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Estimation of Machining Time for CNC Manufacturing using Neural Computing

An approach to solving the problem of machining time estimation in production of complex products within CNC machining systems is presented in the paper. Heuristic analysis of the process is used to define the attributes of influence to machining time. For the problem of estimating machining time the following „Neural Computing techniques“ are used: Back-Propagation Neural Network, Modular Neural Network, Radial Basis Function Neural Network, General Regression Neural Network and Self-Organizing Map Neural Network. Real data from the technological process obtained by measuring are used to design the model used in investigation. The established model is used to carry out the investigation aimed at learning and testing different algorithms of neural networks and the results are given by the RMS error. The best results in the validation phase were achieved by Modular Neural Network (RMSE 1.89 %) and Back-Propagation Neural Network (RMSE 2.03 %) while the worst results were achieved by Self-Organizing Map Neural Network (RMSE 10.05 %).

Process Planning ; Machining time ; Neural Networks ; Estimation ; CNC Manufacturing

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