Martin Baumers

Sustainability of additive manufacturing: measuring the energy consumption of the laser sintering process

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 + 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, time-dependent, 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.

Transparency Built‐in

The 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.

Shape Complexity and Process Energy Consumption in Electron Beam Melting: A Case of Something for Nothing in Additive Manufacturing?

Summary Additive 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 products life cycle, and its implications for value creation possibilities.

Environmental Impacts of Selective Laser Melting: Do Printer, Powder, Or Power Dominate?

This 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.

Environmental Dimensions of Additive Manufacturing: Mapping Application Domains and Their Environmental Implications

Additive 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.

Realised levels of geometric complexity in additive manufacturing

The emergence of Additive Manufacturing (AM) is seen by many as a promising addition to the existing spectrum of manufacturing technology. Assessing a sample of 43 AM produced components, this paper investigates features of complex part geometry. It is found that the measured levels of geometric complexity approximate the normal distribution. Results indicate several factors promoting complexity: membership of the medical industry, organisational stability and the utilisation of powder bed or polymer vat AM technology. The current paper provides some empirical evidence that AM adoption may lead to advances in product performance for a wide range of applications.

Informing Additive Manufacturing Technology Adoption: Total Cost and the Impact of Capacity Utilisation

Informing Additive Manufacturing (AM) technology adoption decisions, this paper investigates the relationship between build volume capacity utilisation and efficient technology operation in an inter-process comparison of the costs of manufacturing a complex component used in the packaging industry. Confronting the reported costs of a conventional machining and welding pathway with an estimator of the costs incurred through an AM route utilising Direct Metal Laser Sintering (DMLS), we weave together four aspects: optimised capacity utilisation, ancillary process steps, the effect of build failure and design adaptation. Recognising that AM users can fill unused machine capacity with other, potentially unrelated, geometries, we posit a characteristic of ‘fungible’ build capacity. This aspect is integrated in the cost estimation framework through computational build volume packing, drawing on a basket of sample geometries. We show that the unit cost in mixed builds at full capacity is lower than in builds limited to a single type of geometry; in our study, this results in a mean unit cost overstatement of 157%. The estimated manufacturing cost savings from AM adoption range from 36 to 46%. Additionally, we indicate that operating cost savings resulting from design adaptation are likely to far outweigh the manufacturing cost advantage.

To kit or not to kit: Analysing the value of model-based kitting for additive manufacturing

Abstract The use of additive manufacturing (AM) for the production of functional parts is increasing. Thus, AM based practices that can reduce supply chain costs gain in importance. We take a forward-looking approach and study how AM can be used more effectively in the production of multi-part products in low to medium quantities. The impact of introducing kitting in AM on supply chain cost is investigated. Kitting approaches are traditionally devised to feed all components belonging to an assembly into individual containers. Where conventional manufacturing approaches are used for kitting, the produced parts pass through inventory and kit preparation steps before being forwarded to the assembly line/station. However, by taking advantage of the object-oriented information handling inherent in the AM process, kitting information can be embedded directly within the digital design data and parts produced in a common build. This model-based kitting practice reduces − even eliminates − the need for a manual kit preparation step and promises additional supply chain benefits. Eight experiments were conducted using laser sintering (LS) to investigate the impact of model-based component kitting on production cost and supply chain cost. The results show that with current state-of-the-art volume packing software, production costs increase with the adoption of kitting. The increased production cost was off-set to different extents by kitting supply chain benefits, including simplified production planning, reduced work-in-progress inventory and elimination of parts fetching prior to assembly. Findings of this research are of interest for manufacturers, service bureaus and practitioners who use AM for low quantity production, as well as developers of AM volume packing and production planning software.

Using total specific cost indices to compare the cost performance of additive manufacturing for the medical devices domain

The label additive manufacturing, also known as three-dimensional printing, serves as an umbrella term for a number of technologies designed to deposit product geometries directly from build materials and digital design information. However, as a relatively recent addition to the spectrum of manufacturing processes, the relationship between process type, system characteristics and cost performance is still broadly unclear for several technology types. To address this gap, the current research develops comprehensive and robust additive manufacturing cost models for two less-studied polymeric additive manufacturing technology variants, material jetting and mask projection stereolithography. Despite sharing the fundamental principle of photopolymerization, the operating processes of both systems are markedly different. This is reflected in the constructed cost models, which incorporate process maps to capture ancillary process elements, ensure efficient capacity utilisation through optimised build volume packing and approximate the expected cost impact of build failure. On this basis, this article estimates a set of specific cost indices reflecting the overall total cost performance of the investigated systems in an example application from the medical devices domain. Specific cost results range from £2.01 to £1.19/cm3 deposited on the Objet Connex 260 system and from £1.59 to £1.00/cm3 of material deposited on the Perfactory system. These results are discussed in the context of similar cost indices extracted from the empirical engineering literature. This article shows that next to increases in build speed, improvements in overall process automation and process stability are needed to enhance the commercial proposition of the investigated technology variants.

Developing an Understanding of the Cost of Additive Manufacturing

This chapter provides a concise and practical introduction to the estimation and modelling of the cost of Additive Manufacturing (AM), which is usually required as a starting point in assessments of commercial viability of the technology. The chapter begins with an overview of cost models in general and specific to AM. This is followed by a step-by-step practical presentation of how such models can be constructed and a discussion of the utility of unit cost functions in breakeven analyses. The chapter also discusses a range of recurring issues in the assessment of the cost performance of AM. These include the capacity utilisation problem, integration with other manufacturing processes, the cost impact of process failure and the requirement to explore the effect of design changes when considering different processes. To provide the reader with some rough, yet readily applicable, information on absolute cost performance, the chapter closes with a presentation of specific cost estimates for a number of AM technology variants and materials.