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.

翻译：工业物联网（IIoT）技术的日益普及实现了制造过程数据的自动化、实时采集，为数据驱动的产品开发开辟了新机遇。当前的数据驱动方法通常应用于特定领域，如设计或制造，而对设计特征与制造过程数据整合的探索有限。由于设计决策显著影响制造结果，如错误率、能耗和加工时间，缺乏此类整合限制了数据驱动产品设计改进的潜力。本文提出一种数据驱动方法，用于映射和分析设计特征与制造过程数据之间的关系。开发了一个全面的系统架构，以确保数据的持续收集与整合。设计特征与制造过程数据之间的关联作为开发机器学习模型的基础，该模型能够提供自动化的设计改进建议。通过将制造过程数据与可持续性指标相结合，该方法为可持续产品开发开启了新的可能性。