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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. “We can leverage small amounts of data to make predictions for a wide range of objects.” “From just five to eight selected objects, we can learn a lot of useful information,” Huang said. Huang said that the research team had aimed to create a model that produced accurate results using the minimum amount of 3-D printing source data. 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. “It can actually take industry eight iterative builds to get one part correct, for various reasons,” Huang said, “and this is for metal, so it’s very expensive.”Įvery 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. “In cases where we are producing a 3-D object similar to the training cases, overall accuracy improvement can be as high as 90 percent.” “What we have demonstrated so far is that in printed examples the accuracy can improve around 50 percent or more,” Huang said. 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. students Yuanxiang Wang, Nathan Decker, Mingdong Lyu, Weizhi Lin and Christopher Henson has so far received $1.4M funding support, including a recent $350,000 NSF grant. The team, led by Qiang Huang, professor of industrial and systems engineering, chemical engineering and materials science, along with Ph.D.
USC PRINTING PRINTME SERIES
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. Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science.
USC PRINTING PRINTME SOFTWARE
Epstein Department of Industrial and Systems Engineering 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. What happens to the unusable print jobs? They must be discarded, presenting a significant environmental and financial cost to industry.Ī team of researchers from the Daniel J. Manufacturers often need to try many iterations of a print before they get it right. Each printer is different, and the printed material can shrink and expand in unexpected ways. But 3-D printing has a high degree of error, such as shape distortion. It allows us to directly build objects from computer-generated designs, meaning industry can manufacture customized products in-house, without outsourcing parts. Image/Pxhereģ-D printing is often touted as the future of manufacturing. PrintFixer is a new machine learning driven software package that will make 3-D printing 50 percent more accurate.
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