A comprehensive review on laser powder bed fusion of steels: Processing, microstructure, defects and control methods, mechanical properties, current challenges and future trends

Abstract

Laser Powder Bed Fusion process is regarded as the most versatile metal additive manufacturing process, which has been proven to manufacture near net shape up to 99.9% relative density, with geometrically complex and high-performance metallic parts at reduced time. Steels and iron-based alloys are the most predominant engineering materials used for structural and sub-structural applications. Availability of steels in more than 3500 grades with their wide range of properties including high strength, corrosion resistance, good ductility, low cost, recyclability etc., have put them in forefront of other metallic materials. However, LPBF process of steels and iron-based alloys have not been completely established in industrial applications due to: (i) limited insight available in regards to the processing conditions, (ii) lack of specific materials standards, and (iii) inadequate knowledge to correlate the process parameters and other technical obstacles such as dimensional accuracy from a design model to actual component, part variability, limited feedstock materials, manual post-processing and etc. Continued efforts have been made to address these issues. This review aims to provide an overview of steels and iron-based alloys used in LPBF process by summarizing their key process parameters, describing thermophysical phenomena that is strongly linked to the phase transformation and microstructure evolution during solidification, highlighting metallurgical defects and their potential control methods, along with the impact of various post-process treatments; all of this have a direct impact on the mechanical performance. Finally, a summary of LPBF processed steels and iron-based alloys with functional properties and their application perspectives are presented. This review can provide a foundation of knowledge on LPBF process of steels by identifying missing information from the existing literature.