Title: A review on in-situ process sensing and monitoring systems for fusion-based additive manufacturing

Authors: Tuğrul Özel

Addresses: Department of Industrial and Systems Engineering, Manufacturing and Automation Research Laboratory (MARLAB), Rutgers University, Piscataway, New Jersey, 08854, USA

Abstract: In additive manufacturing (AM), parts suffer from quality variations, defects, intricate surface topography, and anisotropy in properties that are known to be influenced by factors including process parameters, layerwise processing, and powder melting and fusion. Their influence on process signatures also makes AM processes not fully manageable creating unacceptable levels of inconsistency. To detect the fusion quality with a purpose of quality predictions, in-situ process sensing and monitoring with sensors is often utilised with the goal that AM process can be controlled for consistency in quality. This paper provides a review of the literature on in-situ process sensing and monitoring methods and discusses research challenges and future directions for further efforts. Currently, sensory data is used for data analysis and making mostly off-line quality quantifications and predictions. The future goal is to develop intelligent AM systems that use in-situ process data for making automated intervention and quality control decisions.

Keywords: additive manufacturing; smart manufacturing; PBF; powder bed fusion; metals; sensing; monitoring; fusion; sensors; measurement; quality; defects; machine learning; deep neural network.

DOI: 10.1504/IJMMS.2023.133390

International Journal of Mechatronics and Manufacturing Systems, 2023 Vol.16 No.2/3, pp.115 - 154

Received: 17 Dec 2022
Accepted: 01 Jun 2023
Published online: 14 Sep 2023 *