Abstract
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.
Highlights
Increasing quality requirements, high production rates and progressively more complex product geometries pose manufacturers with the challenges of a systematic automation and an efficient monitoring of blanking processes
The robustness of the MobileNet appears to be higher, which is indicated by lower standard deviations and closer confidence intervals of the model performance
While monitoring approaches of blanking processes are nowadays largely based on time series, this paper presents an approach for image-based tool wear monitoring
Summary
Increasing quality requirements, high production rates and progressively more complex product geometries pose manufacturers with the challenges of a systematic automation and an efficient monitoring of blanking processes. Sensors are increasingly integrated into the processes and attempts are made to identify correlations between process anomalies and features of the recorded time series. Conventional approaches monitor the time series with the help of thresholds [1], linear discriminant functions [2] or envelope curves and can distinguish binary process states from each other. The occurrence of punch wear, which has a negative impact on the resulting product quality [3], is a widely researched application scenario. Knowledge about the current wear can help manufacturers to reduce downtimes and flexibly adapt maintenance intervals to the punch wear state. For example, the edge radius of the punch by means
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