Green manufacturing has become a new production mode for the development and operation of modern and future manufacturing industries. The flexible job shop scheduling problem (FJSP), as one of the key core problems in the field of green manufacturing process planning, has become a hot topic and a difficult issue in manufacturing production research. In this paper, an improved multi-objective wolf pack algorithm (MOWPA) is proposed for solving a multi-objective flexible job shop scheduling problem with transportation constraints. Firstly, a multi-objective flexible job shop scheduling model with transportation constraints is established, which takes the maximum completion time and total energy consumption as the optimization objectives. Secondly, an improved wolf pack algorithm is proposed, which designs individual codes from two levels of process and machine. The precedence operation crossover (POX) operation is used to improve the intelligent behavior of wolves, and the optimal Pareto solution set is obtained by introducing non-dominated congestion ranking. Thirdly, the Pareto solution set is selected using the gray relational decision analysis method and analytic hierarchy process to obtain the optimal scheduling scheme. Finally, the proposed algorithm is compared with other algorithms through a variety of standard examples. The analysis results show that the improved multi-objective wolf pack algorithm is superior to other algorithms in terms of solving speed and convergence performance of the Pareto solution, which shows that the proposed algorithm has advantages when solving FJSPs.

In the printing section of a roll-to-roll gravure printed electronics machine, the plastic film tension is directly associated with the product quality. The temperature distribution of the plastic film in the printing section is non-uniform, because of the higher drying temperature and the lower room temperature. Furthermore, the drying temperature and the room temperature are not constants in industrial production. As the plastic film is sensitive to temperature, the temperature of the plastic film will affects the web tension in the printing section. In this paper, the thermal characteristics of the plastic film tension in roll-to-roll gravure printed electronics are studied in order to help to improve the product quality. First, the tension model including the factor of temperature is derived based on the law of mass conservation. Then, some simulations and experiments are carried out in order to in-depth research the effects of the drying temperature and room temperature based on the relations between system inputs and outputs. The results show that the drying temperature and room temperature have significant influences on the web tension. The research on the thermal characteristics of plastic film tension would benefit the tension control accuracy for further study.

In order to achieve minimum energy consumption in computerized numerical control (CNC) lathe processing under the premise of ensuring the imposed roughness of the machined surface, a black hole-continuous ant colony optimization algorithm (BH-ACO_R) is proposed to optimize the turning parameters. Taking turning specific energy and surface roughness as the optimization objectives, a turning test was designed. Subsequently, a multi-objective mathematical model of the cutting stage was formulated through the application of the least-squares method to fit the test data. The black hole algorithm was introduced to mitigate the shortcomings of the continuous-domain ant colony algorithm, which easily falls into a local optimum, so as to put forward a kind of BH-ACO_R that is applicable to multi-objective optimization. The algorithm was applied to the multi-objective mathematical model in the turning stage to determine the optimal cutting parameters. Through simulation and test verification, the validity and practicability of the proposed method are further proved.

The tension system is one of the most critical systems in gravure printed electronic equipment. It possesses a complex structure that spans the entire feeding process, from unwinding through printing to rewinding. This article focuses on the research of multi-span tension control for printing systems. Firstly, the characteristics and requirements of the tension-control system in the printing section were analyzed, and a multi-span tension-control structure was devised. Then, based on the coupled mathematical model of the multi-span tension system, a static decoupling model was formulated, and a first-order active disturbance rejection control (ADRC) controller was designed utilizing active disturbance rejection control technology. Finally, to verify the control performance of the ADRC decoupling controller for the printing tension system, simulation and experimental studies were conducted using MATLAB/Simulink R2018a and a dedicated experimental platform, and the results were then compared with those obtained from a traditional PID controller. The research findings indicate that the designed multi-span tension-control system demonstrates outstanding decoupling performance and anti-interference capabilities, effectively enhancing the tension-control accuracy of gravure printed electronic equipment.

In the printing section of the shaft-less drive gravure printing machine, as the web material is sensitive to temperature, the web outside the drying devices will be affected by the changes of the environment temperature. In order to improve the printing precision in gravure printing machine, the effects of environment temperature on web tension in the printing section are studied in this article. Based on the law of mass conservation, the tension model including the factor of environment temperature is derived. Then some theoretical analyses and simulations are conducted to in-depth study the effects of environment temperature from the relations between inputs and output in a web span. Finally, some experiments are carried out to verify the characteristics of the tension subsystem including environment temperature. The result shows that the settling times decrease with the rise of the environment temperature in the transient responses, and the absolute changes of the steady-state tension decrease with the rise of the environment temperature in the steady-state responses as the inputs are the velocities of the printing rollers. The study on the effects of environment temperature on web tension would benefit the tension control and the register control for the further study.

The semantic segmentation of the 3D operating environment represents the key to intelligent mining shovels’ autonomous digging and loading operation. However, the complexity of the operating environment of intelligent mining shovels presents challenges, including the variety of scene targets and the uneven number of samples. This results in low accuracy of 3D semantic segmentation and reduces the autonomous operation accuracy of the intelligent mine shovels. To solve these issues, this paper proposes a 3D point cloud semantic segmentation network based on memory enhancement and lightweight attention mechanisms. This model addresses the challenges of an uneven number of sampled scene targets, insufficient extraction of key features to reduce the semantic segmentation accuracy, and an insufficient lightweight level of the model to reduce deployment capability. Firstly, we investigate the memory enhancement learning mechanism, establishing a memory module for key semantic features of the targets. Furthermore, we address the issue of forgetting non-dominant target point cloud features caused by the unbalanced number of samples and enhance the semantic segmentation accuracy. Subsequently, the channel attention mechanism is studied. An attention module based on the statistical characteristics of the channel is established. The adequacy of the expression of the key features is improved by adjusting the weights of the features. This is done in order to improve the accuracy of semantic segmentation further. Finally, the lightweight mechanism is studied by adopting the deep separable convolution instead of conventional convolution to reduce the number of model parameters. Experiments demonstrate that the proposed method can improve the accuracy of semantic segmentation in the 3D scene and reduce the model’s complexity. Semantic segmentation accuracy is improved by 7.15% on average compared with the experimental control methods, which contributes to the improvement of autonomous operation accuracy and safety of intelligent mining shovels.