Altair has officially released a global collection of 100 AI application cases in an electronic book format, covering over 10 industries with 100 AI application scenarios. The publication provides insights into successful global AI-driven engineering design applications and demonstrates how AI technology empowers and revolutionizes the entire product lifecycle in industrial manufacturing.
In the convergence field of artificial intelligence and simulation, new application methods are continuously emerging. Beyond common popular terms like machine learning, generative AI, and synthetic data, the focus should be on how AI empowers simulation in actual engineering, accelerating research and development processes while improving decision-making quality.
**Key Technical Implementation Methods**
In traditional manufacturing industries, companies are actively exploring how to leverage AI to stand out from competition. However, many enterprises still have concerns about the starting path and required skills. It must be clear that AI is not a "plug-and-play" solution; it relies on high-quality data and effective supervised models.
Using large-scale casting as an example, this demonstrates the deep integration of AI and simulation. Through machine learning clustering technology, Altair helps users quickly identify optimal design solutions from massive simulation data, showcasing the enormous potential of "AI-driven simulation" in practical engineering applications.
**Efficient Model Creation** Based on geometric graphics (mesh or CAD format), algorithms can convert these into values for comparing, editing, clustering geometries and dividing them into groups and categories. This makes model organization easier and the modeling process more efficient.
**Multidisciplinary Design Exploration** Using existing results from parametric design, regression analysis can identify correlations and predict individual values or behavior curves, filling gaps in test data.
**Rapid Physical Behavior Prediction** Based on simulation results and geometric structures, neural networks are trained to predict behavior without running new simulations.
**Effective Capture of Complex System Behavior Using Neural Networks** Instead of collaborative simulation using computationally intensive simulations (such as Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), and Finite Element Analysis (FEA)), neural networks are trained as reduced-order models (ROM) to reproduce system behavior. This significantly accelerates system simulation speed, improving it by up to 1,000 times in some cases without affecting accuracy, creating opportunities for innovative thinking and system performance optimization.
Altair romAI is part of the Altair HyperWorks design and simulation platform, providing a toolbox that improves system simulation efficiency. The combination of artificial intelligence and dynamic system theory technologies achieves excellent accuracy while significantly reducing computation time.
**Pattern Recognition and Optimization** Based on simulation results from many design variants, unsupervised machine learning methods create groups with unified behavior patterns, enabling intuitive processing of hundreds of simulations.
A recent large-scale casting example demonstrates the significant advantages of AI-driven methods compared to traditional approaches, showing AI's capability to understand and optimize the behavior of large casting components.
**AI-Enabled Generative Design**
**AI-Supported Generative Design Comprehensive Workflow Optimization** In any development process, numerous requirements must be considered to balance lightweight design, functional requirements, and manufacturability. The optimization process for large casting components includes two phases: starting with topology optimization based on linearized load situations for effective material arrangement, then combining with multidisciplinary optimization to evaluate structural performance and check manufacturability using AI and machine learning-supported casting simulation.
**Topology Optimization** Altair's powerful and proven generative design technology is used for the most effective material arrangement, deriving optimal load paths for multidisciplinary load situations, including hundreds of load cases, variables, and manufacturing constraints for castings.
**Multidisciplinary Optimization** Response Surface Modeling (RSM) optimization is combined with machine learning to meet requirements and provide optimal rib direction and thickness distribution for large casting structures in nonlinear collision and casting simulations. Through clustering and classification of complete simulation results, pure scalar, regression-type objectives can be overcome and compared with expert evaluations to optimize desired component behavior.
**Manufacturability Analysis** Unsupervised machine learning can also evaluate manufacturability. In die-casting process simulations, it identifies uniformity behavior of different design variants, such as flow velocity at gates or flow fronts, and determines optimal quantity, size, and positioning of gate geometries.
**Altair Empowering Engineering and AI**
Altair deeply integrates AI with generative design and advanced CAE technology, forming a complete development process for large casting structures. This method supports multidisciplinary variant analysis, capable of simultaneously processing thousands of simulation scenarios, helping companies quickly identify optimal solutions.
By systematizing and scaling expert knowledge, Altair helps users deeply integrate AI into the entire simulation and manufacturing process, achieving integrated intelligent design from concept to mass production. As a leading provider of engineering data analysis and AI solutions, Altair bridges two languages: engineering and AI. Altair's no-code AI solutions provide easy access to machine learning and AI platforms for both novices and experts.
In summary, Altair helps organizations achieve simulation and digital transformation across various industries, driving the AI-powered industrial transformation journey.
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