Metal additive manufacturing (AM) is capable of producing complex parts, using a wide range of functional metals that are otherwise very difficult to make and involve multiple manufacturing processes. However, because of the involvement of thermal energy in the fabrication of metallic AM parts, residual stress remains one of the major concerns in metal AM. This residual stress has negative effects on part quality, dimensional accuracy, and part performance. This study aims to carry out a comprehensive review and analysis of different aspects of residual stress, including the causes and mechanisms behind the generation of residual stress during metal AM, the state-of-the-art measurement techniques for measuring residual stress, various factors influencing residual stress, its effect on part quality and performance, and ways of minimizing or overcoming residual stress in metal AM parts. Residual stress formation mechanisms vary, based on the layer-by-layer deposition mechanism of the 3D printing process. For example, the residual stress formation for wire-arc additive manufacturing is different from that of selective laser sintering, direct energy deposition, and powder bed fusion processes. Residual stress formation mechanisms also vary based on the scale (i.e., macro, micro, etc.) at which the printing is performed. In addition, there are correlations between printing parameters and the formation of residual stress. For example, the printing direction, layer thickness, internal structure, etc., influence both the formation mechanism and quantitative values of residual stress. The major effect residual stress has on the quality of a printed part is in the distortion of the part. In addition, the dimensional accuracy, surface finish, and fatigue performance of printed parts are influenced by residual stress. This review paper provides a qualitative and quantitative analysis of the formation, distribution, and evolution of residual stress for different metal AM processes. This paper also discusses and analyzes both in situ and ex situ measurement techniques for measuring residual stress. Microstructural evolution and its effect on the formation of residual stress are analyzed. Various pre- and post-processing techniques used to countermeasure residual stress are discussed in detail. Finally, this study aims to present both a qualitative and quantitative analysis of the existing data and techniques in the literature related to residual stress, as well as to provide a critical analysis and guidelines for future research directions, to prevent or overcome residual stress formation in metal AM processes.

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.