Machine learning enabled advanced manufacturing in nuclear engineering applications

Advanced manufacturing has gained tremendous interest in both research and industry in the past few years. Over nearly the same period of time, machine learning (ML) has made phenomenal advancements, finding its way into many aspects of manufacturing. For the nuclear engineering field, the adoption of advanced manufacturing is a compelling argument due to the ambitious challenges the field faces. The combination of advanced manufacturing with ML holds great potential in the nuclear engineering field, and even further development is needed to accelerate their deployment towards real-world applications. This review paper seeks to detail several key aspects of ML enabled advanced manufacturing that are used or could prove useful to nuclear applications ranging from radiation detector materials to reactor parts fabrication. The applications covered here include new material extrapolation, manufacturing defect detection, and additive manufacturing parameters' optimization.