Correcting geometric deviations of CNC Machine-Tools: An approach with Artificial Neural Networks

• An experimental methodology of Design for Manufacturing (DFM) is used for survey and analysis of geometric deviations of a CNC Machine-Tools.
• Artificial Neural Networks (ANN) with back propagation algorithm (BPNN) has been applied to predict the fabrication parameters.
• The performance of the trained neural network has been tested for compensation of geometric deviations of a CNC Machine-Tools.

This paper presents an experimental methodology of Design for Manufacturing (DFM) used for survey and analysis of geometric deviations of CNC Machine-Tools, through their final product. These deviations generate direct costs that can be avoided through the use of Intelligent Manufacturing Systems (IMS), by the application of Artificial Neural Networks (ANNs) to predict the fabrication parameters. Finally, after the experiments, it was possible to evaluate the experimental methodology used, the equations, the variables of data adjustment and thus enable the validation of the methodology used as a tool for DFM with high potential return on product quality, development time and reliability of the process with wide application in various CNC Machines.

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