AI-Driven Generative Experiences
Succeed with AI in Knowledge-Based Engineering
Enhance Your Capabilities with Generative AI
Generative Artificial Intelligence (AI) has emerged as a groundbreaking tool for designers and engineers, offering unprecedented capabilities to create new design concepts, optimize product performance and innovate to solve complex technical challenges. CATIA propose a different approach of Generative AI, with machine and deep learning in various applications to solve product development time and time to market, based on models developed from industry-specific data, generated from knowledge and know-how of industry processes. Your productivity, cost and time constraints can today be solved with AI in CATIA:
- Enhanced Performance to generate higher-performance designs
- Reduction of the time to market up to 300%
- Management of a larger scope of requirements while controlling investment cost
- Reduction of warranty costs by 10%
- Growing reuse by standardization & modularization up to 80%
Generate Automatically High Performance Designs Satisfying Engineering Requirements
Generative AI can now be achieved with Performance Driven Generative Design solutions from CATIA, by:
- The creation of topology optimized parts and assemblies for innovative, high-performance structures and assemblies. This is used by many of our customers in a wide range of engineering disciplines and sectors (aerospace, automotive, Industrial Equipment)
- A dedicated solution to fast generation of Body & Chassis structure to explore thousands of alternatives early in the concept stage, as for new Electric Vehicle platform (chassis-body) architectures
- Multi-Materials Concept studies to optimize the layout and topology of structural components, to maximize stiffness, strength and weight efficiency while satisfying design constraints, like Aircraft wings in composites
Design By Algorithm with CATIA Visual Scripting
With the democratization of Generative AI, designers and engineers are looking for new ways to explore more design possibilities. With CATIA Visual Scripting, you can now easily elaborate complex and intelligent designs thanks to an intuitive no-code, algorithmic approach, while being faster, efficient and flexible in your creation process. Visual Scripting aims to push the boundaries of your designs and address all design and engineering challenges, by:
- Exploring more design alternatives and styling patterns
- Modeling cutting-edge products and intricate textures
- Maximizing team efficiency and collaboration
Managing the Growing Complexity of Sustainability Regulations and Software-Defined Products
Designing and launching a product is always a complex task for designers and engineers due to many constraints (quality, desirability, cost...). In many industries, this complexity is rising because of:
- The sustainability imperative: companies need to consider climate change constraints and sustainability regulations into their products, by calculating the environmental impact over the product lifetime and balancing it with the other decision criteria
- The rise of software defined products: designers and engineers have to manage complex interactions between hardware and software disciplines with different life cycles
Start Your Journey
The world of AI-driven Design in Knowledge-based Engineering is changing. Discover how to stay a step ahead with CATIA
FAQ About AI-Driven Generative Experiences
AI in engineering design refers to using advanced algorithms and machine learning to automate and improve aspects of the design process. It includes:
- generating designs, optimizing them for performance and cost,
- simulating behavior under different conditions,
- analyzing data for patterns, aiding decision-making,
- and facilitating collaboration among stakeholders.
The key applications of AI in engineering design are:
- Automated Design Generation: AI generates design alternatives based on criteria and constraints.
- Optimization: AI optimizes designs for cost, performance, or efficiency.
- Simulation and Analysis: AI conducts complex simulations to predict design behavior under different conditions.
- Generative Design: AI explores and generates innovative design solutions.
- Pattern Recognition: AI identifies patterns in design features or performance data.
- Decision Support: AI provides insights and recommendations for design improvements.
- Collaborative Design: AI facilitates real-time collaboration among design teams.
AI is changing engineering by automating design tasks and predicting maintenance needs. It helps create better designs quickly and improves simulations. AI tools can suggest many design options, find defects, and optimize supply chains. It also helps analyze data and gather information from documents. AI-powered assistants support engineers in their work. Virtual prototypes, made by AI, help reduce development time and costs.
The benefits of using AI in engineering design include:
- Efficiency: AI automates tasks, speeding up the design process.
- Optimization: AI optimizes designs for performance and cost.
- Innovation: AI generates creative design solutions.
- Accuracy: AI improves the accuracy of simulations and analyses.
- Cost-Effectiveness: AI reduces errors and design iteration costs.
- Decision Support: AI provides data-driven insights for informed decision-making.
- Collaboration: AI enhances teamwork and real-time communication.
- Sustainability: AI aids in designing more sustainable solutions.
- Scalability: AI handles large-scale and complex design tasks effectively.
- Competitive Advantage: AI helps companies deliver better products efficiently, gaining a competitive edge.
AI plays a significant role in civil engineering design by:
- Automates Tasks: Speeds up drafting, modeling, and preliminary design processes.
- Optimizes Designs: Improves structural integrity, cost-efficiency, and sustainability through data analysis.
- Predicts Risks: Assists in identifying and mitigating potential design failures or hazards.
- Enhances Decision-Making: Provides data-driven insights for informed decisions throughout the design lifecycle.
- Supports Planning: Analyzes data for urban planning and infrastructure development.
- Facilitates Collaboration: Integrates design inputs and feedback across teams in real-time.
- Drives Smart Cities: Integrates infrastructure with IoT and predictive modeling for efficient management.
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