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Aiming at the problem that machine tool thermal error (MTE) and probe pre-travel error (PPE) seriously weaken measuring accuracy, existing researches are conducted under certain conditions that MTE in a constant ambient temperature is considered or only PPE is compensated. A novel comprehensive error MTE-PPE model with consideration of different constant ambient temperatures is developed to enhance measuring accuracy. Independently developed new type of horizontal CNC gear grinder (L300G) regarded as the research object, MTE-PPE is equivalent to probe slip error (PSE) based on involute property and L300G involute gear measuring principle. Mini-batch gradient descent optimization algorithm (MGDOA) is employed to reckon more reasonable PSE compensation values, on the basis of which grinded topological modification tooth surface is fitted according to the least squares method (LSM) tooth surface fitting principle. The theoretical topological modification tooth surface is constructed founded on the L300G involute helical gear topological modification grinding principle, and morphology deviation is derived from matching the constructed and fitted tooth surfaces. Results show that, before and after PSE compensation, L300G tooth lead measuring accuracy is level 4; after PSE compensation, L300G tooth profile measuring accuracy is improved by 2 grades, reaching level 4, which is consistent with Gleason 350 GMS measuring report; fluctuation range of tooth surface morphology deviation is 0–5 μm, and the tooth profile error and tooth lead error extracted from morphology deviation are consistent with Gleason 350GMS measuring report. The model effectively enhances measuring accuracy, truly reflects the 3D morphology of grinded topological modification tooth surface, and fairly eliminates the limitation of incomplete evaluation of a single error measured item. It is expected to be popularized in high-precision measuring and grinding technology of horizontal or vertical gear grinders. [ABSTRACT FROM AUTHOR]

The primary factors affecting the geometric accuracy of abrasive water jet (AWJ) machining of ultra‐thick carbon fiber‐reinforced polymer (CFRP) laminates are the cutting front drag and kerf width deviation caused by energy dissipation. Firstly, this study comprehensively analyzes the influence of the coupling relationship between cutting front drag and kerf width deviation on the geometric accuracy of hole machining. Secondly, a gradient distribution theory for jet traverse speed with cutting depth is proposed for taper hole machining characteristics. Finally, a general geometric error model applicable to both straight and taper hole machining of ultra‐thick CFRP laminates using AWJ is established, along with a geometric error compensation method. A series of experiments validate the accuracy of the proposed geometric error model and compensation method. The novelty of this research lies in unifying the geometric error models for straight and taper hole machining of ultra‐thick CFRP laminates by considering the gradient distribution of jet traverse speed. The proposed error compensation method reduces the high degree of freedom required for machine tools. This study provides a general geometric error model and compensation strategy for AWJ hole machining of ultra‐thick CFRP laminates, which has significant engineering value for achieving 3D controllable AWJ machining of complex ultra‐thick CFRP components. Highlights: The high geometric precision taper holes machining technique is a necessary prerequisite for achieving precise machining of complex trajectories in ultra‐thick CFRP with abrasive water jet.The influence of the coupling relationship between cutting front drag and kerf taper on the geometric accuracy of hole processing is comprehensively analyzed.A model considering the gradient distribution of traverse speed during taper holes machining was established, and a geometric error model considering this gradient distribution of traverse speed was developed.A taper holes machining geometric error compensation method with high engineering applicability was proposed.Geometric error prediction experiments and error compensation experiments were conducted, and the results proved the accuracy of the geometric error model and error compensation method. [ABSTRACT FROM AUTHOR]

Amidst the accelerating pace of automation in sheet metal bending, the need for small-batch, multi-varietal, efficient, and adaptable production modalities has become increasingly pronounced. To address this need and to enhance the efficacy of the bending process, this study presents the design and development of an embedded soft PLC (Programmable Logic Controller) rooted in the Codesys development platform and leveraging the ARM Cortex-A55 architecture. This controller employs the EtherCAT communication protocol to facilitate seamless and efficient interactions with fully electric servo-driven CNC (Computerized Numerical Control) bending machinery. To mitigate the challenge of bending springback errors, a finite element simulation model is constructed and refined through the application of ALE (Arbitrary Lagrangian-Eulerian) adaptive grid technology, thereby bolstering simulation precision. Subsequently, an enhanced WOA-BP (Whale Optimization Algorithm—Backpropagation) model, integrating Latin hypercube sampling and neural network techniques, is deployed to anticipate and counteract these springback errors. Experimental outcomes demonstrate that the proposed methodology effectively constrains the final forming angle deviation to within 0.3°, significantly enhancing the reliability and precision of the bending system. This achievement not only underscores the technical feasibility but also contributes to advancing the frontier of sheet metal bending automation. [ABSTRACT FROM AUTHOR]

With the continuous development of power system and the growth of load demand, accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method. In the first stage, the original load is processed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The time series features of the load are extracted by temporal convolutional network (TCN), which is used as an input to realize the initial load prediction based on gated recurrent unit (GRU). At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the advantages of low cost, high flexibility, and large workspace, industrial robot has been considered to be the most promising plan to replace traditional CNC machine tool. However, the low absolute positioning accuracy of robot is a key factor that restricts further application in high-precision metal-cutting scenarios. In order to improve the absolute positioning accuracy of robot, a positioning error compensation method based on the stacked ensemble learning is proposed. Firstly, the sources of positioning errors and compensation strategies are clarified by analyzing the kinematic model and structural composition of industrial robot. Then, based on the stacked ensemble learning algorithm, robot positioning error prediction model containing multi-layer learners is constructed. And a discrete grid optimization method is presented for model hyper-parameters optimization calculation. Next, predicted positioning errors are adopted to realize the positioning compensation by offline compensation method. Finally, by setting up a robotic milling platform based on MOTOMAN ES165D robot, a series of error compensation experiments have been implemented to verify the proposed method. After compensation, the maximum absolute position error and average position error have decreased by 83% and 89%, respectively, in the compensation experiments of a single point. Moreover, the error compensation of the end milling experiments has also brought significant accuracy improvement, which proved the effectiveness of the proposed method in robotic machining. [ABSTRACT FROM AUTHOR]

Featured Application: Using a calibration sphere with a known diameter allows for calibration of a robot that includes both geometric errors and end-effector deformation errors. A self-calibration method utilizing spherical constraints is proposed for calibration of robot end-effectors. The method establishes a mathematical model to account for both the geometric errors of the robot and the deformation errors of the end-effector. A nonlinear least-squares parameter identification technique based on spherical constraints is employed to achieve autonomous calibration of the end-effector. Contrasted with methodologies relying on point plane or distance constraints, this novel technique delivers superior positioning accuracy, streamlined operational procedures and enhanced efficiency. Both simulation and experimental validation confirm that the self-calibration method using spherical constraints improves the positioning accuracy of the robot end-effector from 3 mm to 0.3 mm, showing the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect. [ABSTRACT FROM AUTHOR]

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified. [ABSTRACT FROM AUTHOR]

Metal mirrors will be widely used in the coming decades. Therefore, as one of the enabling technologies for metal optical freeform surface manufacturing, ultra-precision (UP) diamond turning error compensation has become a research hotspot. However, for the tool offset error and tool radius error, which are the main errors in UP diamond turning, no precise and efficient calculation method has been found in the literature. In this study, a more precise and efficient algorithm was developed and validated in three ways using on-machine measurement data and profilometer measurement data. After one compensation, the tool offset error can be reduced to below 0.1 μm, and the tool radius error can be reduced to below 1 micrometer, which will significantly improve the UP turning accuracy and efficiency of optical parts. [ABSTRACT FROM AUTHOR]

In vector-controlled Permanent Magnet Synchronous Motors (PMSMs), measuring the motor flux angle, particularly the rotor position, is essential. If a pulse error occurs during motor control using an encoder, it becomes impossible to accurately estimate the rotor position, leading to incorrect position angle information being used in the coordinate transformation. This mismatch causes discrepancies between the commanded three-phase stator current and the actual current applied to the motor. As a result, the motor and inverter output and efficiency decrease, and the control performance deteriorates. Therefore, research is necessary to compensate for such errors. This paper proposes an algorithm that detects pulse errors occurring in incremental encoders and automatically switches to the Hall sensor control mode to compensate for the encoder pulse errors. The proposed algorithm, based on Hall sensors, has the advantage of not significantly affecting control delays, which could be problematic in high-speed operating ranges; thus, effective control is maintained even in such situations. [ABSTRACT FROM AUTHOR]

To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation. [ABSTRACT FROM AUTHOR]

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance. [ABSTRACT FROM AUTHOR]

Several academic and budgetary applications in robotics use low-cost encoders which usually present errors inherent to the fabrication of their components or the surrounding environment. However, the data gathered from these sensors could be used successfully if the estimations based on the data are be compensated. This note presents an efficient method to compensate the error of estimation for the distance traveled by robotic four-wheeled vehicles with a speed control based on pulse width modulation. The current approach uses a methodology based on artificial neural networks which compensate the estimation error and enables a way to model the error for a further method of position estimation. Precisely, the model proposed in this work is based on a neural network with one hidden layer and five nodes. Our approach uses the information from the pulse width modulation value to control the speed of the vehicle as well as the values for the tick counting of the encoders in the four wheels. The back-propagation algorithm was used to calculate the weights of the nodes in the neural network. This method showed an improvement in the results with an error comparable to the intrinsic error due to the precision of the sensor, and gives an error model based on a zero-mean normal distribution. [ABSTRACT FROM AUTHOR]

In response to the problems of cumulative errors and inaccurate correction results in the attitude calculation method of hydraulic supports based on inertial measurement units, a fully mechanized working face hydraulic support attitude monitoring method based on particle swarm optimization (PSO) - extreme learning machine (ELM) is proposed. Using the pitch angle of the hydraulic support top beam as the monitoring object, a tilt sensor and gyroscope are used to collect real-time information on the support attitude of the hydraulic support top beam. The collected data is preprocessed and input into the PSO-ELM error compensation model to obtain the predicted solution error. At the same time, the hydraulic support attitude is calculated through Kalman filtering fusion to obtain the calculated value. Then the method uses the error prediction value to compensate for the error in the calculated value, in order to obtain more accurate data on the top beam support attitude. This method only considers the relationship between acceleration and angular velocity data and solution errors, without relying on specific physical models. It can effectively reduce the cumulative error of attitude solution. The experimental results show that the average absolute error of the pitch angle of the top beam of the hydraulic support has been reduced from 1.420 8° before compensation to 0.058 0°. The error curve has good convergence, verifying that the proposed method can sustainably and stably monitor the support attitude of the hydraulic support.

Aiming at the issue of low accuracy in crop mapping and growth monitoring caused by imprecise calibration of radar time-series data, this paper proposed a Synthetic Aperture Radar (SAR) spatio-temporal error compensation method. By constructing a compensation reference image using the buildings with stable backscattering coefficients in the study area, the error correction of time-series SAR data was realized based on the compensation model. The compensated SAR data were used to establish the Sugarcane Growth Index (SGI) according to the growth curve of sugarcane. The sugarcane area was extracted after combining the compensated SGI and optical image, and the accuracy of F1 on sugarcane distribution extraction was 92.12%. There was an improvement of 4.11% compared with no compensation method. On this basis, the newly planted and ratoon sugarcane were further classified by the expectation maximization algorithm based on the compensated SAR image at the seedling stage. The newly planted sugarcane and the ratoon sugarcane could be accurately distinguished with area extraction accuracy of 82.06% and 80.59%, respectively. Moreover, the compensated SAR data of time series during the tillering to maturity stages were used to estimate the sugarcane height, yielding an R2 value of 0.717 using a quadratic polynomial model. The Spatio-temporal error compensation method for distributed targets proposed in this study can reduce the loss of SAR data across various times and regions, and achieve accurate results of sugarcane distribution extraction and plant height estimation.

The traditional filter weighing method has a cumbersome operation process, a long detection cycle, and a low degree of automation. Although it has high accuracy, it cannot meet the needs of real-time detection of dust concentration. An automatic dust mass concentration detection device was designed, and the temperature and humidity compensation model was established to replace the steps of filter membrane drying after sampling in the manual weighing method, so as to further reduce the dust concentration detection time and the volume of the dust concentration detection device. The prototype device was built and the experiment was conducted. The test results showed that the standard deviation of the CCZ-20A dust sampler commonly used in coal mines and the converted concentration of dust detected by this device was within 5%, and the fitting correlation of the experimental data under the univariate linear regression fitting analysis was good. In order to further improve the detection accuracy of the device, an error compensation method based on the combination of Fourier series, linear fitting and periodic fitting was studied, and the concentration calculation process of the device was established and brought into the original data, and the detection concentration error of the device was concentrated from the original (−7.20%, −1.26%) to (−3.64%, 3.65%). After the introduction of the device concentration calculation process, several comparative experiments were carried out, and the experimental results showed that the detection errors of the device concentration were within this range, which verified the reliability of the device concentration calculation process. The device shortens the time required for dust concentration detection, and at the same time controls the detection error within a reasonable range, which provides a reference for the research of filter membrane weighing method in the online monitoring of dust concentration.

In order to solve the problem of the low end positioning accuracy of large hydraulic rock drilling robotic arms due to machining error and the working environment, this paper proposes an end positioning error compensation method based on an Improved Secretary Bird Optimization Algorithm (ISBOA) optimized Back Propagation (BP) neural network. Firstly, the good point set strategy is used to initialize the secretary bird population position to make the initial population distribution more uniform and accelerate the convergence speed of the algorithm. Then, the ISBOA is used to optimize the initial weights and biases of the BP neural network, which effectively overcomes the defect of the BP neural network falling into a local optimum. Finally, by establishing the mapping relationship between the joint value of the robot arm and the end positioning error, the error compensation is realized to improve the positioning accuracy of the rock drilling robot arm. The experimental results show that the average positioning error of the rock drilling robotic arm is reduced from 187.972 mm to 28.317 mm, and the positioning accuracy is improved by 84.94%, which meets the engineering requirements. [ABSTRACT FROM AUTHOR]

Considering the detrimental impact of thermal phenomena on the geometric precision of machine tools, a machine tool ball screw's omni-directional error model is created using the LSTM neural network algorithm. Subsequently, the machine tool ball screw's omni-directional error compensation module is devised by combining the core functions of the Huazhong numerical control system with the visual programming environment of QT and the numerical computation capability of Matlab. To enhance the practicality and accuracy of the compensation model, this study has employed the Whale Optimization Algorithm (WOA) to optimize the parameters of the LSTM model. This has resulted in an improvement in the model's generalization ability and prediction performance, making it more effective. During the experimental validation phase, the Z-axis error of the machine tool was practically operated and analyzed using the compensation method. Results manifestly show that, after employing the compensation method, the peak amplitude of the Z-axis error fluctuations have been notably curtailed to ±0.006 mm -- a considerable reduction compared to the initial error bandwidth of ±0.0145 mm. These empirical findings substantiate the efficacy of the proposed compensation strategy in substantially boosting the machining precision of products, thus furnishing a substantial and instructive benchmark for future inquiries into CNC machine tool error compensation technologies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-size parts such as aircraft wing structures require a lot of hole machining for assembly between parts. As demand for hole positional accuracy, such as in determinant assembly (DA) concept, securing the positional accuracy of hole machining is becoming more important. Specifically, during the hole machining of large-size parts, significant errors occur due to thermal expansion according to temperature change of a machine tool and a workpiece. In this study, a technique for efficiently reducing the positional error of hole machining in large-size parts is proposed by compensating geometric errors and thermal errors of a machine tool and a workpiece. The thermal displacement error, representing changes in volume error due to temperature variations in the machine tool, is estimated by measuring the distance between the centers of two spheres on the master with low thermal expansion coefficient measured at the reference temperature (20 °C). The thermal expansion error, caused by variations in workpiece temperature, is estimated by assuming the temperature of the workpiece to be that of the cutting fluid just before machining. Hole machining errors are integrally compensated by considering the geometric error measured at the reference temperature and the thermal errors of the machine tool and the workpiece. In the verification experiment, the maximum error was improved by 60.2%, and it was confirmed that the tendency of the error was significantly reduced. [ABSTRACT FROM AUTHOR]

Machining efficiency and accuracy are subjected to higher requirements due to the increasingly widespread application of sculptured surface parts in the fields of aerospace, transportation, and energy. However, the influence of geometric error, tool deflection, and other factors decreases the quality and reliability of CNC machined workpiece. Error compensation is a popular and effective way to improve the machining accuracy of the workpiece. A novel multi-stage error compensation methodology that combines model-based and data-driven approaches is proposed, which realizes the closed-loop quality control process of the sculptured surface by five-axis flank milling. The global process consists of two special stages. First, the compensated machining of stage I is completed by a flexible mirror compensation method after establishing the tool deflection prediction model, which is the major factor causing the geometric error of the machined part. Subsequently, based on the on-machine measurement (OMM) data, the surface is automatically reconstructed by mirror points in 3D software and the toolpath for stage II compensated machining is generated. Finally, the verification experiment on the five-axis machine tool demonstrates that the methodology introduced in this research can effectively enhance the machining accuracy of the workpiece. [ABSTRACT FROM AUTHOR]

Thin-walled parts processed by five-axis CNC machine tools are widely used in aerospace and other fields due to their excellent performance. However, due to the weak rigidity of thin-walled parts, they are prone to deformation during milling, which poses great difficulties for efficient and precise machining of thin-walled parts. This paper introduces the classification and corresponding machining methods of thin-walled parts. By analyzing the causes and evolution mechanisms of errors in the machining process of thin-walled parts, and combining modeling methods with factors such as milling force, residual stress, and cutting chatter, the current research status of domestic and foreign scholars on deformation factors is summarized. At the same time, two deformation control methods, adaptive machining and error compensation, were introduced. Finally, the overall research status of thin-walled parts machining was summarized, and prospects for efficient and precise machining of thin-walled parts were proposed based on actual machining conditions. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous Ground Vehicles (AGVs) can transfer or load and unload material in industrial scenarios due to their flexibility and operability, freeing people from tedious and repetitive labour. However, the dynamic scene and the system model uncertainty reduce the parking accuracy of AGVs in industrial scenes, which seriously affects the autonomous operation robustness and accuracy. Using sliding mode parking control with trajectory planning based on iterative error compensation, this paper proposes a data-driven parking control-planning integration solution for AGVs in complex industrial scenes, which allows AGVs to park accurately and converge to the target parking site quickly. First of all, a data-driven discrete sliding mode controller has been developed to iteratively enhance parking accuracy and effectively rectify the target parking position. This controller showcases insensitivity towards disturbances encountered during the erratic iterative error compensation process, thereby ensuring rapid and asymptotic convergence of the parking error in industrial scenarios. Then, to achieve efficient and smooth planning with the target parking site constantly being corrected, an improved Bi-RRT based trajectory planning scheme is proposed by considering operational constraints and node expanding region division, which provides the trajectory that contributes to parking convergence for the proposed controller promptly. Finally, the efficiency of the proposed method is verified by real-world experiments with self-developed AGV in industrial scenes, and experimental results show that the proposed method achieves accurate parking control with efficient trajectory planning and rapid parking error convergence. [ABSTRACT FROM AUTHOR]

Geometric rotary axis errors are one of the main factors affecting the dispensing positioning accuracy of five-axis vision dispensing machines. Hence, the accurate detection and compensation of these error terms is an important and challenging problem. In this study, a decoupling identification method for both position-dependent geometric errors and position-independent geometric errors of a rotary dispensing table is proposed based on vision measurement technology. According to this strategy, the screw theory and exponential product formula are used to establish a kinematic model of the end-effector, followed by the definition of the mapping relationship between position-independent geometric errors and axis posture. Then, to separate the coupling error terms of the rotating axes, a step-by-step identification strategy is proposed based on the least squares method. Furthermore, a coordinate search-based error compensation algorithm is developed, which avoids the inverse singular value problem and is insensitive to the error model. Finally, the effectiveness of the proposed identification algorithm and compensation algorithm is experimentally validated. [ABSTRACT FROM AUTHOR]

This paper presents an analog delay-locked loop (DLL) with a digital coarse lock and error compensation, designed to enhance locking speed in duty-cycled operation while ensuring reliability. To accelerate coarse locking speed and prevent coarse lock failure, the proposed DLL combines a low-resolution digital-to-analog converter (DAC) with an analog method for accurate lock range identification, efficiently handling scenarios where the DAC's limited resolution could lead to failure. Additionally, it enables the rapid control of voltage adjustments by disconnecting a loop filter during the coarse lock, eliminating the need for a buffer. The DLL improves the coarse lock process reliability by compensating for potential false lock errors caused by circuit non-idealities, such as residual RC delay and amplifier offset. Furthermore, it reuses the previously identified DAC input for the duty-cycled operation to significantly reduce relock time. To mitigate the risk of potential false lock resulting from changes in locking conditions, it can update the previous DAC input upon relocking, ensuring more reliable relocking. The proposed DLL, implemented in a 28 nm CMOS process, reduces initial lock and relock times by an average of 49.3% and 65.9% at a supply voltage of 0.5 V, and 42.4% and 70.2% at 1 V, respectively, compared to the conventional analog DLL. [ABSTRACT FROM AUTHOR]

Fresnel optical films can be efficiently produced in large quantities by manufacturing molds on roller machine tools, using Roll-to-Roll manufacturing technology, and copying the microstructure on the roller mold surface to PET films. The accuracy of the mold determines the quality of the optical films. This paper analyzes the manufacturing principle and processing process of a rotating-tool diamond turning roller mold on roller machine tools. Considering turning efficiency and freedom, two tandem rotating axes were added to the traditional three-axis roller machine tool to form a five-axis roller machine tool. The tandem rotating axis adjusts the tool angle so that the cutting edge directly cuts the Fresnel working surface, increasing the processing efficiency and accuracy. In order to improve manufacturing accuracy, this paper uses spinor theory to establish an error model for machine tools. It uses the Fourier amplitude sensitivity test (FAST) to find the five geometric errors. Finally, an error compensation iterative algorithm is proposed, which quickly and accurately calculates the error compensation amount. Experiments were conducted to study the chatter phenomenon during turning and analyze multiple influencing factors that cause cutting chatter. The experimental results revealed that except for the impact of the stiffness of the workpiece-tool system on cutting chatter, other factors have relatively little influence. Enhancing stiffness can significantly reduce the generation of cutting vibration marks and improve the surface quality of the workpieces. Finally, the Fresnel microstructure was turned on the roller mold. As a result, with reasonable process parameters on the surface, the cutting chatter phenomenon was improved, and the error compensation effect was significant, which verified the reliability of the error compensation method. • Improved roller machine design, and make it possible to manufacture large Fresnel roller molds. • The screw theory was used to model the error of the machine tool, and an error compensation experiment was conducted. • The chatter problem during the cutting process was analyzed through experiments, and the surface quality of the workpiece can be effectively improved by optimizing process parameters. [ABSTRACT FROM AUTHOR]

In order to achieve visual measurement error compensation for the surface of thin-walled components with chatter, a new real-time adaptive correction method for chatter motion error is proposed in this paper. Firstly, according to the mechanism of the chatter effect, an error analysis model of the dynamic structured light phase principal value is derived. Then, a real-time optimization method for the motion phase error in the frame of structured light projection is proposed. Meanwhile, a compensation method based on an adaptive grating projection period is proposed to eliminate the cumulative error of inter-frame chatter motion. Finally, a structured light visual inspection system and an experimental platform for chattering thin-walled parts are established for experimental verification. The experimental results under slight and severe chatter conditions indicate that the proposed method can optimize the phase errors within the projected frame in real time under any scenario, compensate for the cumulative errors between projected frames, and improve the point cloud accuracy of real-time 3D reconstruction. [ABSTRACT FROM AUTHOR]

In order to reduce the adverse effects of structural errors on a novel 5-degrees of freedom (DOF) parallel kinematic mechanism (PKM) with subclosed loops, studies on the elastodynamic modeling and structural error compensation are conducted in this paper. First, considering that introduction of subclosed loops significantly increased the complexity and difficulty of elastodynamic modeling, an equivalent unit modeling method for the branch chain with subclosed loop is proposed based on force analysis and deformation compatibility condition. Then, elastodynamic model of the whole 5-DOF PKM is established with the matrix structural analysis based method. Second, by defining a six-dimensional error (three translational errors and three rotational errors) for each node, structural error model of the PKM is constructed based on closed-loop vector method. To consider the coupling effects of static and dynamic structural errors, the dynamic structural errors derived by above elastodynamic model would be substituted into the structural error model along with given static structural errors. Finally, structural error compensation optimization model is established and structural errors are compensated by adjusting the driven parameters based on the improved particle swarm optimization (PSO) algorithm. Numerical simulations are conducted to verify the proposed elastodynamic modeling and structural error compensation methods. After compensation, the position errors along x, y, and z directions are respectively decreased by 68.036%, 81.409%, and 90.625%, and the angle errors around x and y directions are respectively decreased by 91.426% and 62.042%. [ABSTRACT FROM AUTHOR]

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Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot's accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively. [ABSTRACT FROM AUTHOR]

Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera's field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation. [ABSTRACT FROM AUTHOR]

To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others. [ABSTRACT FROM AUTHOR]

Aiming at the influence of the magnetic flux density uniformity error (MFDUE) of the Lorentz force magnetic bearing (LFMB) on the sensitivity accuracy of magnetically suspended control and sensing gyroscopes (MSCSGs) on the angular rate of a spacecraft, a high precision measurement method of the angular rate of a spacecraft based on the MFDUE compensation of LFMB is proposed. Firstly, the structure of MSCSG and the sensitivity principle of MSCSG to the spacecraft angular rate are introduced. The mechanism influencing the accuracy of MSCSG to spacecraft angular rate sensitivity is deduced based on the definition of magnetic flux density uniformity. Secondly, the 3D magnetic flux distribution of LFMB is analyzed using ANSYS. The relationship between the rotor tilt angle, tilt angular rate, and magnetic flux density is established. The induced current calculation model due to MFDUE is proposed, and the LFMB magnetic flux density error compensation is realized. Finally, the simulation results show that the estimation accuracy of the induced current by the proposed method can reach 96%, and the simulation and the experiment show that the error compensation method can improve the accuracy of MSCSG in measuring the spacecraft angular rate by 12.5%. [ABSTRACT FROM AUTHOR]

The final machining process, high-precision boring, can significantly affect the geometric error index of deep holes in gearboxes with thin-walled features. However, traditional mirror and mean compensation strategies are inadequate in compensating for the complex spatial and vector geometric errors, resulting in machining problems when boring deep holes in thin-walled boxes. This paper proposed a gradient compensation method that considered the geometric error and boring process characteristics to improve the current machining accuracy. The disadvantages of the classical compensation methods were analyzed in detail based on the error characteristics in boring deep holes. A gradient compensation was proposed and derived to improve the machining precision for boring deep holes on thin-walled boxes. On the other hand, a cutting test was designed and implemented using a control variable method in industrial cutting environment. Two different codes were programmed for boring process to validate the advantages of the proposed compensation method than the common. With the proposed compensation method, the geometric accuracy was improved by 86.12%, indicating that the proposed model was efficient for boring deep holes in thin-walled boxes. As a result, the proposed gradient compensation method successfully reduces the machining error of actual boring deep holes in thin-walled boxes. [ABSTRACT FROM AUTHOR]

In this paper, the geometric error modeling and compensation methods of macro–micro composite five-axis turn-milling composite machining center is studied. Firstly, the machine topological structure of the branches, intermediates, and terminal bodies of the macro–micro composite multi-body system is analyzed. Then, the tool chain transformation matrix and the end position of the workpiece chain are established to obtain the geometric error model. Based on the error compensation theory of traditional machine tool structure, the compensation mechanism of macro-level compensation and micro-level sub-micron compensation is proposed. Then, the compensation model of micro-axis error is given. Furthermore, the macro–micro composite error compensation experiment is setup; the laser interferometer is used to judge the positioning accuracy and straightness before and after compensation. The results show that the accuracy of the micro-motion platform after compensation reaches the sub-micron level, which verifies the compensation method, and the machining accuracy of the micron level is achieved through the cutting experiment. [ABSTRACT FROM AUTHOR]

Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the "nine-line method" is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the "S-shaped specimen" to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method. [ABSTRACT FROM AUTHOR]

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The thermal error is a significant factor that influences the machining accuracy of machine tools, and error compensation is an economical and effective method for enhancing the accuracy of machine tools. However, establishing a precise thermal error prediction model is crucial for thermal error compensation. In this paper, the subtraction-average-based optimizer-based CNN-GRU neural network (SABO-CNN-GRU) is applied to integrated thermal error modeling. Through conducting a thermal characteristic experiment, temperature rise data and thermal error data were collected from the linear feed system of LXK300X helical groove CNC machine tool. The fuzzy c-means clustering and grey correlation analysis are employed to identify temperature-sensitive points in the linear feed system. By utilizing the temperature rise data from these sensitive points along with feed shaft thermal errors as data samples, and using the SABO algorithm to optimize the CNN-GRU prediction model, the thermal error prediction model of SABO-CNN-GRU is established. To validate its superiority and practicality, a comparative analysis is conducted with traditional thermal error prediction models based on CNN-GRU and SO-ELM. The results demonstrate that SABO-CNN-GRU model outperforms both models in terms of mean absolute error (MAE), root mean square error (RMSE), remaining prediction deviation (RPD), mean square error (MSE), and determination coefficient (R2) in accurately predicting results. Building upon this achievement, this paper develops a real-time thermal error compensation system which effectively reduces maximum thermal errors from 80.5 to 17.6 μm after implementing compensation measures. Effectively reducing the influence of thermal errors and improving the machining accuracy of machine tools. [ABSTRACT FROM AUTHOR]

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision. [ABSTRACT FROM AUTHOR]

CVD rotary dressers are commonly used in the grinding process to dress wheels. To ensure the accuracy in profile of the dressed wheel, the CVD rotary dresser must achieve micron-level accuracy. However, CVD diamond is hard and brittle, making it difficult to achieve high-accuracy machining. In light of this, a graded grinding method is proposed in this paper to achieve precision machining of the CVD rotary dresser. The normal residual error is determined by measuring the distance between the actual profile curve and the target profile curve. Then, graded grinding is performed according to the normal residual error. In the grinding process, it is proposed that the grinding path can be planned according to the real-time profile of the grinding wheel. As revealed by the experiments conducted on a CVD rotary dresser for the NILES ZP 12 gear grinder, the profile accuracy (peak to valley, PV) of the CVD rotary dresser is improved from 14.79 to 1.57 μ m after grinding. The fluctuation of the arc radii of CVD diamonds in different phases along the circumference is reduced from 11.7 to 1.9 μ m. [ABSTRACT FROM AUTHOR]

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%. [ABSTRACT FROM AUTHOR]

Traditional methods for measuring geometric errors in machine tools, including interferometry and Double Ball Bar (DBB), are known to be expensive and time-intensive. Consequently, a non-contact calibration system called the "Laser R-test" has been developed. This innovative system is designed to measure both position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) efficiently. Since its development in 2000, this tool has been instrumental in analyzing eccentricity errors, angular position errors, and simultaneous trajectory errors. Through extensive research, it has been determined that the total error in a five-axis machine tool can be controlled to below 40 µm after compensating for eccentricity parameters and angular position errors. However, reducing this error to below ± 10 µm is challenging, primarily due to wobble errors in the orientation of the rotary axis without compensating. In this study, a new methodology based on Laser R-test and Rodrigues' rotation formula has been developed to establish a PIGE model of rotary axis. Based on the methodology, the 8 PIGEs can be analyzed by measuring 5 coordinate positions. The compensation of 8 PIGEs in the rotary axis is completed within 30 min using the inspection path. Compatibility with ISO-10791–6 standards for BK1, BK2, and BK4 path tests is confirmed, validating the compensation effects. A precision of below ± 10 µm is achieved, with inspection time reduced by over 50%. This system can complete multiple errors by simply using the different paths. This greatly reduces the setup time for future users, enhancing its commercial applicability. [ABSTRACT FROM AUTHOR]