Sigma Labs, the computer-aided inspection (CAI) software company behind PrintRite3D software, has become a member of the UK’s Manufacturing Technology Centre (MTC).
As a result, Sigma Labs will enable in-process quality monitoring for additive manufacturing systems at the MTC’s National Centre for Additive Manufacturing through its software; the company will also participate in MTC’s member-sponsored programs with a focus on qualification and certification of the additive manufacturing process.
“With Europe at the forefront of many innovative and major developments in the metal AM industry, we believe this agreement, our second major research alliance with a European center of excellence, holds great promise for us and the future of AM,” said John Rice, CEO of Sigma Labs.
Neural networks and advanced algorithms use real-time video to analyze build quality and advise on how to improve it.
For years, engineers at Lawrence Livermore National Laboratory have used sensors and imaging techniques to analyze the physics and processes behind metal 3D printing in order to build high-quality metal parts the first time, every time. Now, they are leveraging machine learning to process data obtained during 3D builds in real time, detecting within milliseconds whether a build will be high quality. More precisely, they are developing convolutional neural networks (CNNs), a type of algorithm commonly used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video.
DNV GL is a quality assurance and risk management company that helps businesses obtain safety and sustainability through classification, certification, and other services to a wide range of industries, including the maritime, oil and gas, power, and renewables industries. The company has been frequently involved with 3D printing lately, particularly in the maritime arena, and now it has published the first classification guideline for the use of additive manufacturing in the maritime and oil and gas industries. The classification guideline is intended to ensure that parts produced via 3D printing, and the materials from which they are printed, have the same level of quality assurance as those produced by traditional means.