Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling
arXiv:2606.11605v1 Announce Type: new Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper proposes a novel knowledge distillation framework designed to achieve high-accuracy predictions in data-scarce scenarios. The framework integrates analytical physics priors, which are systematically extracted from scientific literature via Large Language Models, into a privileged teacher model. We employ a Graph-Masked Attention layer to capture the complex physical de
The increasing sophistication of Large Language Models (LLMs) and the persistent challenges of data scarcity and interpretability in manufacturing provide the impetus for this novel integration of physics priors.
This development offers a pathway to more accurate and interpretable predictive models in manufacturing, potentially reducing experimental costs and accelerating innovation in material science and process optimization.
The ability to systematically extract and integrate physics knowledge via LLMs into AI models introduces a new paradigm for developing robust predictive tools in complex industrial settings, moving beyond purely data-driven black-box approaches.
- · Advanced Manufacturing Sector
- · Materials Science R&D
- · AI/ML Engineering Firms
- · Industrial IoT Platforms
- · Traditional Experimental Labs (high-cost)
- · Purely Black-Box AI Model Providers
More efficient and cost-effective development of new materials and manufacturing processes.
Accelerated discovery of novel material properties, leading to breakthroughs in various industries from aerospace to medicine.
Potential for sovereign control over critical manufacturing IP through advanced, LLM-augmented R&D frameworks.
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