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The assembly sequence in the product assembly process has effect on the final product quality. To solve the assembly sequence optimization problem, such as rotor blade assembly sequence optimization, this paper proposes a small world networks-based genetic algorithm (SWN_GA) to solve the assembly sequence optimization problem. The proposed approach SWN_GA consists of a combination between the standard Genetic Algorithm and the NW Small World Networks.

The selection operation and the crossover operation are improved in this paper. The selection operation remains the elite individuals that have greater fitness than average fitness and reselects the individuals that have smaller fitness than average fitness. The crossover operation combines the NW Small World Networks to select the crossover individuals and calculate the crossover probability.

In this paper, SWN_GA is used to optimize the assembly sequence of steam turbine rotor blades, and the SWN_GA was compared with standard GA and PSO algorithm in a simulation experiment. The simulation results show that SWN_GA cannot only find a better assembly sequence which have lower rotor imbalance, but also has a faster convergence rate.

Finally, an experiment about the assembly of a steam turbine rotor is conducted, and SWN_GA is applied to optimize the blades assembly sequence. The feasibility and effectiveness of SWN_GA are verified through the experimental result.

Once an uneven substrate is aligned, traditional control theories and methods can be used on it, so aligning is of great significance for the development of wire and arc additive manufacturing (WAAM). This paper aims to propose a shape-driven control method for aligning a substrate with slopes to expand the application of WAAM.

A substrate with slopes must be aligned by depositing weld beads with slopes. First, considering the large height differences of slopes, multi-layer deposition is needed, and the number of layer of weld beads must be ascertained. Second, the change in the deposition rate is controlled as a ramp function to generate weld beads with slopes. Third, the variation of the deposition rate must be fine-tuned to compensate for the deviation between the actual and theoretical layer heights at the deposition of each layer. Finally, the parameters of the ramp functions at the deposition of each layer are determined through an optimization method.

First, to model the response function of layer height to deposition rate, the experiments are conducted with the deposition rate jumping from 4 to 8 mm/s and from 8 to 4 mm/s. When the deposition rate jumps from 4 to 8 mm/s and from 8 to 4 mm/s, the difference in the height of each layer decreases as the number of layer increases. Second, the variation of the deposition rate can be fine-tuned based on the deviation between the measured and theoretical layer heights because the variation of the deposition rate is proportional to the layer height when the initial and end deposition rates are near 4 or 8 mm/s, respectively. Third, the experimental results demonstrate that the proposed method is effective for single-layer aligning and aligning a substrate with one or more slopes.

The proposed method can expand the application of WAAM to an uneven substrate with slopes and lays the foundation for aligning tasks focused on uneven substrates with more complex shapes.

Autonomous mobile cleaning robots are widely used to clean solar panels because of their flexibility and high efficiency. However, gravity is a challenge for cleaning robots on inclined solar panels, and robots have problems such as high working power and short battery life. This paper aims to develop a following robot to improve the working time and efficiency of the cleaning robot.

The mechanical structure of the robot is designed so that it can carry a large-capacity battery and continuously power the cleaning robot. The robot determines its position and orientation relative to the edge of solar panel by using optoelectronic sensors. Based on the following distance, the robot changes its state between moving and waiting to ensure that supply cable will not drag.

Prototype following robot test results show that the following robot can stably follow the cleaning robot and supply continuous power to cleaning robot. The linear error of the following robot moving along the solar panel is less than 0.3 m, and the following distance between the robot and the cleaning robot is in 0.5–1.5 m.

The working time of cleaning robots and working efficiency is improved by using following robot, thereby reducing the labor intensity of workers and saving the labor costs of cleaning.

The design of the following robot is innovative. Following robot works with the existing cleaning robots to make up for shortcomings of the existing cleaning system. It provides a more feasible and practical solution for using robots to clean solar panels.