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

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.

Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing

“…Spatter ejections are either caused by a vapor-driven entrapment of powder particles or by unstable solid-liquid transitions leading to molten material ejections (Liu et al, 2015;Khairallah et al, 2016;Ly et al, 2017). The interested reader is referred to McCann et al, 2021;Kumar et al, 2022;Li et al, 2020;Tercan & Meisen, 2022;Liu et al, 2022;Zhang et al, 2022) for review studies devoted to these phenomena and to the development of process monitoring tools in L-PBF.…”

Application of artificial neural network to evaluation of dimensional accuracy of 3D‐printed polylactic acid parts

“…37,38 Recently developed machine learning (ML) algorithms have begun to be used in manufacturing technologies to optimize and predict the AM process. [39][40][41][42] However, there are limited studies for ML on the dimensional accuracy of parts. This study examined the dimensional accuracy of parts with different geometric structures using an artificial neural network (ANN) method.…”

“…Thus, researchers are working to determine the optimum printing parameters and manufacturing devices required for the high dimensional accuracy of 3D printed parts 37,38 . Recently developed machine learning (ML) algorithms have begun to be used in manufacturing technologies to optimize and predict the AM process 39–42 . However, there are limited studies for ML on the dimensional accuracy of parts.…”

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

“…Therefore, it is necessary to implement real-time control strategies to adjust the parameters according to the fabrication environment dynamically. Development and implementation of autonomous controllers based on artificial intelligence techniques have been rapidly studied in the recent literature (Askari et al, 2022;Azimirad et al, 2022;Farbiz et al, 2022;Kumar et al, 2022;Tercan & Meisen, 2022). Advanced control strategies, like adaptive control, intelligent control algorithm, or predictive control, were utilized to achieve closed-loop control in AM system.…”