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Artificial Intelligence for Smart Manufacturing:
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Kim-Phuc Tran

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Kim-Phuc Tran. Artificial Intelligence for Smart Manufacturing: Methods and Applications. Sensors, 2021, Sensors (Basel), 21, 10.3390/s21165584. hal-04427977

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HAL
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Artificial Intelligence for Smart Manufacturing:
Methods and Applications.

Kim-Phuc Tran

► To cite this version:

Kim-Phuc Tran. Artificial Intelligence for Smart Manufacturing: Methods and Applications. Sensors, 2021, Sensors (Basel), 21, 10.3390/s21165584. [https://doi.org/10.3390/s21165587]

HAL Id: hal-04427977

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entific research documents, whether they are pub-
lished or not. The documents may come from
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abroad, or from public or private research centers.

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scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
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Distributed under a Creative Commons Attribution 4.0 International License
Editorial

# Artificial Intelligence for Smart Manufacturing: Methods and Applications

Kim Phuc Tran ID

GEMTEX Textile Research Laboratory, ENSAIT, University of Lille, 59000 Lille, France; kim-phuc.tran@ensait.fr; Tel.: +33-0320258960

The term Industry 4.0 has become increasingly pervasive in the context of industrial manufacturing and it has been considered the fourth industrial revolution (Henning [1]). The industrial transformation in the fourth industrial revolution is fueling the shift to Smart Manufacturing. By integrating advanced technologies like Industrial Internet of Things (IIoT). Big Data, Cloud Computing, Artificial Intelligence (AI), manufacturing will become intelligent and independently perform complex tasks such as predictive maintenance of machines, monitoring and optimizing the quality of products, see [2–5] for example. According to [6], the application of the IIoT in Smart Manufacturing could lead to a decrease in: production costs by 10–30%, logistics costs by 10–30% and quality management costs by 10–30%. It is now at the center of Industry 4.0 and attracts a lot of interest from governments, enterprises, and researchers for implementing Smart Manufacturing, see [7] for more details. Recently, an extensive review of technologies for smart manufacturing systems has been conducted in the research of Alcácer and Cruz-Machado [8].

A vital characteristic of the IIoT is that sensors are embedded in all the components related to the manufacturing process. These sensors act as the “senses” for collecting data from the supply, production, storage, distribution, and consumption of products for development in industrial supply chain analysis and optimization, product quality control, and active maintenance [9,10]. Thanks to the data from these processes, advanced computing technologies now perform efficiently and bring intelligence to manufacturing with AI technology. It brings countless advantages to Smart Manufacturing, involving optimization of all stages of the manufacturing process, reducing waste, and creating new smart products and services with high quality. AI technology now plays the role of a “brain” for Smart Manufacturing.

Towards Smart Manufacturing is a long-term and not straightforward process. It requires deep insight into a multiplicity of advanced and modern technologies that are integrated into this process. This Special Issue aims to offer a systematic overview of this research field and provide innovative developments with respect to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. It provides a leading forum for disseminating the latest results of theoretical research, technological development, and applications of AI in Smart Manufacturing.

The aim of this Special Issue is to highlight innovative developments with respect to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. Topics include but are not limited to the following: real-time monitoring with machine learning and deep learning; artificial intelligence for predictive maintenance; artificial intelligence for smarter cybersecurity; production scheduling with reinforcement learning; artificial intelligence and robotics in smart manufacturing; IoT-enabled smart manufacturing; digital twin-driven smart manufacturing.

This Special Issue uncovers fundamental principles and recent developments in the applications of artificial intelligence in smart manufacturing. The Special Issue contains 19 papers. It attempts to cover the issues related to key enabling technologies for smart manufacturing such as product quality inspection based on deep learning, remaining

<figure><img src="image_3.png" /></figure>

check for
updates

**Citation:** Tran, K.P. Artificial Intelligence for Smart Manufacturing: Methods and Applications. *Sensors* **2021**, *21*, 5584. https://doi.org/10.3390/s21165584

Received: 18 August 2021

Accepted: 18 August 2021

Published: 19 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_4.png" /></figure>

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Editorial

# Artificial Intelligence for Smart Manufacturing: Methods and Applications

Kim Phuc Tran

GEMTEX Textile Research Laboratory, ENSAIT, University of Lille, 59000 Lille, France; kim-phuc.tran@ensait.fr;
Tel.: +33-0320258960

The term Industry 4.0 has become increasingly pervasive in the context of industrial manufacturing and it has been considered the fourth industrial revolution (Henning [1]). The industrial transformation in the fourth industrial revolution is fueling the shift to Smart Manufacturing. By integrating advanced technologies like Industrial Internet of Things (IIoT). Big Data, Cloud Computing, Artificial Intelligence (AI), manufacturing will become intelligent and independently perform complex tasks such as predictive maintenance of machines, monitoring and optimizing the quality of products, see [2–5] for example. According to [6], the application of the IIoT in Smart Manufacturing could lead to a decrease in: production costs by 10–30%, logistics costs by 10–30% and quality management costs by 10–30%. It is now at the center of Industry 4.0 and attracts a lot of interest from governments, enterprises, and researchers for implementing Smart Manufacturing, see [7] for more details. Recently, an extensive review of technologies for smart manufacturing systems has been conducted in the research of Alcácer and Cruz-Machado [8].

A vital characteristic of the IIoT is that sensors are embedded in all the components related to the manufacturing process. These sensors act as the “senses” for collecting data from the supply, production, storage, distribution, and consumption of products for development in industrial supply chain analysis and optimization, product quality control, and active maintenance [9,10]. Thanks to the data from these processes, advanced computing technologies now perform efficiently and bring intelligence to manufacturing with AI technology. It brings countless advantages to Smart Manufacturing, involving optimization of all stages of the manufacturing process, reducing waste, and creating new smart products and services with high quality. AI technology now plays the role of a “brain” for Smart Manufacturing.

Towards Smart Manufacturing is a long-term and not straightforward process. It requires deep insight into a multiplicity of advanced and modern technologies that are integrated into this process. This Special Issue aims to offer a systematic overview of this research field and provide innovative developments with respect to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. It provides a leading forum for disseminating the latest results of theoretical research, technological development, and applications of AI in Smart Manufacturing.

The aim of this Special Issue is to highlight innovative developments with respect to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. Topics include but are not limited to the following: real-time monitoring with machine learning and deep learning; artificial intelligence for predictive maintenance; artificial intelligence for smarter cybersecurity; production scheduling with reinforcement learning; artificial intelligence and robotics in smart manufacturing; IoT-enabled smart manufacturing; digital twin-driven smart manufacturing.

This Special Issue uncovers fundamental principles and recent developments in the applications of artificial intelligence in smart manufacturing. The Special Issue contains 19 papers. It attempts to cover the issues related to key enabling technologies for smart manufacturing such as product quality inspection based on deep learning, remaining

<figure><img src="image_3.png" /></figure>

check for
updates

**Citation:** Tran, K.P. Artificial Intelligence for Smart Manufacturing: Methods and Applications. *Sensors* **2021**, *21*, 5584. https://doi.org/10.3390/s21165584

Received: 18 August 2021

Accepted: 18 August 2021

Published: 19 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_4.png" /></figure>

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
useful life prediction for predictive maintenance based on deep learning, Machine Vision Systems, intelligent recommender system, Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning, Real-Time and Explainable Process Monitoring, Intelligence-Driven Decision Support System. These contributions represent an advance in the state-of-the-art of key enabling technologies for smart manufacturing [11–29]. The richness and diverseness of the papers submitted to this Special Issue confirm the importance of applications of AI in Smart Manufacturing. The hope is that the research ideas, results and achievements will inspire active researchers in this field and will contribute to the further development of this important domain.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** I would like to thank all the authors who have submitted their papers to this Special Issue and the anonymous Reviewers for their contributions. I am grateful to the Sensors Editorial Office for giving us the opportunity to publish this Special Issue and for their great support in management of the review and organization of this Special Issue.

**Conflicts of Interest:** The author declares no conflict of interest.

## References

1. Henning, K. *Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0*; National Academy of Science and Engineering: Washington, DC, USA, 2013.

2. Nguyen, H.; Tran, K.; Zeng, X.; Koehl, L.; Castagliola, P.; Bruniaux, P. Industrial Internet of Things, big data, and artificial intelligence in the smart factory: A survey and perspective. In Proceedings of the ISSAT International Conference on Data Science in Business, Finance and Industry, Danang, Vietnam, 5–9 July 2019.

3. He, Z.; Tran, K.P.; Thomassey, S.; Zeng, X.; Xu, J.; Yi, C. A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. *Comput. Ind.* **2021**, *125*, 103373. [CrossRef]

4. He, Z.; Tran, K.P.; Thomassey, S.; Zeng, X.; Xu, J.; Yi, C. Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. *J. Manuf. Syst.* **2021**, in press. [CrossRef]

5. He, Z.; Tran, K.P.; Thomassey, S.; Zeng, X.; Xu, J.; Changhai, Y. Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest. *Text. Res. J.* **2020**, *90*, 896–908. [CrossRef]

6. Huong, T.T.; Bac, T.P.; Long, D.M.; Luong, T.D.; Dan, N.M.; Thang, B.D.; Tran, K.P. Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach. *Comput. Ind.* **2021**, *132*, 103509. [CrossRef]

7. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. *Int. J. Prod. Econ.* **2019**, *210*, 15–26. [CrossRef]

8. Alcácer, V.; Cruz-Machado, V. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. *Eng. Sci. Technol. Int. J.* **2019**, *22*, 899–919. [CrossRef]

9. Song, Z.; Sun, Y.; Wan, J.; Liang, P. Data quality management for service-oriented manufacturing cyber-physical systems. *Comput. Electr. Eng.* **2017**, *64*, 34–44. [CrossRef]

10. Xu, Y.; Sun, Y.; Wan, J.; Liu, X.; Song, Z. Industrial big data for fault diagnosis: Taxonomy, review, and applications. *IEEE Access* **2017**, *5*, 17368–17380. [CrossRef]

11. Huang, P.M.; Lee, C.H. Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion. *Sensors* **2021**, *21*, 5338. [CrossRef]

12. Kim, T.H.; Kim, H.R.; Cho, Y.J. Product Inspection Methodology via Deep Learning: An Overview. *Sensors* **2021**, *21*, 5039. [CrossRef]

13. Huang, Y.C.; Chen, Y.H. Use of Long Short-Term Memory for Remaining Useful Life and Degradation Assessment Prediction of Dental Air Turbine Handpiece in Milling Process. *Sensors* **2021**, *21*, 4978. [CrossRef] [PubMed]

14. Kim, J.; Ko, J.; Choi, H.; Kim, H. Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder. *Sensors* **2021**, *21*, 4968. [CrossRef] [PubMed]

15. Xia, K.; Saidy, C.; Kirkpatrick, M.; Anumbe, N.; Sheth, A.; Harik, R. Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding. *Sensors* **2021**, *21*, 4276. [CrossRef] [PubMed]

16. Sharma, S.; Koehl, L.; Bruniaux, P.; Zeng, X.; Wang, Z. Development of an Intelligent Data-Driven System to Recommend Personalized Fashion Design Solutions. *Sensors* **2021**, *21*, 4239. [CrossRef]

17. Yang, S.; Xu, Z.; Wang, J. Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning. *Sensors* **2021**, *21*, 1019. [CrossRef]
useful life prediction for predictive maintenance based on deep learning, Machine Vision Systems, intelligent recommender system, Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning, Real-Time and Explainable Process Monitoring, Intelligence-Driven Decision Support System. These contributions represent an advance in the state-of-the-art of key enabling technologies for smart manufacturing [11–29]. The richness and diverseness of the papers submitted to this Special Issue confirm the importance of applications of AI in Smart Manufacturing. The hope is that the research ideas, results and achievements will inspire active researchers in this field and will contribute to the further development of this important domain.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** I would like to thank all the authors who have submitted their papers to this Special Issue and the anonymous Reviewers for their contributions. I am grateful to the Sensors Editorial Office for giving us the opportunity to publish this Special Issue and for their great support in management of the review and organization of this Special Issue.

**Conflicts of Interest:** The author declares no conflict of interest.

## References

1. Henning, K. *Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0*; National Academy of Science and Engineering: Washington, DC, USA, 2013.

2. Nguyen, H.; Tran, K.; Zeng, X.; Koehl, L.; Castagliola, P.; Bruniaux, P. Industrial Internet of Things, big data, and artificial intelligence in the smart factory: A survey and perspective. In *Proceedings of the ISSAT International Conference on Data Science in Business, Finance and Industry*, Danang, Vietnam, 5–9 July 2019.

3. He, Z.; Tran, K.P.; Thomassey, S.; Zeng, X.; Xu, J.; Yi, C. A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. *Comput. Ind.* **2021**, *125*, 103373. [CrossRef]

4. He, Z.; Tran, K.P.; Thomassey, S.; Zeng, X.; Xu, J.; Yi, C. Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. *J. Manuf. Syst.* **2021**, in press. [CrossRef]

5. He, Z.; Tran, K.P.; Thomassey, S.; Zeng, X.; Xu, J.; Changhai, Y. Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest. *Text. Res. J.* **2020**, *90*, 896–908. [CrossRef]

6. Huong, T.T.; Bac, T.P.; Long, D.M.; Luong, T.D.; Dan, N.M.; Thang, B.D.; Tran, K.P. Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach. *Comput. Ind.* **2021**, *132*, 103509. [CrossRef]

7. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. *Int. J. Prod. Econ.* **2019**, *210*, 15–26. [CrossRef]

8. Alcácer, V.; Cruz-Machado, V. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. *Eng. Sci. Technol. Int. J.* **2019**, *22*, 899–919. [CrossRef]

9. Song, Z.; Sun, Y.; Wan, J.; Liang, P. Data quality management for service-oriented manufacturing cyber-physical systems. *Comput. Electr. Eng.* **2017**, *64*, 34–44. [CrossRef]

10. Xu, Y.; Sun, Y.; Wan, J.; Liu, X.; Song, Z. Industrial big data for fault diagnosis: Taxonomy, review, and applications. *IEEE Access* **2017**, *5*, 17368–17380. [CrossRef]

11. Huang, P.M.; Lee, C.H. Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion. *Sensors* **2021**, *21*, 5338. [CrossRef]

12. Kim, T.H.; Kim, H.R.; Cho, Y.J. Product Inspection Methodology via Deep Learning: An Overview. *Sensors* **2021**, *21*, 5039. [CrossRef]

13. Huang, Y.C.; Chen, Y.H. Use of Long Short-Term Memory for Remaining Useful Life and Degradation Assessment Prediction of Dental Air Turbine Handpiece in Milling Process. *Sensors* **2021**, *21*, 4978. [CrossRef] [PubMed]

14. Kim, J.; Ko, J.; Choi, H.; Kim, H. Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder. *Sensors* **2021**, *21*, 4968. [CrossRef] [PubMed]

15. Xia, K.; Saidy, C.; Kirkpatrick, M.; Anumbe, N.; Sheth, A.; Harik, R. Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding. *Sensors* **2021**, *21*, 4276. [CrossRef] [PubMed]

16. Sharma, S.; Koehl, L.; Bruniaux, P.; Zeng, X.; Wang, Z. Development of an Intelligent Data-Driven System to Recommend Personalized Fashion Design Solutions. *Sensors* **2021**, *21*, 4239. [CrossRef]

17. Yang, S.; Xu, Z.; Wang, J. Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning. *Sensors* **2021**, *21*, 1019. [CrossRef]
18. Kim, K.; Jeong, J. Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism. *Sensors* **2020**, *20*, 7099. [CrossRef]

19. Oh, C.; Jeong, J. VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring. *Sensors* **2020**, *20*, 6858. [CrossRef]

20. Viana, M.S.; Morandin, O., Jr.; Contreras, R.C. A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem. *Sensors* **2020**, *20*, 5440. [CrossRef]

21. Lee, K.; Lee, S.H. Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams. *Sensors* **2020**, *20*, 4693. [CrossRef]

22. Yang, H.; Jiao, S.J.; Yin, F.D. Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network. *Sensors* **2020**, *20*, 4638. [CrossRef]

23. Cordero, J.; Aguilar, J.; Aguilar, K.; Chávez, D.; Puerto, E. Recognition of the Driving Style in Vehicle Drivers. *Sensors* **2020**, *20*, 2597. [CrossRef] [PubMed]

24. Vo, N.D.; Hong, M.; Jung, J.J. Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System. *Sensors* **2020**, *20*, 2510. [CrossRef] [PubMed]

25. Elkatatny, S.; Al-Abduljabbar, A.; Abdelgawad, K. A New Model for Predicting Rate of Penetration Using an Artificial Neural Network. *Sensors* **2020**, *20*, 2058. [CrossRef]

26. Alsabaa, A.; Gamal, H.; Elkatatny, S.; Abdulraheem, A. Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System. *Sensors* **2020**, *20*, 1669. [CrossRef]

27. Philipsen, M.P.; Moeslund, T.B. Cutting Pose Prediction from Point Clouds. *Sensors* **2020**, *20*, 1563. [CrossRef]

28. Zhuang, Z.; Liu, Y.; Ding, F.; Wang, Z. Online Color Classification System of Solid Wood Flooring Based on Characteristic Features. *Sensors* **2021**, *21*, 336. [CrossRef] [PubMed]

29. Chadha, G.S.; Islam, I.; Schwung, A.; Ding, S.X. Deep Convolutional Clustering-Based Time Series Anomaly Detection. *Sensors* **2021**, *21*, 5488. [CrossRef]
18. Kim, K.; Jeong, J. Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism. *Sensors* **2020**, *20*, 7099. [CrossRef]

19. Oh, C.; Jeong, J. VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring. *Sensors* **2020**, *20*, 6858. [CrossRef]

20. Viana, M.S.; Morandin, O., Jr.; Contreras, R.C. A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem. *Sensors* **2020**, *20*, 5440. [CrossRef]

21. Lee, K.; Lee, S.H. Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams. *Sensors* **2020**, *20*, 4693. [CrossRef]

22. Yang, H.; Jiao, S.J.; Yin, F.D. Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network. *Sensors* **2020**, *20*, 4638. [CrossRef]

23. Cordero, J.; Aguilar, J.; Aguilar, K.; Chávez, D.; Puerto, E. Recognition of the Driving Style in Vehicle Drivers. *Sensors* **2020**, *20*, 2597. [CrossRef] [PubMed]

24. Vo, N.D.; Hong, M.; Jung, J.J. Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System. *Sensors* **2020**, *20*, 2510. [CrossRef] [PubMed]

25. Elkatatny, S.; Al-Abduljabbar, A.; Abdelgawad, K. A New Model for Predicting Rate of Penetration Using an Artificial Neural Network. *Sensors* **2020**, *20*, 2058. [CrossRef]

26. Alsabaa, A.; Gamal, H.; Elkatatny, S.; Abdulraheem, A. Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System. *Sensors* **2020**, *20*, 1669. [CrossRef]

27. Philipsen, M.P.; Moeslund, T.B. Cutting Pose Prediction from Point Clouds. *Sensors* **2020**, *20*, 1563. [CrossRef]

28. Zhuang, Z.; Liu, Y.; Ding, F.; Wang, Z. Online Color Classification System of Solid Wood Flooring Based on Characteristic Features. *Sensors* **2021**, *21*, 336. [CrossRef] [PubMed]

29. Chadha, G.S.; Islam, I.; Schwung, A.; Ding, S.X. Deep Convolutional Clustering-Based Time Series Anomaly Detection. *Sensors* **2021**, *21*, 5488. [CrossRef]