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SummaryAdditive manufacturing (AM) proposes a novel paradigm for engineering design and manufacturing, which has profound economic, environmental, and security implications. The design freedom offered by this category of manufacturing processes and its ability to locally print almost each designable object will have important repercussions across society. While AM applications are progressing from rapid prototyping to the production of end-use products, the environmental dimensions and related impacts of these evolving manufacturing processes have yet to be extensively examined. Only limited quantitative data are available on how AM manufactured products compare to conventionally manufactured ones in terms of energy and material consumption, transportation costs, pollution and waste, health and safety issues, as well as other environmental impacts over their full lifetime. Reported research indicates that the specific energy of current AM systems is 1 to 2 orders of magnitude higher compared to that of conventional manufacturing processes. However, only part of the AM process taxonomy is yet documented in terms of its environmental performance, and most life cycle inventory (LCI) efforts mainly focus on energy consumption. From an environmental perspective, AM manufactured parts can be beneficial for very small batches, or in cases where AM-based redesigns offer substantial functional advantages during the product use phase (e.g., lightweight part designs and part remanufacturing). Important pending research questions include the LCI of AM feedstock production, supply-chain consequences, and health and safety issues relating to AM.

The term additive manufacturing (AM) describes a collection of production techniques enabling the layer-by-layer manufacture of components using digital data and raw material as inputs. The AM technology variant most frequently used in the production of end use parts is laser sintering (LS).It has been suggested that efficient usage of the energy inputs is one of the advantages of the technology. This paper presents a comparative assessment of the electricity consumptions of two major polymeric LS platforms: the Sinterstation HiQ þ HS from 3D Systems and the EOSINT P 390 from EOS GmbH. The energy inputs to a build consisting of two prosthetic parts were recorded during power-monitoring experiments conducted on both platforms. This paper injects clarity into the ongoing research on the AM energy consumption by applying a novel classification system; it is argued that the AM energy usage can be divided into the job-dependent, timedependent, geometry-dependent, and Z-height-dependent energy consumption values. The recorded mean real power consumption conforms to the values that have been reported for similar platforms. The measured energy consumption rates are higher than reported elsewhere. It is also shown that the purely time-dependent energy consumption is the main energy drain. Furthermore, the presentation of results in the context of previous literature highlights the caveats attached to summary metrics of the AM input usage.

Additive manufacturing offers great potential for both product and process innovation in manufacturing across a wide range of industry sectors. To date, most applications that have been reported use additive manufacturing to produce either customized parts or produce at small scale, while the volume manufacture of standard parts largely remains a conjecture. In this article, we report on a series of experiments designed to elucidate how quantity, quality and cost relate in additive manufacturing processes. Our findings show that traditional economies of scale only partially apply to additive manufacturing processes. We also identify four build failure modes and quantify their combined effect on unit cost, exposing an unusual property whereby the cost‐optimal operating point occurs below maximum machine capacity utilization. Furthermore, once additive manufacturing technology is used at full capacity utilization, we find no evidence of a positive effect of increased volume on unit cost. We do, however, identify learning curve effects related to process repetition and operator experience. Based on our findings we propose a set of general characteristics of the additive manufacturing process for further testing.

SummaryThis life cycle assessment measured environmental impacts of selective laser melting, to determine where most impacts arise: machine and supporting hardware; aluminum powder material used; or electricity used to print. Machine impacts and aluminum powder impacts were calculated by generating life cycle inventories of materials and processing; electricity use was measured by in-line power meter; transport and disposal were also assessed. Impacts were calculated as energy use (megajoules; MJ), ReCiPe Europe Midpoint H, and ReCiPe Europe Endpoint H/A. Previous research has shown that the efficiency of additive manufacturing depends on machine operation patterns; thus, scenarios were demarcated through notation listing different configurations of machine utilization, system idling, and postbuild part removal. Results showed that electricity use during printing was the dominant impact per part for nearly all scenarios, both in MJ and ReCiPe Endpoint H/A. However, some low-utilization scenarios caused printer embodied impacts to dominate these metrics, and some ReCiPe Midpoint H categories were always dominated by other sources. For printer operators, results indicate that maximizing capacity utilization can reduce impacts per part by a factor of 14 to 18, whereas avoiding electron discharge machining part removal can reduce impacts per part by 25% to 28%. For system designers, results indicate that reductions in energy consumption, both in the printer and auxiliary equipment, could significantly reduce the environmental burden of the process.

SummaryAdditive manufacturing (AM) technology is capable of building up component geometry in a layer-by-layer process, entirely without tools, molds, or dies. One advantage of the approach is that it is capable of efficiently creating complex product geometry. Using experimental data collected during the manufacture of a titanium test part on a variant of AM technology, electron beam melting (EBM), this research studies the effect of a variation in product shape complexity on process energy consumption. This is done by applying a computationally quantifiable convexity-based characteristic associated with shape complexity to the test part and correlating this quantity with per-layer process energy consumption on the EBM system. Only a weak correlation is found between the complexity metric and energy consumption (ρ = .35), suggesting that process energy consumption is indeed not driven by shape complexity. This result is discussed in the context of the energy consumption of computer-controlled machining technology, which forms an important substitute to EBM. This article further discusses the impact of available additional shape complexity at the manufacturing process level on the incentives toward minimization of energy inputs, additional benefits arising later within the product's life cycle, and its implications for value creation possibilities.

Keywords:digital supply chain energy consumption industrial ecology manufacturing process rapid manufacturing rapid prototyping SummaryThe supply chains found in modern manufacturing are often complex and long. The resulting opacity poses a significant barrier to the measurement and minimization of energy consumption and therefore to the implementation of sustainable manufacturing. The current article investigates whether the adoption of additive manufacturing (AM) technology can be used to reach transparency in terms of energy and financial inputs to manufacturing operations.AM refers to the use of a group of electricity-driven technologies capable of combining materials to manufacture geometrically complex products in a single digitally controlled process step, entirely without molds, dies, or other tooling. The single-step nature affords full measurability with respect to process energy inputs and production costs. However, the parallel character of AM (allowing the contemporaneous production of multiple parts) poses previously unconsidered problems in the estimation of manufacturing resource consumption.This research discusses the implementation of a tool for the estimation of process energy flows and costs occurring in the AM technology variant direct metal laser sintering. It is demonstrated that accurate predictions can be made for the production of a basket of sample parts. Further, it is shown that, unlike conventional processes, the quantity and variety of parts demanded and the resulting ability to fully utilize the available machine capacity have an impact on process efficiency. It is also demonstrated that cost minimization in additive manufacturing may lead to the minimization of process energy consumption, thereby motivating sustainability improvements.