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A three-dimensional (3D) printing error simulation approach is proposed to analyze the influence of tilted vertical beams on the 3D printing accuracy. The purpose of this study is to analyze the influence of such errors on printing accuracy and printing quality for delta-robot 3D printer.

First, the kinematic model of a delta-robot 3D printer with an ideal geometric structure is proposed by using vector analysis. Then, the normal kinematic model of a nonideal delta-robot 3D robot with tilted vertical beams is derived based on the above ideal kinematic model. Finally, a 3D printing error simulation approach is proposed to analyze the influence of tilted vertical beams on the 3D printing accuracy.

The results show that tilted vertical beams can indeed cause 3D printing errors and further influence the 3D printing quality of the final products and that the 3D printing errors of tilted vertical beams are related to the rotation angles of the tilted vertical beams. The larger the rotation angles of the tilted vertical beams are, the greater the geometric deformations of the printed structures.

Three vertical beams and six horizontal beams constitute the supporting parts of the frame of a delta-robot 3D printer. In this paper, the orientations of tilted vertical beams are shown to have a significant influence on 3D printing accuracy. However, the effect of tilted vertical beams on 3D printing accuracy is difficult to capture by instruments. To reveal the 3D printing error mechanisms under the condition of tilted vertical beams, the error generation mechanism and the quantitative influence of tilted vertical beams on 3D printing accuracy are studied by simulating the parallel motion mechanism of a delta-robot 3D printer with tilted vertical beams.

The purpose of this study is to propose an automatic leveling method for a printing platform based on a three-point coordinate feedback. The proposed method is used in fused deposition modeling additive manufacturing systems. The coordinate error of the leveled plane is constrained to within  ± 0.2 mm, which is less than the printed layer thickness.

First, the model of the forward and inverse solutions of the parallel arm is obtained based on the principles of vector algebra. Second, the automatic leveling mechanism for collecting the z-coordinate is designed. The best position of the virtual origin plane is obtained by comparing the z-coordinates of the test points. Finally, after making multiple adjustments through a closed-loop z-coordinate feedback, the parallelism of the printing plane and the virtual origin plane is limited to an effective range.

The experimental results show that after three leveling attempts, the z-coordinate of the test points can be constrained to within  ± 0.2 mm, which shows that this method can effectively achieve automatic leveling in a delta three-dimensional (3D) printer.

This study presents a novel and distinctive delta 3D printer leveling system by designing a leveling mechanism and a leveling algorithm. The method uses a closed-loop feedback mode to make the leveling process simple, convenient and efficient without requiring major changes to the printer. The error after leveling is less than the printed layer thickness, which fully guarantees the accuracy of the leveling process.

This paper aims to offer a hybrid genetic algorithm and the ant colony optimization (GA-ACO) algorithm for task mapping and resource management. The paper aims to reduce the makespan and total response time in fog computing- medical cyber-physical system (FC-MCPS).

Swift progress in today’s medical technologies has resulted in a new kind of health-care tool and therapy techniques like the MCPS. The MCPS is a smart and reliable mechanism of entrenched clinical equipment applied to check and manage the patients’ physiological condition. However, the extensive-delay connections among cloud data centers and medical devices are so problematic. FC has been introduced to handle these problems. It includes a group of near-user edge tools named fog points that are collaborating until executing the processing tasks, such as running applications, reducing the utilization of a momentous bulk of data and distributing the messages. Task mapping is a challenging problem for managing fog-based MCPS. As mapping is an non-deterministic pol ynomial-time-hard optimization issue, this paper has proposed a procedure depending on the hybrid GA-ACO to solve this problem in FC-MCPS. ACO and GA, that is applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as the main approach and GA as the local search. GA-ACO is a memetic algorithm using GA as the main approach and ACO as local search.

MATLAB is used to simulate the proposed method and compare it to the ACO and MACO algorithms. The experimental results have validated the improvement in makespan, which makes the method a suitable one for use in medical and real-time systems.

The proposed method can achieve task mapping in FC-MCPS by attaining high efficiency, which is very significant in practice.

The proposed approach can achieve the goal of task scheduling in FC-MCPS by attaining the highest total computational efficiency, which is very significant in practice.

This research proposes a GA-ACO algorithm to solve the task mapping in FC-MCPS. It is the most significant originality of the paper.