Modeling of manufacturing processes with ANNs for intelligent manufacturing

Modern manufacturing often caters to rapidly changing product specifications determined by the continuously increasing productivity, flexibility and quality demands. Metal forming and machining are two important manufacturing processes in present day manufacturing. Automatic selection of tools and accessories in these processes heavily relies on forming force/cutting force estimation. Complex relationships exist between process parameters and these forces. In the present work, the applicability and relative effectiveness of Artificial Neural Network based models has been investigated for rapid estimation of these, invoking the function approximation capabilities of the ANN models. The results obtained are found to correlate well with the finite element simulation data in cases of metal forming, and experimental data in cases of metal cutting. This work has considerable implications in selection of the tools and on-line monitoring of tool wear. The actual forming and cutting forces can be compared with predicted ones to signal the onset of tool wear, and thus prevent damage to the tool and work piece during the course of manufacturing. (C) 2000 Elsevier Science Ltd. All rights reserved.