Study On Wear Failure Of Adapter Based On Morphology Change

With the development of railway freight transport,the safety and reliability of railway freight transport has become research hotspot.It has always been the most concerned problem for railway operation and maintenance department to maximize the economic benefits of vehicles in the life span with the minimum maintenance cost.At present,the main maintenance to adapter in railway departments is still manual periodic maintenance,with high work intensity and low efficiency,the test results only record whether it is qualified or not,without recording the specific wear value,so the health state of the adapter cannot be accurately evaluated,nor can it fit the concept of sustainable development.In this paper,the adapter of railway freight is taken as the research object,combined with the maintenance standard of the adapter and the actual demand of railway maintenance departments,an automatic testing system is built to collect and measure the wear of the adapter.A wear degradation model of adapter is established based on Archard wear model,and detection and life prediction system of adapter is developed.The specific research work of this paper is as follows:(1)A point cloud data acquisition system of adapter based on robot control system and 3D scanning technology is designed to realize high-speed automatic point cloud acquisition of adapter.To solve the point cloud data of adapter is hard to collect,according to the actual conditions,set up a non-contact point cloud data acquisition system of adapter using the mechanical system,electrical control system,robot control system and the visual system.It greatly reduces the influence of artificial factors,improves the efficiency of high point cloud acquisition,and reduces the secondary wear in the acquisition process.(2)The data processing and wear measurement analysis of adapter’s point cloud are completed.Firstly,topological relationship is established for the disordered point cloud collected by 3D scanner based on KD-Tree algorithm.Secondly,data processing methods to point cloud such as point cloud filtering,point cloud segmentation and point cloud registration are studied and compared.Gaussian filtering,voxel filtering,ICP point cloud registration and region growth algorithm are used to complete the preprocessing of point cloud data and reduce the impact of noise and background in point cloud data.Then,based on the function of point cloud measuring software,measure and analyze the wear and partial wear of the top surface,the wear of the inner adapter face,the wear of the inner side of the guide frame rim,the wear of the bottom surface of the guide frame and the wear of the thrust shoulder as stipulated in the Railway Section Repair Standard.Compared with the load-bearing saddle data collected by the system,it is found that the top surface bias wear and the inner adapter wear are the most prone parts of adapter;Finally,the precision of point cloud measurement is tested,and the results show that the precision of this paper is high and can meet the requirements of adapter.(3)A wear degradation analysis method of adapter based on Archard wear model and finite element analysis is proposed.Firstly,the cause and development of adapter wear are studied.Then,based on the Archard wear model,the degradation relationship between the wear depth and the sliding distance is calculated by using the discrete calculation method.Finally,the degradation model is transformed into the relationship between wear depth and mileage based on the actual load-bearing saddle data,and is applied to the wear prediction of adapter.(4)The adapter detection and life prediction system is developed.Integrating point cloud data acquisition system of adapter and life prediction model,then completing system software development through QT and My SQL database,the system with automatic detection,analysis,life prediction,abnormal alarm,data storage query and other functions,and realizing automation and intelligence of saddle maintenance and management..