| Computerized flat knitting machine is one of the most important types of knitting machinery,and the flatness of its needle bed base is a key factor to ensure the quality of the flat knitting machine and the knitting efficiency.At present,textile enterprises generally use contact measurement to test the flatness of the needle bed base,the workload is heavy and complex,and can only be distinguished by the naked eye,the measurement accuracy cannot be guaranteed.With the development of 3D measurement technology,laser scanning equipment has been widely used in various fields.Non-contact measurement based on laser scanning can efficiently and accurately measure the measured plane.At the same time,the obtained laser point cloud data contains the plane measured,and the three-dimensional coordinate information of the plane is convenient for the calculation of the flatness error.In view of the above problems,this thesis takes the three-dimensional laser scanning point cloud data of the needle bed base as the processing object,and studies the flatness error detection method of the needle bed base.The main works are as follows:(1)A flatness error detection method based on point cloud optimization and fitting is proposed.This method firstly performs preprocessing such as simplification and denoising on the point cloud data collected by the 3D laser profile sensor to obtain high-precision point cloud raw data.Based on the constraint of normal vector angle difference,a point cloud optimization fitting algorithm based on RANSAC is designed to accurately restore the complete topography of the needle bed base plane,and use the least square method to evaluate the plane error.The simulation and actual point cloud data are used for verification.The experimental results show that the algorithm not only has good fitting stability,but also has a higher accuracy than the traditional RANSAC algorithm for the topography restoration of the base frame of the needle bed,and can accurately detect the flatness error of the needle bed base.(2)A computerized flat knitting machine needle bed base plane intelligent detection system is developed a computerized flat knitting machine needle bed base plane intelligent detection system based on point cloud optimization and fitting.According to the actual needs,the overall plan of the intelligent detection system for the plane of the needle bed base is formulated,the hardware platform is built,and the corresponding software is developed.The system is mainly divided into point cloud data processing module,visual motion processing module and human-computer interaction module.Among them,the point cloud data processing module adopts the flatness detection algorithm based on point cloud optimization and fitting proposed in this paper to detect the plane of the needle bed base frame and guide rail in real time.The stability and real-time performance of the detection system are tested in the actual environment.The results show that the detection system developed in this thesis can detect and calculate the flatness error of the needle bed base frame and guide rail,and the average defective detection rate of the frame is 99.32%,the average defective detection rate of the guide rail is 98.87%,and the defect area can be located at the same time,which meets the high-precision and high-efficiency detection requirements of textile enterprises for computerized flat knitting equipment. |
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