# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 1 Introduction  
In traditional punching machine processing enterprises, the loading and unloading work is repeated, and the labor intensity is large, which occupies a considerable proportion of the total workload. Therefore, the current domestic and foreign market has a strong demand for complete sets of intelligent equipment. For sheet metal processing enterprises, it is urgent to automate the integration of CNC punch, CNC bending machine and other sheet metal processing equipment to greatly improve production efficiency and benefit. Therefore, as an important part of the automatic intelligent production line of sheet metal, the quality and reliability of the loading and unloading truss robot for CNC punch is very important. At present, the reliability level of metal plate automatic processing production line manufactured in China is low, so it is urgent to study the reliability of the loading and unloading truss robot for CNC punch, to improve its reliability level in the automatic production line. The loading and unloading truss robot for CNC punch belongs to an industrial robot, which can not only be used with CNC punching machine alone, but also can be combined with other machine tools to form an automatic production line. As industrial robots and automated production lines have been gradually popularized in recent years, their reliability research is still relatively weak at present. [PERSON] of Dalian University of Technology designed a loading and unloading truss robot system for CNC lathe. Although finite element analysis and simulation tests were carried out on the system, reliability analysis was not carried out [1]. Although [PERSON] from South China University of Technology designedand studied a small stamping and loading pneumatic manipulator and conducted reliability analysis on its control circuit [2], he only conducted reliability research on the control circuit and the reliability analysis involved was not deep enough. [PERSON] et al. [3] proposed a fuzzy Markov method based on the advantages of fault tree, fuzzy comprehensive evaluation and Markov methods, which can improve the reliability of input fault data and get the updated model which can best reflect the state of the system. [PERSON] et al. [4] put forward a software reliability prediction algorithm based on ELM and SVM for the problem of low accuracy of fault prediction based on standard machine learning technology, and studied the factors affecting the prediction accuracy. [PERSON] et al. [5] proposed a reliability prediction method with the introduction of interval hierarchy analysis, aiming at the problem that it is difficult to accurately assess the difference degree of reliability level between evaluation objects and similar products when the traditional similarity comparison method is used for reliability prediction of CNC machine tools. [PERSON] et al. [6] studied a reliability fuzzy prediction method based on fuzzy theory in order to overcome the lack of reliability data of existing welding robot systems. [PERSON] et al. [7] proposed a new reliability prediction method based on \"function-motion-action\" structural decomposition, aiming at the problem of a large deviation of predicted results due to the large difference between the selected similar products and the evaluation objects when using the traditional similar product method for reliability prediction. For failure modes and effects analysis (FMEA), as early as 1978, some scholars predicted the reliability level of CNC machine tools under given operating conditions and analyzed different types of failure. In recent years, the theory and application of fault mode analysis are more mature. [PERSON] [8] started from the positioning of FMEA in the software life cycle, elaborated the process steps of FMEA and the custom definition that can be used for software development, and gave the analysis results of FMEA. [PERSON] et al. [9] proposed the FMEA method based on functional modeling, gave the analysis process, and verified the FMEA method based on functional modeling.