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December 5, 2023 · Technical collaboration agreement will focus on innovating high-temperature ceramics processing viathe use of Lithoz 3D printing technology.
September 8, 2023 · Technical ceramics AM is expected to top $2 billion, traditional ceramic and sand AM targeting $1.7 billion by 2032.
August 22, 2023 · In the collaboration, the companies plan to leverage and develop their existing expertise to produce high-value resins and 3D print ceramic components.
August 11, 2023 · Tech will be used to make ceramic components.
February 27, 2023 · Using advanced machine learning and optimization, the Ceramic Crown 3D Printing Ecosystem features Crown Design Services powered by artificial intelligence.
July 12, 2022 · The new printer targets full-scale industrial ceramic 3D printing production.
August 26, 2021 · The partnership allows U.S.-based companies to serve a range of industrial applications as well as classified projects with government agencies.
September 3, 2020 · Companies are collaborating to offer ceramic 3D printing along with investment casting.
October 3, 2019 · The new, dedicated systems are the latest step in XJet’s journey to enhance metal and ceramic additive manufacturing, company says.
July 25, 2018 · XJet is staffed with many veterans of Objet Geometries, developers of PolyJet AM technology (now owned and marketed by Stratasys).
April 23, 2018 · The Air Force Research Laboratory Aerospace Systems Directorate has entered into a cooperative research and development/material transfer agreement with HRL Laboratories to test silicon oxycarbide (SiOC) materials for additive manufacturing to help achieve hypersonic flight.
January 15, 2016 · HRL Laboratories has developed a way to print complex ceramics that can withstand high temperatures.
May 2026 Special Focus: Artificial Intelligence in Design and Simulation
In this Special Focus Issue, learn about the latest developments in the integration of artificial intelligence into engineering workflows.
Cut Retrieval-Augmented Generation (RAG) Hallucinations by 50%
Most teams hit the same wall with enterprise AI: LLMs that hallucinate, pipelines that don’t scale, and infrastructure that’s harder to design than the models themselves.
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