Organizations make big investments in Additive Manufacturing. AM machines, new materials, experts in AM processes, testing, analysis, and simulation – no expense is spared. These costs feel justified in the light of the benefits that AM can bring – parts that can be printed-to-order, new lightweight components with previously unachievable shapes, or reduced manufacturing lead times.
The rate of adoption of additive manufacturing (AM) is incredible. AM brings a physicality to ideas, and offers ways for people to touch upon solutions that would have been impossible to otherwise imagine. Equally impressive is the scale of investment in machines for producing AM parts, which is of course supported by business cases highlighting reduced development times, fewer prototype costs, reduced part counts, and flexible manufacturing. But, I am seeing more and more evidence that the prescribed route to this ‘Nirvana’ is via a process of trial and error for settings, powders, and even machine capability.
Considerable investments are made in AM Research & Development to make high performance parts, research designs and materials, and optimize production processes. One of the central aims of this R&D is reducing the variability of processes and producing unique parts ‘right first time’, leaving no room for error. Progress in this area requires effective use of large quantities of specialist information. First, you must understand the fast-evolving landscape of machines and materials in order to set up a research or manufacturing project. These programs then generate huge amounts of data: material properties, process parameters, test data, simulation results, and on qualification of parts. How can we make best use of this data? And how can we best leverage process simulations, ensuring traceability for both virtual and test data?