The invention relates to a tool state detection method based on image processing. The method comprises the following steps of acquiring a surface state picture of a tool after each cutting in real time through image acquisition equipment arranged in a spare area near a main shaft on a side wall of a machine tool; implementing state detection and classification of the tool through a tool state detection model established by a convolutional neural network on the basis of regional cutting, grayscale processing, denoising and signal enhancement of the picture; and transmitting a prediction resultto a relevant macro variable of a CNC system through a TCP/IP protocol, so as to be used for the control of a CNC machining procedure or the control of a tool change procedure. According to the tool state detection method, a tool image is acquired through a simple image acquisition equipment microscope, so that the wear state can be detected without detaching the tool; high detection speed is achieved; the machining process is not influenced; and the production cost is lowered. The wear state of the tool is detected by using a convolutional neural network technology, so that the detected wearstate conforms to the judgment based on actual worker experience, and the obtained result is similar to human judgment.

Tool wear state automatic detection method based on image processing

The invention discloses a size sorting system which comprises a controller The controller is connected with a camera and a grabbing mechanism The size sorting system further comprises a plurality ofstorage baskets The size sorting system is simple in structure and capable of directly conducting judgment according to the sizes of objects to be sorted and sorting the objects The invention further discloses a size sorting method A mature size neural network is established to accurately correspond to the return path of the grabbing mechanism Objects of different sizes are stored in different storage baskets, and manpower is greatly saved

Size sorting system and method

The invention discloses a color sorting system which comprises a controller. The controller is connected with a camera and a grabbing mechanism. The color sorting system further comprises a pluralityof storage baskets. The structure is simple. Judgment can be directly conducted according to the surface color RGB value of an object to be sorted. The object is sorted, and the sorting efficiency andaccuracy are high. The invention further discloses a color sorting method. A mature color neural network is established to accurately correspond to the return path of the grabbing mechanism. Objectsof different colors are stored in different storage baskets. Manpower is greatly saved.

Color sorting system and color sorting method

The invention discloses a shape sorting system which comprises a controller. The controller is respectively connected with a camera and a grabbing mechanism, and the shape sorting system also comprises a plurality of containing baskets. The shape sorting system is simple in structure, judgment is performed directly according to the shapes of to-be-sorted objects, and the objects are sorted. The invention also discloses a shape sorting method. By establishing a mature shape neural network to accurately correspond to a return path of the grabbing mechanism, the objects with different shapes areaccommodated in the different containing baskets, and manpower is greatly saved.

Shape sorting system and method

The invention provides a material distribution decision-making method and device based on a machine learning model and a storage medium, and relates to the field of cloud computing. The method comprises the following steps of carrying out multi-angle shooting on a to-be-distributed component to obtain a plurality of images of the to-be-distributed component; determining at least one defect type and the confidence corresponding to the at least one defect type existing in the to-be-distributed part according to the plurality of images; and taking the at least one defect type existing in the to-be-distributed part and the confidence coefficient corresponding to the at least one defect type as the input parameters of the machine learning model to determine a material distribution decision result corresponding to the to-be-distributed part. According to the embodiment of the invention, the detection and material distribution efficiency is improved, and the product parts can be comprehensively detected.

Material distribution decision-making method and device based on machine learning model and storage medium

The invention discloses a micro part quality detection system based on a convolutional neural network. The micro part quality detection system comprises: A, collecting the surface image information ofa micro part by an image acquisition module formed by microscopic vision; B, detecting the image collected by the microscopic vision by using a convolutional neural network model, and classifying thedetected defect images; C, transmitting the classifying result into a main controller, and sending a control signal to a terminal actuator; and D, carrying out picking and classifying on the corresponding micro part by the terminal mechanical arm actuator according to the control signal transmitted by the controller so as to convey the part into the corresponding receiving box, such that the whole system completes the detection and defect classification on the surface quality of the micro part. According to the present invention, the system can effectively used for the detection of micro parts, and can improve the automation degree and the efficiency of detection, and reduce the influence of human factors on the detection process and the labor intensity of workers.

Liu Cong

Papers

Micro part quality detection system based on convolutional neural network