Neural networks for estimating the tool path length in concurrent engineering applications

This paper deals with the development of a neural computing system that can predict the cutting tool path length for milling an arbitrary pocket defined within the domain of a product design, in a computer numerically controlled (CNC) setting. Existing computer aided design and manufacturing systems (CAD/CAM) consume significant amounts of time in terms of data entry pertaining to the geometries and subsequent modifications to them. In the concurrent engineering environment, where even the designer needs information from the CAD/CAM systems, such time-consuming processes can be expensive. To alleviate this problem, a neural network system can be used to estimate machining time by predicting cost-dependent variables such as tool path length for the pocket milling operation. Pockets are characterized and classified into various groups. A randomized design is described so that the training samples that have been chosen represent the domain evenly. An appropriate network was built and trained with the sample pocket geometries. The analysis of the performance of the system in terms of tool path length prediction for new pocket geometries is presented.