FEA Approach for Wear and Damage Prediction of Tools for the Progressive Die Stamping of Steel Washers

Abstract

In progressive die stamping processes, maintenance activities caused by tool damage, and wear represent economic losses for companies. An effective predictive maintenance strategy can only be implemented if maintenance data coming from the operations are correlated to specific process-related information. As a part of a more general data-based predictive maintenance strategy, the main causes of tool damage and wear in a progressive die stamping factory that produces automotive metal washers have been identified by means of FEA simulations. In this study, the progressive die stamping of a dented conical washer is simulated with Transvalor FORGE FEA software by implementing the process parameters used in a real case. In this study, two indicators called FEAwear and FEAdamage are proposed for prediction of die wear and damage for tools with high risk of failure. For validating the accuracy of the FEA simulations, dimension and geometry comparisons are performed between FEA and real washer, and then real and FEA maximum press force comparison is performed. In the end, FEA simulations demonstrated their accuracy in predicting the stamping force of the press and the final part quality, and proposed FEA damage and wear indicators accurately predicted the most critical tools and stations, as confirmed by the real maintenance data. Finally, the simulations also correctly detected potential damage zones of the tools.