Abstract
In this article, we proposed an intelligent approach for an optimal production based on the manufacturing process. By examining the manufacturing process, we draw a big idea of how this industry manufacturing performing his production and analysing those process to determine the control factors that control it, by using those information we build a control system based on the factory real control system. To simplify the complexity of the manufacturing process, we divide it into a sub-process based on the production line and the number of processes and create for each sub-process a logical model. By accumulating all the logical model, we have a big hierarchy logical model for the manufacturing process. Each of those sub-system logical models examined using an artificial neural network, this model based on the control system that is created. The goal of the neural network is to determine how the control factors interfere with manufacturing production. From results obtained from the neural network and by using Pareto front, we determine a set of an optimal configuration for the control system. the conclusions determine in this article can be extended to the processing industry worldwide.
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Yassine, H.M., Shkodyrev, V.P. (2021). Optimal Production Manufacturing Based on Intelligent Control System. In: Schaumburg, H., Korablev, V., Ungvari, L. (eds) Technological Transformation: A New Role For Human, Machines And Management. TT 2020. Lecture Notes in Networks and Systems, vol 157. Springer, Cham. https://doi.org/10.1007/978-3-030-64430-7_18
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