Machine Learning Advances Additive Manufacturing in January Issue

The January issue of Additive Manufacturing magazine looks at three ways machine learning is advancing understanding of 3D printing processes and materials.

Compared to conventional processes, additive manufacturing (AM) lacks the years—and in some cases, centuries—of knowledge building that makes machining or casting predictable. But it’s not just about age; AM also has a greater number of variables that can affect the final outcome of a part.

The diversity of these variables and the difficulty in learning about them through trial and error makes AM a good match for another emerging technology: machine learning, or the application of computer algorithms to identify patterns in data. When coupled with human judgement, machine learning has the potential to accelerate additive manufacturing’s advance.

Related Stories

The January issue of Additive Manufacturing explores this potential with three stories of machine learning applications within AM:

  • ADAPT, the Alliance for the Development of Additive Processing Technologies, is using machine learning to map variables and outcomes for metal 3D printing, with the goal of developing a predictive model.
  • A computer vision system developed at Carnegie Mellon University has learned to identify metal powders for 3D printing with 95 percent accuracy, a capability that could speed material qualification for AM.
  • GE’s Global Research Center is building a digital library of AM parameters within its Predix Cloud that will one days enable machine learning at the machine.

Read these stories and more in the digital edition.