# Simulation-Based Reliability Design Optimization Method for Industrial Robot Structural Design
## 2 Simulation-Based Reliability Design Optimization Method (SBRDOM)  
### SBRDOM Procedure  
In the SBRDOM, the Latin hypercube sampling (LHS), simulation technology, response surface method (RSM) and SORA (Sequential Optimization and Reliability Assessment) algorithm were integrated to complete the structural design of the robot. The proposed method integrated the joint simulation of MTLAB and ANSYS into approximate technology so as to obtain the performance function of an industrial robot. In addition, the proposed method employed the SORA algorithm to solve the problem of low computational efficiency in engineering applications. The main procedure of the SBRDOM is shown in Figure 1.  
The main steps of the proposed method are summarized as follows.  
Step 1: Determining the initial design parameters and variables of the industrial robot. In this step, the geometric configuration, load, material and working environment of the industrial robot were analyzed to define the uncertainty design parameters and variables.  
Step 2: Sampling the design variables based on LHS. In this step, the LHS [25] was used to obtain the samples of the design variables. The bounds of each variable were regarded as an interval. This interval was divided into \(n\) small intervals, and then a sample point was randomly extracted from each small interval. The final samples were obtained by combining all the sample points.  
Step 3: Constructing the performance function of the industrial robot based on computer simulation and RSM. In this step, the joint simulation of MTLAB and ANSYS was  
Figure 1: Main procedure of the SBRDOM.  
integrated with RSM to obtain the preformance function of the industrial robot. The detailed operation of this step is shown in Figure 2 and explained as follows,  
Step 3.1: Updating the simulation models. Firstly, the 3D parameter model of the industrial robot was constructed by using the ANSYS software. Then, the computer simulation model was updated with the sample point produced in step 2.  
Step 3.2: Performing the simulations. The ANSYS simulation was performed with each sample, and then the response values corresponding to each sample were obtained.  
Step 3.3: Approximating the performance function by using RSM. In this step, MATLAB was used to apply the approximation technology. Based on the samples and responses, the performance function could be obtained by using the approximation approach. The general used approximate model included a polynomial model, a kriging model and a support vector machine model.  
Step 4: Constructing the reliability design optimization model of the industrial robot based on the probabilistic reliability theory. Based on the probability reliability theory [26], the design parameters and variables were modeled as random parameters and variables, and then the reliability design optimization model of the industrial robot could be constructed.  
Step 5: Solving the reliability design optimization problem by using the SORA algorithm. The reliability design optimization model of the industrial robot was carried out by employing the SORA algorithm in this step. The process of SORA is detailed in Section 2.3.  
Step 6: Obtaining the optimal design of the industrial robot. In this step, the optimization results obtained by SORA were evaluated. If the results were not reasonable, we turned back to Step 2 to perform the process again. The evaluation criterion was that the difference between two optimization results had to be within the allowable accuracy range.