Monitoring of cutting tool systems are very important in machine tools and manufacturing equipment due to the impact they have in quality products and economy production. The cutting tool condition can be determined by direct or indirect sensing methods. Indirect methods are the only practical approach that offers better results by exploiting data sensor fusion techniques, which help to make a more robust and stable diagnosis. Different successful approaches from the Artificial Intelligence (AI) community are reviewed. A discussion of the implementation and evaluation of two AI techniques is done. Hidden Markov Model (HMM) based and Bayesian Networks based into an industrial machining center are tested. Excellent results demonstrated that HMM-based approach has a potential industrial application.
[Show abstract][Hide abstract] ABSTRACT: The aim of this research is to present a new methodology for predicting and optimizing the surface roughness during machining of 1018 and 4140 Steel. There is particular interest in finding the best machining value parameters that should be used to achieve good surface roughness. These parameter values can be found by this neural intelligent approach. This methodology analyzes and identifies the parameters involved in the machining process; with this information the model is able to predict the surface roughness value in different conditions and then optimize the results with different intelligent heuristics. The experimental results show that we may conclude that this intelligent system is a suitable methodology for predicting and optimizing surface roughness during the machining of 1018 and 4140 Steel.
Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08; 11/2008
[Show abstract][Hide abstract] ABSTRACT: The process of titanium’s machining in the aerospace industry today is by trial and error, it produce non efficient results, because this material is classified by the high chemical reaction with other materials and its low thermal conductivity such as a difficult to machine, so the process of finding the correct parameters for machining are hard to determine, and today researchers are looking to develop new models to predict and optimize these parameters. A recently developed optimization algorithm called particle swarm optimization is used to find optimum process parameters.Accordingly, the results indicate that a system where neural network is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to multi-objective optimization of titanium’s machining process
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