A data-driven approach to linking design features with manufacturing process data for sustainable product development

TL;DR

This paper introduces a data-driven system that links design features with manufacturing process data using machine learning, aiming to improve product design and sustainability through integrated analysis of manufacturing outcomes.

Contribution

It presents a novel system architecture and machine learning approach for integrating design and manufacturing data to enhance sustainable product development.

Findings

Successful development of a comprehensive data integration system.

Machine learning model provides automated design improvement suggestions.

Integration of sustainability metrics with manufacturing data enables eco-friendly product design.

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.