Finite Element Analysis And Structural Reliability Research On Key Compoments Of Arc Welding Robot

In today's industrial production, the welding robot is more and more important, and the overseas welding robot has been achieved the level of universal design and higher product quality. While there are some deficiencies in the design and use of the domestic welding robot, so the reliability of the welding robot has yet to be improved. In this paper, the structural reliability of the existing six kilograms arc welding robot (HHR6) will be analyzed. Because the goal of the design of the arc welding robot is reducing the weight and the cost, and then there are a variety of structures for lower weight in the design, the structural reliability becomes more important. For the reliability problem of HHR6, this paper has contained the following studies:The structures of the key components of the arc welding robot have been analyzed with the finite element method; meanwhile, the strength and the stiffness have been evaluated. In addition, the natural frequencies and vibration mode shapes have been obtained to assess the dynamic characteristic and stiffness of each component.With the above analyzed results, the structural reliability of key components has been researched by ANSYS software. The finite element model has been created by the parameter language. The Monte-Carlo method has been used to analyze the reliability. The statistics results have been obtained and the factors affecting the reliability of the structure have been analyzed.For the results above, using the first order second moment method in the structural reliability analysis and the basic theory of elasticity, the structural reliability of the key components has been verified by the calculation and deduction. The structural reliability level of the key components has been derived from both simulation and theoretical proof.On the basis of the Monte-Carlo methods above, the K-nearest neighbor algorithm of the pattern recognition has been introduced to structural reliability analysis. The method based on K-nearest neighbor algorithm and the Monte-Carlo method (KNN-MC) for the structural reliability has been raised. Furthermore, the KNN-MC method has been improved with fuzzy theory, and the structural reliability analysis methods based on the fuzzy K-nearest neighbor algorithm (FUZZY-KNN-MC) has been proposed. The above methods based on pattern recognition theory have been proved by using the simulated data of the forearm, and the solution for the problem in the using has been improved.