Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Embedded fuzzy-control system for machining processes results of a case study
Computers in Industry
Feedback Linearization Using Neural Networks: Application to an Electromechanical Process
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
ICCS'03 Proceedings of the 1st international conference on Computational science: PartI
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Nowadays, the modeling of complex manufacturing tasks is a key issue. In this work, as a case study is selected the application of a dynamic model to predict cutting force in machining processes. A model created using Artificial Neural Networks (ANN), able to predict the process output is introduced in order to deal with the characteristics of such an ill-defined process. This model describes the dynamic response of the output before changes in the process input command (feed rate) and process parameters (depth of cut). Experimental tests are made in a professional machining centre, with different cutting conditions, on real time data. The model provides sufficiently accurate prediction of cutting force, since the process-dependent specific dynamic properties are adequately reflected.