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The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light.

The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained.

The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy.

BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.

A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.

The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.

A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

There is an increasing demand for higher-accuracy dimensional measurements of nano- and micro-structures. Recently, the authors presented a fiber Bragg grating (FBG) sensor-based dynamic nano-coordinate-measuring machine (CMM) probe for true three-dimensional coordinate measurement, in which a specific mechanical structure with several FBG sensors was developed to provide the probe with sensitivity to loading in all directions.

The study presents a three-dimensional sensing and demodulation system based on an improved matched filter design and the time division multiplexing technique that helps solve the problem of multiplex FBG-signals conflicts. In addition, the application of the dynamic mode of the probe system effectively solves the problem presented by the surface interaction forces.

Consequently, this FBG-based vibrating probe system has increased sensitivity to strain, while maintaining smaller contact force. The experiments for testing probe performance show that the prototype yielded a measurement resolution of 13 nm, a repeatability of 50 nm and a vertical measurement force of less than1.5 mN.

The force tests in the horizontal directions are difficult to conduct because both the probe and the dynamometer are only adaptable to vertical use.

Development of the FBG-based dynamic nano-coordinate-measuring machine probe will achieve a new and inexpensive method for higher-accuracy dimensional measurements of nano- and micro-structures, such as micro-electromechanical systems, micro-fluidic chips, inkjet and diesel engine injector nozzles that are in overall dimensions within the micrometer scale.

The study presents a three-dimensional sensing and demodulation system for the vibrating nano-coordinate-measuring machine probe based on FBG sensors. The prototype yielded a measurement resolution of 13 nm, a repeatability of 50 nm and a vertical measurement force of less than1.5 mN.

The purpose of this paper is the presentation and research of a novel robot vision system, which uses laser dynamic triangulation, to determine three-dimensional (3D) coordinates of an observed object. The previously used physical operation principle of discontinuous scanning method is substituted by continuous method. Thereby applications become possible that were previously limited by this discretization.

The previously used prototype No. 2, which uses stepping motors to realize a discontinuous laser scan, was substituted by the new developed prototype No. 3, which contains servomotors, to achieve a continuous laser scan. The new prototype possesses only half the width and turns out to be significantly smaller and therefore lighter than the old one. Furthermore, no transmissions are used, which reduce the systematic error of laser positioning and increase the system reliability.

By using a continuous laser scan method instead of discontinuous laser scan method, dead zones in the laser scanner field can be eliminated. Thereby, also by changing the physical operation principle, the implementation of applications is allowed, which previously was limited by the fixed step size or by the object distance under observation. By using servomotors instead of stepping motors, also a significant reduced positioning time can be accomplished maintaining the relative positioning error less than 1 per cent.

The originality is based on the substitution of the physical operation principle of discontinuous by continuous laser scan. The previously used stepping motors discretized the laser scanner field and thereby produced dead zones, where 3D coordinates cannot be detected. These stepping motors were substituted by servomotors to revoke these disadvantages and provide a continuous laser scan, where dead zones in the field of view get eliminated and the step response of the laser scanner accelerated.

The weld joint of large thin-wall metal parts which deforms in manufacturing and clamping processes is very difficult to manufacture for its shape is different from the initial model; thus, the space normals of the part surface are uncertain.

In this paper, an effective method is presented to calculate cutter location points and to estimate the space normals by measuring some sparse discrete points of weld joint. First, a contact-type probe fixed in the end of friction stir welding (FSW) robot is used to measure a series of discrete points on the weld joint. Then, a space curve can be got by fitting the series of points with a quintic spline. Second, a least square plane (LSP) of the measured points is obtained by the least square method. Then, normal vectors of the plane curve, which is the projection of the space curve on the LSP, are used to estimate the space normals of the weld joint curve. After path planning, a post-processing method combing with FSW craft is elaborated.

Simulation and real experiment demonstrate that the proposed strategy, which obtains cutter locations of welding and normals without measuring the entire surface, is feasible and effective for the FSW of large thin-walled complex surface parts.

This paper presents a novel method which makes it possible to accurately weld the large thin-wall complex surface part by the FSW robot. The proposed method might be applied to any multi-axes FSW robot similar to the robot studied in this paper.

The purpose of this paper is to develop a transform method and a deep learning model to identify the inner surface shape based on the measurement temperature at the outer boundary of the pipe.

The training process is assisted by the finite element method (FEM) simulation which solves the direct problem for the data preparation. To avoid re-meshing the domain when the inner surface shape varies, a new transform method is proposed to transform the shape identification problem into the effective thermal conductivity identification problem. The deep learning model is established to set up the relationship between the measurement temperature and the effective thermal conductivity. Then the unknown geometry shape is acquired by the mapping between the inner shape and the effective thermal conductivity through the inverse transform method.

The new method is successfully applied to identify the internal boundary of a pipe with eccentric circle, ellipse and nephroid inner geometries. The results show that as the measurement points increased and the measurement error decreased, the results became more accurate. The position of the measurement point and mesh density of the FEM model have less effect on the results.

The deep learning model and the transform method are developed to identify the pipe inner surface shape. There is no need to re-mesh the domain during the computation progress. The results show that the proposed method is a fast and an accurate tool for identifying the pipe inner surface.

This paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.

The paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.

The paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.

Machine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

The purpose of this paper is to achieve the trajectory tracking measurement of a moving target based on double position sensitive detectors (PSDs).

In this paper, first, a double PSD-based measurement system including hardware system and software system is built up. Then, the working principle is studied to calculate parameters, and calibration experience is conducted. Finally, this double PSD-based measurement system is used to test angular displacement and axial displacement on the tool magazine and automatic tool changer.

In the experiment, the maximum position error of a space point based on double PSD measurement system is 0.8566 mm, and the average error is 0.4716 mm. These results show that the built double PSD-based measurement system of trajectory tracking of a moving target is reasonable.

Combining the characteristics of the PSD and principles of binocular visual measurement, a non-contact three-dimensional measuring system based on double PSDs is developed. The designed double-based measurement system is quite suitable for measurement of a fast-changing illuminant or in the case that the tracking accuracy is not tight.

Laser scanning is increasingly used in many three‐dimensional (3‐D) measurement and modeling applications. It is the latest technique used in 3‐D measurement, and is becoming increasingly important within a number of applications. However, many applications require photogrammetric data in addition to laser scanning data. The purpose of this paper is to present a range and image sensor combination for three‐dimensional reconstruction of objects or scenes.

In this study, a Nikon D80 camera was mounted on an Ilris 3D laser scanner and CPP was estimated according to the laser scanner coordinate system. The estimated CPP was controlled using three different methods which were developed in this study and a sample application as coloring of point cloud using image taken by the camera mounted on the laser scanner was performed.

It was found that when a high‐resolution camera is mounted on laser scanners, camera position parameters (CPP) should be estimated very accurately with respect to the laser scanner coordinate system.

The paper shows that the combination of high‐resolution camera and laser scanners should be used for more accurate and efficient results in 3D modeling applications.

The purpose of this paper is to increase the measurement accuracy of assembly deviations of an inertial navigation system, a new evaluation and optimal method of assembly metrology system is proposed, which takes into account the uncertainty from laser tracker hardware and coordinate system transformation, and is based on the Monte Carlo method.

The uncertainty model of the laser tracker is established and its parameters are obtained from the known repeated test data by kriging interpolation and the least squares method. The errors of coordinate transformation are reduced by using a weighted point matching method, and the uncertainty of the transformation parameters is obtained based on the generalized inverse theory. The weighting coefficients of each reference point are optimized by the particle swarm optimization method according to the assembly requirements.

The experiment results show that measurement error and predicted results match well, and the assembly deviation uncertainty of large component is reduced by about 10 per cent compared with the singular value decomposition method.

This paper proposes a method to evaluate and eliminate the influence of random errors of the laser tracker during evaluation process of coordinate translation parameters and assembly deviations. The proposed method would be useful to improve the assembly measurement accuracy through less measurement times.