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.

Being dependent on the above approach is stressful, anti-innovative, and a waste of both resource and money. Although Thomas Edison famously made thousands of failed light bulbs before he got one to work, I’m sure he would have preferred to have taken a more methodical and knowledge-based approach if he could.

For additive manufacturing especially, a comprehensive approach to managing machine, powder and settings selection is needed to close the loop of create, test, improve. Robust, pedigreed, and version-controlled data is required to evaluate and support design and manufacturing processes. Intelligent materials data management accelerates the time to value for AM machinery, and reduces the time taken to achieve qualification and certification of AM parts.


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