Abstract
This article proposes a deep learning technique for the prevision of the geometric accuracy in single point incremental forming. Moreover, predicting geometric accuracy is one of the most crucial measures of part quality. Accordingly, roundness and positioning deviation are two indicators for measuring geometric accuracy and presenting two output variables. Two types of artificial intelligence learning approaches, that is, shallow learning and deep learning, are investigated and compared for forecasting geometrical accuracy in the single point incremental forming process. Therefore, the back-propagation neural network with one hidden layer is selected as the representative for shallow learning and deep belief network and stack autoencoder are chosen as the representatives for deep learning. Accurate prediction is closely related to the feature learning of single point incremental forming process parameters. The following six parameters were considered as input variables: sheet thickness, tool path direction, step depth, speed rate, feed rate, and wall angle. The results of these studies indicate that deep learning could be a powerful tool in the current search for geometric accuracy prediction in single point incremental forming. Otherwise, the deep learning approach shows the best performance prediction with shallow learning. In addition, the deep belief network model achieves superior performance accuracy for the prediction of roundness and position deviation in comparison with the stack autoencoder approach.
Highlights
Single point incremental forming (SPIF) is an indispensable manufacturing process for customized production with high flexibility and rapid prototyping of sheet metal parts.[1]
The main drawbacks of such process consist in the high production time and the limit of the geometric accuracy pertaining to the finished part
The results showed that geometrical accuracy is very dependent on tool diameters and tool step-downs, and the SPIF process is a combination of three modes of forming
Summary
Single point incremental forming (SPIF) is an indispensable manufacturing process for customized production with high flexibility and rapid prototyping of sheet metal parts.[1]. The main drawbacks of such process consist in the high production time and the limit of the geometric accuracy pertaining to the finished part
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