Machine Learning and 3D Printing

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3D printing is high-tech and a fairly predictable process. However, one issue that still persists is how to avoid printing objects that don’t meet expectations and thus can’t be used, leading to a waste in materials and resources. Scientists at the University of Southern California’s (USC’s) Viterbi School of Engineering have come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs.

A machine learning algorithm could theoretically optimize the path taken by the 3D print head to simplify the printing process.

ML identifies, analyzes, and monitors huge volumes of data, allowing it to provide a real-time status of processes and machinery. Manufacturers who can take advantage of ML to predict when equipment and parts will fail, and subsequently employ 3D printing to proactively print and ship replacement parts ahead of these failures, will enjoy significantly reduced spare parts costs and delivery times, and higher customer satisfaction.

Industrial robots have sensors that detect how much current is being used to heat up the metal, how hot that metal gets, and where exactly the welds are being applied. The next phase is combining that data with machine-learning algorithms to help the robot learn what welds are likely to pose problems.