Surface Failure Method Of Used Parts For Remanufacturing Testing Service

Remanufacturing service is an important way to realize the recycling and sustainable development of retired machinery products.Remanufacturing testing service is an important part of it,which plays an important role in improving remanufacturing efficiency and reducing remanufacturing cost.Due to the large number of retired machinery products,the variety is complex,How to make a scientific decision on remanufacturing program by measuring its failure state quickly and accurately and judging its remanufacturable value,It has become a difficult problem in remanufacturing service research.Therefore,this paper takes the retired parts as the research object,the remanufacturing testing service system and the surface failure state of retired parts were studied,It can be used for reference to extract and analyze the failure information of retired parts.The main contents of this paper are as follows:(1)Explore the remanufacturing testing service system of retired parts.Firstly,the significance and function of remanufacturing testing service for retired parts are described,highlight its importance in remanufacturing services,at the same time,it expounds its service process.Focus on remanufacturing testing service requirements,It leads to the failure state detection of retired parts,summarize its content method and so on.Through the analysis of the background environment and the advantages and disadvantages of the existing methods to further reflect the necessity of this study,according to the failure characteristics of the retired parts,the feasibility of the proposed idea is proved.(2)It is difficult to divide the surface failure area of retired parts and classify the failure form accurately,the failure classification model of retired parts was established,The failure classification model of retired parts was established.Firstly,based on the principle of visual correlation technology,the image of standard parts is extracted.By improving the Gaussian background modeling method,A ROI Gaussian learning strategy is proposed to segment the failure region,the image features of this region are extracted.In order to reduce redundant data,feature dimensionality reduction algorithm is selected to screen image features and improve recognition rate.Then,support vector machine(SVM)was used to establish the failure mode classification model,Cross validation method(K-CV)was used to optimize the penalty factor and kernel parameters,and the best failure form classifier was obtained.Finally,based on classification accuracy,the advantages of the proposed method are verified by comparing different dimensionality reduction algorithms and the effects of classifiers,to realize the classification of the surface failure forms of retired parts.(3)It is difficult to quantify the failure degree of retired parts,the accurate failure degree is obtained from the concept of multi-source information fusion.According to the effect of remanufacturing pretreatment on the surface of parts,the damage calculation model is put forward;The failure information transmission chain is constructed by the connection between the functional areas of retired parts,a polychromatic set is used to describe the transfer and correlation of failure features in part structure,On this basis,the polychromatic model is improved,the failure degree of each failure area is inferred,Finally,the reasoning result is corrected by combining the factors such as repair environment.(4)Example validation.The effectiveness and practicability of the method presented in this paper are verified by taking the failure state detection of retired gear as an example.