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A study on the accuracy of multi-task learning structure artificial neural network applicable to multi-quality prediction in injection molding process  

Lee, Jun-Han (Molding and Metal Forming R&D Department, Korea Institute of Industrial Technology)
Kim, Jong-Sun (Molding and Metal Forming R&D Department, Korea Institute of Industrial Technology)
Publication Information
Design & Manufacturing / v.16, no.3, 2022 , pp. 1-8 More about this Journal
Abstract
In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.
Keywords
Injection molding; Process parameter; Product quality; Artificial neural network; Single-task learning Multi-task learning;
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