Fatigue And Reliability Analysis Of The TBM Cutterhead

Cutterhead is one of the important parts of a tunnel boring machine(TBM). Its performance directly affects the TBM’s lifetime and the quality of tunnel construction. In this paper, taking the load of cutterhead as randomized, the author analyzed the reliability and fatigue characteristics of TBM cutterhead and performed size-optimization on its weak part using ANSYS and WORKBENCH. The main work of this paper includes:(1)Establishment of the 3-D model of the TBM cutterhead. In order to improve the computational accuracy, we obtained the cutterhead’s hexahedral mesh model through solid-cutting of the cutterhead and rigid connection of the rib-corresponding nodes. On the base of analyzing the characteristics of the forced cutterhead the application of randomized load on the cutterhead is achieved by establishing key points at the tool bores of the disc cutters.(2)Reliability analysis of TBM cutterhead. With consideration of randomized load of the cutterhead and using the probability analysis module in ANSYS based on response surface method, we acquired the failure probability of the cutterhead structure, determined the primary and secondary factors of its failure probability, investigated the effect of the ratio of tangential force to propulsive force on its failure probability and contrastively analyzed the varying relationship between the sensitivity and the tangential/propulsive ratio factor.(3)Fatigue analysis of TBM cutterhead. According to the results of finite element analysis, fatigue analysis of the cutterhead was performed based on the modified S-N curve theory. The S-N curve and fatigue-sensitivity curve of the cutterhead structure were draw.The primary factors affecting cutterhead’s fatigue life was analyzed. This will provided evidence for optimization of cutterhead design.(4)Optimization of TBM cutterhead structure. By parametric description of critical sizes of the cutterhead structure’s weak parts, we optimized these sizes in order to improve the minimal lifetime. After optimization, the minimal lifetime was increased by 230.6% and the maximal equivalent stress was decreased by 29.3%. By sensitivity analysis of relevant sizes, we determined the critical factors of the minimal lifetime and provided reference for design improvement.