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Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified Cook’s increment, which could help adaptively remove the noise points that exceeds the threshold.

This paper proposes a robust point cloud plane fitting method based on ICOOK and WTLS to improve the robustness to noise in point cloud fitting. The ICOOK to denoise the initial point cloud was set and verified with experiments. In the meanwhile, weighted total least squares method (WTLS) was adopted to perform plane fitting on the denoised point cloud set to obtain the plane equation.

(a) A threshold-adaptive Cook’s distance method is designed, which can automatically match a suitable threshold. (b) The ICOOK is fused with the WTLS method, and the simulation experiments and the actual fitting of the surface of the DD motor are carried out to verify the actual application. (c) The results shows that the plane fitting accuracy and unit weight variance of the algorithm in this paper are substantially enhanced.

The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed. The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed.

Aircraft structures are mainly connected by riveting joints, whose quality and mechanical performance are directly determined by vertical accuracy of riveting holes. This paper proposed a combined vertical accuracy compensation method for drilling and riveting of aircraft panels with great variable curvatures.

The vertical accuracy compensation method combines online and offline compensation categories in a robot riveting and drilling system. The former category based on laser ranging is aimed to correct the vertical error between actual and theoretical riveting positions, and the latter based on model curvature is used to correct the vertical error caused by the approximate plane fitting in variable-curvature panels.

The vertical accuracy compensation method is applied in an automatic robot drilling and riveting system. The result reveals that the vertical accuracy error of drilling and riveting is within 0.4°, which meets the requirements of the vertical accuracy in aircraft assembly.

The proposed method is suitable for improving the vertical accuracy of drilling and riveting on panels or skins of aerospace products with great variable curvatures without introducing extra measuring sensors.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

The purpose of this paper is to investigate the effect of round pits arrangement patterns on tribological properties of journal bearing. In this paper, the tribological behaviors of journal bearing with different arrangement patterns under lubrication condition were studied based on M-2000 friction and wear tester.

The friction and wear of journal bearing contact surface were simulated by ANSYS. The wear mechanism of bearing contact surfaces was investigated by the means of energy dispersive spectrum analysis on the surface morphology and friction and wear status of the journal bearing specimens by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometer (EDS). Besides, the wearing capacity of the textured bearing was predicted by using the GM (1,1) and Grey–Markov model.

As the loads increase, the friction coefficient of journal bearing specimens decrease first and then increase slowly. The higher rotation speed, the lower friction coefficient and the faster temperature build-up. The main friction method of the bearing sample is three-body friction. The existence of texture can effectively reduce friction and wear. In many arrangement patterns, the best is 4# bearing with round pits cross-arrangement pattern. Its texturing diameters are 60 µm and 125 µm, and the spacing and depth are 200 µm and 25 µm, respectively. In addition, the Grey–Markov model prediction result is more accurate and fit the experimental value better.

The friction and wear mechanism is helpful for scientific research and engineers to understand the tribological behaviors and engineering applications of textured bearing. The wear capacity of textured bearing is predicted by using the Grey–Markov model, which provides technical help and theoretical guidance for the service life and reliability of textured bearing.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this paper investigates dynamic ease distributions of clothes at bust and waist lines with different body surface angle by using a Qualisys three-dimensional motion capture system (3DMCS).

The current method first obtain the specific markers of participants and their clothes along the bust and waist lines through 3DMCS, then using the least square method and four piecewise polynomial fitting participants and their clothes' bust and waist curves. The coordinates of the markers were tracked by the 3DMCS, while the participants under different body surface angle walked on a treadmill calculated the distances of markers coordinates to the participants' bust and waist curves. Finally, the data of samples were analyzed. It was found that the dynamic ease distributions showed different patterns at different body surface angle.

The results revealed the bust convex angle is 26.53 degrees (Specification:X3) and back slope angle is 13.96 degrees (Specification: Y1), the fluctuation of participant ease distributions on bust section was most obvious, and the maximum fluctuation value was ±20 mm and ±25 mm. The ease distributions of participant waist section fluctuated most obviously when the bust convex angle is 28.10 degrees (Specification: X5) and the back slope angle is 13.96 degrees (Specification: Y1), and the maximum fluctuation was ±30 mm and ±20 mm. The bust convex angle has the greatest influence on 1# garment, and the back slope angle has the greatest influence on 2# garment.

Currently, there is little information in the literature about dynamic ease distributions of garment on a different body types. This paper takes different body surface angles as the research objects to analyze the ease distributions of different clothes, the conclusion can provide reference data for 3D garment modeling and improve the authenticity of virtual garment fitting.

This study aims to propose a semiempirical and semitheoretical cyclic compaction constitutive model of coarse-grained soil filler for the high-speed railway (HSR) subgrade under cyclic load.

According to the basic framework of critical state soil mechanics and in view of the characteristics of the coarse-grained soil filler for the HSR subgrade to bear the train vibration load repeatedly for a long time, the hyperbolic empirical relationship between particle breakage and plastic work was derived. Considering the influence of cyclic vibration time and stress ratio, the particle breakage correction function of coarse-grained soil filler for the HSR subgrade under cyclic load was proposed. According to the classical theory of plastic mechanics, the shearing dilatation equation of the coarse-grained soil filler for the HSR subgrade considering particle breakage was modified and obtained. A semiempirical and semitheoretical cyclic compaction constitutive model of coarse-grained soil filler for the HSR subgrade under cyclic load was further established. The backward Euler method was used to discretize the constitutive equation, build a numerical algorithm of “elastic prediction and plastic modification” and make a secondary development of the program to solve the cyclic compaction model.

Through the comparison with the result of laboratory triaxial test under the cyclic loading of coarse-grained soil filler for the HSR subgrade, the accuracy and applicability of the cyclic compaction model were verified. Results show that the model can accurately predict the cumulative deformation characteristics of coarse-grained soil filler for the HSR subgrade under the train vibration loading repeatedly for a long time. It considers the effects of particle breakage and stress ratio, which can be used to calculate and analyze the stress and deformation evolution law of the subgrade structure for HSR.

The research can provide a simple and practical method for calculating deformation of railway under cyclic loading.