Modeling of manufacturing processes with ANNs for intelligent manufacturing

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