Supervisión inteligente de un proceso complejo basada en un modelo neuronal. Un caso real de aplicación.

An intelligent supervisory system inspired on a Neural Network Output Error model is presented herein. The application for predicting tool wear in a milling process is selected as a case study. The supervision block consists of a neural model, the weighted sum of squared residuals method and the tool condition index for a decision-making. This work shows the combined use of residual vector norm and the norm of the residual vector derivative to compute adaptive thresholds. The study analyses the influence of infinity and Euclidean norm on the results. Experimental tests are run in a professional machining centre under different cutting conditions using real-time data and new, half-worn and worn tools. The results show this supervisory system’s suitability.

[1]  Spyros G. Tzafestas,et al.  Modern approaches to system/sensor fault detection and diagnosis , 1990 .

[2]  R. Haber,et al.  Application of Knowledge Based Systems for Supervision and Control of Machining Processes , 2022 .

[3]  Karina Cantillo,et al.  Networked sensing for high-speed machining processes based on CORBA , 2005 .

[4]  Y. G. Srinivasa,et al.  A back-propagation algorithm applied to tool wear monitoring , 1994 .

[5]  Ruxu Du,et al.  Tool condition monitoring in turning using fuzzy set theory , 1992 .

[6]  Santanu Das,et al.  Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach , 1996, Neural Networks.

[7]  D. E. Dimla,et al.  Neural network solutions to the tool condition monitoring problem in metal cutting—A critical review of methods , 1997 .

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

[9]  Steven Y. Liang,et al.  Fuzzy Control of Spindle Torque in High-Speed Milling Processes , 2006 .

[10]  Steven Y. Liang,et al.  Feed rate optimization and depth of cut control for productivity and part parallelism in grinding , 1999 .

[11]  Rodolfo E. Haber,et al.  Controlling a complex electromechanical process on the basis of a neurofuzzy approach , 2005, Future Gener. Comput. Syst..

[12]  Bernhard Sick.  On-line tool wear monitoring in turning using neural networks , 2005, Neural Computing & Applications.

[13]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[14]  Rodolfo E. Haber,et al.  A neural network-based model for the prediction of cutting force in milling process. A progress study on a real case , 2000, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[15]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..