Research On Optimization Of Deburring Efficiency Of Industrial Robots Based On Machine Vision

Burr is unavoidably generated during the machining and manufacturing of mechanical parts.The existence of burr will have a negative impact on the appearance quality,machining accuracy,assembly accuracy and repositioning of the parts.Due to high processing costs and low processing efficiency,the manual deburring method can not meet the actual production needs.Compared with machining equipment such as CNC and machining centers,industrial robots are very suitable for complex trajectory automated operation such as deburring.However,the robots deburring industry in our country is in the initial stage,and the machining efficiency of robots deburing is relatively low.Therefore,it is imperative to study how to use industrial robots to remove burrs efficiently for complex materials and shape of the workpieces.When deburring using industrial robots,the processing efficiency of deburring should be improved in the following two aspects:on the one hand,it can improve the processing efficiency and processing quality during deburring process by optimizing the parameters;on the other hand,it can improve the efficiency by reasonably planning the deburring processing path.Based on this idea,this paper presents a method to optimize the efficiency of industrial robots deburring based on ant colony algorithm and machine vision.Firstly,the research background and significance of the paper are introduced.Research and application of industrial robots deburring,optimization of process parameters,deburring path planning and industrial robots based on machine vision are introduced.The main contents and framework of the paper are proposed.Secondly,the characteristics of burr are studied,and the information of deburring cutting force is analyzed.The optimization variables,objective function and constraint conditions of industrial robots deburring are determined,and the mathematic model of process parameters optimization of industrial robots deburring is established.The unified object method is used to transformed the multi-obj ective optimization problem into single-obj ective optimization problem.The penalty function method is used to convert the constrained optimization problem into unconstrained optimization problem.Then,ant colony algorithm is used to optimize the mathematical model of the processing parameters.Thirdly,in view of the characteristics that the burr shape is complex and difficult to be extracted information,the method of using machine vision to extract burr information is put forward.After the images taken by CCD camera are filtered,threshold segmented and edge detected,the position information of the burr in the image is obtained,and then the actual physical information of the burr is extracted according to the system calibration theory.After the burr information is obtained,the problem of deburring path planning is transformed into TSP problem.Then,the ant colony algorithm is used to solve the problem,and the global path planning method of robots deburring based on machine vision and ant colony algorithm is obtained.Finally,a industrial robots deburring experimental platform based on machine vision was built which including industrial robots and their control systems,machine vision detection systems and rigid motorized spindle tool systems.Aiming at the die castings of motor rotor shell,the efficiency optimization experiment of industrial robots deburring was carried out.The optimized parameters and path of the deburring process are compared with the traditional process parameters and path.The practicability and feasibility of the proposed method based on ant colony algorithm and machine vision for the optimization of robots deburring efficiency are verified.