3D Modeling System for Custom Fit Accuracy
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Solution Overview
Problem
Current 3D modeling methods for creating custom-fit models are time-consuming, error-prone, and require expensive human resources, with low accuracy and the need for individual design of each model, leading to variations in accuracy due to human error.
Innovation Solution
A system and method for generating 3D models by acquiring digital representations of objects, determining geometric design definitions, and computing these definitions to create models that fit specific objects, with iterative improvement using learning modules to enhance fit accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If individual 3D models are designed manually for each product using 3D CAD modeling, then custom fit accuracy can be achieved, but the process becomes time-consuming and requires expensive human resources
Solution Approach 1:
The patent replaces manual 3D CAD modeling with an automated computational system that uses machine learning algorithms to generate 3D models from point cloud data. The system automatically performs coordinate system alignment, parameter extraction, and model generation without human intervention, eliminating the need for manual drafting while maintaining high accuracy through iterative validation against fit accuracy parameters.
Solution Approach 2:
The system enables self-service 3D modeling where the computational algorithm autonomously processes point cloud data, aligns coordinate systems, extracts geometric parameters, and generates customized 3D models. The machine learning model continuously improves through iterative validation, allowing the system to serve itself without requiring expert human operators for each modeling task.
2Productivity
If computational methods are used to create 3D models from measurements, then the process becomes faster and more automated, but the accuracy decreases due to measurement limitations
Solution Approach 1:
The patent implements iterative validation where generated 3D models are continuously compared against fit accuracy parameters and measurement data. The machine learning model receives feedback from validation results and adjusts its parameters to improve accuracy. This closed-loop feedback system allows the computational method to achieve high accuracy while maintaining fast automated processing speeds.
Solution Approach 2:
The system performs preliminary coordinate system alignment and parameter extraction from point cloud data before final model generation. By pre-processing the data to establish accurate reference frames and extract key geometric features beforehand, the system ensures that subsequent computational modeling starts with high-quality input data, thereby improving final model accuracy without sacrificing processing speed.
3Adaptability or versatility
If manual 3D CAD modeling is used, then design flexibility and adaptability are maintained, but human error increases variations in model accuracy
Solution Approach 1:
The patent uses machine learning models that can dynamically adjust parameters based on input point cloud data characteristics. The system changes geometric parameters, coordinate system orientations, and model generation parameters automatically according to the specific features of each object being modeled. This parameter adaptation maintains design flexibility while eliminating human error through automated, consistent processing.
Solution Approach 2:
The patent replaces human operators with an automated computational system that eliminates variability introduced by human error. The machine learning model consistently applies the same algorithms and validation criteria to every modeling task, ensuring uniform accuracy standards across all generated models while maintaining adaptability through data-driven parameter adjustment.
4Manufacturing precision
If expensive human resources are allocated for individual model design, then high accuracy can be achieved, but the cost and complexity of the system increase
Solution Approach 1:
The patent creates a universal machine learning-based system that can handle multiple types of 3D modeling tasks with a single platform. The system processes various object types, coordinate systems, and model requirements through standardized algorithms, eliminating the need for multiple specialized human experts. This multi-functional approach maintains high accuracy across diverse applications while reducing system complexity through automation.
Solution Approach 2:
The patent replaces expensive human expertise with an automated machine learning system that encapsulates complex modeling knowledge in algorithms. The system performs coordinate alignment, parameter extraction, and model generation automatically, converting human expertise into reusable computational processes. This substitution reduces system complexity by eliminating the need for trained human operators while maintaining or improving accuracy through consistent algorithmic application.
Data Source
AI summary
Systems and methods for creating a geometric design definition for 3D models designed to fit physical or digital template objects is disclosed. The 3D models can transform to fit specific physical or digital objects which are different from but topologically isomorphic to the original template objects based on visual or mathematical inputs. To validate the fit, the generated 3D model can be compared with the specific physical or digital objects for which the 3D model is generated to fit, and the geometry of generated 3D model can be adjusted to improve the fit if the generated 3D model is not validated. The accuracy of the fit of the generated 3D models can be improved iteratively.