打通设计与制造数据，用机器学习推动绿色产品设计
A data-driven approach to linking design features with manufacturing process data for sustainable product development
- 构建系统集成设计特征与制造数据流
- 实现设计优化建议自动化生成
- 融合能耗等指标支持可持续设计
工业互联网（IIoT）技术的普及使制造过程数据得以实时自动采集，为数据驱动的产品开发带来新机遇。现有数据方法多局限于设计或制造单一领域，缺乏对设计特征与制造数据的整合分析。由于设计决策显著影响缺陷率、能耗和加工时间等制造结果，这种割裂限制了产品设计的优化潜力。本文提出一种数据驱动方法，建立设计特征与制造过程数据间的映射关系。通过构建完整系统架构，实现持续数据采集与融合，并基于此开发机器学习模型，实现设计改进的自动化建议。结合制造数据与可持续性指标，该方法为可持续产品开发开辟了新路径。
原文摘要 · Abstract (English)
The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data, unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains, such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach opens new possibilities for sustainable product development.
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