This paper describes the training and implementation of an artificial neural network model (a multilayer feedforward backpropagated neural network) for the real-time, online estimation of key machining parameters. The neural network model is customized, configured, and trained off-line and then integrated with the automated fixture design (AFD) system for on-line prediction of the machining parameters (according to given workpiece representations and required processing information) as needed in the fixture design synthesis process. The paper reports the implementation of a prototype system and discusses several issues regarding the integration aspects in a case study.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science Applications
- Computational Theory and Mathematics
- Artificial Intelligence