8TH Thermal and Fluids Engineering Conference (Hybrid)
Industry 4.0 requires flexible advanced manufacturing technologies and schemes that will realize a connected and distributed manufacturing infrastructure that will rapidly respond to national and global manufacturing needs with the use of artificial intelligence and machine learning. In manufacturing complex components, additive manufacturing (AM) technologies are ideal for their ability to rapidly configure a variety of products. Furthermore, AM systems can be equipped with sensory systems for data driven optimization, anomaly detection, and feeding data into AI and ML algorithms tied to local, state, and national cyber-physical manufacturing systems. However, the current state-of-the-art of an increasing number of AM methods and systems require nationally and globally accepted codes and standards from design to quality control that will increase confidence in the use of AM. Lack of such codes inhibit the wide adaptation of AM technology in producing load bearing components. Moreover, the large number of variables associated with AM processes creates challenges in standardization efforts. In addressing these challenges, physics and data informed process monitoring and control systems are expected to play a critical role. In this talk, Dr. Ozdemir discusses the role of thermal and fluids sciences in decoding AM process sensitivities and applying the knowledgebase to develop physics-informed quality control procedures with examples from Cold Spray Metal Additive Manufacturing.
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