Control En Procesos De Perforacion

556

Flexible Automation and Intelligent Manufacturing, FAIM2004, Toronto, Canada

A Proposal to Use Artificial Neural Networks for Process Control of Punching/Blanking Operations
Jannes Slomp1 and Warse Klingenberg
Production Systems Design Group, Faculty of Management and Organization University of Groningen P.O. Box 800, 9700 AV Groningen, The Netherlands

ABSTRACT Punching/blanking isamong the most important sheet metal manufacturing processes in mass production of metal parts and components in a great variety of industries. In recent years, a further understanding of the technological aspects of the punching/blanking process has been gained, offering the opportunity to integrate the new insights in the process control of the manufacturing process. This paper indicates thenew insights in the punching/blanking process and shows how these findings can be used in a process control concept. The application of an artificial neural network plays an important role in this concept.

1. INTRODUCTION During the past century, mechanized and automated industries have created wealth by mass-production of industrial and consumer goods at affordable prices. During the pastdecades, product diversity has drastically increased and product life cycles decreased, while competitive forces have forced companies to carry the lowest possible levels of raw material, work in progress and finished goods stock. This places high demands on manufacturing organizations in terms of flexibility, process quality and cost minimization. In a global competitive environment, where labor ratesdiffer significantly between regions, high-tech manufacturing industries attempt to utilize their technological assets by drastically improving the organization, planning, control and ultimately the productivity of their operations, often through knowledge-intensive solutions. Further research of process planning and in-process control play an increasingly important role in the quest for low costflexible manufacturing operations. In recent years, the first tangible concepts, which could serve as bases for in-process control solutions for sheet metal operations, started to emerge, combining an understanding of the principles of in-process control of manufacturing processes and the technological aspects of the process in question [1-4]. For manufacturing processes outside of sheet metalforming, for example turning, on-line monitoring of tool wear and in-process control principles were reported by other researchers [5,6]. Punching/blanking is among the most important sheet metal manufacturing processes in mass production of metal parts and components in a great variety of industries. Punching and blanking are probably as old as any sheet metal forming operation, which is said todate back to at least 250 BC. However, the operation is still not fully understood or captured by any comprehensive process model, although significant progress was made, through experimental modeling [2,7,8] analytical modeling [1,2,9-13] and numerical (Finite Element) modeling [1,2,14-17] of the punching/blanking process. These models, however, may still not be sufficiently adequate to accuratelypredict the process in all circumstances. Therefore, researchers have started to search for methods to monitor and control some key aspects of the punching/blanking process in real time. Some of these research efforts focus on Artificial Neural Networks (ANN), for example by training an ANN with input from the results of a large number of Finite Element simulations [16]. Others propose to usepattern recognition techniques to monitor and control tool wear, for example from measurements of the peak force [18]. The latter can be categorized as a Statistical Process Control (SPC) solution: a procedure that uses statistical methods to analyze the measured results of a process and uses the results of the analysis to control and manage it [19]. Based on statistical information, SPC aims to...