Missing Connection Element Detection in Vehicle Digital Twins via 2D Rendering
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Solution Overview
Problem
Existing methods for detecting missing connecting elements in 3D vehicle geometry data of digital twins are inefficient, time-consuming, and prone to errors due to manual inspection and subjective expertise, leading to delays and inaccuracies in physical vehicle assembly.
Innovation Solution
A method using a trained detection model for image recognition on 2D images generated from 3D vehicle geometry data to automatically detect missing connecting elements, with backprojection to determine their 3D positions, and subsequent correction of the digital twin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection by experts is used to detect missing connecting elements, then detection can be performed with human judgment and experience, but the process is time-consuming and expensive
Solution Approach 1:
The patent replaces manual visual inspection by experts with an automated computer-based system that uses rendering engines to generate 2D images from 3D digital twin data and applies image recognition algorithms to detect missing connecting elements. This substitution eliminates human time constraints while maintaining high detection accuracy through systematic automated analysis of all connecting element counterparts in the digital twin.
Solution Approach 2:
The patent creates a digital copy (2D rendered image) of the 3D digital twin geometry data to enable automated analysis. By rendering the 3D model from specific camera positions and angles, the system generates 2D images that can be processed by image recognition algorithms, allowing rapid detection without manually examining the complex 3D geometry.
2Reliability
If manual inspection is used, then expert experience can be applied, but the quality of inspection fluctuates due to lack of experience or concentration
Solution Approach 1:
The patent replaces variable human performance with a consistent automated system. The computer-based image recognition process applies the same detection algorithms and criteria to every inspection case, eliminating fluctuations in inspection quality that occur with manual review. The system maintains consistent attention and expertise levels across all digital twin inspections.
Solution Approach 2:
The patent transforms the inspection process from subjective human judgment to objective parameter-based detection. By defining specific detection parameters such as connecting element counterpart identification, occupancy status determination, and systematic search criteria, the system achieves consistent, repeatable results that do not depend on individual expert variability.
3Reliability
If random checking for missing fasteners is performed, then some errors can be found, but problem areas are inevitably overlooked
Solution Approach 1:
The patent segments the digital twin into individual vehicle parts and further into specific connecting element counterparts. This systematic segmentation allows the automated system to methodically examine each potential connecting element location, ensuring complete coverage without missing problem areas. Each connecting element counterpart is individually identified and checked for occupancy status.
Solution Approach 2:
The patent performs preliminary identification of all connecting element counterparts during the rendering and image generation phase. By pre-processing the 3D digital twin data to locate and mark all potential connecting element positions before the actual detection algorithm runs, the system ensures comprehensive coverage of all areas that require inspection, eliminating random oversight.
4Measurement precision
If 3D digital twin data is directly analyzed, then accurate spatial information is available, but image recognition models work better with 2D images
Solution Approach 1:
The patent creates 2D rendered copies of the 3D digital twin from strategically selected camera positions and angles. These 2D images serve as input for the image recognition model while preserving the spatial relationships and geometric features needed for accurate detection. The rendering process maintains precision by using the same 3D geometry data as the source.
Solution Approach 2:
The patent transforms the 3D digital twin geometry data into 2D rendered images, changing the dimensional representation to match the input requirements of the image recognition model. This dimensionality transformation enables the use of成熟的 image processing algorithms while the backprojection process later recovers the 3D spatial positions of detected connecting elements.
Data Source
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AI summary
The invention relates to a method and a device (100) for detecting missing connecting elements (40') at connecting element counterparts (22, 32) of a digital twin (10) to be examined for the physical structure of a vehicle. One or more 2D images for a vehicle part (20) are generated (S200) from one or more spatial directions of the digital twin (10) by rendering digital 3D vehicle geometry data. The rendered 2D image is input into a trained detection model (130) trained to detect unoccupied connecting element counterparts (22').The method further allows the detection (S500) of the absence of a connecting element (40') at a 3D location of the digital twin (10) for use in the construction of the vehicle based on the detection (S400) of a missing connecting element (40') in the input 2D image by the trained detection model (130), the digital 3D vehicle geometry data, the spatial direction and the detected position in the 2D image.