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The purpose of this paper is to improve the positioning accuracy of 6-Dof serial robot by the way of error compensation and sensitivity analysis.

In this paper, the Denavit–Hartenberg matrix is used to construct the kinematics models of the robot; the effects from individual joint and several joints on the end effector are estimated by simulation. Then, an error model based on joint clearance is proposed so that the positioning accuracy at any position of joints can be predicted for compensation. Through the simulation of the curve path, the validity of the error compensation model is verified. Finally, the experimental results show that the error compensation method can improve the positioning accuracy of a two joint exoskeleton robot by nearly 76.46%.

Through the analysis of joint error sensitivity, it is found that the first three joints, especially joint 2, contribute a lot to the positioning accuracy of the robot, which provides guidance for the accuracy allocation of the robot. In addition, this paper creatively puts forward the error model based on joint clearance, and the error compensation method which decouples the positioning accuracy into joint errors.

It provides a new idea for error modeling and error compensation of 6-Dof serial robot. Combining sensitivity analysis results with error compensation can effectively improve the positioning accuracy of the robot, and provide convenience for welding robot and other robots that need high positioning accuracy.

The purpose of this paper is to improve the accuracy of fused deposition modeling (FDM) machines.

An integrated error model and compensation methods are developed to improve the accuracy of FDM machines. The effects of machine-dependent and process-dependent errors are included in this integrated model. The error model is then used to obtain compensated values for the printed object. A three-dimensional artifact is designed for the FDM machine characterization. This process takes place only once and an error model for the machine is then developed. An artifact is designed that is feature rich and its coordinates are measured by the coordinate measuring machine (CMM). The CMM digitized values for the three-dimensional artifact are used to calculate the coefficients of the model. The integrated error model of the machine can be used to obtain the compensated values for any given part models. The coefficients of the integrated error model are machine-dependent and represent machine error estimation. To demonstrate this, two test examples are used and modified based on the machine model to verify the effectiveness of the proposed method.

The errors from machine mechanical structure and process are evaluated. The variation trend of each error is analyzed. The uncompensated and compensated models are compared, and the effectiveness of the integrated error model and compensation method is analyzed and validated.

An effective integrated error model with compensation is developed, which can be used to improve the FDM machines accuracy.

The lack of geometric and dimensional accuracy of parts produced by additive manufacturing (AM) is directly related to the machine, material and process used. This paper aims to propose a method for the analysis and compensation of machine-related geometric errors applicable to any AM machine, regardless of the manufacturing process and technology used.

For this purpose, an error calculation model inspired by those used in computerized numerical control machines and coordinate measuring machines was developed. The error functions of the model were determined from the position deviations of a set of virtual points that are not sensitive to material and process errors. These points were obtained from the measurement of an ad hoc designed and manufactured master artefact. To validate the model, off-line compensation was applied to both the original designed artefact and an example part.

The geometric deviations in both cases were significantly smaller than those found before applying the geometric compensation. Dimensional enhancements were also achieved on the example part by using a correction parameter available in the three-dimensional printing software, whose value was adjusted from the measurement of the geometrically compensated master artefact.

The errors that persist in the part derive from both material and process. Compensation for these type of errors requires a detailed analysis of the influencing parameters, which will be the subject of future research.

The use of the virtual-point-based error model increases the quality of additively manufactured parts and can be used in any AM system.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The purpose of this study is to improve the accuracy of a fused deposition modeling three-dimensional (3D) printer by identifying and compensating for position-independent geometric errors using a face-diagonal length test featuring a designed artifact and a Vernier caliper.

An artifact that does not require support when printing was designed and printed to allow performance of the face-diagonal length test. A Vernier caliper was used to measure the lengths of diagonals in the XY, YZ and ZX planes of the printed artifact specimen; this completed the face-diagonal length test. The relationships between position-independent geometric errors of the linear axes X, Y and Z and the measured diagonal lengths of the three planes were determined to identify geometric errors.

The approach was applied to a commercial fused deposition modeling 3D printer, and three position-independent geometric errors were rapidly identified. The artifact was re-printed after model-based compensation for these errors and the diagonal lengths were re-measured. The results were verified via coordinate measuring machine measurement of a simple test piece without and with model-based compensation for identified geometric errors. Furthermore, the proposed approach was applied to a commercial 3D printer.

The measured diagonal lengths of the printed artifacts varied greatly. Thus, further studies should investigate the effects of printing materials and parameters on the length discrepancies of 3D printed artifacts.

A software-based compensation of identified position-independent geometric errors has to be used at commercial 3D printers for accuracy improvements of printed parts.

Thus, the approach is of practical utility; it can be periodically used to identify position-independent geometric errors and ensure that the 3D printer is consistently accurate.

The purpose of this paper is to describe a novel error‐ranking methodology and two compensation strategies for hybrid parallel kinematic machines (HPKMs).

The paper outlines an error analysis methodology developed for HPKMs and applies the technique to a typical industrial HPKM. Based on the results of this, two compensation strategies are developed and implemented, for both mass‐induced and thermal errors.

The paper demonstrates and quantifies the performance improvements possible with appropriate error compensation strategies.

The paper introduces a novel and generic methodology for error source analysis and describes two fully implemented compensation strategies which result in a significantly improved level of system performance.

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

The pivotal aspect of aircraft assembly lies in precise measurement accuracy. While a solitary digital measuring tool suffices for analytical and small surfaces, it falls short for extensive synthetic surfaces like aircraft fuselage panels and wing spars. The purpose of this study is to develop a “combined measurement method” (CMM) that enhances measurement quality and expands the evaluative scope, addressing the limitations posed by singular digital devices in meeting measurement requirements across various aircraft components.

The study illustrated the utilization of the CMM by combining a laser tracker and a portable arm-measuring machine. This innovative approach is tailored to address the intricate nature and substantial dimensions of aircraft fuselage panels. The portable arm-measuring machine performs precise scans of panel components, while common points recorded by the laser tracker undergo coordinate conversion to reconstruct the fuselage panel’s shape. The research outlines the CMM’s measurement procedure and scrutinizes the data processing technique. Ultimately, the investigation yields a deviation vector matrix and chromatogram deviation distribution, pivotal in achieving enhanced measurement precision for the novel CMM device.

The use of CMM noticeably enhances fuselage panel assembly accuracy, concurrently reducing assembly time and enhancing efficiency compared to conventional measurement systems.

The research’s practical implication lies in revolutionizing aircraft assembly by mitigating accuracy issues through the innovative digital CMM for aircraft synthetic structure type product (aircraft fuselage panel). This ensures safer flights, reduces rework and enhances overall efficiency in the aerospace industry.

Introducing a new aircraft assembly accuracy compensation method through digital combined measurement, pioneering improved assembly precision. Also, it enhances aerospace assembly quality, safety and efficiency, offering innovative insights for optimized aviation manufacturing processes.

The purpose of this paper is to analyze the sensitivity of the motion error parameters in microassembly process, thereby improving the assembly accuracy. The motion errors of the precision motion stages directly affect the final assembly quality after the machine visual alignment.

This paper presents the error parameters of the in-house microassembly system with coaxial alignment function, builds the error transfer model by the multi-body system theory, analyzes the error sensitivity on the sensitive direction using the Sobol method, which was based on variance, and then gets the ones which made a great degree of influence. Before the sensitivity analyzing, parts of the error sources have been measured to obtain their distribution ranges.

The results of the sensitivity analysis by the Sobol method, which was based on variance, are coincident with the theoretical analysis. Besides, the results provide a reference for the error compensation in control process, for the selection of the precision motion stages and for the installation index of the motion stages of the assembly system with coaxial alignment.

This kind of error sensitivity analysis method is of great significance for improving the assembly accuracy after visual system positioning, and increasing efficiency from the initial motion stage selection to final error compensation for designers. It is suitable for general precision motion systems be of multi-degree of freedom, for the method of modeling, measuring and analyzing used in this paper are all universal and applicative.

The purpose of this study is to develop a modified parameter identification method and a novel measurement method to calibrate a 3 degrees-of-freedom (3-DOF) parallel tool head. This parallel tool head is a parallel mechanism module in a five-axes hybrid machine tool. The proposed parameter identification method is named as the Modified Singular Value Decomposition (MSVD) method. It aims to overcome the difficulty of choosing the algorithm parameter in the regularization identification method. The novel measurement method is named as the vector projection (VP) method which is developed to expand the measurement range of self-made measurement implements.

Newton Iterative Algorithm based on Least Square Method is analyzed by using the Singular Value Decomposition method. Based on the analysis result, the MSVD method is proposed. The VP method transforms the angle measurement into the displacement measurement by taking full advantage of the ability that the 3-DOF parallel tool head can move in the X – Y plane.

The kinematic calibration approach is verified by calibration simulations, a Rotation Tool Center Point accuracy test and an experiment of machining an “S”-shaped test specimen.

The kinematic calibration approach with the MSVD method and VP method could be successfully applied to the 3-DOF parallel tool head and other 3-DOF parallel mechanisms.