| Title | Life cycle assessment on fabrication and characterization techniques for additively manufactured polymers and polymer composites |
|---|---|
| ID_Doc | 10393 |
| Authors | Al Rashid, A; Khan, SA; Koç, M |
| Title | Life cycle assessment on fabrication and characterization techniques for additively manufactured polymers and polymer composites |
| Year | 2024 |
| Published | |
| Abstract | Life cycle assessment (LCA) is a powerful tool to evaluate the environmental impacts of any domestic or industrial activity consuming goods and services. There has been an increasing interest in recycling polymers and polymer composites for circular economy and sustainability; however, the widespread use of polymers should be avoided upfront, especially for research and development. LCA is performed to evaluate the environmental impact of two characterization approaches (numerical modeling and experimentation) used for additively manufactured polymers and composites. The current study was conducted using GaBi software, per the International Organization for Standardization (ISO) 14040 and 14044 guidelines. Five different environmental impacts, namely global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), ozone layer depletion (ODP), and fossil fuel depletion (FFD), were evaluated and compared for numerical modeling and experimental characterization approaches. Electrical energy used during the filament fabrication, 3DP process, and mechanical testing was identified as a hotspot in the environmental impacts (GWP, AP, EP, and FFD) for experimental design. However, the materials consumed during the experimental characterization contributed considerably to ODP. Results concluded that using the numerical modeling approach could significantly reduce the environmental impact caused due to extensive resource utilization in experiments. The numerical modeling approach can help promote sustainability as these tools can adequately predict the response of 3D-printed structures; therefore, they should be explored and improved further. |
| https://doi.org/10.1016/j.cesys.2023.100159 |
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