3D printing smarter with machine learning
PrintFixer is a machine learning driven software package that will make 3D printing 50% more accurate. Image/pxhere
PXHERE/University of Southern California | Viterbi School of Engineering

3D printing smarter with machine learning

Manufacturers and medical device companies will soon have access to powerful AI-driven 3D printing software.

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3D allows objects from computer-generated designs to be directly built, but 3D printing can have a high degree of error, such as shape distortion. Each printer is different, and the printed material can shrink and expand in unexpected ways. Manufacturers often need to try many iterations of a print before they get it right.

A team of researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering tackled the problem with a new set of machine learning algorithms and a software tool called PrintFixer, to improve 3D printing accuracy by 50% or more, making the process vastly more economical and sustainable.

The result of six years of research, the work, recently published in IEEE Transactions on Automation Science and Engineering, describes a process called convolution modeling of 3D printing.

Led by Qiang Huang, associate professor of industrial and systems engineering, chemical engineering and materials science, along with Ph.D. students Yuanxiang Wang, Nathan Decker, Mingdong Lyu, Weizhi Lin, and Christopher Henson, the team has received $1.4 million funding support, including a recent $350,000 National Science Foundation (NSF) grant. Their objective is to develop an AI model that accurately predicts shape deviations for all types of 3D printing and make 3D printing smarter.

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Ph.D. student Weizhi Lin’s work is ensuring 3D printed models of dental patients’ teeth are accurately matched to their designs. The red points on the model are automatically-selected landmarks to ensure the complex shape can be printed accurately.

Every 3D printed object results in some slight deviation from the design, whether due to printed material expanding or contracting when printed, or due to the way the printer behaves.

PrintFixer uses data gleaned from past 3D printing jobs to train its artificial intelligence (AI) to predict where the shape distortion will happen, in order to fix print errors before they occur.

Huang says the research team had aimed to create a model that produced accurate results using the minimum amount of 3D printing source data.

“From just five to eight selected objects, we can learn a lot of useful information,” Huang says. “We can leverage small amounts of data to make predictions for a wide range of objects.”

The team has trained the model to work with the same accuracy across a variety of applications and materials – from metals for aerospace manufacturing, to thermal plastics for commercial use. The researchers are also working with a dental clinic in Australia on the 3D printing of dental models.

Decker says that users could opt to print with a different, higher-quality printer and use the software to predict whether that would provide a better result.

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A screenshot from PrintFixer shows the predicted variations in a printed shape, with expanded areas highlighted in red and smaller areas marked in blue.

“But if you don’t want to change the printer, we also have incorporated functionality into the software package allowing the user to compensate for the errors and change the object’s shape – to take the parts that are too small and increase their size, while decreasing the parts that are too big,” Decker notes. “And then, when they print, they should print with the correct size the first time.”

The team’s objective is for the software tool to be available to everyone, from large scale commercial manufacturers to 3D printing hobbyists. Users from around the world will also be able to contribute to improving the software AI through sharing of print output data in a database.

“Say I’m working with a MakerBot 3D printer using PLA (a bioplastic used in 3D printing), I can put that in the database, and somebody using the same model and material could take my data and learn from it,” Decker says. “Once we get a lot of people around the world using this, all of a sudden, you have a really incredible opportunity to leverage a lot of data, and that could be a really powerful thing.”