# Simulation-Based Reliability Design Optimization Method for Industrial Robot Structural Design
## 4 Conclusions  
Considering that the influence of uncertainties in the structural design of industrial robots is important for improving the performance of the robots, in this paper, a simulation-based reliability design optimization method is proposed to improve the reliability of robots. Four steps including LHS, approximating the performance function based on simulation, establishing the reliability design optimization model and optimization solution based on the SORA algorithm were involved in the proposed method. The effectiveness of the proposed method was verified by the design of the big arm and the small arm of an industrial robot with a load of 6 Kg. Compared with the deterministic design optimization, the proposed method appeared to be more efficient in ensuring performance reliability. The verification results showed that the proposed method can successfully realize the reliability design of industrial robots. The verification results also demonstrated that the proposed method has a better computational efficiency compared with the reliability design optimization of the double-loop method.  
In this paper, the reliability design optimization of the big arm and small arm of an industrial robot was performed under conditions which did not consider the dynamic performance and the design optimization of other connecting parts, and therefore a whole reliability design optimization method can be developed on the basis of the proposed method in the future. The influence of the jump resonance on the reliability of the robotic arms was also not considered in this research. In addition, the study considered a single-disciplinary reliability design optimization. However, the structural design of industrial robots is a problem involving multiple disciplines such as mechanical and electrical system and control. Consequently, the development of a multidisciplinary reliability design optimization method simultaneously considering static performance, dynamic performance and jump resonance will be the focus of future work, so as to further improve the performance of the industrial robot.  
Conceptualization, L.-X.Z. and X.-J.M.; methodology, L.-X.Z. and X.-J.M.; software, [PERSON]-J.D. and H.-X.H.; validation, [PERSON]-X.Z., [PERSON]-J.D. and H.-X.H.; formal analysis, X.-J.M.; investigation, [PERSON]-J.D. and X.-J.M.; resources, X.-J.M.; data curation, L.-X.Z., [PERSON]-J.D. and H.-X.H.; writing--original draft preparation, [PERSON]-X.Z. and Z.-J.D.; writing--review and editing, [PERSON]-X.Z. and X.-J.M.; visualization, L.-X.Z., X.-J.M. and Z.-J.D.; supervision, [PERSON]-J.M.; project administration, [PERSON]-J.M.; funding acquisition, X.-J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hebei Province of China, grant number E2021402052, and the Science & Technology Research Program of Handan City of China, grant number 21422021299.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Big Arm**} & \multicolumn{2}{c}{**Small Arm**} \\ \cline{2-5}  & **[(x\({}_{1}\), x\({}_{2}\), x\({}_{3}\)), Weight]** & **Time** & **[(x\({}_{4}\), x\({}_{5}\), x\({}_{6}\)), Weight]** & **Time** \\ \hline Proposed & [(8.79, 3.54, 2) mm, 9.4306 Kg] & 2097.35 s & [(12.08, 10.58, 13.58) mm, 2.1133 Kg] & 1857.24 s \\ double-loop & [(8.75, 3.56, 2) mm, 9.4295 Kg] & 5853.61 s & [(12.06, 10.52, 13.61) mm, 2.1061 Kg] & 4558.22 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different reliability design optimization methods.