ADVANCED MATERIALS & PROCESSES | JULY 2026 60 3D PRINTSHOP BINDER-JET PRINTING RECYCLED GLASS A collaboration between Oak Ridge National Laboratory (ORNL) and Vitriform3D resulted in development of a technique that uses binder-jet printing to create new products from recycled glass. Vitriform3D’s glass recycling process begins with crushing bottles down into a sandy powder. A robotic arm smoothly spreads the tiny glass fragments into a square pile, and nozzles along the arm spray an adhesive that glues the particles together, while other nozzles distribute ink to add fresh color. As the arm swings back and forth, adding more glass particles and adhesive each time, layers build up until the powdered glass is transformed. The new object is then dusted off and heated in an oven to set the final shape. This 3D-printing process, known as “binder jetting,” had already been used with many types of powders, including metal, wood fiber, and sand, but it had not yet been used for crushed glass, which itself is a combination of silica (the main constituent of sand), soda ash, and limestone. “Essentially, you’re glueing the powdered glass together,” says Alex Stiles, Vitriform3D’s cofounder. He says the final product is considered engineered stone, with a composition of 90-95% glass waste and 5-10% of a binder polymer adhesive. Next steps for Vitriform3D include collaboration with DOE’s Building Technologies Research and Integration Center at ORNL. The partnership aims to develop recycled glass cladding: the exterior walls that give a building its finished look as well as protection from the elements. vitriform3d.com. AI PREDICTS MICROSCOPIC DEFECTS IN METAL 3D PRINTING Researchers from POSTECH and the Korea Institute of Materials Science created a data selection machine learning (DSML) framework to help detect microscopic internal defects in 3D-printed parts. Rather than attempting to eliminate defects entirely, the team reframed the problem by focusing on understanding and predicting defects scientifically. The team integrated porosity data alongside process parameters, microstructural features, and mechanical property data to train an AI model. They applied the DSML technique to identify only the most influential variables from the dataset, effectively filtering out noise and focusing the model on the factors that matter most. The approach is analogous to a physician interpreting a CT scan to diagnose disease: The AI analyzes the internal microstructure and defect characteristics of metal components to anticipate their mechanical behavior before any physical testing is performed. To validate the framework, the team fabricated AlSi10Mg alloy, one of the most widely used aluminum alloys in 3D-printed aerospace and automotive components, under a variety of process conditions. The AIbased model successfully predicted the yield strength of components with a mean absolute error of just 9.51 MPa within seconds, eliminating the need for complex experimental procedures. This represents more than a 4-fold improvement in prediction accuracy compared to conventional approaches, demonstrating the framework’s robustness and practical utility. The research team envisions this framework serving as the basis for a “defect-aware design map,” enabling the prediction and design of material performance based on process conditions in advance, thereby significantly reducing the trial-and-error typically associated with materials development. www.postech.ac.kr. Bottles of various colors can be selected for different binder-jet printed designs. Courtesy of Amy Smotherman Burgess/ ORNL, U.S. Dept. of Energy. Schematic diagram of the DSML framework and experimental validation of AlSi10Mg. Courtesy of POSTECH.
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