Renishaw opens AM demo center

Renishaw has opened an additive manufacturing (AM) demonstration center inside Ibex Engineering’s headquarters in Newbury Park, California. Visitors can explore, interact with, and use Renishaw’s latest metal AM systems.

The center can print high-precision titanium (Ti64Al4V) parts on Renishaw’s RenAM 500 series of laser-powder-bed-fusion AM systems. For industrial production applications, the RenAM 500 series allows automatic powder sieving and recirculation within the compact system, reducing manual handling and material exposure.

Union Tech SL demo center opens

Union Tech Inc.’s Chicago, Illinois, stereolithography (SL) demonstration center allows customers to evaluate the company’s commercial and production SL equipment.

The center includes:

• Pilot 250 and Pilot 450 – building parts in Somos EVOLVE, Element, WaterShed; Industrial RSPRO 600 and 800
• Benchmarking part builds for evaluation of SL technology machines as a 3D printing option
• Investment casting to rapid tooling, metal clad composites, and prototyping applications
• Warehousing, logistic support
• Final assembly, managing quality control of the equipment

Jim Reitz, general manager of UnionTech says the center provides “a convenient venue for a first-hand, close-up examination and hands-on evaluation of the UnionTech fresh dimensions approach to stereolithography, open-sourced materials, software options, and robust, keep-it-simple-and-solid engineering that provides cost-effectiveness with excellent part aesthetics.”

Senvol earns NIST grant for AM analytics

The National Institute of Standards and Technology (NIST) awarded a grant to Senvol for its “Continuous Learning for Additive Manufacturing Processes Through Advanced Data Analytics,” project, demonstrating that data analytics can be applied to additive manufacturing (AM) data to establish process-structure-property (PSP) relationships. Senvol ML data-driven machine learning software for AM will be used to conduct the analyses; data will come from NIST’s various round-robin test studies and its AM Benchmark Test Series.

Senvol ML will analyze model reliability, adaptive sampling, generative learning, hybrid modeling, and transfer learning. Additionally, Senvol will parameterize in-situ monitoring data, non-destructive testing (NDT) data, and microstructure data so it can be incorporated into NIST’s AM Material Database (AMMD). The project will culminate by integrating Senvol ML and AMMD, so data stored within AMMD can be analyzed by Senvol’s machine learning software.

“The work in this project will demonstrate the power of a data-driven machine learning approach for additive manufacturing process understanding and material characterization… [showcasing] hybrid modeling, whereby physics-based models and data-driven models are joined under a single framework,” says Yan Lu, senior research scientist at NIST.