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초록

To maximize intelligent manufacturing efficiency under the Industry 4.0 paradigm, this study addresses the critical challenge of precision design change detection and manufacturing cost prediction for plastic products by leveraging exact Boundary Representation (B-Rep) geometric information and data-driven learning models. Unlike conventional mesh-based approaches that suffer from tessellation approximation errors, the proposed method performs direct parametric sampling on NURBS surfaces from STP (CAD) files. This enables the noise-free, micrometer-level detection of fine shape displacements and the rigorous extraction of 12 key engineering feature vectors relevant to machining decision-making. Based on the extracted features, a hierarchical LightGBM-based learning structure utilizing a leaf-wise growth strategy is proposed to classify seven core manufacturing processes, including NC machining and EDM, and to predict the associated processing time and costs. Furthermore, an LLM- and RAG-based explainable AI (XAI) framework is incorporated to provide natural language interpretations of the engineering rationale underlying the numerical predictions. To ensure model robustness and prevent overfitting, the framework was evaluated on a dataset of 15,120 change patches using 5-fold cross-validation, with cost labels validated against real-world Enterprise Resource Planning (ERP) data. Experimental results demonstrate a process classification accuracy of 94.2% and a Mean Absolute Percentage Error (MAPE) of 7.8% in cost prediction, significantly outperforming conventional baselines such as Linear Regression, Random Forest, and XGBoost. The proposed approach reduces the design change analysis lead time from several days to under 3 minutes, demonstrating its effectiveness for real-time decision support in intelligent manufacturing environments.

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