitoring in Machining Processes using Artificial Intelligence

High Speed Machining (HSM) has become one of the leading methods in the improvement of machining productivity. The term HSM covers high spindle speeds, high feed rates, as well as high acceleration and deceleration rates. Furthermore, HSM does not imply only working with high speeds but also with high levels of precision and accuracy. Additional to the HSM, many companies producing machine tools are interested in new technologies which provide intelligent features. Several research works (Koren et al., 1999; Erol et al., 2000; Liang et al., 2004) predict that future manufacturing systems will have intelligent functions to enhance their own processes, and the ability to perform an effective, reliable, and superior manufacturing procedures. In the areas of process monitoring and control, these new systems will also have a higher process technology level. In any typical metal-cutting process, the key indexes which define the product quality are dimensional accuracy and surface roughness; both directly influenced by the cutting tool condition. One of the main goals in a Computer Numerically Controlled (CNC) machining centre is to find an appropriate trade-off among cutting tool condition, surface quality and productivity. A cutting tool condition monitoring system which optimizes the operating cost with the same quality of the product would be widely appreciated, (Saglam & Unuvar, 2003; Haber & Alique, 2003). For example, in (Tonshoff et al., 1988), it has been demonstrated that effective machining time of the CNC milling centre could be increased from 10 to 65% with a monitoring and control system. Also, (Sick, 2002) mentions that any manufacturing process can be significantly optimized using a reliable and flexible tool monitoring system. The system must develop the following tasks: • Collisions detection as fast as possible. • Tool fracture identification. • Estimation or classification of tool wear caused by abrasion or other influences. While collision and tool fracture are sudden and mostly unexpected events that require reactions in real-time, the development of wear is a slow procedure. This section focuses on O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

[1]  F. Musharavati.  RECONFIGURABLE MANUFACTURING SYSTEMS , 2010 .

[2]  Ruben Morales-Menendez,et al.  DESIGNING A COST-EFFECTIVE SUPERVISORY CONTROL SYSTEM FOR MACHINING PROCESSES , 2007 .

[3]  Joseph C. Chen,et al.  A tool breakage detection system using an accelerometer sensor , 1999, J. Intell. Manuf..

[4]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[5]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[6]  Paul D. Gader,et al.  Generalized hidden Markov models. I. Theoretical frameworks , 2000, IEEE Trans. Fuzzy Syst..

[7]  Les E. Atlas,et al.  Hidden Markov models for monitoring machining tool-wear , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[8]  James A. Stori,et al.  A Bayesian network approach to root cause diagnosis of process variations , 2005 .

[9]  M. R. Ito,et al.  Open system architecture modular tool kit for motion and machining process control , 2000 .

[10]  Rubén Morales-Menéndez,et al.  Diagnosis of a Cutting Tool in a Machining Center , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[11]  P. Gader,et al.  Generalized Hidden Markov Models — Part I : Theoretical Frameworks , 2008 .

[12]  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 .

[13]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[14]  A. Unuvar,et al.  Tool condition monitoring in milling based on cutting forces by a neural network , 2003 .

[15]  Bernhard Sick,et al.  Fusion of hard and soft computing techniques in indirect, online tool wear monitoring , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[16]  Steven Y. Liang,et al.  Machining Process Monitoring and Control: The State-of-the-Art , 2004 .

[17]  Yoram Koren,et al.  Reconfigurable Manufacturing Systems , 2003 .

[18]  H.J.J. Kals,et al.  Developments and Trends in Monitoring and Control of Machining Processes , 1988 .

[19]  Rubén Morales-Menéndez,et al.  Intelligent monitoring and decision control system for peripheral milling process , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[20]  Rubén Morales-Menéndez,et al.  Tool-Wear Monitoring Based on Continuous Hidden Markov Models , 2005, CIARP.

[21]  Lane M. D. Owsley,et al.  Self-organizing feature maps and hidden Markov models for machine-tool monitoring , 1997, IEEE Trans. Signal Process..

[22]  A. Alique,et al.  Intelligent process supervision for predicting tool wear in machining processes , 2003 .

[23]  Rodolfo E. Haber,et al.  An investigation of tool-wear monitoring in a high-speed machining process , 2004 .