As an advanced intelligent manufacturing technology

additive manufacturing (AM) can directly produce metallic components with complex macroscopic structure in short lead time and has attracted lots of attention in recent years. It has been widely used in many advanced manufacture fields such as aerospace

medical device

and so on. However

there exhibit limited number of alloy systems suitable for AM printing

and the complex printing process makes it easy to introduce defects. Consequently

the large-scale application of AM is hindered. Machine learning has been widely used in various daily life and industrial production fields due to its excellent data processing and analysis capabilities. In this paper

the application of machine learning in the AM process including processing window establishing

printing quality control

printed metallic microstructure identification

and mechanical properties exploration are reviewed. In the end

the opportunities and challenges of machine learning in AM are discussed

and the further research directions are proposed.