The company has installed an additive manufacturing (AM) machine at Oak Ridge National Laboratory and will train researchers to use its EosState MeltPool monitoring software.
Oak Ridge plans to use a range of defect and anomaly detection processes to assess the accuracy of QA in sample builds and is developing “a characterization methodology using computed tomography (CT)” to identify defects and anomalies in 3D printed builds.
“As additive manufacturing continues to generate greater levels of appeal for mass production, there is a growing requirement to ensure quality, and build upon one of the greatest strengths of 3D printing which is the capability to monitor parts as they are being built, layer-by-layer,” said Dr. Ankit Saharan, senior manager, metals technology at EOS. “If we can improve quality control processes to the point of making adjustments to builds in real-time, as anomalies are detected, that will save organizations time and energy while improving overall quality control.”
“If we can successfully demonstrate the ability to correlate in-situ data with metallurgical inspection, this could significantly change the paradigm of AM and enable mass production of AM components for a variety of high value industries,” added Ryan Dehoff, ORNL researcher for deposition science and technology.
The agreement will run for two years, and result in a co-authored final report.
