Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems
Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenge...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/21/10962 |
| _version_ | 1797469237995896832 |
|---|---|
| author | Emanuele Carpanzano Daniel Knüttel |
| author_facet | Emanuele Carpanzano Daniel Knüttel |
| author_sort | Emanuele Carpanzano |
| collection | DOAJ |
| description | Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems’ self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed. |
| first_indexed | 2024-03-09T19:18:33Z |
| format | Article |
| id | doaj.art-db8e280df7ed4832be1466029ab6a845 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:18:33Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-db8e280df7ed4832be1466029ab6a8452023-11-24T03:35:43ZengMDPI AGApplied Sciences2076-34172022-10-0112211096210.3390/app122110962Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing SystemsEmanuele Carpanzano0Daniel Knüttel1SUPSI, University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette 11, 6928 Manno, SwitzerlandIntelligent Production Machines, Inspire AG, Via la Santa 1, 6962 Viganello, SwitzerlandIndustrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems’ self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed.https://www.mdpi.com/2076-3417/12/21/10962control systemsindustrial automationartificial intelligencemachine learningself-learning machine toolsadaptive production systems |
| spellingShingle | Emanuele Carpanzano Daniel Knüttel Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems Applied Sciences control systems industrial automation artificial intelligence machine learning self-learning machine tools adaptive production systems |
| title | Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems |
| title_full | Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems |
| title_fullStr | Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems |
| title_full_unstemmed | Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems |
| title_short | Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems |
| title_sort | advances in artificial intelligence methods applications in industrial control systems towards cognitive self optimizing manufacturing systems |
| topic | control systems industrial automation artificial intelligence machine learning self-learning machine tools adaptive production systems |
| url | https://www.mdpi.com/2076-3417/12/21/10962 |
| work_keys_str_mv | AT emanuelecarpanzano advancesinartificialintelligencemethodsapplicationsinindustrialcontrolsystemstowardscognitiveselfoptimizingmanufacturingsystems AT danielknuttel advancesinartificialintelligencemethodsapplicationsinindustrialcontrolsystemstowardscognitiveselfoptimizingmanufacturingsystems |