As one of the core processes in modern manufacturing, injection molding quality is critically dependent on dozens of process parameters, such as melt temperature and packing time. Common defects like flash, short shots, and silver streaks frequently arise during production. However, current industrial fault diagnosis heavily relies on the experiential knowledge of engineers and manual consultation of technical documentation. This approach is not only inefficient but also hinders the accumulation and transfer of specialized expertise. To address this issue, this paper integrates large language models and retrieval-augmented generation (RAG) technology to establish an efficient and reliable intelligent question-answering system for injection molding defect diagnosis for the first time. First, multi-source domain-specific corpora were collected to construct a high-quality instruction dataset, and the Qwen3-8B model was fine-tuned using low-rank adaptation (LoRA) for parameter efficiency, achieving domain adaptation for injection molding knowledge. Simultaneously, key modules were deeply enhanced and customized. To address the prevalence of complex documents in this field, the parsing strategy was transformed into a "layout-priority combined with regional optical character recognition" approach, significantly improving parsing accuracy and retrieval citation quality of industrial documents. Second, a text classifier was introduced as a pre-retrieval module to identify whether user queries pertain to the injection molding domain, thereby activating knowledge base retrieval only for professional inquiries and effectively boosting system response efficiency. Furthermore, an independent knowledge graph retrieval route was incorporated to achieve fused multi-source knowledge recall. Finally, the system was evaluated in detail using multi-dimensional metrics, including faithfulness, answer accuracy, context recall, recall-oriented understudy for gisting evaluation (ROUGE) , and BERTScore. Experimental results confirm that the proposed "fine-tuned model + enhanced retrieval" integrated system significantly outperforms traditional RAG systems and other baseline models across all evaluated metrics. This system offers an efficient and reliable intelligent solution for injection molding defect diagnosis, demonstrating substantial practical value and broad potential for industrial application.

The optimization of injection molding process parameters is crucial in contemporary manufacturing, as it affects not only product quality but also production costs and efficiency. Traditional manual tuning methods rely on trial and error, leading to extended production cycles and increased costs. To address this, reinforcement learning (RL) technology is applied, for the first time, to the tuning of injection molding process parameters, proposing a novel automatic tuning algorithm based on RL for injection molding process parameters. The paper first models the injection molding process parameter tuning problem as a sequential decision-making problem and designs a customized Markov Decision Process model. Subsequently, a model-free RL method is proposed, implementing the automatic selection of injection molding process parameters based on the Q-learning algorithm. Compared with traditional methods, this algorithm can automatically explore and optimize process parameter configurations in dynamically changing production environments. Finally, the feasibility and effectiveness of the proposed method are experimentally validated, demonstrating significant application potential.