Research On Cutting Tool Wear Condition Monitoring Based On Computer Vision

Cutting t ool c ondition monitoring b ased one omputer v ision h as a dvantage w ith which traditional monitoring methods aren't provided. In this paper, cutting tool condition monitoring method based on computer vision is researched. The research is composed of feature extraction of workpiece surface texture and segment of tool wear image, which are based on the model of Markov Random Field. Cutting tool condition monitoring based on computer vision has provided a new method for the development of TCMS. Each chapter research contents are as follows:The first chapter talks about the research significance and background. Some traditional tool condition monitoring methods are researched. The theory and advantage of Computer vision are expounded.The second chapter analyzes the characters of tool wearing surface and the factors effecting workpiece surface texture. A reasonable experiment scheme is designed. Based on the experiment datum, the rationality of the selection about cutting parameters is analyzed.The third chapter gives the brief expatiation about the concept of texture. The features of workpiece surface texture are extracted with gray co-occurrence matrix and the disadvantage of this method is pointed out.The fourth chapter introduces the theory of Fractional Brown Movement (FBM). The image texture of turning workpiece is analyzed using FBM model. After abstracting the texture character, the cutting tool wear status can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum.Compared with the experimental facts, the result of this method is satisfying.The fifth chapter introduces the theory of Markov Random Field. Markov Random Field texture model is established to analyze workpiece surface texture image. Two parameter estimation methods of the texture model based on the Gibbs distribution and neighbor pixels are adopted to extract the features of workpiece surface texture. Tool wear condition is recognized by the feature parameters of texture. The experiment datum indicates that the feature parameters of MRF model have the rotative invariability for the texture image with small angle rotation.* The sixth chapter presents a tool wear area image segmentation algorithm based on Markov Random Field m odel. The refresh formula of relaxation labeling is deduced. With the algorithm, the segmentation result composed of wear area, background area and tool body area is obtained according to the MAP(maximum a posterior) criterion. By using peak-band algorithm, the aim area (B area) is segmented from the wear area image. Aiming at the edge noise of image, mathematical morphology theory is applied to integrate edge. Test results show that the segmentation algorithm is effective in identifying tool wear degree.The seventh chapter researches the application of tool condition monitoring technology based on computer vision in the turning monitoring. First, the basic structure of tool condition monitoring system and the monitoring approach of tool wear status are presented. Secondly, based on the obtained data of cutting experiment, several texture analysis techniques are used to analyze workpiece surface texture. Lastly, the image segmentation model based on MRF is applied to acquire the wear area of flank face of tool and the segmentation result is analyzed.The eighth chapter summarizes the main fruits and prospects the future of the research.