Simufact & Renishaw partner to improve the success of Renishaw’s laser powder bed fusion (LPBF) builds.
The collaboration includes the integration of Renishaw’s QuantAM build preparation software into Simufact Additive 2020. Simufact has re-thought design optimization by enhancing the creative process so that designers have more time to optimise the product’s concept and integrate additional features that add value.
The teams from both companies are also working together on improved simulation of the complex metallurgy of commonly used materials to enable successful prediction, and compensation, of stress and distortion during the printing process.
Simufact Additive 2020 includes an integrated Renishaw QuantAM API for build job preparation and export, which provides the user an error-free data transmission from Simufact Additive directly to Renishaw systems. This functionality also improves productivity because the entire work process from design to printed part is possible within the software. The QuantAM API now enables multi-laser build file preparation for Renishaw’s popular RenAM 500Q quad-laser machine.
“Simufact Additive 2020 broadens the scope of our build optimisation software and underlines our commitment to openness and interoperability with third party products,” commented Dr. Hendrik Schafstall, CEO and Managing Director at Simufact. “With the integration of the QuantAM build processor, we can provide an end-to-end build optimisation process, helping the users of Renishaw systems to produce AM components with repeatable dimensional accuracy right first time.”
The collaboration extends to improving the simulation process itself. The increasing adoption of multi-laser LPBF systems for production applications presents new simulation challenges. Faster energy input from multiple lasers means that metal components often experience higher temperatures during the printing process, affecting their microstructure and resulting in different residual stress and distortion. The two companies are researching such phenomena to improve the accuracy of build simulations, enabling prediction of any distortion that may arise. This knowledge can be used to optimise the build process, and compensate the design to accommodate expected distortion.
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