Energy Assessment of Powder Bed Fusion Additive Manufacturing Processes at Industrial Scale: Experiments and Simulations
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Abstract
Abstract Powder bed fusion processes are additive manufacturing (AM) techniques that are increasingly used for industrial applications. These AM technologies are today mature enough to be used intensively for the manufacturing of metal parts. Nevertheless, energy efficiency and productivity play a crucial role in scaling up AM to higher volumes, especially for their relatively slow deposition speed per unit mass compared to conventional manufacturing methods. To the current state of the art, the energy consumption analysis of these processes under real industrial conditions and not in a lab environment is of primary interest to contribute to reaching the sustainability development goals posed by United Nations. However, in the existing literature, very few of these consider real case studies typically faced in industry. Also, the existing research on AM production is mainly focused on the quality of produced parts and printing technology rather than factory-level management. Literature results demonstrate that discrete event simulation (DES) methods can successfully help to increase the productivity of conventional production systems. However, these methods have not yet been extensively reported for AM facilities. The aim of the work is dual: on the one hand, to provide reliable primary energy data to be used for carrying out LCA analyses on AM; on the other hand, to provide process guidelines and insights to reduce energy consumption in AM industrial operations. For this purpose, an experimental campaign has been carried out by using primary energy data of different printed jobs deriving from the same AM manufacturing cell. A DES has been also carried out to estimate the influence of schedule issues on real energy consumption. The results obtained showed that, under fixed process parameters, both the job schedule and the job design (in terms of the number and dimension of the parts printed) have a non-negligible effect on the energy consumption of the process.
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Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Injection Molding Process and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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