Nothing to be thick-headed about: an early experience with quality assurance for additive manufacturing

Posted by Ian Wing

My first lesson in quality assurance for additive manufacturing was a hard one. As a young engineer, I built a set of two identical head-shaped devices to measure helmet fit. As both devices looked faintly human-like, I’ll give them each a name for the purposes of this article: let’s call them Starsky and Hutch.

The devices looked great. The matte finish of the jet-black ABS which made up the somewhat angular shape of the headforms gave them a futuristic look, which was only compounded by the real-time readings indicating the helmet “tightness” tracing across the screens adjacent laptops. Except for the serial numbers, they appeared identical. During initial testing, the devices worked wonderfully on their own, however, when we ran tests comparing the two one would always read “tighter” than the other.

Turns out, much like their TV namesakes, Hutch was a little bigger, head-wise, than Starsky. While that might have been obvious for the television characters, it took a fair amount of troubleshooting and eventually a laser scan to determine that our Hutch was a few thousandths of an inch bigger than our Starsky – too small to see with the eye, but large enough to influence our helmet fit results. (Thankfully we detected and rectified the error before it could influence our findings).

The point is, even though the two parts were nominally the same — printed from the same file, built on the same printer and assembled the same way — the end products were slightly different in geometry, illustrating the need for broad quality assurance in AM applications.

Years later I became a consultant and faced this problem again. I studied how subtle defects in additively manufactured parts could have much larger consequences than anomalous laboratory results. The consequences of component failure can be extreme, particularly in aerospace, defense, medical, and automotive applications. In fact, many leaders in the field cite quality assurance as the biggest single barrier to widespread adoption of AM.1

As a former engineer, I enjoyed diving back into the technical literature to research the state-of-the-art in quality assurance for additively-manufactured parts and products. The science and engineering community is gravitating toward a solution space, which I see as having three quality dimensions: Build planning, build monitoring and feedback control.

  • First, detailed numerical simulations are used to predict the geometry and properties that result from various combinations of build parameters, including tool path, laser power, et cetera, as a result of the physics that govern the various AM processes. My co-authors and I call this build planning in our paper.
  • Second, sensors – primarily imaging technology like high speed and thermal cameras – are used to monitor the build process as it happens. We call this build monitoring in the paper.
  • Third pillar is feedback control, which ties the previous two prongs together by continuously using information from the sensors to update the build planning simulations and adjust the build parameters in real time.

There’s a lot more to QAAM than that though.

I hope you will find, as I have, that it’s important to use your head when considering quality assurance in additive manufacturing! I encourage you to learn more by checking out the full paper, part of Deloitte’s continually growing 3D Opportunity collection on Deloitte University Press.

1 Rose Hansen, “Building the future: Modeling and uncertainty quantification for accelerated certification,” Science and Technology Review, January/February 2015,, accessed November 5, 2015.

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