Abstract
The aim of this research is to reduce the gap between manufacture expertise and management expertise by using modern technology like Manufacturing Execution System (MES) via Artificial Intelligence (AI) and Machin Learning (ML). A design of MES has been proposed and implemented on El-Araby Plastic Injection Molding (PIM) factory. This work is based on the International Society of Automation Standard (ISA-S95). A fully automated data management system has been designed and implemented to control data follow between shop floor e.g. (machines and operators) and management floor e.g. (production, quality, inventory and Enterprise Resource Planning (ERP) staff). A real-time MES quality control and monitoring has been also designed and implemented using either classic computing, or AI and ML techniques. Fuzzy Logic (FL) controllers have been designed and implemented as feedforward controllers; to improve the performance of existed classical PID controller of injection parameters. An expert FL system has been used as one of AI techniques to implement manufacturer expertise in MES. An FL product quality classifier as ML has been designed and implemented depending on injection molding conditions to give the expected product quality. An expert system has been devolved based on machine manufacturers, raw material suppliers and production engineer expertise’s using FL to give the injection parameters set points according to the product quality measure. The final results of this work are an intelligent computing system named (I-MES).
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Mahmoud, M.I., Ammar, H.H., Eissa, M.H., Hamdy, M.M. (2018). Improved Production Key Performance Indicators (KPI’s) Using Intelligent-Manufacturing Execution Systems (I-MES). In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_43
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