3D Object Deformation Assessment via Model Alignment
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Solution Overview
Problem
In 3D manufacturing, predicting and inferring object deformation during the manufacturing process is challenging due to thermal diffusion, uneven heating, and manufacturing errors, especially in high-volume production environments where manual assessment of deformation is impractical.
Innovation Solution
The use of machine learning models, such as deep neural networks, to predict object deformation by aligning 3D object models with scanned point clouds and calculating deformation metrics, including mean, median, and standard deviation, to assess manufacturing accuracy and detect deviations from tolerated accuracy levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of deformation is used, then measurement precision can be maintained, but productivity decreases significantly in high-volume production environments
Solution Approach 1:
The patent replaces manual mechanical measurement with automated computer vision systems. The system captures images of manufactured objects, processes them through image processing algorithms, and automatically determines deformation metrics without human intervention, thereby maintaining precision while dramatically increasing productivity.
Solution Approach 2:
The system creates digital copies (images) of the physical objects and analyzes these copies to determine deformation. By working with image data rather than direct physical measurement, the system can rapidly assess multiple objects without the time constraints of manual measurement, resolving the contradiction between precision and productivity.
2Productivity
If automated assessment systems are implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The system uses the manufactured objects themselves as part of the measurement process. By capturing images of the objects in their final position and using their visual characteristics for deformation assessment, the system eliminates the need for separate complex measurement fixtures or specialized equipment, reducing overall system complexity while maintaining automation.
Solution Approach 2:
The image processing system serves multiple functions: it captures object geometry, determines deformation, and provides assessment data all through a single unified system. This multi-functionality reduces the need for separate specialized equipment, thereby reducing device complexity while maintaining high productivity.
3Manufacturing precision
If thermal energy is used to fuse material, then manufacturing precision can be achieved, but object deformation occurs due to thermal diffusion and uneven heating
Solution Approach 1:
The system captures images of the manufactured objects and automatically analyzes their geometry to detect deformation. This feedback loop allows the system to identify thermal-induced geometric changes and provide data for process optimization, enabling compensation for thermal diffusion and uneven heating effects.
Solution Approach 2:
The system determines deformation metrics before final use by analyzing images captured during or after manufacturing. This preliminary assessment allows for identification and compensation of thermal effects on geometry, enabling corrective actions to be taken while the object is still controllable, thereby maintaining manufacturing precision despite thermal challenges.
Data Source
AI summary
Examples of methods for object deformation determination are described herein. In some examples, a method includes aligning a first bounding box of a three-dimensional (3D) object model with a second bounding box of a scan. In some examples, the method includes determining a deformation between the 3D object model and the scan based on the alignment.