Additive Industries and Materialise have released a new build processor for MetalFab metal additive manufacturing equipment, with company benchmarking showing a reduction in preparation time from 46 minutes to six. The software is available to existing MetalFab customers and provides additional controls for laser exposure and build strategies.
The Next-Generation Build Processor was developed jointly by the Dutch machine manufacturer and Belgian software specialist. It replaces the earlier processing approach for preparing instructions used in laser beam powder bed fusion, while retaining compatibility with established Materialise Magics workflows. Users can import profiles from the existing Classic Build Processor when moving to the new version.
The reported performance comparison used Classic Build Processor version 2.6, which required 46 minutes for the benchmark preparation task. The next-generation product completed the same task in six minutes. That is a reduction of approximately 87% in computational preparation time, attributed by the companies to multiprocessing and algorithm improvements.
The result must be distinguished from the time taken to manufacture a metal component. A build file may be processed in minutes while the machine subsequently operates for many hours, depending on the geometry, material, laser parameters and number of layers. The companies have not released an independent benchmark methodology or shown that every customer build will experience the same improvement.
In laser powder bed fusion, a prepared arrangement of parts and supports must be translated into instructions describing where the laser or lasers will apply energy on each layer. The software establishes exposure vectors and processing parameters from the component geometry, the selected material profile and the build strategy. Its output governs the movements and energy application requested from the manufacturing equipment.
Large or intricate builds can generate substantial amounts of geometric information. Processing may become particularly demanding where many features, support structures and scanning regions are involved. The preparation workload can increase further when equipment uses multiple lasers that must be assigned coordinated areas of activity.
Multiprocessing enables parts of the computational work to be handled in parallel where the algorithms allow it. Improvements in how information is organised and calculated can also reduce processing demands. Their combined effect will vary according to the geometry, processor resources and preparation strategy, so the published benchmark should be treated as evidence for one reported test rather than a general production guarantee.
The new software adds features originally developed for the MetalFab 420K platform. These include optimised laser load balancing, variable beam diameter control by vector type, ghost vectors intended to improve thermal stability, and additional controls for exposure strategies. The available functions are intended to let engineers adjust the way energy is delivered to different regions of a component.
Laser load balancing is relevant where several beams contribute to one build. The software must assign work in a way that respects equipment constraints while avoiding unnecessary waiting or interference. Materialise’s wider processor technology accounts for matters including laser overlap and process gas movement, but the companies have not disclosed the complete implementation or quantitative throughput effect for the MetalFab release.
Variable beam diameter control can allow different regions to use exposure characteristics appropriate to their geometry. Fine boundaries and larger internal areas do not necessarily benefit from identical beam behaviour. Process engineers still need to establish suitable parameters for the material and machine rather than assuming that a more detailed control automatically creates a better finished part.
The companies describe ghost vectors as a means of improving thermal stability. They have not published sufficiently detailed measurement results to establish the contribution of that individual function to residual stress, distortion or finished material properties across different applications. Its intended purpose should therefore be separated from demonstrated material performance.
Process strategy affects the way heat is introduced and distributed during manufacturing. Local thermal conditions can influence melt behaviour, dimensional conformity and the risk of defects. A build processor provides more options for controlling exposure, but inspection and qualification remain necessary where components must satisfy demanding industrial specifications.
The new processor operates within Materialise Magics, supporting the established sequence of build preparation, configuration and job submission. It can also connect build information with Materialise’s CO-AM ecosystem, which the companies intend to use for wider production data management and automation.
Existing MetalFab users can import profiles from the Classic Build Processor. That provides a starting point for migration, but imported values alone do not show that a revised method of generating files produces identical manufacturing outcomes. Users with validated processes must examine whether the new instructions or exposure options affect their specific qualification requirements.
Swiss space hardware manufacturer SWISSto12 participated in beta testing. Its manufacturing engineer Lionel Simon reported opportunities to improve the efficiency of print preparation, although the public account does not specify independent measurements of final part properties, machine utilisation or cost per part.
Reducing file preparation time may increase engineering capacity where operators frequently set up complex builds. It will have a smaller effect where printing, machine changeover, heat treatment or inspection is the principal production constraint. The immediate verified change is the availability of a faster processor with additional controls, while gains in finished component output will depend on how the software performs within each manufacturing operation.
