As the main component of the 3D printing industry, the fused deposition process covers all aspects of the industry with its advantages of low R&D investment, high practicability, and open source programs. However, due to process problems, problems have arisen in terms of printing efficiency and molding quality. To this end, we designed a large-scale multinozzle FDM printing device using the high-current fused deposition (FDM) printing principle. The defects of small size, slow printing speed, and low precision are deeply studied, and the machine structure is optimized according to the structural strength analysis. In this paper, the theoretical design and static analysis of the overall mechanical part of the large-scale FDM device are carried out, and then, the selection of the movement organization structure and movement method is theoretically analyzed. A modular flow chart is designed for the control system to coordinate and control the parallel and precise operation of multiple nozzles, and the relationship function between the main controller, power driver, and heating module is designed. By modifying the firmware parameter command, we can find out the optimal method running on the platform and discuss the function usage of the slicing software in detail. According to the current problems of FDM printing equipment, various factors affecting printing speed were analyzed from the perspective of printing accuracy, and the process parameters of 3D printer were studied through orthogonal experiments. Speed, nozzle temperature, idling speed, and fill rate were studied, and the relationship between factors affecting printing speed and printing accuracy was obtained. Use a simple model print to measure the overall performance of your product. The stability of the system is verified by short-term and long-term printing tests. The analysis results show that the forming performance and stability of the large-scale FDM are improved significantly.

In this study, we are concerned with the optimization of fuzzy clustering (Fuzzy C-Means) on the basis of a collection of distributed datasets without violating data confidentiality and security. The optimization of fuzzy clusters is realized using the differential evolution algorithm in a federated learning environment. Fuzzy clustering plays an important role in revealing the underlying structure of a given dataset. However, traditional iterative method is easy to get stuck at local optimum. With the growing concerning on data confidentiality and security, how to reveal the underlying structure of the data that are stored locally across different sites is becoming an urgent problem. In order to overcome these two obstacles, we propose a federated differential evolution algorithm to realize fuzzy clustering. We augment the well-known differential evolution algorithm such that it can work in a federated learning environment to ensure local data privacy. The design practice of the federated differential evolution is elaborated on by highlighting its effectiveness in finding the optimal fuzzy clusters on the basis of distributed datasets. The performance of the proposed method is compared with traditional fuzzy clustering algorithm. Experimental studies completed on a series of real-world datasets coming from machine learning repository are reported to demonstrate the superiority of the proposed algorithm.

A sliding mode control for active disturbance rejection was utilized for uniaxial tracking control, and a cross-coupled contour error compensation controller based on nonlinear position domain (PD) was devised for enhanced contour machining precision and robustness of biaxial moving platform. For single-axis tracking control, the total disturbance of the system was initially considered to be the external disturbance plus internal unmodeled dynamics, and the overall disturbance was estimated and compensated in real time using a linear extended state observer. A sliding mode controller was built on this foundation to obtain satisfactory uniaxial control performance. Secondly, based on the estimated value of each axis component of contour error, a nonlinear PD contour error compensation controller was designed to coordinate the tracking motion between axes and further reduced the contour error. The proposed work is compared against state-of-the-art works from literature for various metrics such as tracking error curves, average comparison of tracking errors, and cross-coupled controller parameters. The simulation results showed that the proposed method can effectively improve the contour processing accuracy of the system.