Abstract
AbstractManufacturing systems for individualized production require workflows depending on individual objects. Machine learning (ML) offers the possibility to classify different objects by training a neural network. Depending on the output values of the network, decisions for the following production step can then be controlled. The question arises whether it is possible to execute the neural network in real time in coordination with the machine and motion control tasks. In this paper, this question is investigated using a programmable logic controller (PLC) runtime environment on a standard industrial PC. The execution times of different neural network implementation methods are measured and compared. The fastest neural network requires an average execution time of only 54 µs. The characteristics of the different methods with respect to training and implementing the neural networks in the controller are also discussed.KeywordsMachine learningIntelligent Manufacturing SystemsIndividualized production
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