Evaluating Artificial Intelligence Effects on Additive Manufacturing by Machine Learning Procedure

Abstract

Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0.

Keywords: additive manufacturing, machine learning, powder bed fusion