Zeiss and Oak Ridge Develop New Characterization Methodology
The R&D project will use the Zeiss 3D ManuFact Solution to develop and commercialize a comprehensive powder-to-part characterization methodology for additive manufacturing.
Carl Zeiss Industrial Metrology LLC has partnered with Oak Ridge National Laboratory (ORNL) to develop and commercialize a comprehensive powder-to-part characterization methodology for additive manufacturing (AM).
The collaboration between ORNL and Zeiss, “Leveraging Artificial Intelligence (AI) to Enable Reliable Non-Destructive Characterization of Additively Manufactured (AM) Parts Using X-ray CT,” is led by Dr. Amir Koushyar Ziabari, an R&D staff scientist at ORNL. The goal of the project is use the Zeiss 3D ManuFact Solution to develop and commercialize the methodology, which reportedly can rapidly qualify new alloys, powder materials and processes for printed parts, and enable quick certification and qualification of additively manufactured components.
Zeiss 3D ManuFACT includes advanced X-ray CT-based (XCT) characterization tools such as Zeiss Versa, Zeiss Metrotom and Zeiss VoluMax. According to the company, XCT characterization provides insights into processing, microstructure, material properties and performance of complex AM parts. ORNL's algorithm leverages existing CAD models for 3D-printed parts – along with the physics-based information – to train a deep convolutional neural network that learns to remove the noise and artifacts from synthetic XCT data. It then applies the trained network to actual experimental XCT data.
Dr. Bhattad, business development manager for AM at Zeiss, says, “The enhancement of non-destructive evaluation could translate to 100 percent inspection of high-volume products ranging from electronics to batteries to even digital twins for turbine blades in aerospace applications or additively manufactured medical and dental implants. We see real value in this algorithm facilitating rapid inspection of the entire production chain with XCT for both NDE and metrology.”
The expected outcomes for the first year of the project include quantifying the improvement in porosity detection capability and resolution, quantifying the improvement in metrology by comparing XCT results against Zeiss CMM measurements and generalizing the network so it can work on multiple resolutions.
