Sensitivity-Based Sizing Optimization for Local Design Pattern Control
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Solution Overview
Problem
Existing computer-based optimization methods for designing real-world objects often result in impractical designs for manufacturing, particularly due to the lack of consideration for physical requirements and the inability to effectively implement local control of design patterns on surfaces using sensitivity-based sizing optimization.
Innovation Solution
The implementation of sensitivity-based sizing optimization using Maximum Local Relative mass or Maximum Local Absolute mass design responses to determine optimized designs for manufacturing, which incorporates local volume or mass constraints to enhance physical properties and manufacturability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing computer-based optimization methods are used to design real-world objects, then design automation and simulation capabilities are improved, but manufacturability and practical implementation are worsened due to lack of consideration for physical requirements
Solution Approach 1:
The patent applies local quality by implementing local volume or mass constraints at specific elements of the finite element model. This allows different regions of the object to have different design patterns and material distributions optimized for their specific functional requirements while maintaining overall manufacturability. The local control enables practitioners to specify minimum and maximum volume or mass ratios for individual elements, creating spatially varying properties that balance automation with manufacturing practicality.
2Ease of manufacture
If sensitivity-based sizing optimization is implemented with local volume constraints, then manufacturing feasibility and physical property requirements are improved, but design complexity and computational requirements increase
Solution Approach 1:
The patent employs parameter changes by introducing volume ratio or mass ratio parameters for each element in the finite element model. These parameters represent the proportion of material present in each element and serve as design variables in the sensitivity-based optimization. By changing these parameters within specified minimum and maximum bounds, the method achieves local control over material distribution while maintaining a manageable optimization framework that balances manufacturing feasibility with computational complexity.
3Reliability
If local control of design patterns is implemented on surfaces, then mechanical properties and robustness are improved, but computational time and processing requirements increase
Solution Approach 1:
The patent applies partial action by implementing local volume or mass constraints only on specific elements or regions of the finite element model where design pattern control is most critical. Rather than applying constraints uniformly across the entire model, the method focuses computational effort on key areas that most influence mechanical properties and robustness. This selective approach reduces overall computational time while maintaining improved reliability in critical regions.
Data Source
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AI summary
Embodiments automatically determine optimized designs for manufacturing real-world objects. An embodiment begins with defining a finite element model comprised of a plurality of elements that represents a real-world object. Next, equilibriums and design responses of the object in response boundary conditions are determined, which includes calculating a local volume constraint for a given element of the finite element model. Then, design response sensitivities of the object in response to the boundary conditions are determined, which includes differentiating the calculated local volume constraint to determine sensitivity of a sizing design variable. In turn, the model is iteratively optimized with respect to the sizing design variable using the determined equilibriums and the determined design responses, including the calculated local volume constraint, and the determined design response sensitivities, including the determined sensitivity of the sizing design variable to determine an optimized value of the sizing design variable.