Production costs of additive manufacturing – a case study for powder bed fusion technologies

One principal driver behind the adoption of new technology is its capability to provide net benefits to potential users [1,2] as it generates competitive advantage [3]. In other words, for private firms within the commercial manufacturing sectors, acquiring new technology is targeted at enhancing profits [2,4]. In the case of AM technologies, an increase in revenue of at least 30% to 40% is supposedly required [5] to make commercial sense and promote its adoption.

Generally, each advantage associated with AM provides value-enhancing opportunities for manufacturers or products end users. Similarly, a cost-increase is usually associated with AM limitations. In other words, the cost structure associated with AM processes affect the development, diffusion and societal impact of the technology.

Monetary costs associated with the operation of AM determine the net benefits achievable with these techniques. In this post, we present a quick case study to estimate costs of efficient machines operation in the context of a cost-minimising manufacturing business. We review the expenditures involved in running two different powder bed fusion processes in order to assess their economic viability.

We compare the costs of two different powder bed fusion processes, Electron Beam Melting (EBM) [6] and Direct Metal Laser Sintering (DMLS) [7].

Both platforms are aimed at and used for the production of end-use metal components [8].

SLM machines are capable of producing multiple, potentially unrelated parts in parallel [9] made of the same material. Indeed, it makes commercial sense to take advantage of the full build volume capability for efficient machine operation [10].

Five metal components are chosen to be manufactured using EBM and DMLS: a bearing block, a turbine wheel, a belt link, an end cap and a venturi. The two different packing configurations generated for each systems use a build volume packing algorithm [12] based on the available platform size. 53 components can be built on the EBM platform. 85 components can be built on the SLM system.
Manufacturing efficiency is usually based on build production rate. Even though two different build materials are used here, the mass-based material deposition rate observed on the S12 EBM system (69.24 g/h) is 84% greater than that observed on the M270 DMLS system (37.58 g/h).

The cost model used here is based on the actual weight of the parts generated by SLM  and EBM: the material taken into account comprises the component and its anchors (supports). Raw material and energy consumption (direct costs) are combined with time-dependent (indirect) costs such as machine running time, operator time, etc.. In this case, ill-structured costs relating to quality, build failure and logistics [5] are excluded from the analysis.

One metric for the comparison of manufacturing cost is the total cost per cm3 of material used. This metric is calculated by attributing total cost to the total volume of AMed parts, net of anchor structures. The total specific cost is 2.39 £/cm3 for the build performed on the EBM system and 6.18 £/cm3 for the build done using DMLS [12].

Broken down costs for EBM (Ti64) and SLM (steel) [12].

A second measure illustrates the two technologies’ efficacy in turning raw material and energy into products. It is the ratio of all direct costs over total cost. For the EBM platform, the results suggest that for every £1 spent, £0.42 worth of raw Ti6a materials and energy are converted into products (and anchor structures). For DMLS, £0.12 worth of steel are converted into products [11]. In other words, ~10% of component cost is attributable to raw steel material.

References:

[1] Stoneman, P. (Ed.), 2002a. The economics of technological diffusion. Blackwell, Oxford.

[2] Foster, A.D., Rosenzweig, M.R., 2010. Microeconomics of technology adoption. Center discussion paper no. 984 ([Online], available from: http://www.econ.yale.edu/growth_pdf/cdp984.pdf [Accessed 10.09.2013]).

[3] Walsh, S.T., 2004. Roadmapping a disruptive technology: a case study – the emerging Microsystems and top-down nanosystems industry. Technol. Forecast. Soc. Chang. 71, 161–185.

[4] Hurkens, K.,Wynstra, F., 2004. The concept of “total value of ownership”: a case study approach, Summer School 2003-2004 Publications. International Federation of Purchasing and Supply Management, Satzberg, pp. 51–62.

[5] Bourell, D.L., Leu, M.C., Rosen, D.W. (Eds.), 2009. Roadmap for additive manufacturing, identifying the future of freeform processing. The University of Texas at Austin, Austin.

[6] ArcamAB., 2013. Corporate website. [Online], available from: www.arcam.com

[7] EOS, GmbH, 2013. Corporate website. [Online], available from: www.eos.info/en

[8] Wohlers, T. (Ed.), 2012. Wohlers Report 2012: additive manufacturing and 3D printing state of the industry.Wohlers Associates Inc., Fort Collins

[9] Ruffo, M., Hague, R., 2007. Cost estimation for rapid manufacturing – simultaneous production of mixed components using laser sintering. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 221, 1585–1591.

[10] Baumers, M., Tuck, C.,Wildman, R., Ashcroft, I., Hague, R., 2011b. Energy inputs to additive manufacturing: does capacity utilizationmatter? Proceedings of the Solid Freeform Fabrication (SFF) Symposium. The University of Texas at Austin.

[11] Atzeni, E., Salmi, A., 2012. Economics of additivemanufacturing for end-usable metal parts. Int. J. Adv. Manuf. Technol. 62 (9), 1147–1155.

[12] Baumers, M., et al., The cost of additive manufacturing: machine productivity, economies of scale and technology-push, Technol. Forecast. Soc. Change (2015), http://dx.doi.org/10.1016/j.techfore.2015.02.015