A Data-Driven Approach for Improving Sustainability Assessment in Advanced Manufacturing

Published

December 11, 2017

Author(s)

Yunpeng Li, Heng Zhang, Utpal Roy, Yung-Tsun Lee

Abstract

Sustainability assessment (SA) has been one of the prime contributors to advanced manufacturing analysis, and it traditionally involves life cycle assessment (LCA) techniques for retrospective and prospective evaluations. One big challenge to reach a reliable sustainability assessment comes from the inadequate understandings of the underlying activities related to each of the product lifecycle stages based on expert knowledge. Data-driven modeling, on the other hand, is an emerging approach that takes advantage of machine-learning methods in building models that would complement or replace the knowledge-based models capturing physical behaviors. Incorporating suitable data analytics models to utilize real-time product and process data could significantly improve LCA techniques. To address the complexity and uncertainty involved in multi-level SA decision-making activities, this paper proposes a modular LCA framework to accommodate a hybrid modeling paradigm that includes knowledge-based and data-driven models. The feasibility and benefits of the proposed modular, hybrid sustainability assessment methodology have been illustrated with an injection molding case study, incorporating an overall modular Scorecard-based LCA architecture with a Bayesian Network predictive model.

Proceedings Title

2017 IEEE International Conference on Big Data (BigData 2017), 2nd Symposium on Data Analytics
for

Conference Dates

December 11-14, 2017

Conference Location

Boston, MA, US

Pub Type

Conferences

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Keywords

Sustainability assessment, Data-driven modeling, DMN, PMML, Bayesian Network

Citation

Li, Y. , Zhang, H. , Roy, U. and Lee, Y. (2017), A Data-Driven Approach for Improving Sustainability Assessment in Advanced Manufacturing, 2017 IEEE International Conference on Big Data (BigData 2017), 2nd Symposium on Data Analytics for, Boston, MA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924679 (Accessed October 15, 2024)