sheet metal

Workpiece Image-based Tool Wear Classification in Blanking Processes Using Deep Convolutional Neural Networks Machine Learning

Blanking processes belong to the most widely used manufacturing techniques due to their economic efficiency. Their economic viability depends to a large extent on the resulting product quality and the associated customer satisfaction as well as on possible downtimes. In particular, the occurrence of increased tool wear reduces the product quality and leads to downtimes, which is why considerable research has been carried out in recent years with regard to wear detection. While processes have widely been monitored based on force and acceleration signals, a new approach is pursued in this paper. Blanked workpieces manufactured by punches with 16 different wear states are photographed and then used as inputs for Deep Convolutional Neural Networks to classify wear states. The results show that wear states can be predicted with surprisingly high accuracy, opening up new possibilities and research opportunities for tool wear monitoring of blanking processes.

Fuzzy set theory applied to bend sequencing for sheet metal bending


Brake forming is widely applied in the high variety and small batch part manufacturing of sheet metal components, for the bending of straight bending lines. Currently, the planning of the bending sequences is a task that has to be performed manually, involving many heuristic criteria. However, set-up and bend sequencing procedures and knowledge have to be formally formalized and modeled, for the development of computer-aided process planning systems for sheet metal forming. This paper describes the application of fuzzy set theory for the normalization and modeling of the set-up and bend sequencing process for sheet metal bending. A fuzzy-set based methodology is used to determine the optimal bending sequences for the brake forming of sheet metal components, taking into account the relative importance of handling and accuracy.