Towards Machine Learning for Error Compensation in Additive Manufacturing
Additive Manufacturing (AM) of three-dimensional objects is now being progressively realised with its ad-hoc approach with minimal material wastage (lean manufacturing) being one of its benefit by default. It could also be considered as an evolutional paradigm in the manufacturing industry with its...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2375 |
| _version_ | 1827601798970474496 |
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| author | Amzar Omairi Zool Hilmi Ismail |
| author_facet | Amzar Omairi Zool Hilmi Ismail |
| author_sort | Amzar Omairi |
| collection | DOAJ |
| description | Additive Manufacturing (AM) of three-dimensional objects is now being progressively realised with its ad-hoc approach with minimal material wastage (lean manufacturing) being one of its benefit by default. It could also be considered as an evolutional paradigm in the manufacturing industry with its long list of application as of late. Artificial Intelligence is currently finding its usefulness in predictive modelling to provide intelligent, efficient, customisable, high-quality and sustainable-oriented production process. This paper presents a comprehensive survey on commonly used predictive models based on heuristic algorithms and discusses their applications toward making AM “smart”. This paper summarises AM’s current trend, future opportunity, gaps, and requirements together with recommendations for technology and research for inter-industry collaboration, educational training and technology transfer in the AI perspective in-line with the Industry 4.0 developmental process. Moreover, machine learning algorithms are presented for detecting product defects in the cyber-physical system of additive manufacturing. Based on reviews on various applications, printability with multi-indicators, reduction of design complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of as prospective opportunities for further research. |
| first_indexed | 2024-03-09T05:04:34Z |
| format | Article |
| id | doaj.art-f44ab320d3334818a39988fbf66cd27b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:04:34Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f44ab320d3334818a39988fbf66cd27b2023-12-03T12:56:49ZengMDPI AGApplied Sciences2076-34172021-03-01115237510.3390/app11052375Towards Machine Learning for Error Compensation in Additive ManufacturingAmzar Omairi0Zool Hilmi Ismail1Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, MalaysiaCentre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, MalaysiaAdditive Manufacturing (AM) of three-dimensional objects is now being progressively realised with its ad-hoc approach with minimal material wastage (lean manufacturing) being one of its benefit by default. It could also be considered as an evolutional paradigm in the manufacturing industry with its long list of application as of late. Artificial Intelligence is currently finding its usefulness in predictive modelling to provide intelligent, efficient, customisable, high-quality and sustainable-oriented production process. This paper presents a comprehensive survey on commonly used predictive models based on heuristic algorithms and discusses their applications toward making AM “smart”. This paper summarises AM’s current trend, future opportunity, gaps, and requirements together with recommendations for technology and research for inter-industry collaboration, educational training and technology transfer in the AI perspective in-line with the Industry 4.0 developmental process. Moreover, machine learning algorithms are presented for detecting product defects in the cyber-physical system of additive manufacturing. Based on reviews on various applications, printability with multi-indicators, reduction of design complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of as prospective opportunities for further research.https://www.mdpi.com/2076-3417/11/5/2375additive manufacturingmachine learningdata-driven artificial intelligencecyber-physical systemerror process control |
| spellingShingle | Amzar Omairi Zool Hilmi Ismail Towards Machine Learning for Error Compensation in Additive Manufacturing Applied Sciences additive manufacturing machine learning data-driven artificial intelligence cyber-physical system error process control |
| title | Towards Machine Learning for Error Compensation in Additive Manufacturing |
| title_full | Towards Machine Learning for Error Compensation in Additive Manufacturing |
| title_fullStr | Towards Machine Learning for Error Compensation in Additive Manufacturing |
| title_full_unstemmed | Towards Machine Learning for Error Compensation in Additive Manufacturing |
| title_short | Towards Machine Learning for Error Compensation in Additive Manufacturing |
| title_sort | towards machine learning for error compensation in additive manufacturing |
| topic | additive manufacturing machine learning data-driven artificial intelligence cyber-physical system error process control |
| url | https://www.mdpi.com/2076-3417/11/5/2375 |
| work_keys_str_mv | AT amzaromairi towardsmachinelearningforerrorcompensationinadditivemanufacturing AT zoolhilmiismail towardsmachinelearningforerrorcompensationinadditivemanufacturing |