A Parametric Life Cycle Modeling Framework for Identifying Research Development Priorities of Emerging Technologies: A Case Study of Additive Manufacturing
Procedia CIRP, ISSN: 2212-8271, Vol: 80, Page: 370-375
2019
- 12Citations
- 73Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Life Cycle Assessment (LCA) has been used to assess the environmental implications of emerging technologies in different manufacturing sectors. However, it is challenging to use the traditional LCA method to model the relationships between Life Cycle Inventory (LCI) data and key technical parameters, preventing further analysis for understanding key driving factors and determining priorities for research and technology development. Furthermore, the sensitivity analysis of traditional LCA could be misleading for decision making or strategic planning given that the potential/possibility of improving specific parameters are commonly not taken into consideration. In this work, a novel parametric analysis framework was developed to address the methodological challenge. The modeling framework integrates process-based engineering models with LCA, Life Cycle Cost analysis (LCC), and optimization. The framework is demonstrated through a case study of additive manufacturing (AM).
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2212827119300393; http://dx.doi.org/10.1016/j.procir.2019.01.037; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85067183390&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2212827119300393; https://dx.doi.org/10.1016/j.procir.2019.01.037
Elsevier BV
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