Design of an optimized procedure to predict opposite performances in porthole die extrusion

The main objective of advanced manufacturing control techniques is to provide efficient and accurate tools in order to control the set-up of machines and manufacturing systems. Recent developments and implementations of expert systems and neural networks support this aim. This research explores the combined use of neural networks and Taguchi’s method to enhance the performance of porthole die extrusion process; the energy saving and the quality of the welding line are two conflicting objectives of the process taken into account. The complexity of the analysis, due to the number of the involved variables, does not allow the representation of the specified outputs by means of a simple analytical approach. The implementation of a more accurate and sophisticated tool, such as the neural network, results more efficient and easier to be integrated into a simple “ready to use” procedure for predicting the investigated outputs. The main limit to wider implementation of neural networks is the huge computation resources (times and capacities) required to build the data set; a finite element approach was adopted to overcome the time and money wasting typical of experimental investigations. Satisfactory results in terms of prediction capability of the highlighted outputs were found. Finally, a simple and integrated interface was designed to make easier the application of the proposed procedure and to allow the generalization to other manufacturing processes.