Intelligent Monitoring and Supervisory Control System in Peripheral Milling Process in High Speed Machining
Vallejo Guevara, Antonio Jr.
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This research is leading to solve a real problem in High Speed Machining processes (HSM), specifically in the peripheral milling process. Nowadays, the machining processes have increased their complexity by considering the HSM, because of the high dimensional precision, high surface quality, and the minimum cost in the demanded products. The general scope of this research is: Design and implement an intelligent monitoring and supervisory control system for peripheral milling process in HSM. The main objectives of this research are defined as follows: � Implement a general model to predict the surface roughness by considering several aluminium alloys, cutting parameters, geometries, and cutting tools. � Design and implement a monitoring and diagnosis system for the cutting tool wear condition during the machining process. � Design and develop an intelligent process planning system, which includes a merit variable to compute the optimal cutting parameters and a decision-making module to recommend some actions in agreement with the cutting tool wear condition. The design and implementation of the system implied to make research, exhaust experiments, and write several papers to validate the proposal ideas and algorithms. The main contributions can be summarized as follows: � A complete data acquisition system was implemented in a machining center HS-1000 Kondia. Several sensors were installed to characterize the surface roughness (Ra) and flank wear of the cutting tool with the process state variables. The Mel Frequency Cepstrum Coefficients (MFCC) computed from the process signals were used for modelling the Ra with ANN models. � Related with the Ra modelling, the most important factors affecting the Ra were deduced by applying the screening factorial design. Also, Response Surface Methodology was applied with excellent results for modeling the Ra. The models were computed for a new, half-new, half-worn, and worn cutting tool condition. Multi-sensor and data fusion were used to build ANN models with excellent results. � New ideas based in the Hidden Markov Models (HMM) and the MFCC were developed for monitoring and diagnosis the cutting tool wear condition for peripheral milling process in HSM. The system was implemented for recognizing on-line four cutting tool wear conditions: new, half-new, half-worn, and worn condition. � The design and implementation of the intelligent monitoring and process planning system (IMPPS) represented the main module of the intelligent monitoring and supervisory control system. In this module, Genetic Algorithms with the RSM models were used to compute the optimal cutting parameters in Pre-process operating mode with excellent results. Another contribution was the implementation of the Markov Decision Process in the optimization process. This algorithm recommends optimal actions for minimizing the operation cost during the production of specific workpieces.