APPLYING A HYBRID MACHINE LEARNING APPROACH TO THE PRODUCTION FLOOR: A CASE OF PAPER COUNTING SYSTEM IN PRINTING INDUSTRY
Keywords:
Recurrent Neural Network, Forecasting, Printing Industry, EstimationAbstract
To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a hybrid machine learning approach and is able to provide a real-time and noncontact measurement of its quantity. With a brief introduction of the system architecture, our main work focuses on the forecasting approach using neural network in different environment. The basic principle is to identify each paper profile from various attributes from the company’s Enterprise Resource Planning (ERP) system together with measured heights of the pallet and provide workers an estimated sheet paper count. According to experiments and tests in real production lines, our hybrid approach can reach a very high measuring accuracy for printing papers or cards with any thickness.
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