Abstract:

To address the challenges of optimizing low-carbon manufacturing processes， a structured feature analysis and an intelligent mapping-driven method were proposed to link part design models to carbon emissions assessment during the manufacturing phases. First， the geometric and topological information were extracted by parsing STEP files， and an extended attributed adjacency graph and the corresponding matrix representation were constructed， then feature submatrix matching against a feature library was utilized to identify typical manufacturing characteristics. Second， based on the identification results， corresponding parameter-extraction rules were triggered to obtain geometric feature dimensions， thereby achieving a structured representation of manufacturing information. Finally， a collaborative quantification model linking manufacturing carbon emissions and processing time was constructed. A multi-objective process optimization framework targeting low-carbon efficiency was established， and the NSGA-Ⅱ algorithm was employed to determine the Pareto-optimal solution set， providing decision support for low-carbon manufacturing process planning of the parts.

Key words: design information mining, feature recognition, carbon emission assessment, multi-objective optimization

摘要：

针对零件设计模型与制造阶段的碳排放评估之间因缺乏有效关联与映射而导致低碳工艺优化困难的问题，提出了一种结构化特征解析与智能映射驱动的零件低碳工艺优化方法。首先通过解析STEP文件提取几何与拓扑信息，构建扩展属性邻接图及其对应的矩阵表示，并利用特征子矩阵匹配特征库实现典型制造特征识别；然后依据识别结果触发对应的参数提取规则，获取特征几何尺寸实现制造信息结构化表达；最后构建制造环节碳排放与加工时间的协同量化模型，以低碳高效为目标构建多目标工艺优化框架，通过NSGA-Ⅱ算法求解帕累托最优解集，为零件的低碳制造工艺规划提供决策支持。

关键词: 设计信息挖掘, 特征识别, 碳排放评估, 多目标优化