Simulation-Guided Design Generation for Industry-Specific Optimization
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Solution Overview
Problem
Existing digital simulation technologies lack the ability to automatically generate optimized designs that reflect industry-specific characteristics and user needs.
Innovation Solution
A design generation device and method that includes a design generator, simulator, and optimization engine to derive optimization parameters, utilizing machine learning and parameter optimization models to iteratively refine designs until predefined objectives are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional digital simulation technology is used to evaluate product designs, then development cost reduction and performance optimization are achieved, but the system lacks the ability to automatically generate optimized designs reflecting industry-specific characteristics and user needs
Solution Approach 1:
The system is divided into distinct functional modules: a design generator that creates initial designs, a simulator that evaluates designs, and an optimization engine that refines designs based on simulation results. This segmentation allows each component to specialize in specific tasks, enabling automatic design generation while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The optimization engine uses simulation results as feedback to iteratively refine design parameters. The system continuously cycles through generation, simulation, and optimization stages, with each cycle improving the design based on predefined objectives and industry-specific characteristics. This feedback mechanism enables automatic optimization without requiring complex manual intervention.
2Manufacturing precision
If comprehensive simulation and optimization processes are implemented to reflect industry-specific characteristics, then design precision and user needs fulfillment are improved, but processing time and computational resources increase
Solution Approach 1:
The design generator pre-generates multiple candidate designs with varying parameters before simulation. The optimization engine pre-defines industry-specific characteristics and objective functions before the optimization process begins. This preliminary preparation reduces the need for extensive iterative adjustments during the main optimization phase, thereby improving design precision while reducing overall processing time.
3Reliability
If iterative optimization processes are performed to satisfy multiple optimization objectives, then design optimization quality is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The optimization engine systematically varies design parameters within predefined ranges and constraints to explore the design space. By changing parameters iteratively based on simulation feedback and industry-specific objectives, the system achieves high optimization quality. The parameter changes are guided by mathematical optimization algorithms that reduce computational complexity compared to exhaustive search methods.
Data Source
AI summary
The present disclosure relates to a design generation device including a design generator configured to generate at least one design from design data when design data is input, a simulator configured to simulate the at least one design and evaluate a result of the simulating, and an optimization engine configured to derive an optimization parameter for the at least one design by inputting the evaluation result obtained from the simulator into a parameter optimization model provided in advance, wherein the design generator modifies the at least one design according to the optimization parameter. By doing so, an optimized design may be automatically generated by reflecting industry-specific characteristics and user needs.