ML-Based Physical Component Generation for Specification Compliance

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Solution Overview

Problem

The process of designing and creating physical components is often manual, time-consuming, and inefficient, requiring engineers to manually verify specifications and ensure compatibility with other components.

Innovation Solution

A component generation system utilizing machine learning models, such as conditional normalizing flow and diffusion denoising probabilistic models, to automatically generate physical components based on user-provided specifications, images, and mesh data, streamlining the design process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual design process is used, then design flexibility and control are maintained, but time consumption and efficiency deteriorate

Engineering Contradiction:

Improvecomponent generation speedVSAvoidmanual verification time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The generated physical component automatically verifies its own specifications and compatibility through the machine learning model, eliminating the need for manual verification by engineers. The system self-validates that the component meets all constraints and interfaces correctly with other components.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical design and verification process is replaced with an automated machine learning system that generates components and validates their specifications programmatically, dramatically reducing time consumption while maintaining design quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated generation is implemented, then productivity and speed are improved, but system complexity increases

Engineering Contradiction:

Improvecomponent generation efficiencyVSAvoidmachine learning system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model serves multiple functions simultaneously: it generates physical components, validates their specifications, ensures compatibility with other components, and optimizes design parameters. This multi-functionality reduces the need for separate systems for each task.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Manufacturing precision

If manual design verification is performed, then specification accuracy is ensured, but time consumption increases

Engineering Contradiction:

Improvespecification complianceVSAvoiddesign throughput

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The machine learning model incorporates feedback loops that continuously validate generated components against specified constraints and compatibility requirements. The system iteratively refines component designs until all specifications are met, ensuring high precision while maintaining automated efficiency.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250391113A1Generating physical components based on machine learning models

Publication Date: 2025.12.25 VOLKSWAGEN AG

AI summary

A method, apparatus and system are provided to generate and/or design physical components. A complex Gaussian distribution is generated based on a set of specifications for a physical component, a set of images of the physical component, and a set of meshes for the physical component. A randomly generated point cloud is obtained. A component point cloud is generated based on the complex Gaussian distribution, the randomly generated point cloud, and a diffusion denoising model. The component point cloud represents the physical component.