To realise the intelligent decision-making of dynamic scheduling and reconfiguration, we studied the intelligent scheduling and reconfiguration with dynamic job arrival for a reconfigurable flow line (RFL) using deep reinforcement learning (DRL), for the first time. The system architecture of intelligent scheduling and reconfiguration in smart manufacturing is proposed, and the mathematical model is established to minimise total tardiness cost. In addition, a DRL system of scheduling and reconfiguration is proposed by designing state features, actions, and rewards for scheduling and reconfiguration agents. Moreover, the advantage actor-critic (A2C) is adapted to solve the studied problem. The training curve shows the A2C-based agents have effectively learned to generate better solutions for unseen instances. The test results show that the A2C-based approach outperforms two traditional meta-heuristics, iterated greedy (IG) and genetic algorithm (GA), in solution quality and CPU times by a large margin. Specifically, the A2C-based approach outperforms IG and GA by 57.43% and 88.30%, using only 0.46‱ and 2.20‱ CPU times of IG and GA. The trained model can generate a scheduling or reconfiguration decision within 1.47 ms, which is almost instantaneous and can satisfy real-time optimisation. Our work shows a promising prospect of using DRL for intelligent scheduling and reconfiguration.

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

Aerospace engine is the source of power for the development of national defense and aerospace. Engine bolt tightening is an important part of the aerospace engine production process. At present, most of the bolt tightening methods of aerospace engines are manual tightening or traditional rigid tightening, which lead to the problem of low efficiency and large engine deformation. This paper proposes a process planning method for bolt tightening of aerospace engine based on digital twin, including flexible tightening mechanism design, tightening sequence optimization, the establishment of the digital twin system of the tightening mechanism, etc. The flexible tightening mechanism is suitable for different types of aerospace engines. Based on bolt tightening sequence optimization, the deformation and stress of the cabin section during the tightening process are reduced. The bolt tightening work is completed in the digital twin system, the real-time communication of physical and digital space synchronizes the operations of them. After tightening, the residual pre-tightening force of bolts are detected based on the ultrasonic method. The results show that the proposed method can solve the problems of low efficiency of aerospace engine bolt tightening and the problem of the cabin’s large deformation. Furthermore, it provides technical reference and theoretical basis for the bolt tightening technology of aerospace engine.

Reverse engineering is an important process of real model digitization. However, the existing methods are limited by the characteristics of the modeling object; the modeling is low efficient, has poor versatility, and unstable accuracy. To complete the reverse engineering of a real workpiece with irregular deformation based on the ideal computer-aided design (CAD) model, a high-precision reverse engineering method of the workpiece based on the CAD model prior was presented. Through the registration of the ideal CAD model and the point cloud model of the real workpiece, the geometric feature position information and feature constraint information contained in the CAD model are transmitted to the modeling process, which helps to improve the accuracy and efficiency of reverse engineering. This method is applied to the reverse engineering process of the engine compartment with slight irregular deformation, the modeling accuracy reaches 0.04 mm, and the high-precision reverse engineering of the engine compartment is successfully realized.

Tendon-sheath actuators (TSAs), similar to Bowden cable-based series elastic actuators (SEAs), have a wide range of applications in robots. However, when applying them to manipulators, force sensors cannot be mounted due to limited space. Therefore, this paper proposes a new method for compliant control based on a dual encoder, and the existing transmission model was improved by considering the joint torque. To validate the proposed method, experimental setups composed of a motor, a tendon-sheath component, a robotic joint, force sensors, and position sensors are established. The transmission processes are interpreted clearly through a position control mode. Parameter identifications without external disturbance force are conducted to acquire the ideal transmission model, and the error value between the experiment data and the fitted curve is measured. The availability of the presented scheme is verified by compliant control experiments, including collision detection and hand guiding, and the frequency characteristics of the actuator are analyzed. The results show that the function can be realized without force and torque sensors during the whole process.