Additive manufacturing (AM) is widely recognized as a critical pillar of advanced manufacturing and is moving from the design shop to the factory floor. As AM processes become more popular, it is paramount that engineers and policymakers understand and then reduce their environmental impacts. This article structures the current work on the environmental impacts of metal powder bed processes: selective laser melting (SLM), direct metal laser sintering (DMLS), electron beam melting (EBM), and binder jetting (BJ). We review the potential benefits and pitfalls of AM in each phase of a part's lifecycle and in different application domains (e.g., remanufacturing, hybrid manufacturing etc.). We highlight critical uncertainties and future research directions throughout. The environmental impacts of AM are sensitive to the specific production and use-phase context; however, several broad lessons can be extracted from the literature. Unlike in conventional manufacturing, powder bed production impacts are dominated by the generation of the direct energy (electricity) required to operate the AM machines. Combined with a more energy-intensive feedstock (metal powder) this means that powder bed production impacts are higher than in conventional manufacturing unless production volumes are very small (saving tool production impacts) and/or there are significant material savings through part light weighting or improved buy-to-fly ratios.
Bayesian inference allows the transparent communication and systematic updating of model uncertainty as new data become available. When applied to material flow analysis (MFA), however, Bayesian inference is undermined by the difficulty of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 US steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' informative priors. Sensible, weakly informative priors are adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey and the World Steel Association. The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed using 2012 data; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre‐assumed data noise levels, providing a more robust basis for decision‐making that affects the system.
Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. However, the method is undermined by the difficultly of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 U.S. steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods.The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' prior. A sensible, weakly-informative prior is also adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey (USGS) and the World Steel Association (WSA).The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.
Aluminum recycling requires less energy and releases fewer greenhouse emissions than primary production from naturally occurring ores; however, a significant fraction of the furnace charge is lost to dross generation during remelting. In this article, we use an electric furnace to remelt clean aluminum sheet and machining chip process scrap of varying thickness, surface roughness, and composition. The metal recovery results show that magnesium-containing alloys (e.g., 2xxx, 5xxx, 6xxx, and 7xxx alloys) accelerate dross generation and lower metal recovery. This is likely due to magnesium having a higher reactivity than aluminum, with the magnesium content detected in the dross (using Energy-dispersive X-ray spectroscopy) greater than the magnesium content in the alloy. Metal recovery decreased when remelting thinner scrap. Metal recovery for clean machining chips was lower than for aluminum sheet scrap of the same thickness and composition. This disparity was likely due to the greater surface roughness of the machining chips, which will increase the surface area for oxidation and potentially the wetting of the oxide by the Wenzel effect. The decreased metal recovery for scratch brushed aluminum sheets confirmed the effect of surface roughness. Subsequently, a “squeeze” cutting tool was designed and manufactured, which smooths the otherwise rough back-side of the machining chips. These smoother machining chips exhibited increased metal recovery during remelting.
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