Hostname: page-component-cb9f654ff-5jtmz Total loading time: 0 Render date: 2025-08-19T11:11:26.496Z Has data issue: false hasContentIssue false
  • English
  • Français

Data-driven life cycle assessment for mechatronic systems: a comparative analysis of environmental impact assessments

Published online by Cambridge University Press:  16 May 2024

Artur Krause ,
Steffen Wagenmann ,
Katharina Ritzer ,
Albert Albers  and
Nikola Bursac
Artur Krause*
Affiliation:
Hamburg University of Technology, Germany
Steffen Wagenmann
Affiliation:
Karlsruhe Institute of Technology, Germany
Katharina Ritzer
Affiliation:
Hamburg University of Technology, Germany
Albert Albers
Affiliation:
Karlsruhe Institute of Technology, Germany
Nikola Bursac
Affiliation:
Hamburg University of Technology, Germany
*
artur.krause@tuhh.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The growing emphasis on sustainability integrates eco-design and life cycle analysis into product development. Despite the value of LCAs, data limitations lead to assumptions, impacting accuracy. This study compares an estimation-based LCA with a data-driven approach, focusing on a laser machine's operational phase. The significant influence of resource consumption during operation underscores the necessity of optimization. Applying a data-driven approach reveals a 24% difference compared to the estimation-based method, emphasizing the challenges in obtaining accurate data for effective LCAs.

Keywords

sustainabilitydata-driven designlife cycle assessment (LCA)

Information

Type
Design for Sustainability
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1339 - 1348
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Albers, A., Rapp, S., Heitger, N., Wattenberg, F. and Bursac, N. (2018), “Reference Products in PGE – Product Generation Engineering: Analyzing Challenges Based on the System Hierarchy”, Procedia CIRP, Vol. 70, pp. 469–474.CrossRefGoogle Scholar
Bauer, C. and Poganietz, W.-R. (2007), “Prospektive Lebenszyklusanalyse oder die Zukunft in der Ökobilanz”, TATuP-Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis, pp. 17–23.CrossRefGoogle Scholar
Bhatia, P., Cummis, C., Brown, A., Draucker, L., Rich, D. and Lahd, H. (2011), Product Life Cycle Accounting and Reporting Standard, available at: ISBN: 978-1-56973-773-6.Google Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, 1st, Springer Publishing Company, Incorporated.CrossRefGoogle Scholar
Chavez, Z., Gopalakrishnan, M., Nilsson, V. and Westbroek, A. (2022), “Exploring Data-Driven Decision-Making for Enhanced Sustainability”, pp. 392–403.CrossRefGoogle Scholar
Deutsches Institut für Normung (2009), DIN EN ISO 14040: Umweltmanagement–Ökobilanz–Grundsätze und Rahmenbedingungen, Beuth Verlag Berlin.Google Scholar
EUROPEAN PARLIAMENT (2022), European Corporate Sustainability Reporting Directive: DIRECTIVE (EU) 2022/2464.Google Scholar
Frischknecht, R. (2020), Lehrbuch der Ökobilanzierung, Springer.CrossRefGoogle Scholar
Guinée, J., Heijungs, R. and Frischknecht, R. (2021), “Multifunctionality in Life Cycle Inventory Analysis: Approaches and Solutions”, in Ciroth, A. and Arvidsson, R. (Eds.), Life Cycle Inventory Analysis Methods and Data, Springer International Publishing, Cham, pp. 73–95.CrossRefGoogle Scholar
Hauschild, M.Z., Rosenbaum, R.K. and Olsen, S.I. (2018), Life Cycle Assessment, Springer International Publishing, Cham.CrossRefGoogle Scholar
Hellweg, S. and Milà i Canals, L., (2014), “Emerging approaches, challenges and opportunities in life cycle assessment”, Science (New York, N.Y.), pp. 1109–1113.CrossRefGoogle Scholar
Inkermann, D. (2022), “Potentials of integrating MBSE and LCA to handle uncertainties and variants in early design stages”, 33. DfX-Symposium 2022, pp. 1–13.Google Scholar
Jeswiet, J. and Hauschild, M. (2005), “EcoDesign and future environmental impacts”, Materials & Design, Vol. 26 No. 7, pp. 629–634.CrossRefGoogle Scholar
Kokoschko, B.R., Augustin, L., Beyer, C. and Schabacker, M. (2021), “Ansatz zur Erarbeitung einer Methodenauswahl für nachhaltige Produktentwicklung in KMUs”.CrossRefGoogle Scholar
Ma, S., Zhang, Y., Liu, Y., Yang, H., Lv, J. and Ren, S. (2020), “Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries”, Journal of Cleaner Production.CrossRefGoogle Scholar
Nakayama, H., Nishino, N., Oda, S.H. and Ueda, K. (2005), “Decision Making of Economic Agents for Durable-Goods Recycling”, in 2005 4th International Symposium on Environmentally Conscious Design and Inverse Manufacturing, 12-14 Dec. 2005, Tokyo, Japan, IEEE, pp. 43–50.CrossRefGoogle Scholar
Provost, F. and Fawcett, T. (2013), “Data Science and its Relationship to Big Data and Data-Driven Decision Making”, Big Data, Vol. 1 No. 1, pp. 51–59.CrossRefGoogle ScholarPubMed
Schäfer, M. and Löwer, M. (2021), “Ecodesign. A Review of Reviews”, Sustainability, Vol. 13 No. 1.Google Scholar
Schmitt, R.H., Kurzhals, R., Kiesel, R., Nilgen, G., Schlegel, P., Dietrich, E., Krauß, J., Latz, A., Ellerich, M. and Miller, N. (2020), “Predictive Quality–Data Analytics zur Steigerung unternehmerischer Nachhaltigkeit”, pp. 289–318.Google Scholar
Stock, T. and Seliger, G. (2016), “Opportunities of Sustainable Manufacturing in Industry 4.0”, pp. 536–541.CrossRefGoogle Scholar
Wagenmann, S., Krause, A., Rapp, S., Hünemeyer, S., Albers, A. and Bursac, N. (2022), “Process Model for the Data-driven Identification of Machine Function Usage for the Reduction of Machine Variants”, available at: https://www.researchgate.net/publication/366464714.Google Scholar