3D printing allows users to directly build objects from computer-generated designs, meaning industry can manufacture customized products in-house without outsourcing parts. But 3D printing has 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. And unusable print jobs must be discarded, presenting a significant environmental and financial cost.
Researchers have developed a new set of machine learning algorithms and a software tool called PrintFixer to improve 3D printing accuracy by 50 percent or more, making the process vastly more economical and sustainable. The AI model accurately predicts shape deviations for all types of 3D printing and makes 3D printing smarter.
Every 3D-printed object results in some slight deviation from the design, whether this is 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 AI to predict where the shape distortion will happen in order to fix print errors before they occur.
The team aimed to create a model that produced accurate results using the minimum amount of 3D printing source data. The software can leverage small amounts of data to make predictions for a wide range of objects.
The model was trained 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 on the 3D printing of dental models.
Users could opt to print with a different, higher-quality printer and use the software to predict whether that would provide a better result. Functionality also is incorporated into the software package that allows 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.
The 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.
For more information, contact Amy Blumenthal at