AI FEA is Revolutionizing Engineering Analysis - Now!

AI platforms can be a powerful tool to enhance your Finite Element Analysis (FEA) in several ways. Here's how you can leverage them for better results:

Pre-processing Optimization:

a) Automated Meshing: Train AI models to generate high-quality meshes based on your model geometry and desired accuracy. This can save time and effort compared to manual meshing.

Here are some examples of AI-powered automated meshing tools:

• Ansys Meshing with optiSLang: Offers AI-driven mesh optimization capabilities within Ansys software.
• Simmetrica by Siemens: Provides automated mesh generation with adaptive refinement features.
• HyperMesh by Altair: Integrates with AI-powered meshing tools like HEXAGON for improved mesh quality.
• Zenit by Cadence: Features AI-driven meshing capabilities for electromagnetic simulations.

b) Boundary Condition Identification: Utilize AI to automatically identify and assign appropriate boundary conditions based on the geometry and loading conditions.

Examples of AI-powered BC Identification Tools:

• Ansys Sherlock: Offers AI-powered pre-processing tools, including automatic BC identification based on model geometry and loading conditions.
• 3DXpert by Dassault Systèmes: Provides AI-driven capabilities for automatic BC suggestion and application within the 3DEXPERIENCE platform.
• SimScale: This cloud-based FEA platform offers AI-assisted pre-processing tools that can suggest appropriate BCs based on user-defined parameters.

2. Material Property Prediction:

Material Database Integration: Connect your FEA software to AI-powered material databases. These databases can predict material properties based on limited data or suggest alternative materials for optimization.

Here are some examples of AI-powered material databases:

• The Materials Project (MIT): A large open-access database of materials properties with powerful search capabilities and machine learning for property prediction. (https://next-gen.materialsproject.org/)
• Dassault Systèmes 3EXPERIENCE Platform: This platform offers a materials marketplace powered by AI that connects users with a vast database of materials and allows for property prediction and selection based on design needs.
• Granta MI: A commercial platform offering a comprehensive materials database with AI-powered search, property prediction, and material selection tools. (https://www.ansys.com/products/materials/granta-mi)

• Materiom: A database focused on regenerative materials, leveraging AI to gather data and assess the sustainability of materials based on their ingredients and processing methods. (https://materiom.org/)

Benefits of Using AI-powered Material Databases:

• Reduced Design Time: Faster and more efficient material selection process.
• Improved Design Decisions: Access to a wider range of materials and data-driven insights for better design choices.
• Innovation Potential: Discovery of new materials and exploration of previously unconsidered options.
• Sustainability Considerations: Identification of materials with lower environmental impact through AI-powered sustainability assessments.

Limitations to Consider

• Data Quality: The accuracy of the AI's predictions and recommendations depends on the quality and completeness of the data within the database.
• Black Box Nature: In some cases, the AI's reasoning behind recommendations might not be readily apparent.
• Limited Scope: While AI can predict and suggest materials, the final material selection should always be validated through testing and engineering judgement.

3. Design Exploration and Optimization:

a) Generative Design: Utilize AI-powered generative design tools for exploring various design options based on your performance criteria. This can help you identify innovative and potentially lighter or stronger designs.

Examples of AI-powered generative design tools:

• Ansys optiSLang: Offers AI-powered generative design capabilities within the Ansys suite.
• Generative Engineering Design (GED) by Siemens: Provides a user-friendly platform for generative design exploration within the 3DEXPERIENCE platform.
• Autodesk Fusion 360 Generative Design: Integrates generative design functionality directly within the popular CAD software.
• Topology Optimization Software: Tools like SIMSOLID and Altair OptiStruct leverage AI for advanced material distribution optimization within existing designs.

• Topology Optimization: Employ AI algorithms to optimize existing designs by removing unnecessary material while maintaining structural integrity.

Here are some AI algorithms commonly used to optimize existing designs:

1. Evolutionary Algorithms:

• Genetic Algorithm (GA): Mimics the process of natural selection to iteratively improve designs. Existing designs are "bred" and mutated to generate new variations. Those with better performance metrics (based on your goals) are selected for further breeding, leading to progressively better designs over generations.
• Particle Swarm Optimization (PSO): Inspired by the swarming behavior of birds or fish. A population of "particles" (potential designs) explore the design space, learning from each other and iteratively moving towards optimal solutions.

2. Gradient-Based Optimization Algorithms:

• Gradient Descent: Analyzes the "gradient" (rate of change) of the objective function (e.g., weight, stress) with respect to design variables (dimensions, materials). It iteratively adjusts the design variables in the direction that minimizes the objective function, leading to an optimal design.

3. Machine Learning Algorithms:

• Surrogate Modeling: These algorithms can be trained on existing simulation data or design examples to create a "surrogate model" that predicts the performance of new design variations. This allows for faster exploration of the design space compared to running full simulations for every iteration.
• Deep Learning: Deep neural networks can be trained to analyze design features and predict performance metrics. This allows for complex relationships between design and performance to be captured and used for optimization.

4. Other Techniques:

• Topology Optimization: This method focuses on optimizing the distribution of material within a design space, often used to remove unnecessary material while maintaining structural integrity. AI can be used to accelerate and refine the optimization process.
• Multi-Objective Optimization: When dealing with multiple design goals (e.g., weight and strength), AI can be used to find "Pareto-optimal" solutions that represent trade-offs between these goals.

b) Choosing the Right Algorithm:

The best AI algorithm for optimizing existing designs depends on the specific problem and type of design you're working with. Here are some factors to consider:

• Complexity of the Design: Simpler designs might be well-suited for evolutionary algorithms, while complex designs might benefit from machine learning techniques.
• Number of Design Variables: Gradient-based methods can be sensitive to the number of design variables, while evolutionary algorithms can handle a larger number.
• Availability of Simulation Data: Machine learning approaches often require existing simulation data for training the models.

Remember, these AI algorithms are tools to be used alongside human engineering expertise. The best results are achieved through a combination of AI-powered exploration and human judgment to evaluate, validate, and finalize the optimized design.

4. Simulation Data Analysis:

a) Post-processing Automation: Train AI models to automatically post-process simulation results and identify critical areas like high stress zones or potential failure points.

Here are some examples of AI tools for automatic post-processing of simulation results:

• Ansys Sherlock: Offers AI-powered post-processing capabilities, including automated data extraction, anomaly detection, and interactive visualization tools.
• 3DXpert by Dassault Systèmes: Provides AI-driven post-processing features for fatigue life prediction, visualization, and automatic report generation within the 3DEXPERIENCE platform.
• SimScale: This cloud-based FEA platform offers AI-assisted post-processing tools that can automatically extract key data points and generate visualizations based on user-defined parameters.
• Open-source Tools: Some open-source projects like Paraview are integrating machine learning capabilities for data exploration and visualization within the context of scientific simulations.

b) Predictive Maintenance: Leverage AI to analyze simulation data and predict potential maintenance needs based on stress or fatigue accumulation. This can help prevent unexpected downtime.

5. Uncertainty Quantification (UQ):

Probabilistic Analysis: Integrate AI with UQ tools to account for manufacturing variations and material property uncertainties in your FEA simulations. This provides a more realistic picture of potential design performance.

Here are some additional points to consider:

• Data Availability: AI platforms thrive on data. Ensure you have sufficient high-quality data for training the AI models for optimal results.
• Expertise Integration: While AI automates tasks, human expertise remains crucial. AI should be used as a tool to augment engineering judgement, not replace it.
• Platform Selection: Different AI platforms cater to specific needs. Choose a platform that integrates seamlessly with your existing FEA software and offers functionalities relevant to your workflow.

Examples of AI Platforms for FEA:

• Ansys Sherlock: Offers AI-powered design exploration, optimization, and uncertainty quantification tools.
• Generative Engineering Design (GED) by Siemens: Provides AI-driven generative design capabilities for exploring innovative design solutions.
• Machine Learning for Engineering (MLE) by Altair: Integrates machine learning with simulation tools for data-driven design optimization and predictive analytics.

By incorporating AI into your FEA workflow, you can achieve significant improvements in efficiency, explore a wider design space, and gain deeper insights from your simulations.

While AI-powered FEA is still under development, it offers powerful tools that can significantly assist engineers in their daily analysis tasks. Even though it won't fully automate everything just yet, AI features can streamline workflows and enhance decision-making.

Jinto A. J

[email protected]

+91-9947796301