A data-driven approach to linking design features with manufacturing process data for sustainable product development
🤖 AI Summary
Existing data-driven approaches are largely confined to isolated domains—such as design or manufacturing—lacking deep integration between design features and manufacturing process data, thereby hindering sustainability-oriented design optimization. This paper proposes a novel system architecture that, for the first time, enables real-time mapping and fusion of design features with multidimensional manufacturing data—including dimensional error rates, energy consumption, and processing time—and constructs an interpretable association model. Sustainability metrics are embedded into a closed-loop, data-driven design framework integrating IIoT data acquisition, heterogeneous data fusion, feature engineering, and supervised machine learning (regression and classification). Validated on an industrial production line, the approach achieves a 32% improvement in the accuracy of design improvement recommendations, a 14.7% reduction in average energy consumption, and a 21.3% decrease in machining defect rate—significantly advancing intelligent, green, manufacturing-aware design.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Mapping design features to manufacturing process data
Linking design decisions with sustainability metrics
Enabling automated design improvements via machine learning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrating design features with manufacturing process data
Developing machine learning model for design improvements
Linking manufacturing data to sustainability metrics
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