Parametric investigation, formulation, and benchmarking of energy consumption for the powder bed fusion process

Autor: Akshar Kota, Venkata Reddy Nallagundla, Carla Susana A Assuad, Kristian Martinsen, Suryakumar Simhambhatla
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Cleaner and Responsible Consumption, Vol 14, Iss , Pp 100205- (2024)
Druh dokumentu: article
ISSN: 2666-7843
DOI: 10.1016/j.clrc.2024.100205
Popis: Energy consumption is an important metric used to evaluate the sustainability potential of manufacturing processes. Due to the low volume and mass customization potential, additive manufacturing (AM) processes have experienced exponential growth in recent years, resulting in heightened ecological consciousness surrounding energy usage. Gaining insight into the energy-intensive sub-systems and sub-processes and identifying strategies for their minimization enables manufacturers to save on energy costs and also aids in reducing their carbon footprint. This study delves into the energy consumption characteristics of the powder bed fusion (PBF) process, particularly selective laser melting (SLM). Through experimental investigation, we investigate how certain factors impact energy usage, namely capacity utilization, layer thickness, and part orientation. We present a novel formulation for estimating primary and total energy consumption in PBF processes, offering a comprehensive energy consumption model. Our results demonstrate significant energy savings with increased capacity utilization—up to a 32.68% reduction in total energy consumption (TEC) per part. Layer thickness variations show the lowest TEC at 25 μm, which can be attributed to the SLM machine's reduced operational time and energy usage of auxiliary components. Furthermore, altering part orientation for the given case study yielded a 50% reduction in TEC, highlighting orientation as a critical factor in energy efficiency. Our formulation, benchmarked against experimental data and specific energy consumption (SEC) values from the literature, effectively captures these parameters' influence on energy usage. The insights from this research advance our understanding of energy dynamics in SLM processes and pave the way for more energy-efficient practices in AM.
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