ADVANCED MATERIALS & PROCESSES | SEPTEMBER 2024 19 Are you maximizing your ASM membership? Expand your knowledge and apply your ASM International member-only discounts to a variety of professional development resources: • AM&P Digital Edition • ASM Archive • Discounts on event registration Learn more about your membership benefits by visiting asminternational.org/membership integration with sensor networks and AI-powered anomaly detection algorithms enables the model to preemptively pinpoint and correct potential flaws or deviations from the set printing standards, ensuring the uniform quality and performance of the ceramic components produced. Incorporating NLP-driven LLMs with AI techniques offers a comprehensive strategy for predicting process parameters in ceramic 3D printing. By tapping into the vast knowledge contained in textual data and applying adaptive learning algorithms, manufacturers can reach new heights of efficiency, accuracy, and dependability in AM. This powerful combination of language processing and AI has the potential to transform the ceramic AM sector, setting the stage for novel advancements and applications in the production of high-performance ceramics. CONCLUSION The widespread adoption of ML and AI holds immense potential to revolutionize ceramic AM. By harnessing ML techniques for process optimization, anomaly detection, and quality assurance, and combining AI for predictive modeling and adaptive learning, manufacturers can achieve unprecedented levels of efficiency and precision. Embracing these technologies promises to overcome inherent challenges in ceramic AM, paving the way for enhanced product quality, reduced waste, and accelerated innovation. The integration of ML and AI stands to usher in a new era of advanced, sustainable, and efficient ceramic additive manufacturing processes.~AM&P For more information: Bhargavi Mummareddy, additive manufacturing engineer, Dimensional Energy, Ithaca, NY 14850, bhargavi@dimensionalenergy.com. References 1. L. Meng, et al., Machine Learning in Additive Manufacturing: A Review, JOM Journal of the Minerals Metals and Materials Society, 72(6), p 2363–2377, 2020. 2. C. Tan, et al., Machine Learning in Additive Manufacturing: State-ofthe-Art and Perspectives, Additive Manufacturing, 36, p 101538, 2020. 3. Nvidia AI in Manufacturing, (n.d.), Nvidia. Retrieved February 29, 2024, from https://www.nvidia.com/en-us/ industries/manufacturing/. 4. S.A. Shevchik, et al., Acoustic Emission for In Situ Quality Monitoring in Additive Manufacturing using Spectral Convolutional Neural Networks, Additive Manufacturing, 21, p 598–604, 2018. 5. P.K. Rao, et al., Online RealTime Quality Monitoring in Additive Manufacturing Processes using Hetero- geneous Sensors, Journal of Manu- facturing Science and Engineering, 137(6), p 061007, 2015. 6. Y. Lakhdar, et al., Additive Manufac- turing of Advanced Ceramic Materials, Progress in Materials Science, 116, p 100736, 2021.
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