Speaker
Description
Reduction of defects such as cracking, porosity, lack of fusion, distortion, and surface roughness and control of grain structure and phase formation are needed to improve part quality, reduce cost, and increase the market penetration of 3D printed or additively manufactured components. Reduction in defects and control of microstructure cannot be done by time-consuming and expensive experimental trials because of the involvement of many variables with a large parameter window. Physics-based mechanistic models are often used as an alternative. However, the evolution of microstructures and defects depends on many complex physical processes, and the mechanistic understanding of many of these processes is not fully developed. The use of emerging artificial intelligence (AI) tools such as machine learning and deep learning can automate several steps, including process monitoring, defect detection, sensing, and process control, and can help in the selection of appropriate processing conditions to improve structure and properties. This would minimize the need for human intervention and significantly improve the process efficiency, productivity, and part quality and reduce materials and energy waste and cost. In this work, the effectiveness of AI tools has been evaluated in reducing defects and improving the microstructure and properties of additively manufactured metallic components. Several experimental data for additive manufacturing processes were gathered, which were then used to train the machine learning and deep learning algorithms. Artificial neural networks, decision trees, random forests, and support vector machines were tested under various conditions and materials. The results indicated that the integration of AI tools in additive manufacturing can reduce cracking, residual stresses, lack of fusion, and balling defects. In addition, several examples of the use of machine learning and deep learning for in-situ process monitoring, sensing and control, parameter optimization, and controlling microstructure and properties have been provided. It has also been shown that AI tools perform better if they are trained using the variables computed using mechanistic models of the additive manufacturing process. In addition, Several examples of using generative AI tools such as ChatGPT to write codes to use machine learning and mechanistic modeling in additive manufacturing are included.
