Research And Implementation Of A Vision-based Chip Capacitance Detection And Sorting System

As one of the electronic components,chip capacitors have the characteristics of large output,wide variety,and small size.Capacitors need to be classified and counted before leaving the factory,and defective products are eliminated,so as to facilitate subsequent management and use.The use of traditional manual inspection methods has problems such as errors in visual recognition of micro-sized originals,resulting in low work efficiency and high inspection costs,making it difficult to meet the actual needs of modern industrial production.And machine vision technology has the advantages of high accuracy,high efficiency,non-contact,etc.Therefore,the combination of machine vision and robot technology for the detection and sorting of micro components such as chip capacitors can effectively solve the traditional detection methods.problem.The vision-based chip capacitor detection and sorting technology proposed in this paper is mainly to complete the series of operations such as chip capacitor rejection,target detection and positioning,grabbing and placing,so as to realize the automation process of industrial production and greatly improve industrial production efficiency..The main work content of this thesis for chip capacitance detection has the following aspects:(1)Due to the small size of the chip capacitor to be tested(1.0mm±0.5mm),this places high requirements on the positioning accuracy of the vision guidance system,and also brings difficulty to the detection of defects on the surface of the chip capacitor.The accuracy of visual inspection is mainly controlled and optimized from the image processing algorithm and the hardware equipment of the image acquisition system.According to the characteristics of the chip capacitors to be tested and the requirements of the system’s grasping accuracy,the selection of the main hardware equipment such as the light source,camera,and lens of the vision system has been completed through comparative analysis of related hardware facilities and experiments.(2)The defect detection method of chip capacitor is studied.Mainly through image preprocessing,including image filtering,image segmentation and morphological processing.Then use feature extraction to calculate the rectangularity of the chip capacitor to determine whether it is a missing corner;finally,dynamic threshold segmentation combined with morphological methods is used to detect surface scratches,and the scratch defects are extracted and counted.(3)The principle and main methods of camera calibration are studied.First understand the conversion relationship between the small hole imaging model and each coordinate system under the model.Then the current camera calibration methods are compared and analyzed.The nine-point calibration method is finally selected in this paper,and the accuracy test of camera calibration is carried out through experiments.(4)The communication between workpiece target positioning and system modules is studied.First of all,to realize the grasping of the workpiece,the position information of the workpiece needs to be obtained.The workpiece positioning adopts the Blob analysis algorithm,which mainly includes image segmentation,morphological processing,connectivity analysis,feature value calculation and other steps,and uses the smallest bounding rectangle to get The centroid coordinates of the workpiece are used as the key point of the robotic arm grasping;secondly,the communication between the various modules and the robotic arm control are studied to complete the detection and grasping actions of the entire system.The premise is to realize the image acquisition module and image processing module.Communication with the motion actuator and control of the robotic arm.