# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 3 Results
### Reliability Prediction
#### 3.2.2 Reliability Prediction of Mechanical System and Pneumatic System  
The mechanical system and pneumatic system of the loading and unloading truss robot for CNC punch include a large number of parts and components, and the failure mechanisms of the parts and components of the system are different. The expert grading method based on fuzzy theory [21] is used to predict the reliability of the mechanical system and pneumatic system of the loading and unloading truss robot for CNC punch. According to the fuzzy reliability prediction model [22], the loading and unloading truss robot of CNC punch is predicted, the specific implementation methods are as follows: the mechanical system (M), pneumatic system (P) and electrical control system (E) were scored by five expert group members based on fuzzy theory according to complexity, technical level, working time and environmental conditions. The grades of the four evaluation factors are divided into 10 grades, and then fuzzy theory is introduced to deal with the continuous triangular fuzzy numbers of the linguistic variables. The grades and relative importance fuzzy numbers of the four evaluation factors are shown in Table 2.  
Now five experienced loading and unloading truss robot of CNC punch designers and experts are invited to independently score the corresponding evaluation factors. Under the four scoring factors of complexity, technical level, working time and environmental conditions, the expert scoring results table of each system is shown in Table 3.  
As can be seen from Table 3, different experts give inconsistent scores to the same index. Therefore, expert scoring is subjective to a certain extent. To eliminate the influence of experts' personal opinions, the analytic hierarchy process is used to calculate the weight of each expert, and then, according to the weight of each expert, the evaluation value of each expert is calculated based on fuzzy theory. The specific methods for determining expert weight are as follows:  
In the hierarchical structure model, the top layer is the target layer, the second layer is the factor layer, the third layer is the sub-factor layer and the bottom layer is the expert layer. The objective layer is expert weight, the factor layer constitutes factor set B = {B1,B2,B3,B4} = [professional title, educational background, seniority, age], and then sub-divide each factor to form a sub-factor layer {Bij}(j = 1,2,...,m), a set of factors of expert layer \(Z\) = {Z1,Z2,Z3,Z4}. The hierarchical model for each expert weight is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Factors** & **Expert** & **M** & **P** & **E** \\ \hline \multirow{5}{*}{Complexity} & Z1 & \((8,9,10)\) & \((1,2,3)\) & \((6,7,8)\) \\  & Z2 & \((7,8,9)\) & \((2,3,4)\) & \((7,8,9)\) \\  & Z3 & \((8,9,10)\) & \((1,2,3)\) & \((6,7,8)\) \\  & Z4 & \((8,9,10)\) & \((3,4,5)\) & \((8,9,10)\) \\  & Z5 & \((7,8,9)\) & \((2,3,4)\) & \((6,7,8)\) \\ \hline \multirow{5}{*}{Technical level} & Z1 & \((1,2,3)\) & \((2,3,4)\) & \((5,6,7)\) \\  & Z2 & \((1,1,2)\) & \((3,4,5)\) & \((4,5,6)\) \\  & Z3 & \((1,1,2)\) & \((2,3,4)\) & \((6,7,8)\) \\  & Z4 & \((2,3,4)\) & \((2,3,4)\) & \((5,6,7)\) \\  & Z5 & \((1,1,2)\) & \((4,5,6)\) & \((7,8,9)\) \\ \hline \multirow{5}{*}{Working hours} & Z1 & \((5,6,7)\) & \((8,9,10)\) & \((8,9,10)\) \\  & Z2 & \((7,8,9)\) & \((9,10,10)\) & \((9,10,10)\) \\  & Z3 & \((6,7,8)\) & \((7,8,9)\) & \((9,10,10)\) \\  & Z4 & \((6,7,8)\) & \((8,9,10)\) & \((8,9,10)\) \\  & Z5 & \((7,8,9)\) & \((8,9,10)\) & \((8,9,10)\) \\ \hline \multirow{5}{*}{Environmental conditions} & Z1 & \((5,6,7)\) & \((6,7,8)\) & \((1,1,2)\) \\  & Z2 & \((2,3,4)\) & \((5,6,7)\) & \((1,1,2)\) \\ \cline{1-1}  & Z3 & \((5,6,7)\) & \((7,8,9)\) & \((1,2,3)\) \\ \cline{1-1}  & Z4 & \((4,5,6)\) & \((7,8,9)\) & \((1,2,3)\) \\ \cline{1-1}  & Z5 & \((4,5,6)\) & \((6,7,8)\) & \((1,1,2)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Each system expert score results table.