CAD Model Update via Point Cloud Density Clustering
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Solution Overview
Problem
Current CAD systems face challenges in accurately updating 3D models of complex environments, such as process facilities, due to frequent changes and incompatibilities between 3D and 2D representations, leading to inefficiencies and potential catastrophic issues from outdated designs.
Innovation Solution
A method that generates a point cloud from received signals, identifies clusters based on density, maps these clusters to existing CAD diagrams, and automatically updates the CAD model, allowing for spatial and mechanical changes to be reflected, using a system with a processor, emitter, and receiver to emit and receive signals, and a digital processing module to process the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual updating of CAD models is used, then accuracy can be maintained, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system enables self-service by automatically updating CAD models using point cloud data from laser scanning. The automated process includes: (1) capturing current environment data with laser scanner, (2) processing points to identify objects and their positions, (3) automatically updating the CAD model without manual intervention. This eliminates the need for manual measurement and redrawing, thereby maintaining accuracy while significantly reducing update time.
Solution Approach 2:
The patent replaces manual mechanical processes with automated digital processing. Instead of manual measurement and model updating, the system uses laser scanning to capture spatial data, processes points through computational algorithms, and automatically regenerates or updates the CAD model. This substitution of mechanical manual work with automated digital systems resolves the contradiction between accuracy and time consumption.
2Manufacturing precision
If 3D CAD models are used for complex environments, then detailed representation is achieved, but incompatibility between 3D and 2D representations causes errors
Solution Approach 1:
The system implements feedback by continuously comparing the updated 3D CAD model with the actual environment captured by laser scanning. The process captures current spatial data, processes points to identify objects, and updates the model accordingly. This feedback loop ensures that the 3D model remains consistent with the actual environment, resolving incompatibility issues between 3D representations and real-world objects.
Solution Approach 2:
The patent applies preliminary action by capturing the current environment state before making any updates. The laser scanner captures spatial data of the actual environment, which serves as the basis for subsequent processing and model updates. This preliminary capture ensures that all updates are based on accurate, current information, preventing inconsistencies between 3D models and reality.
3Adaptability or versatility
If frequent updates of CAD models are performed, then the model reflects current environment changes, but the complexity of managing updates increases
Solution Approach 1:
The system enables self-service by automating the entire update process. The automated process includes: (1) laser scanning to capture current environment, (2) processing points to automatically identify objects and positions, (3) automatic model regeneration or updating. This eliminates manual intervention in update management, allowing frequent updates while reducing the complexity of managing these updates through automation.
Solution Approach 2:
The patent utilizes parameter changes by transforming the environment representation from manual 2D drawings to automated 3D point cloud data. The system processes spatial parameters from laser scanning directly into updated CAD models, changing the fundamental parameters of how environmental data is captured and stored. This parameter transformation simplifies update management by providing a unified, automated workflow.
4Productivity
If automated point cloud processing is used, then update efficiency increases, but the complexity of signal processing and data processing increases
Solution Approach 1:
The system applies segmentation by dividing the complex processing task into distinct manageable stages: (1) signal capture by laser scanner, (2) point cloud generation, (3) point processing to identify objects and positions, (4) model updating. This segmentation reduces the perceived complexity by breaking down the automated processing into clear, sequential steps, while maintaining high productivity through automation.
Solution Approach 2:
The patent uses an intermediary approach by introducing a dedicated processing module that acts as a mediator between the laser scanner and the CAD model. This intermediary process handles the complex signal processing and point cloud analysis, isolating the complexity from the user perspective. The intermediary layer transforms raw scan data into updated model information automatically, increasing productivity while managing complexity through a dedicated processing layer.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate updating of CAD models, reducing the need for manual intervention and re-engineering, while providing interactive views and metadata, thus improving facility maintenance and operations by maintaining up-to-date models of complex environments.
Implementation Method 1
generating a point cloud in computer memory, the point cloud representing one or more objects of an environment based on received signals, where the received signals have reflected off one or more real-world objects of an environment
Data Source
AI summary
An embodiment of the present invention provides a method of updating a CAD model representing an environment. Such an embodiment begins by generating a point cloud representing one or more objects of an environment based on received signals, where the received signals reflected off the one or more objects of the environment. Next, one or more clusters of the point cloud are identified based on a density of points that includes the one or more clusters. In turn, the one or more clusters are mapped to existing CAD diagrams and a CAD model of the environment is automatically updated using the existing CAD diagrams.