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.
Feature Article - Metal AM Magazine
Intellegens features in the Winter 2021/22 edition of Metal AM Magazine, discussing applications of machine learning to additive manufacturing. The article explores some of the challenges of applying machine learning in AM projects, how these are being overcome, and the resulting benefits.
Intellegens launches new AM package
Intellegens has launched a New Alchemite™ AM Package. The ready-to-go bundle of machine learning software, analysis tools, and implementation services is designed to help AM teams extract value from their data, optimise build parameters and ensure more repeatable AM processes, while greatly reducing the need for testing.
Intellegens AM solution wins awards
The Intellegens additive manufacturing solution was a winner in the Innovation Awards at the 2021 AM Tech Forum event, organised by the American Society of Mechanical Engineers (ASME). Intellegens scooped two of the awards – Best in Class Software, and the Startup Award. You can now view a recording of the award-winning presentation.
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
The Alchemite™ for AM package provides the Alchemite™ model-building and analytics tools, plus the right implementation services to help you get up-and-running, fast. Scientists and engineers can apply powerful deep learning methods to get more from their AM project data.
- Auto-generate and refine models that identify key property / process relationships
- Design optimal powder properties and machine parameters to achieve target outcomes
- Monitor production data to enable failure analysis and maintain quality control
- Gap-fill and validate data to improve data quality
- Apply adaptive Design of Experiments (DoE) to focus testing, saving time and expense