Reduction of defects such as cracking, porosity, lack of fusion, distortion, and surface roughness and control of microstructure and properties are needed to improve part quality, reduce cost, and increase the market penetration of additively manufactured components. Reduction in defects and control of microstructure and properties 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, properties, 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.
We evaluate the effectiveness of AI tools in reducing defects and improving the microstructure and properties of additively manufactured metallic components. We gather several experimental data for additive manufacturing processes, which we then use to train the machine learning and deep learning algorithms. We test artificial neural networks, decision trees, random forests, and support vector machines under various conditions and materials. In this research, we focus on the application of machine learning and deep learning using open-source packages such as Weka and Scikit Learn. In addition, we show how emerging AI tools such as ChatGPT can benefit additive manufacturing. This research aims to explore the benefits of these tools in the additive manufacturing industry and provide a deeper understanding of their capabilities.
Results and discussions:
Our preliminary results indicate that the integration of AI tools in additive manufacturing can reduce cracking, residual stresses, lack of fusion, and balling defects. In addition, we provide 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. 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. Several examples of using ChatGPT to write codes to use machine learning and mechanistic modeling in additive manufacturing are included.
Summary and conclusions:
In summary, our research demonstrates the potential of emerging AI tools in the additive manufacturing of metallic materials. The integration of these tools can lead to significant improvements in efficiency, quality, and sustainability. Further research is needed to optimize the performance of these tools and explore their full capabilities in the manufacturing industry. The findings of this research have implications for the adoption of AI tools in various industries and can contribute to the development of smarter and more efficient manufacturing processes.