3D CAD to 2D Shaded Contour Rendering With Neural Mapping
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Solution Overview
Problem
Current methods for creating contour and surface shading renderings in CAD models are time-consuming, prone to human error, and lack accuracy, leading to inefficiencies in product design documentation.
Innovation Solution
A system utilizing neural networks, including CNNs, U-Nets, and GANs, to transform 3D CAD models into 2D shaded contour renderings in real-time, ensuring high accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual drafting is used to create contour and surface shading renderings, then the process allows for human control and adjustment, but it is time-consuming and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical drafting process with an automated computational system that uses machine learning models to generate contour and surface shading renderings. This substitution eliminates human error and significantly reduces the time required while maintaining high accuracy through algorithmic precision and automated feature extraction from CAD models.
Solution Approach 2:
The system enables self-service rendering by automatically processing CAD models through trained predictive models to generate shaded contour renderings without human intervention. The automated pipeline extracts features, generates renderings, and outputs results independently, eliminating the need for manual drafting while ensuring consistent, error-free results.
2Productivity
If algorithmic approaches are used to generate renderings, then time and resources are reduced, but accuracy and predictability are compromised
Solution Approach 1:
The patent applies preliminary action by training predictive models in advance on extensive datasets of CAD models and their corresponding shaded contour renderings. This pre-training enables the system to rapidly generate accurate renderings in real-time without compromising precision, as the complex computational work of learning feature mappings is completed beforehand during the training phase.
Solution Approach 2:
The system changes parameters by using trained machine learning models with optimized weights and biases that were tuned during training to achieve high accuracy. These parameter adjustments allow the algorithm to maintain precision while operating at high speed during inference, resolving the contradiction between rendering speed and accuracy.
3Productivity
If frequent renderings are performed using current methods, then design documentation is updated regularly, but significant time and resources are invested
Solution Approach 1:
The patent replaces manual rendering processes with automated machine learning-based rendering that can be executed frequently without proportionally increasing time investment. The automated system processes CAD models rapidly and consistently, enabling high-frequency rendering updates while reducing total time investment through elimination of manual labor and optimization of computational efficiency.
Data Source
AI summary
The embodiments describe herein relate to a system for real-time transformation of 3D models to 2D shaded contour renderings. The system comprises a processor in communication with a memory. The memory storing executable instructions that when executed by the processor configure the system for receiving, a 3D model input corresponding to a physical object, and generating, based on the 3D model input, a data structure including one or more features of the physical object, and one or more 2D renderings of the physical object. The processor further configures the system for correlating, the one or more features with the one or more 2D renderings of the physical object and determining, based on the one or more features, a 2D shaded contour rendering of the physical object. The system is configured for transmitting, to a display device the 2D shaded contour rendering of the physical object.