Manufacturers, medical device companies and
the general public will soon have access to powerful AI-driven 3-D printing software. 3-D printing is often touted as the future
of manufacturing. It allows us to directly build objects from
computer-generated designs, meaning industry can manufacture customized products in-house,
without outsourcing parts. But 3-D 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. The unusable print jobs must be discarded,
presenting a significant environmental and financial cost to industry. A team of researchers from the University
of Southern California is tackling this problem, with a new set of machine learning algorithms
and a software tool called PrintFixer, to improve 3-D printing accuracy by 50 percent
or more, making the process vastly more economical and sustainable. The work, recently published in IEEE Transactions
on Automation Science and Engineering, describes a process called “convolution modeling of
3-D printing.” It’s among a series of 15 journal articles
from the research team covering machine learning for 3-D printing. Their objective is to develop an AI model
that accurately predicts shape deviations for all types of 3-D printing and make 3-D
printing smarter. Every 3-D 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 3-D
printing jobs to train its AI to predict where the shape distortion will happen, in order
to fix print errors before they occur. The research team had aimed to create a model
that produced accurate results using the minimum amount of 3-D printing source data. 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 3-D printing of dental models. The team’s objective is for the software tool
to be available to everyone, from large scale commercial manufacturers to 3-D 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

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