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Merging the data management abilities of Ansys’ Granta MI with the machine learning platform of Alchemite is a perfect fit, promising to offer deep insights to Additive Manufacturing workflows.
FREMONT, CA: Machine learning specialist Intellegens and engineering simulation leader Ansys collaborates to integrate machine learning methods into Additive Manufacturing (AM) workflows, boosting the development of reliable and repeatable AM operations. Combining the technologies will make it rapid and easy for AM project teams to analyze data from the experiment, simulation, or production, creating models that capture vital insights. These models are leveraged to optimize process parameters and powders, enhancing the quality of AM parts.
The agreement will couple Intellegens’ machine learning technology, Alchemite, within the Ansys materials data management platform, Granta MI. Alchemite deep learning algorithms rapidly find relationships within complex datasets, even when that data is sparse. This makes Alchemite best for AM teams seeking to exploit data brought together from many sources. It extracts all knowledge from the data to find the vital combinations of factors that ultimately control AM parts' performance. Alchemite requires no prior knowledge of which parameters are likely to be important - a significant benefit in this emerging technology area. Uses throughout the AM workflow include process parameter optimization for AM operations, computational design of AM materials, failure analysis and quality control, data validation and gap-filling, and assisted Design of Experiments (DoE) for AM.
Granta MI is the standard for materials data management in engineering firms and is applied in AM applications to capture all of a firm’s AM data. This comprises data on the properties of powders and raw materials, post-build processing data, machine build parameters, test results for AM parts, and simulation data from the Ansys AM simulation suite. Combining Alchemite into this holistic system will make it straightforward to analyze the full range of this data searching for key process relationships and continuously enhancing models as the data is updated.