研究目的
To address the need for designing, programming, analyzing, testing, and verifying machine vision applications early in the design phase using a Virtual Machine Vision concept to reduce commissioning time and improve automation in aero engine manufacturing.
研究成果
The VMV concept was successfully implemented and validated, showing that machine vision systems can be designed and tested early in the development phase using simulation. The same programs used in the virtual environment were applicable in the real world, with accuracy comparable to physical systems (e.g., maximum deviation of 0.7 mm). This approach reduces commissioning time and allows for faster production start-up, emphasizing the potential for industrial use in aero engine manufacturing.
研究不足
The simulation environment is less detailed than some other systems, potentially limiting accuracy in modeling physics of vision like shadows and reflections. It may not fully replicate real-world ambient conditions such as light and material properties. The system is not real-time due to computational demands, and validated camera models for industrial cameras are not commercially available.
1:Experimental Design and Method Selection:
The study uses a Virtual Machine Vision (VMV) concept integrating a real vision software with a commercial Computer Aided Robotics (CAR) software. It involves simulating a machine vision system in a virtual environment before physical implementation.
2:Sample Selection and Data Sources:
A demonstrator setup is modeled using CAD software and a 3D scan of a real component. Images are captured from the simulated environment.
3:List of Experimental Equipment and Materials:
Includes a robot simulation software (e.g., RobotStudio from ABB), MATLAB with Image Processing Toolbox for vision software, virtual cameras modeled as pinhole cameras, and communication protocols like RS
4:Experimental Procedures and Operational Workflow:
2 Steps include modeling the production system cell, programming devices, testing for collisions and reach, translating to robot-specific language, and uploading to a physical robot controller. Virtual images are captured, processed using Canny edge detection, camera calibration is performed with a checkerboard pattern, and stereo vision algorithms are applied to determine a 3D weld joint.
5:Data Analysis Methods:
Data is analyzed using optimization for camera calibration (least squares error criterion), image processing algorithms in MATLAB, and comparison of virtual and real system results for accuracy.
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