Virtual Kinematic Joint Detection From Point Cloud Link Data
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Solution Overview
Problem
Current CAD and PDM systems require manual and time-consuming processes for defining kinematics in virtual kinematic devices, especially for manufacturing plants with numerous kinematic devices.
Innovation Solution
The implementation of a method that uses Machine Learning algorithms to analyze point cloud representations of kinematic device links, automatically identifying joint types and descriptors, thereby streamlining the kinematic definition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual kinematic definition is used for virtual devices, then kinematic capabilities can be defined accurately, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of kinematic definition with an automated computer-based system. The system automatically detects links and joints in 3D CAD models and generates kinematic definitions without requiring manual intervention, thereby maintaining accuracy while dramatically reducing the time required for kinematic capability identification.
Solution Approach 2:
The system enables the virtual device models to define their own kinematic capabilities automatically. By analyzing the geometric features and constraints within the 3D CAD models, the system allows the devices to self-identify their links and joints, eliminating the need for external manual definition while preserving kinematic accuracy.
2Reliability
If simulation engineers manually model kinematics for each device, then accurate kinematic chains are created, but the process requires experienced users and consumes precious time
Solution Approach 1:
The patent replaces the expert manual modeling process with an automated algorithmic system. The system uses computer-based analysis to detect kinematic features in 3D CAD models and generate accurate kinematic chains automatically, eliminating the need for experienced simulation engineers while maintaining model reliability and significantly increasing productivity.
Solution Approach 2:
The system creates accurate copies of the physical device's kinematic behavior by analyzing the 3D CAD model geometry. By copying the geometric constraints and relationships from the digital model, the system generates reliable kinematic definitions without requiring manual interpretation or expert knowledge.
3Adaptability or versatility
If 3D device libraries are used, then device availability is improved, but most models lack kinematics definition requiring manual addition
Solution Approach 1:
The patent replaces the manual process of adding kinematics to library devices with an automated detection system. The system automatically analyzes the 3D models in the device library, identifies kinematic features, and generates the necessary kinematic definitions, thereby maintaining broad device library coverage while eliminating the complexity of manual kinematic addition.
Solution Approach 2:
The system performs preliminary kinematic analysis on 3D device library models during the modeling phase. By pre-detecting and defining kinematic capabilities before the devices are used in simulations, the system eliminates the need for subsequent manual kinematic definition, thereby maintaining device library versatility while reducing overall process complexity.
Data Source
AI summary
Systems and a method for determining a joint in a virtual kinematic device. Input data are received which contain data on two point cloud representations of two given links of a given virtual kinematic device and data on the specific joint type associated with the two links. A specific joint descriptor analyzer is applied to the input data. The specific joint descriptor analyzer is modeled with a function trained by a machine learning (ML) algorithm and the specific joint descriptor analyzer generates output data. The output data contains specific joint descriptor data for determining the mutual motion capabilities of the specific joint type associated with the two given links. From the output data, at least one joint is determined in the virtual kinematic device.