Additive Manufacturing could be transformational – delivering lighter, stronger parts and novel product capabilities. But ensuring repeatable AM processes is a challenge, particularly given limitations on the available data. Sometimes there is too little data. Or, you may have large project datasets where the data is sparse (e.g., every attribute is not captured for every test) or noisy.
With the Alchemite deep learning software, you can gap-fill and get much more from your data. Understand critical property/process relationships. Decide which changes in material or processing will give the best results. Design more focused testing programs to help deliver projects faster.
Optimising AM processes with the AMRC and Boeing
In this collaboration, machine learning technology is being applied to make the additive manufacturing (AM) process of metallic alloys for aerospace cheaper and faster, encouraging the production of lightweight, energy-efficient aircraft to support net-zero targets for aviation. The project was covered as a case study in a recent webinar – a short (01:18) sample video clip is provided here, and you can view the full recording.
Designing a material for direct laser deposition
The process to ‘print’ materials is often poorly understood and subject to significant variation based on the exact processing parameters and conditions. In our example, limited data was available on a direct laser deposition method, consisting of only ten sets of processing variables. This dataset was not enough for traditional machine learning techniques to be able to predict the properties of a wider family of processing variables. We optimized the process using historical welding data and the available sparse direct laser deposition dataset, potentially saving years of research.
Integration with materials data management for AM
Intellegens is partnering with engineering simulation leader Ansys to integrate Alchemite technology into the Granta MI materials data management system for AM applications. For Granta MI user organisations, this will provide an additional way to access Alchemite capabilities from within their existing workfows.
Alchemite for additive manufacturing
With the Alchemite software scientists and engineers can apply powerful deep learning methods to get more from their AM project data.
- Gap-fill and validate sparse, noisy AM project data from suppliers, experiment, simulation, and builds
- Auto-generate and refine models that identify key property / process relationships
- Quantify uncertainty to understand and improve reliability
- Design optimal powder properties and machine parameters to achieve target outcomes
- Monitor production data to help refine models and optimise processes
- Capture AM process knowledge as models for sharing and re-use