Knowledge-enhanced Generative Large Model for Intelligent Injection Molding Process Optimization

ZHU Ningdi; PAN Yifeng; YAO Hanfei; ZHOU Jingning; CAI Qize; LYU Yiru; ZHOU Fei; JIAO Xiaolong; ZHAO Peng

doi:10.3969/j.issn.1674-6457.2026.05.026

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (5) : 277-287. DOI: 10.3969/j.issn.1674-6457.2026.05.026

Advanced Manufacturing Technology and Equipment

Knowledge-enhanced Generative Large Model for Intelligent Injection Molding Process Optimization

ZHU Ningdi¹, PAN Yifeng^2,3,*, YAO Hanfei^2,3, ZHOU Jingning⁴, CAI Qize⁴, LYU Yiru⁴, ZHOU Fei¹, JIAO Xiaolong¹, ZHAO Peng^2,3,*

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Abstract

The work aims to propose a knowledge-enhanced generative large model-based intelligent optimization method, to explore the application of artificial intelligence in the optimization of manufacturing processes and address the challenges in injection molding process tuning, such as heavy reliance on empirical knowledge, high trial-and-error costs, and long optimization cycles. An optimization framework that integrated domain knowledge and real-time feedback was constructed. Firstly, 819 Chinese and English technical documents on injection molding were automatically collected; Then, these documents were chunked and embedded into a vectorized knowledge base accessible to the large model; Finally, combining locally deployed generative large model, parameter adjustment recommendations were dynamically generated based on the deviation between actual and target part weight, accompanied by interpretable reasoning. Experimental results showed that the proposed method achieved precise convergence to the target weight within three iterations for multiple materials, including PC and PMMA. In contrast, a knowledge-agnostic large model, initialized at the center of the parameter space and lacking understanding of material properties and molding physics set an excessively low initial packing pressure, leading to low initial holding pressure, large first-round deviations and even counterproductive adjustments. Compared with traditional manual trial-and-error, which relies on subjective experience and requires more than seven iterations to meet the target, our approach significantly shortens the tuning cycle through knowledge-guided automated reasoning. By effectively fusing process mechanism knowledge with intelligent inference, the method substantially enhances the efficiency and generalization capability of injection molding optimization, offering a reliable pathway for deploying large models in industrial manufacturing.

Key words

retrieval-augmented generation / generative large model / process parameter optimization / injection molding / effective convergence

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ZHU Ningdi, PAN Yifeng, YAO Hanfei, ZHOU Jingning, CAI Qize, LYU Yiru, ZHOU Fei, JIAO Xiaolong, ZHAO Peng. Knowledge-enhanced Generative Large Model for Intelligent Injection Molding Process Optimization[J]. Journal of Netshape Forming Engineering. 2026, 18(5): 277-287 https://doi.org/10.3969/j.issn.1674-6457.2026.05.026

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Funding

The Key Project of Science and Technology of “Innovation Yongjiang 2035” of Ningbo (2025Z010); The National Natural Science Foundation of China (52535010); Zhejiang provincial teams of leading talents in Innovation and Entrepreneurship (2024R01002)