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Scanning projection-based stereolithography (SPSL) is a powerful technology for additive manufacturing with high resolution as well as large building area. However, the surface quality of stitching boundary in an SPSL system has been rarely studied, and no positive settlement was proposed to address the poor stitching quality. This paper aims to propose an approach of multi-pass scanning and a compensation algorithm for multi-pass scanning process to address the issue of poor stitching quality in SPSL systems.

The process of multi-pass scanning is realized by scanning regions repeatedly, and the regions can be cured simultaneously because of the very short repeat exposure time and very fast scanning. Then, the poor stitching quality caused by the non-simultaneous curing can be eliminated. Also, a compensation algorithm is designed for multi-pass scanning to reduce the stitching errors. The validity of multi-pass scanning is verified by curing depth test, while the performance of multi-pass scanning as well as proposed compensation algorithm is demonstrated by comparing with that of a previous SPSL system.

The results lead to a conclusion that multi-pass scanning with its compensation algorithm is an effective approach to improve the stitching quality of an SPSL system.

This study can provide advice for researchers to achieve the satisfactory surface finish with SPSL technology.

The authors proposed a process of multi-pass scanning as well as a compensation algorithm for SPSL additive manufacturing (system to improve the stitching quality, which has rarely been studied in previous work.

The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

Assembly variations, which will propagate along the assembly process, are inevitable and difficult to analyze in Aeronautical Thin‐Walled Structures (ATWS) assembly. The purpose of this paper is to present a new method for analyzing the variation propagation of ATWS with automated riveting.

The paper addresses the variation propagation model and method by first, forming a novel Stage‐State model to represent the process of automated riveting. Second, the effect of positioning error on assembly variation is defined as propagation variation (PV), and propagation matrix of key characteristic points (KCP) is discussed. Third, the effect between the variations in each stage is defined as expansion variation (EV). According to the analysis of mismatch error and the reference transformation, the expansion matrix is formed.

The model can solve the variation propagation problem of ATWS with automated riveting efficiently, which is shown as an example of this paper.

The variation obtained by the model and method presented in this paper is in conformity with the variation measured in experiments.

The propagation variation and expansion variation is proposed for the first time, and variations are studied according to novel propagation matrix and expansion matrix.

This paper aims to present the development of an electrospinning-based rapid prototyping (ESRP) technique for the fabrication of patterned scaffolds from fine fiber.

This ESRP technique unifies rapid prototyping (RP) and electrospinning to obtain the ability of RP to create a controllable pattern and of electrospinning to create a continuous fine fiber. The technique follows RP process of fused deposition modeling, but instead of using extrusion process for fiber creation, electrospinning is applied to generate a continuous fiber from a liquid solution. A machine prototype has been constructed and used in the experiments to evaluate the technique.

Three different lay-down patterns: 0°/90°, 45°/135° and 45° twists were used in the experiments. According to the experimental results, stacks of patterned layers could be created with the ESRP technique, and the fabrication process was repeatable and reproducible. However, the existing machine vibration influenced the fiber size and the ability to control straightness and gap size. Also, incomplete solidification of the fibers prior to being deposited obstructed the control of layer thickness. Improvement on vibration suppression and fiber solidification will strengthen the capability of this ESRP technique.

This research is currently limited to the introduction of the ESRP technique, to the development of the machine prototype, to the demonstration of its capability and to the evaluation of the structural properties of the fabricated patterned scaffolds. Further studies are required for better control of the patterned scaffolds and for investigation of mechanical and biological properties.

This unification of the two processes allows not only the fabrication of controllable patterned scaffolds but also the fabrication of both woven and non-woven layers of fibers to be done on one machine.

Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map.

The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data.

On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines.

A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

The purpose of this paper is to present a finite element formulation of enhanced two‐node parabolic cable element for the static analysis of cable structures.

Unlike the assumed polynomial displacement interpolation functions, the present approach uses the analytical cable dynamic stiffness matrix to obtain the explicit expression of the static stiffness matrix of an inclined sagging cable by setting the frequency at zero. The Newton‐Raphson‐based iterative method is used to obtain the solution.

It is demonstrated that the present results agree well with those obtained from the nonlinear analytical theory of a parabolic cable and previous reported methods in the literature.

This paper proposes a two‐node parabolic cable element. For comparable accuracy with the truss element method, fewer numbers of such cable elements are needed.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

The conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.

The multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.

Benchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.

Because of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate.

In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section.

The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

The purpose of this paper is to construct an unbiased interval grey number prediction model with new information priority for dealing with the jumping errors from difference equation to the differential equation in the prediction model of interval grey number.

First, this study obtains a set of linear equations about the model parameters by taking the minimum error sum of squares between the accumulative sequence and its simulation values as criterion, and solves them on the basis of the Crammer rule. Then, according to the new information priority principle, it selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method to establish the interval grey number prediction model.

This paper provides an unbiased interval grey number prediction model with new information priority, and the example analysis shows that the method proposed in this paper has higher prediction precision and practicality.

If there is a better method to whiten the interval grey number, so as to fully tap the grey information contained in it, the accuracy of the model will be higher.

The model proposed in this paper can avoid the error caused by jumping from difference equation to differential equation and make full use of new information. It can be better used in a problem where new information has a great influence on prediction results.

This paper selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method. Then, it constructs an unbiased interval grey number prediction model with new information priority.