AI-Generated 3D Cellular Structures for Lightweight Vehicular Components
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Solution Overview
Problem
Designing vehicular components with cellular structures that balance performance, weight, and material efficiency is time-consuming and resource-intensive, requiring precise expertise.
Innovation Solution
A method using a 3D denoising-diffusion model to generate vehicular components with desired properties by iteratively estimating and removing noise from a 3D noise tensor, generating a 3D cellular structure optimized for mechanical, thermal, or electromagnetic performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If traditional design methods are used to develop cellular structures with tailored geometrics, then appropriate strength and mechanical performance can be achieved, but the design process becomes burdensome in terms of time, resources, and personnel requirements
Solution Approach 1:
The patent replaces traditional mechanical design methods with an AI-based denoising-diffusion model that generates cellular structures through probabilistic modeling. The system substitutes expert human judgment and iterative mechanical design processes with automated neural network-based generation, where the model learns from training data to produce optimized cellular geometries directly, eliminating time-consuming manual design cycles while maintaining mechanical performance requirements
Solution Approach 2:
The patent transforms the design problem by changing parameters from fixed geometric specifications to probabilistic distributions. Instead of designing specific cellular geometries through traditional methods, the system defines target properties (density, strength, compliance) as parameter distributions, allowing the AI model to generate multiple valid configurations that satisfy the specified parameter ranges, thereby reducing design time while achieving appropriate mechanical performance
2Strength
If traditional design methods are used to develop cellular structures with tailored geometrics, then appropriate mechanical properties can be achieved, but resource consumption and personnel requirements increase
Solution Approach 1:
The patent replaces complex human expert systems with an automated AI model. The denoising-diffusion neural network encapsulates design expertise within its trained parameters, eliminating the need for specialized personnel and reducing design complexity from a human-centric process to an automated computational process that requires only specification of target properties
Solution Approach 2:
The patent creates a universal design system that can handle multiple cellular structure design problems through a single AI model. The denoising-diffusion model is trained to generate cellular structures with various geometries and properties by adjusting input parameters, making the system multi-functional and adaptable to different design requirements without requiring separate specialized processes for each case
3Weight of moving object
If cellular structures are designed to reduce weight and material quantity, then performance efficiency improves, but achieving appropriate strength and mechanical properties becomes more challenging
Solution Approach 1:
The patent uses parameter changes by defining target mechanical properties (strength, compliance, density) as probabilistic distributions rather than fixed values. The AI model learns the relationship between cellular geometry parameters and mechanical properties from training data, enabling it to generate lightweight structures that satisfy minimum strength requirements by optimizing the distribution of material within the cellular architecture
Solution Approach 2:
The patent applies local quality by generating cellular structures with spatially varying properties. The denoising-diffusion model creates non-uniform cellular patterns where material distribution is optimized locally to achieve desired mechanical properties while minimizing overall weight. Different regions of the cellular structure have different cell sizes, shapes, and densities tailored to local loading conditions
Data Source
AI summary
Devices, systems, and methods related to design of structural portions of a vehicular component can include estimating noise within a 3D noise tensor via a 3D denoising-diffusion model and removing noise from the 3D noise tensor based on the estimated noise. Structural portions can be generated including a 3D cellular structure. Design of the vehicular component can be adapted based on the generated structural portion.