AI for CAx & Simulation
Engineers and designers face challenges ranging from managing enormous datasets and conducting complex simulations to maintaining accuracy and efficiency in design processes.
With AI, vast and complex datasets become manageable and informative. AI algorithms can quickly analyze and visualize simulation results. AI-driven tools can automate repetitive and time-consuming modeling tasks, allowing engineers to focus on creative solutions and innovation.
Why Renumics
We Know How to Navigate The Challenges for ML in CAx
At Renumics, we understand that integrating Machine Learning (ML) into Computer-Aided Design (CAD) and Engineering (CAE) environments presents possibilities and challenges. Our expertise in this specialized field enables us to navigate the hurdles effectively.
- Handling Complex CAx Data from Different Sources
- Seamless Integration into existing CAx Workflows
- Expertise and Training to Ensure an Effective Use of ML-powered Tools
Experience the advances in AI for CAx & Simulation
Discover how Renumics can assist you in utilizing AI advancements for CAx and simulation. We are your partner for industrial AI.
Automate the Postprocessing
Large-scale simulations, such as NVH (Noise, Vibration, and Harshness) analysis and crash testing, pose challenges for manual analysis and visualization of the vast amount of data. With pattern recognition for 3D simulation results, as well as for 2D curves and more, we automate repetitive tasks, greatly reducing the effort involved. This not only reduces costs but also allows for new ML-based visualization techniques suitable for handling large volumes of data.
Quality Control of CFD Simulations
Machine Learning facilitates the early detection of issues in CFD simulations, reducing costs by minimizing unnecessary iterations and computation. By utilizing advanced machine learning techniques, we can detect errors and anomalies in simulation outputs, including residuals and physical quantities. This capability allows for prompt intervention by the simulation engineer and also enables deeper investigation to determine underlying causes through interactive visualization.
Geometry understanding for CAE/CAD
Approaches to understanding 3D geometry allow us to implement systems for automating modelling tasks in the simulation setup for CAE. This is particularly helpful when many simulations of the same type would have to be set up manually, or when many individual parts need to be considered and modelled.
- Benefits
- Increase Efficiency
- Consistency and Accuracy
- Scalability
- Outcomes
- Improve Product Quality
- Boost return on Investment
- Expand market reach
AI in Material and CFD Surrogate Modelling: Predicting Behavior with Precision
Traditional model-based material descriptions can be replaced with a data-driven approach for material modeling. This allows us to decrease the modeling time and surpass the limitations of conventional material descriptions in the context of using new materials, whose behaviors are becoming increasingly complex.
We apply Machine learning methods to predict fluid flow based solely on the shape of the object being simulated. This reduces the time required to reach solutions while maintaining the accuracy levels comparable to those of traditional computational fluid dynamics (CFD) solvers.
Research: Machine Learning for 3D Engineering Data
Our advanced machine learning technologies are revolutionizing the 3D engineering landscape.
We develop data-efficient and generalizable 3D machine learning solutions using foundation models and multimodal approaches that combine various data sources, including 3D meshes, point clouds, photographs, and text.
- Effortless Preprocessing
Automate the labeling and segmentation of CAD models to create structured hex meshes suitable for high-requirement simulations with minimal manual effort.
- User-tailored Search
Discover CAD models that meet your specific needs with customizable combined queries for shape and text.
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