Kumar, S and Gopi, T and Harikeerthana, N and Gupta, MK and Gaur, V and Krolczyk, GM and Wu, CS (2022) Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control. In: Journal of Intelligent Manufacturing .
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Abstract
For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of Intelligent Manufacturing |
| Publisher: | Springer |
| Additional Information: | The copyright for this article belongs to the Author(s). |
| Keywords: | 3D printers; Additives; Data mining; Design; Flow control; Industrial research; Industry 4.0; Machine learning; Process control; Wages, Design-process; Machine learning algorithms; Machine learning techniques; Machine-learning; Manufacturing; Manufacturing methods; Manufacturing process; Smart manufacturing; State-of-the art reviews; Traditional manufacturing, Production control |
| Department/Centre: | Division of Mechanical Sciences > Mechanical Engineering |
| Date Deposited: | 31 Oct 2022 09:01 |
| Last Modified: | 31 Oct 2022 09:01 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77657 |
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