Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Sachin Kumar (), T. Gopi (), N. Harikeerthana (), Munish Kumar Gupta (), Vidit Gaur (), Grzegorz M. Krolczyk () and ChuanSong Wu ()
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Sachin Kumar: Indian Institute of Science (IISc) Bengaluru
T. Gopi: Indian Institute of Technology (IIT) Palakkad
N. Harikeerthana: Nitte Meenakshi Institute of Technology Bengaluru
Munish Kumar Gupta: Opole University of Technology
Vidit Gaur: Indian Institute of Technology (IIT) Roorkee
Grzegorz M. Krolczyk: Opole University of Technology
ChuanSong Wu: Shandong University Jinan

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 1, No 2, 55 pages

Abstract: 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.

Keywords: Manufacturing; Industry 4.0; Machine learning; Additive manufacturing; Smart manufacturing (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-022-02029-5

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