New Decision Framework Ranks Metal 3D Printing Technologies for Every Part

Choosing the right metal additive manufacturing technology has long been more art than science. Engineers face a crowded field of competing processes, each promising different combinations of precision, speed, cost and mechanical performance, and no single technology wins on every front. Now, a team of researchers in Spain has turned that fuzzy judgment call into a rigorous, mathematically grounded procedure, publishing a two-stage selection framework in the open-access journal Heliyon that combines a feasibility filter with the Analytical Hierarchical Process, a classic multi-criteria decision-making method developed by Thomas Saaty in the 1970s.

The study, led by Virginia Uralde and Fernando Veiga with colleagues Alfredo Suarez and Tomas Ballesteros, addresses a problem that has grown acute as metal 3D printing has matured. Unlike conventional subtractive manufacturing, in which milling, turning, grinding and drilling remove material from a solid workpiece, additive manufacturing builds parts layer by layer, unlocking geometries that would otherwise be impossible: hollow sections, intricate internal lattices and topology-optimised structures that slash weight. That design freedom, however, comes at the price of complexity. With each process offering a different balance of feedstock, energy source, deposition rate, accuracy, build volume and post-processing burden, the question of which technology to use no longer has an obvious answer.

The researchers’ first move is deceptively simple: rule out what cannot work. Before any ranking takes place, three design-phase criteria act as exclusion filters: the geometric complexity of the part, the material it must be made from, and its overall dimensions. A technology that cannot print a particular alloy, or whose build volume cannot accommodate a large component, is eliminated before the analysis begins. This pre-screening stage, the authors argue, is the study’s key novelty, because it tailors the subsequent comparison to the specific application rather than producing a generic, one-size-fits-all ranking. The underlying hypothesis is blunt: there is no universally optimal additive manufacturing technology for all materials, sizes and shapes.

Once the field has been narrowed, the framework deploys the Analytical Hierarchical Process, or AHP, to rank the surviving candidates. AHP decomposes a decision into a hierarchy: the overall objective at the top, criteria in the middle, and alternatives at the bottom. Decision-makers compare elements in pairs using Saaty’s famous 1-to-9 scale, where 1 means two items are equally preferable and 9 means one is extremely preferable to the other. These subjective judgments are converted into numerical weights through eigenvector calculations, and a consistency ratio, which must fall below 0.1, verifies that the comparisons are logically coherent rather than arbitrary. The method’s strength, the authors note, is precisely its ability to transform qualitative expert judgment into transparent mathematics, a valuable asset in a field where quantitative data can be scarce, unreliable or quickly outdated.

The team considered five technological alternatives spanning the main families of metal additive manufacturing. Powder Bed Fusion, or PBF, which fuses fine metal powders layer by layer, offers high geometric complexity, tight tolerances and smooth surface finishes, with typical layer thicknesses of just 0.02 to 0.06 millimetres, but limited productivity and build size. Material Extrusion, or MEX, uses metal-filled filaments followed by debinding and sintering, offering accessible equipment and low costs at the expense of mechanical performance. Directed Energy Deposition, or DED, which deposits material directly into a melt pool, was subdivided by energy source into laser-beam, electron-beam and arc-based variants, the last of which stands out for very high deposition rates of roughly 10 kilograms per hour and suitability for large components and repair work.

To weight the criteria, the researchers surveyed 260 respondents: 130 additive manufacturing experts, 70 designers and 60 customers of metal parts. The questionnaire posed 17 paired comparisons on a 1-to-9 scale, asking participants to trade off criteria against one another. Nine sub-criteria were organised into two phases of the additive workflow. The print phase covered machine price, manufacturing cost, productivity, reliability and part finish after printing; the final phase covered final part properties, post-processing complexity, process sustainability and user preferences. The results were striking: the print phase received a weight of 0.683, more than double the final phase’s 0.317, and reliability emerged as the single most important criterion overall, with a global weight of 0.304. Final part properties followed at 0.186, then manufacturing cost at 0.147, while sustainability, machine price and user preferences ranked lowest.

Interesting differences emerged between respondent profiles. Experts and designers both weighted the print phase heavily, drawn to factors such as productivity and process reliability, with the experts placing particular emphasis on reliability. Customers, by contrast, cared more about the finishing phase, because the final properties of the delivered part matter most to them. For criteria that could not be answered from the literature, such as sustainability perceptions and user preferences, a second survey of purely technical interviewees supplied the paired comparisons needed to score each alternative.

When all five technologies were evaluated against the full set of criteria, arc-based Directed Energy Deposition, or DED-Arc, came out on top with a final score of 0.249, narrowly ahead of Powder Bed Fusion at 0.231, with DED-EB, DED-LB and MEX trailing at 0.179, 0.172 and 0.170 respectively. The result tracks the underlying physics and economics. DED-Arc machines cost roughly 110,000 euros, the lowest of the group, and its arc-welding deposition achieves productivity around 10 kilograms per hour, roughly fifty to one hundred times that of PBF, which manages only 0.1 to 0.2 kilograms per hour. PBF, however, dominates on quality: surface roughness of Ra 6 to 12 micrometres, tensile strengths of 800 to 1100 megapascals in Ti6Al4V, and the highest survey scores for reliability and as-printed finish, reflecting the fine powder particles that yield smoother surfaces. Its weakness is cost, with the highest manufacturing cost of any alternative, driven by material waste and energy consumption.

The framework’s real power shows itself when the pre-selection filter is applied. In a worked example, the researchers considered a part of medium-high geometric complexity, medium size, made from aluminium. The filter excluded DED-EB and DED-Arc, leaving PBF, DED-LB and MEX as viable candidates. Re-running the AHP with only these three alternatives reshuffled the ranking dramatically: PBF surged to first place with a score of 0.415, followed by MEX at 0.306 and DED-LB at 0.279. The lesson is that the ‘best’ technology is not an intrinsic property of a process but a function of the part, the material and the constraints, and that a ranking computed without feasibility screening can be actively misleading.

The authors are candid about the method’s limits. The criterion weights reflect the preferences of the surveyed group rather than universal priorities, and different expert populations could produce different rankings. Technical characteristics were drawn from literature and expert assessment and vary with machine, material and process parameters, so the output should be read as decision support rather than a definitive verdict. AHP also assumes criteria are independent and uses deterministic comparisons, leaving uncertainty and interactions unmodelled; the researchers suggest fuzzy or probabilistic extensions as future work. And because metal additive manufacturing is evolving rapidly, the technical data underpinning the framework need periodic updating, though the two-stage structure itself can absorb new alternatives, screening conditions and weights without modification. For manufacturers weighing six-figure machine investments against part quality, throughput and sustainability, the study offers something rarer than a winner: a defensible, customisable way to find the right tool for each job.

Subject of Research: Multi-criteria decision-making for selecting optimal metal additive manufacturing technologies using the Analytical Hierarchical Process

Article Title: Method for selecting the optimal technology in metal additive manufacturing using an analytical hierarchical process

Article References: Uralde, V., Veiga, F., Suarez, A., & Ballesteros, T. (2026). Method for selecting the optimal technology in metal additive manufacturing using an analytical hierarchical process. Heliyon, 12(15), Article e45519. https://doi.org/10.1016/j.heliyon.2026.e45519

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45519

Keywords: metal additive manufacturing, analytical hierarchical process, multi-criteria decision making, powder bed fusion, directed energy deposition, material extrusion, DED-Arc, reliability, manufacturing cost, productivity, technology selection, 3D printing