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Open AccessArticle

The Neural Network Revamping the Process’s Reliability in Deep Lean via Internet of Things

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Department of Industrial Engineering, Zagazig University, Zagazig 44519, Egypt
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Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia
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Department of Industrial Engineering, Alexandria Higher Institute of Engineering and Technology (AIET), Alex 21311, Egypt
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Department of Computer and Systems Engineering, Zagazig University, Zagazig 44519, Egypt
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Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia
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Author to whom correspondence should be addressed.
Processes 2020, 8(6), 729; https://doi.org/10.3390/pr8060729
Received: 12 May 2020 / Revised: 14 June 2020 / Accepted: 15 June 2020 / Published: 23 June 2020
Deep lean is a novel approach that is concerned with the profound analysis for waste’s behavior at hidden layers in manufacturing processes to enhance processes’ reliability level at the upstream. Ideal Standard Co. for bathtubs suffered from defects and cost losses in the spraying section, due to differences in the painting cover thickness due to bubbles, caused by eddies, which move toward the bathtubs through hoses. These bubbles and their movement are considered as a form of lean’s waste. The spraying liquid inside the tanks and hoses must move with uniform velocity, viscosity, pressure, feed rate and suitable Reynolds circulation values to eliminate the eddy causes. These factors are tackled through the adoption Internet of Things (IoT) technologies that are aided by neural networks (NN) when an abnormal flow rate is detected using sensor data in real-time that can reduce the defects. The NN aimed at forecasting eddies’ movement lines that carry bubbles and works on being blasted before entering the hoses through using Design of Experiment (DOE). This paper illustrates a deep lean perspective as driven by the define, measure, analysis, improvement and control (DMAIC) methodology to improve reliability. The eddy moves downstream slowly with an anti-clockwise flow for some of the optimal values for the influencing factors, whereas the circulation of Ω increases, whether for vertical or horizontal travel. View Full-Text
Keywords: deep learning; DMAIC; eddy waste control; circulation number; Reynolds number deep learning; DMAIC; eddy waste control; circulation number; Reynolds number
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Abed, A.M.; Elattar, S.; Gaafar, T.S.; Alrowais, F.M. The Neural Network Revamping the Process’s Reliability in Deep Lean via Internet of Things. Processes 2020, 8, 729.

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