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Rongchai Wang Jul 22, 2024 18:14 HP 3D Printing and NVIDIA Modulus team up to enhance manufacturing digital twins using physics-informed machine learning. HP 3D Printing and NVIDIA Modulus have announced a collaboration to develop an open-source manufacturing digital twin, leveraging physics-informed machine learning (physics-ML). This partnership aims to foster innovation in AI engineering applications by embedding physical laws into the learning process, according to NVIDIA Technical Blog. Advancements in Physics-ML Physics-ML is a burgeoning field that incorporates physical laws into machine learning models, enhancing the generalizability and efficiency of neural networks. NVIDIA Modulus, an open-source framework, facilitates the building, training, and fine-tuning of these models with a simple Python interface. The framework offers reference applications to help domain experts apply physics-ML to real-world use cases. The Digital Twin team at HP 3D Printing Software Organization has utilized physics-ML models for their manufacturing digital twin and contributed this work to Modulus. HP, a leader in additive manufacturing, aims to accelerate the onboarding of new applications and adopt this technology in production environments. Dr. Jun Zeng, HP’s distinguished technologist, emphasized the importance of physics simulation engines grounded in manufacturing process variability, noting the significant speedups achieved with well-trained physics-ML models. Digital Twins in Additive Manufacturing HP has a rich history of technological innovation, including the development of thermal inkjet technology. The company’s latest innovation, HP Metal Jet, enables the production of industrial-grade 3D metal parts. HP is developing a digital twin for Metal Jet technology to optimize design parameters and process control, thereby improving part quality and manufacturing yield. The HP team created the Virtual Foundry Graphnet model, applying physics-ML to accelerate the computation of metal powder material phase transitions. This model has achieved significant speedups, enabling near real-time, high-fidelity emulation of the metal sintering process. The model has also demonstrated…

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Filed under: News - @ July 23, 2024 5:16 pm