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

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.