A Soft Computing System for Modelling the Manufacture of Steel Components

Abstract

In this paper we present a soft computing system developed to optimize the laser milling manufacture of high value steel components, a relatively new and interesting industrial technique. This multidisciplinary study is based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a laser milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures steel components like high value molds and dies. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough based on the existence of internal patterns. The second phase is focus on identifying a model for the laser-milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel components.

Meta-heuristic improvements applied for steel sheet incremental cold shaping

“…Over recent years, there has been a significant increase in the use of artificial intelligence and soft computing (SOCO) methods to solve real world problems. Many different SOCO applications have been reported: the use of exploratory projection pursuit (EPS) and ARMAX for modelling the manufacture of steel components [3]; EPS and neural networks (NN) for determining the operating conditions in face milling operations [15] and in pneumatic drilling processes [17]; genetic algorithms and programming for trading rule extraction [4] and low quality data in lighting control systems [21]; feature selection and association rule discovery in high dimensional spaces [20] or NNs and principal component analysis and EPS in building energy efficiency [18,19].…”