2022
DOI: 10.37965/jdmd.2022.118
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Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Abstract: Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions. This ideology is strengthened by Industry 4.0, which aims to continuously monitor high-value manufacturing assets. This article builds upon the Industry 4.0-concept to improve the efficiency of manufacturing systems. The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding (FSW) process. It consists of a CNC manufacturing machine, sensors… Show more

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Cited by 5 publications
(4 citation statements)
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“…The emergence of deep learning has revolutionized the landscape of polymer processing detection, enabling researchers to harness the capabilities of NNs and advanced deep learning techniques. By leveraging the power of NNs and other deep learning techniques, researchers across different fields can more effectively analyze large datasets and extract meaningful insights from complex systems 18–23 . As a result, deep learning is becoming an increasingly popular tool for optimizing processing procedures and improving the quality and efficiency of polymer products.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The emergence of deep learning has revolutionized the landscape of polymer processing detection, enabling researchers to harness the capabilities of NNs and advanced deep learning techniques. By leveraging the power of NNs and other deep learning techniques, researchers across different fields can more effectively analyze large datasets and extract meaningful insights from complex systems 18–23 . As a result, deep learning is becoming an increasingly popular tool for optimizing processing procedures and improving the quality and efficiency of polymer products.…”
Section: Introductionmentioning
confidence: 99%
“…By leveraging the power of NNs and other deep learning techniques, researchers across different fields can more effectively analyze large datasets and extract meaningful insights from complex systems. [18][19][20][21][22][23] As a result, deep learning is becoming an increasingly popular tool for optimizing processing procedures and improving the quality and efficiency of polymer products.…”
mentioning
confidence: 99%
“…Recently, deep learning models have emerged as a powerful approach that eliminates the need for extensive feature engineering by automatically learning hierarchical representations from raw data [ 25 ]. The method presents advantages in comparison with signal processing methods and traditional machine learning techniques in different areas [ 26 , 27 , 28 ]. Deep neural networks, a popular type of deep learning model, have demonstrated impressive performance in addressing complex tasks like image recognition, natural language processing, and condition monitoring of assets [ 29 , 30 , 31 ].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, with the development of the industrial 4.0 system, bigdata analysis techniques based on real-time condition monitoring have been widely applied [12,13]. The main goal is to continuously monitor and assess high-value equipment in real-time.…”
Section: Introductionmentioning
confidence: 99%