Purpose
This paper aims to automatically plan sequence for complex assembly products and improve assembly efficiency.
Design/methodology/approach
An assembly sequence planning system for workpieces (ASPW) based on deep reinforcement learning is proposed in this paper. However, there exist enormous challenges for using DRL to this problem due to the sparse reward and the lack of training environment. In this paper, a novel ASPW-DQN algorithm is proposed and a training platform is built to overcome these challenges.
Findings
The system can get a good decision-making result and a generalized model suitable for other assembly problems. The experiments conducted in Gazebo show good results and great potential of this approach.
Originality/value
The proposed ASPW-DQN unites the curriculum learning and parameter transfer, which can avoid the explosive growth of assembly relations and improve system efficiency. It is combined with realistic physics simulation engine Gazebo to provide required training environment. Additionally with the effect of deep neural networks, the result can be easily applied to other similar tasks.
A new proportional integral derivative (PID) control method is proposed for the 3D laser scanning system converted from 2D Lidar with a pitching motion device. It combines the advantages of a fuzzy algorithm, a radial basis function (RBF) neural network and a predictive algorithm to control the pitching motion of 2D Lidar quickly and accurately. The proposed method adopts the RBF neural network and feedback compensation to eliminate the unknown nonlinear part in the Lidar pitching motion, adaptively adjusting the PID parameter by a fuzzy algorithm. Then, the predictive control algorithm is adopted to optimize the overall controller output in real time. Finally, the simulation results show that the step response time of the Lidar pitching motion system using the control method is reduced from 15.298 s to 1.957 s with a steady-state error of 0.07°. Meanwhile, the system still has favorable response performance for the sinusoidal and step inputs under model mismatch and large disturbance. Therefore, the control method proposed above can improve the system performance and control the pitching motion of the 2D Lidar effectively.
Preliminary structure of light rail vehicle (LRV) carbody made of steel was designed considering its usage, strength, manufacturing, etc. Based on the finite element analysis, the optimization of design parameters associated with thickness of LRV carbody is carried out to increase the whole strength of the carbody and to reduce its mass. With the aids of the substructure technique and the modified technique with discrete variables in the optimization based on the finite element method, the consumed computing time is reduced dramatically. The optimized LRV carbody is re-analyzed by FEM to obtain its static strength and vibrating mode and is manufactured. The mass of the optimized carbody reduces about 1.3 kg, and the relative reduction ratio is about 10%. Then, the strength test of the real carbody under the static load is executed. It is shown by the numerical and test results that the design requirements of the LRV carbody are satisfying. The newly designed carbody is used in the LRV, which is the first one used commercially developed by China independently. Nowadays, the LRV is running on the transportation circuit in Changchun of China.
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