(749a) Life Cycle Modeling for Emerging Technologies: A Parametric Approach to Guide Research and Development for Additive Manufacturing

A quantitative understanding of the environmental and economic implications of emerging technologies in the early stages is vital to further research and development (R&D). Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) has been used to assess the environmental impacts and economic feasibility of emerging technologies in different manufacturing sectors. However, traditional LCA do not model relationships between Life Cycle Inventory (LCI) data and key technical parameters, leading to difficulties in understanding the impacts of R&D improvement in the future and determining R&D priorities in the short-term and long-term to reduce the environmental footprints and costs of emerging technologies.

In this work¹, a parametric life-cycle modeling framework was developed to address this methodology gap. The framework integrates LCA, Life Cycle Cost (LCC) analysis and optimization. The LCI data was parameterized through process-based engineering and supply chain models, and the key R&D parameters were modeled as decision variables. The objective function is to maximize the total possibility of improving specific technical parameters through R&D in a given timeframe. Such improvements are subject to constraints based on the goals of reducing specific environmental impacts and life-cycle costs. The framework is capable of identifying the most feasible pathways to achieve economic, environmental, and energy expectations of emerging technology and identify the R&D priority areas within a reasonable timeframe (e.g., future 10 years). The framework has the potential to be used by policymakers, scientists, and engineers for accelerating the development and deployment of emerging technologies to reduce industrial-wide energy and GHG footprints.

The framework is demonstrated through a case study of the next-generation manufacturing technology - additive manufacturing (AM). The framework was applied to an AM case study for tooling and the optimization model was solved using the genetic algorithm. Four conceptual goals were set in this case study for future 20 years, including 20% reduction of life-cycle cost, 5% reduction of unit energy consumption of the parts, 20% reduction of unit costs of the parts, and no increase in the upfront costs. The model identified technical parameters that are most likely to be improved by R&D to achieve those goals simultaneously, which shed light on synergetic opportunities to improve both energy efficiency and cost-effectiveness. Such insights could be useful for future integrated design and applications of AM in the manufacturing industries. Although this case study mainly focused on energy and costs, the life cycle modeling framework and the parametric approaches presented can be applied to other indicators such as GHG emissions, as well as other emerging technologies.

1. Yao, Y.; Huang, R., A Parametric Life Cycle Modeling Framework for Identifying Research Development Priorities of Emerging Technologies: A Case Study of Additive Manufacturing. Procedia CIRP 2019, 80, 370-375.