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Structural optimization can easily go wrong if it’s not approached correctly. In this Sharpen-the-Saw session, Gerrit Visser walks through the fundamental principles of structural optimization, explaining what “optimal” really means and how to define optimization problems that deliver meaningful, reliable results. Using a practical example, the session covers common mistakes, best-practice workflows, and different optimization methods — from sizing and shape optimization to topology and generative design. 🔍 What you’ll learn: What structural optimization actually means in practice How to define design variables, cost functions, and constraints Why minimizing mass alone leads to invalid designs The correct way to combine mass minimization with response constraints Common pitfalls: local vs global minima, missing load cases, robustness issues Overview of optimization types: sizing, shape, topology, topography, and generative design Using optimization for model correlation and test matching 🎯 Who should watch: Engineers, analysts, and students working with FEA who want to apply optimization effectively — without falling into the most common traps. 00:00 – Introduction & Session Objectives 02:45 – What Is Structural Optimization? 06:00 – Design Variables, Cost Functions & Constraints 09:30 – Example Model Setup 12:20 – How Optimization Goes Wrong (Common Mistakes) 17:30 – Why Weighted Cost Functions Can Be Misleading 21:30 – The Correct Optimization Approach: “Lightest Valid Design” 26:00 – Design vs Response Constraints Explained 29:30 – Multiple Load Cases & Valid Designs 32:30 – Local vs Global Optima & Robustness 35:30 – Types of Optimization: Sizing, Shape, Topology & Generative Design 38:00 – Beyond Optimization: Model Matching & Correlation 40:00 – Key Takeaways & Where to Begin