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3D Printing in Education and Research: Your advantages in working with 2onelab
Material & Strategy Freedom
Enjoy the freedom to choose your printing materials and design your printing strategies. Our Additive Manufacturing solutions let you explore and create without limits. This freedom helps speed up innovation and deepens exploration in 3D metal printing, making your scientific and educational projects always ahead.
Wide Material Spectrum
Explore a wide range of materials, including reactive and non-reactive metals. We provide many parameter sets for free, making it easy to start with your research or educational projects. Our wide material range opens up many possibilities, letting you explore Powder Bed Fusion (PBF) technology and Additive Manufacturing in your projects.
Compact & Cost-effective
Our small size and very affordable solution is perfect for any research budget. Made in Germany, our offerings are high-quality but priced sensitively. The cost advantage helps you do more in your research and educational projects, making every resource count in advancing your 3D metal printing projects.
current research project
Where Advanced Science Meets Industry Expertise: Elevating Standards with Precision & Traceability
The new 2Connect WebApp
Project “DiSer – Digital Service Platform for Resource-Efficient Processes in Additive Manufacturing”
In a collaborative initiative between the esteemed Technical University of Darmstadt and 2oneLab, a leading company with profound expertise in the 3D metal printing of dental components, a groundbreaking digital platform is in development. This platform is primarily focused on the exchange of process parameters in Selective Laser Melting (SLM) within the dental sector. Our objective is to significantly elevate resource efficiency in additive manufacturing. Through precise adjustments of 3D printing parameters and a reliable traceability of printed components back to their process parameters, we are setting new industry standards. Leveraging advanced analytical tools, we can efficiently evaluate STL data, dynamically identifying areas in 3D models that require process parameter adjustments.