About Course
Generative Design & AI in Engineering: Optimization & Innovation Training Course
Introduction
The fields of engineering and product development are undergoing a radical transformation, driven by the power of Generative Design and Artificial Intelligence (AI). This Generative Design & AI in Engineering: Optimization & Innovation Training Course is specifically designed for mechanical engineers, product designers, industrial designers, architects, manufacturing engineers, and R&D professionals who aim to revolutionize their design processes. It explores how AI-driven tools can rapidly explore countless design possibilities, optimize performance, reduce material usage, and accelerate innovation beyond human intuition.
Participants will gain a comprehensive understanding of how to define design goals and constraints, leverage AI algorithms for design exploration and optimization, and integrate these advanced capabilities with additive manufacturing (3D printing). The curriculum delves into practical applications using leading generative design software platforms, covering techniques like topology optimization, lattice structures, and multi-objective optimization. By mastering these cutting-edge methodologies, you will be equipped to create highly innovative, performant, and sustainable products, driving efficiency and competitive advantage in the modern engineering landscape.
Target Audience
- Mechanical and Aerospace Engineers.
- Product and Industrial Designers.
- Architects and Civil Engineers.
- Manufacturing and Production Engineers.
- Research & Development (R&D) Professionals.
- CAD/CAM/CAE Software Users and Administrators.
Duration
10 days
Course Objectives
- Understand the fundamental concepts of generative design and its transformative impact on engineering and product development.
- Grasp how Artificial Intelligence and Machine Learning algorithms are applied to optimize and explore design solutions.
- Gain practical experience using leading generative design software to define design spaces, goals, and constraints.
- Master techniques for topology optimization, lightweighting, and creating complex lattice structures.
- Learn to design for additive manufacturing (DfAM) specifically using generative design principles.
- Implement multi-objective optimization strategies to balance conflicting engineering requirements.
- Integrate simulation and analysis tools within generative design workflows for performance validation.
- Identify and apply generative design solutions to real-world engineering challenges across various industries.
Course Content
Course Content
Module 1. Introduction to Generative Design & AI in Engineering
- What is Generative Design (GD)? Beyond traditional CAD
- The evolution of design tools: Manual to parametric to generative
- How AI is transforming the engineering and product development lifecycle
- Key benefits of GD: Faster iteration, optimized performance, material efficiency, innovation
- Overview of industries adopting GD (automotive, aerospace, medical, consumer goods)
Module 2. Core Principles of Generative Design
- Defining the design problem: Formulating clear goals and objectives
- Setting up design constraints: Loads, boundary conditions, fixed geometries
- Specifying materials properties and manufacturing processes as inputs
- Understanding design space exploration and candidate generation
- The iterative feedback loop in generative design workflows
Module 3. AI & Machine Learning for Engineering Design
- Optimization algorithms: Evolutionary algorithms, genetic algorithms for design
- Neural Networks in design: Predicting performance, exploring design variations
- Reinforcement Learning for sequential design decisions
- Surrogate models: Accelerating simulations and design space exploration
- Human-AI collaboration in the generative design process
Module 4. Generative Design Software & Platforms
- Overview of leading generative design tools: Autodesk Fusion 360, Altair Inspire, PTC Creo
- Introduction to specialized tools: nTopology, ANSYS Discovery
- Practical hands-on sessions with selected software (e.g., Fusion 360 Generative Design)
- User interface, basic setup, and running generative studies
- Data input and output formats for generative models
Module 5. Topology Optimization & Lattice Structures
- Principles of topology optimization: Distributing material for optimal performance
- Lightweighting: Reducing mass while maintaining structural integrity
- Creating complex, organic geometries impossible with traditional methods
- Designing and implementing lattice structures for enhanced properties (e.g., energy absorption)
- Applications in aerospace components, medical implants, and consumer products
Module 6. Design for Additive Manufacturing (DfAM) with GD
- Additive Manufacturing (AM) overview: 3D printing processes (SLA, FDM, SLM)
- How generative design complements additive manufacturing
- Design rules for AM: Overhangs, support structures, build orientation
- Leveraging GD to create geometries specifically optimized for 3D printing
- Minimizing material waste in AM through generative design
Module 7. Multi-Objective Optimization & Design Space Exploration
- Handling conflicting design requirements (e.g., strength vs. weight vs. cost)
- Pareto fronts: Visualizing optimal trade-offs between objectives
- Techniques for navigating and selecting from a vast design space
- Sensitivity analysis: Understanding the impact of input changes on design outcomes
- Post-processing and analyzing generative design results
Module 8. Simulation & Analysis in Generative Workflows
- Integrating Finite Element Analysis (FEA) for structural validation
- Computational Fluid Dynamics (CFD) for fluid flow and thermal performance
- Stress, strain, displacement analysis of generatively designed parts
- Automated simulation setup and result interpretation
- Iterative refinement of designs based on simulation feedback
Module 9. Materials Informatics & AI-Driven Material Selection
- Introduction to materials informatics: Using data science for materials discovery
- AI for predicting material properties and performance
- Generative models for new material design
- AI-assisted material selection in generative design workflows
- Case studies in advanced materials development
Module 10. Generative Design in Product Development Workflow
- Integrating GD into existing CAD/CAM and PLM/PDM systems
- Workflow automation: From concept to manufacturing
- Collaboration between designers, engineers, and manufacturing teams
- Managing design versions and iterations in generative projects
- Practical tips for incorporating GD into your organization's processes
Module 11. Ethical Considerations & Future of GD
- Design autonomy vs. human oversight: The role of the human in the loop
- Intellectual property implications of AI-generated designs
- Bias in training data impacting generative outcomes
- Sustainability and material usage: Ensuring environmentally responsible designs
- The evolving relationship between designers, engineers, and AI
Module 12. Advanced Applications & Case Studies
- Aerospace: Lightweighting aircraft components, bracket optimization
- Automotive: Chassis design, engine components, custom parts
- Medical Devices: Personalized implants, prosthetics, surgical tools
- Consumer Goods: Ergonomic designs, customizable products, footwear
- Architecture & Construction: Structural optimization, complex facades, sustainable building elements
General remarks
General remarks
- Customizable courses are available to address the specific needs of your organization.
- The participant must be conversant in English
- Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
- The course fee for onsite training includes facilitation training materials, tea break and lunch.
- Accommodation and airport pick up are made upon request
- For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
- Payment should be made to our bank
