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Partnership Aligns to Advance Characterization of AM Parts Using X-ray CT and AI

Carl Zeiss Industrial Metrology has partnered with Oak Ridge National Laboratory (ORNL) on a project chosen to receive funding through the U.S. Department of Energy’s (DOE’s) Technology Commercialization Fund (TFC) project solicitation. The 2021 TFC project selections represent 12 DOE National Labs across the nation and are supported by partners in 25 states and four countries.

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. ZEISS and ORNL currently have a cooperative research and development agreement (CRADA) to develop and commercialize a comprehensive powder-to-part characterization methodology for additive manufacturing using the unique ZEISS 3D ManuFACT Solution — a holistic, integrated inspection workflow. The new methodology will rapidly qualify new alloys, powder materials and processes for printed parts and enable rapid certification and qualification of additively manufactured components.

“We want our X-ray CT (XCT) machines to be able to drive AM processes toward printing consistent, predictable and repeatable parts,” says Dr. Pradeep Bhattad, Business Development Manager for Additive Manufacturing, ZEISS Industrial Quality Solutions. “Due to the complexity of AM parts, X-ray CT is increasingly becoming the preferred technology to qualify the parts. However, AI-based X-ray CT has the potential to revolutionize non-destructive testing (NDT) and metrology beyond just the AM industry.”

ZEISS 3D ManuFACT includes advanced XCT characterization tools such as ZEISS Versa, ZEISS METROTOM and ZEISS VoluMax. Dr. Ziabari and co-investigators Dr. Singanallur Venkatakrishnan, Dr. Philip Bingham, Dr. Ryan Dehoff and Dr. Vincent Paquit developed an algorithm for use with these computational imaging systems. “The AI-based technology enhances XCT capabilities, producing fast and high-quality images with significant improvement in noise and artifact suppression, thus improving accuracy of measurements,” explains Dr. Ziabari.

X-ray CT-based characterization provides insights into processing, microstructure, material properties and performance of complex AM parts. ZEISS XCT systems’ non-destructive evaluation (NDE) capabilities are particularly critical for inspecting metal AM parts. “A scanner will acquire a series of projections of a part, such as an airplane engine turbine blade from different angles, and these are then reconstructed into a 3D volume using a mathematical model such as FDK,” says Dr. Ziabari. ORNL’s algorithm improves this process by leveraging existing computer-aided design (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 artifact from synthetic XCT data, and then applies the trained network to actual experimental XCT data.

According to Dr. Ziabari, “The relevant artifacts here are metal artifacts and beam hardening which are due to the polychromatic nature of the light resulting in higher absorption of low-energy X-ray photons by dense materials such as metals. Other artifacts and noise stem from reducing the scan time for such dense materials. Our framework has been able to reduce noise and beam hardening artifacts significantly, while enhancing the defect detection capabilities and aims to reduce the scan times by a factor of six.” This is a fundamentally different approach than any previous 3D reconstruction method.

“We are really satisfied and excited by the hard work Dr. Ziabari and his team have put in,” says Dr. Bhattad. “The results show great potential not just for AM but for a diversity of applications, multi-material systems and complex geometries. The enhancement of NDE could easily translate to 100% inspection of high-volume products ranging from electronics to batteries to even digital twins for critical components such as 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.”

ORNL receives funding from the DOE’s Advanced Manufacturing Office (AMO). Dr. Ziabari and his team have outlined a five-step research and development plan in coordination with ZEISS for the project’s first year:

– Phase one is testing and evaluating the algorithms on several real datasets

– Phase two will leverage novel domain adaptation techniques to ensure the proposed method translates to a range of samples and for multi-material structures

– Phase three includes documentation, development of the final executable user interface and modeling that will be verified by ZEISS’ test data

The expected outcomes in this first year include quantifying the improvement in porosity detection capability and resolution, quantifying the improvement in metrology by comparing XCT results against ZEISS CMM measurements of samples and generalizing the network so it can work on multiple resolutions. “Our ultimate goal is to further develop and mature this technology so that it can be commercialized and adopted into the broader XCT market,” says Dr. Ziabari.

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