AI detects ‘personalities’ of individual 3D printers to cut manufacturing errors
Imagine buying three identical 3D printers. Despite being the same brand, the same model and even having similar serial numbers, each machine may behave slightly differently. Over time and at scale, these differences can accumulate into significant manufacturing defects.
Additive manufacturing has revolutionized manufacturing by enabling customized, cost-effective products with minimal waste. However, with the majority of 3D printers operating on open-loop systems, they are notoriously prone to failure. Minor changes, like adjustments to nozzle size or print speed, can lead to print errors that mechanically weaken the part…
Scientists and engineers at Lawrence Livermore National Laboratory (LLNL) have developed a camera-based inspection system that can monitor complex 3D-printed structures layer by layer, using AI and machine learning (ML) to measure tiny variations and potentially identify problems before a part ever leaves the printer.
As 3D printers have become cheaper and more widely accessible, novice makers within a rapidly growing community are fabricating their own objects. To do this, many of these amateur artisans access free, open-source repositories of user-generated 3D models that they download and fabricate on their 3D printers.