At the core of this research is the pursuit of enhancing the trajectory tracking performance of six-degree-of-freedom (6-DOF) collaborative robots, with a particular focus on addressing the challenges posed by uncertainties in real-world applications. One of the primary issues encountered with existing methods is the susceptibility of trajectory tracking to uncertainties, which can significantly hinder the performance of robotic systems. To address these challenges, we propose an advanced control method, known as the Model-based proportional-derivative controller, or MPDP controller for short, which represents an innovative fusion of model-based PD control principles with a robust control algorithm. This amalgamation is driven by the need to mitigate the impact of uncertainties and external disturbances on trajectory tracking. A comprehensive assessment grounded in Lyapunov theory has been undertaken to validate the effectiveness of our approach. The analysis has firmly established that our method ensures not only the ultimate boundedness but also the uniform boundedness of the robotic system, which is critical for its operational stability. Both experimental and simulation studies have been meticulously conducted to benchmark the performance of the MPDP controller against the conventional proportional-integral-derivative (PID) controller, which serves as a widely adopted baseline in the field. The results unequivocally demonstrate the superiority of the MPDP controller across multiple dimensions. It exhibits exceptional robustness, resulting in a smaller steady-state tracking error, a critical advantage when addressing inherent uncertainties and external disturbances that can perturb the robot system. This translates to a more stable trajectory tracking performance. Furthermore, the MPDP controller empowers the robot with the capability to precisely follow predefined trajectories, thus ensuring high-precision and reliable execution of tasks. This feature significantly contributes to an overall enhancement of system performance and productivity.
{"title":"Advanced robust control design and experimental verification for trajectory tracking of model-based uncertain collaborative robots","authors":"Shengchao Zhen, Runtong Li, Xiaoli Liu, Ye-hwa Chen","doi":"10.1088/1361-6501/ad179d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad179d","url":null,"abstract":"\u0000 At the core of this research is the pursuit of enhancing the trajectory tracking performance of six-degree-of-freedom (6-DOF) collaborative robots, with a particular focus on addressing the challenges posed by uncertainties in real-world applications. One of the primary issues encountered with existing methods is the susceptibility of trajectory tracking to uncertainties, which can significantly hinder the performance of robotic systems. To address these challenges, we propose an advanced control method, known as the Model-based proportional-derivative controller, or MPDP controller for short, which represents an innovative fusion of model-based PD control principles with a robust control algorithm. This amalgamation is driven by the need to mitigate the impact of uncertainties and external disturbances on trajectory tracking. A comprehensive assessment grounded in Lyapunov theory has been undertaken to validate the effectiveness of our approach. The analysis has firmly established that our method ensures not only the ultimate boundedness but also the uniform boundedness of the robotic system, which is critical for its operational stability. Both experimental and simulation studies have been meticulously conducted to benchmark the performance of the MPDP controller against the conventional proportional-integral-derivative (PID) controller, which serves as a widely adopted baseline in the field. The results unequivocally demonstrate the superiority of the MPDP controller across multiple dimensions. It exhibits exceptional robustness, resulting in a smaller steady-state tracking error, a critical advantage when addressing inherent uncertainties and external disturbances that can perturb the robot system. This translates to a more stable trajectory tracking performance. Furthermore, the MPDP controller empowers the robot with the capability to precisely follow predefined trajectories, thus ensuring high-precision and reliable execution of tasks. This feature significantly contributes to an overall enhancement of system performance and productivity.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"26 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Atmospheric reanalysis plays an important role in retrieving the atmospheric tropospheric delays with ray tracing for space geodetic techniques. In order to represent the complex weather and climate conditions better, the spatiotemporal resolutions of the newly developed atmospheric reanalysis products are improved significantly. The increased spatiotemporal resolution provides a great opportunity to improve the accuracy of the tropospheric delays derived from ray tracing, but it remains a challenge due to the highly increased computation costs. In this paper, we develop a rapid ray tracing method with refined height interval determination to accommodate the increased spatiotemporal resolution of the atmospheric reanalysis products. The accuracy of this method was validated by the 2010 International Association of Geodesy (IAG) Working Group 4.3.3 ray tracing Comparison Campaign reference results. Zenith and slant delays were obtained by tracing 342 global International GNSS Service (IGS) stations. Compared to the traditional method, this reduced memory footprint by 16.1%, global refractivity field construction time by 13.6%, and per ray trace time by 22.5% while maintaining accuracy. Based on this methodology, ray tracing using state-of-the-art fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and second Modern-Era Retrospective Analysis for Research and Applications (MERRA2) at 342 IGS stations assessed tropospheric delay performance in 2021. Results showed significant ERA5 and MERRA2 slant delay and mapping factor differences up to the decimeter level, especially for the wet component. Additionally, using IGS Zenith Total Delay (ZTD) as a reference, ERA5 ZTD bias and Root Mean Square Error (RMSE) were 2.3 and 11.9 mm, versus that of 1.8 and 16.2 mm for MERRA2 ZTD. At extreme weather-affected AIRA stations over August 5-9, 2021, ERA5 ZTD mean and RMSE differences were -3.0 and 19.8 mm, and -5.3 and 21.7 mm for MERRA2 ZTD. Tropospheric delays and models derived from ERA5 can support space geodetic applications given improved performance and temporal resolution.
{"title":"A rapid ray tracing method to evaluate the performances of ERA5 and MERRA2 in retrieving global tropospheric delay","authors":"Mingyuan Zhang, Peng Yuan, Weiping Jiang, Yong Zou, Wenlan Fan, Jian Wang","doi":"10.1088/1361-6501/ad1707","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1707","url":null,"abstract":"\u0000 Atmospheric reanalysis plays an important role in retrieving the atmospheric tropospheric delays with ray tracing for space geodetic techniques. In order to represent the complex weather and climate conditions better, the spatiotemporal resolutions of the newly developed atmospheric reanalysis products are improved significantly. The increased spatiotemporal resolution provides a great opportunity to improve the accuracy of the tropospheric delays derived from ray tracing, but it remains a challenge due to the highly increased computation costs. In this paper, we develop a rapid ray tracing method with refined height interval determination to accommodate the increased spatiotemporal resolution of the atmospheric reanalysis products. The accuracy of this method was validated by the 2010 International Association of Geodesy (IAG) Working Group 4.3.3 ray tracing Comparison Campaign reference results. Zenith and slant delays were obtained by tracing 342 global International GNSS Service (IGS) stations. Compared to the traditional method, this reduced memory footprint by 16.1%, global refractivity field construction time by 13.6%, and per ray trace time by 22.5% while maintaining accuracy. Based on this methodology, ray tracing using state-of-the-art fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and second Modern-Era Retrospective Analysis for Research and Applications (MERRA2) at 342 IGS stations assessed tropospheric delay performance in 2021. Results showed significant ERA5 and MERRA2 slant delay and mapping factor differences up to the decimeter level, especially for the wet component. Additionally, using IGS Zenith Total Delay (ZTD) as a reference, ERA5 ZTD bias and Root Mean Square Error (RMSE) were 2.3 and 11.9 mm, versus that of 1.8 and 16.2 mm for MERRA2 ZTD. At extreme weather-affected AIRA stations over August 5-9, 2021, ERA5 ZTD mean and RMSE differences were -3.0 and 19.8 mm, and -5.3 and 21.7 mm for MERRA2 ZTD. Tropospheric delays and models derived from ERA5 can support space geodetic applications given improved performance and temporal resolution.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"5 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.1088/1361-6501/ad1742
Weiyu Liu, Shengbao Yu, Xinhao Zhang
In shallow surface electromagnetic detection, the square wave scheme is generally used in conventional transmission systems. Based on frequency-domain electromagnetic (FDEM) sounding theory, high-frequency measurement helps to improve vertical resolution. However, long grounded cable inductance produces severe reactive power suppression at high frequency transmission frequencies, which will reduce detection. To further improve detection accuracy and efficiency, a dual-frequency transmitter configuration is proposed in this article for shallow surface detection. The transmitter simultaneously powers two LC series resonant circuits for the detection of shallow and deep area. Dual-frequency control strategy is adopted, with both bridge arms being provided with constant switching frequency operation. According to the equivalent model of the transmission system, the control of the load branches is independent of each other. The LC series resonant circuit guarantees a wide passband to match long cable inductance that cannot be accurately estimated in advance. Simulations and experimental tests were carried out using this transmitter configuration and control technique. The simulation and experimental results are in general agreement, verifying the feasibility and effectiveness of the proposed dual-band transmitter configuration.
{"title":"Dual-frequency transmitter configuration for shallow surface electromagnetic detection","authors":"Weiyu Liu, Shengbao Yu, Xinhao Zhang","doi":"10.1088/1361-6501/ad1742","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1742","url":null,"abstract":"\u0000 In shallow surface electromagnetic detection, the square wave scheme is generally used in conventional transmission systems. Based on frequency-domain electromagnetic (FDEM) sounding theory, high-frequency measurement helps to improve vertical resolution. However, long grounded cable inductance produces severe reactive power suppression at high frequency transmission frequencies, which will reduce detection. To further improve detection accuracy and efficiency, a dual-frequency transmitter configuration is proposed in this article for shallow surface detection. The transmitter simultaneously powers two LC series resonant circuits for the detection of shallow and deep area. Dual-frequency control strategy is adopted, with both bridge arms being provided with constant switching frequency operation. According to the equivalent model of the transmission system, the control of the load branches is independent of each other. The LC series resonant circuit guarantees a wide passband to match long cable inductance that cannot be accurately estimated in advance. Simulations and experimental tests were carried out using this transmitter configuration and control technique. The simulation and experimental results are in general agreement, verifying the feasibility and effectiveness of the proposed dual-band transmitter configuration.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":" 17","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the carbon fiber-reinforced plastic milling process, the high abrasive property of carbon fiber will lead to the rapid growth of tool wear, resulting in poor surface quality of parts. However, due to the signal data distribution discrepancy under different working conditions, addressing the problem of local degradation and low prediction accuracy in tool wear monitoring model is a significant challenge. This paper proposes an entropy criterion deep conditional domain adaptation network, which effectively exploits domain invariant features of the signals and enhances the stability of model training. Furthermore, a novel unsupervised optimization method based on tool wear distribution is proposed, which refines the monitoring results of data-driven models. This approach reduces misclassification of tool wear conditions resulting from defects in data-driven models and interference from the manufacturing process, thereby enhancing the accuracy of the monitoring model. The experimental results show that the hybrid method provides assurance for the accurate construction of tool wear monitoring model under different working conditions.
{"title":"Multi-condition tool wear prediction for milling CFRP base on a novel hybrid monitoring method","authors":"Shipeng Li, Siming Huang, Hao Li, Wentao Liu, Weizhou Wu, Jian Liu","doi":"10.1088/1361-6501/ad1478","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1478","url":null,"abstract":"In the carbon fiber-reinforced plastic milling process, the high abrasive property of carbon fiber will lead to the rapid growth of tool wear, resulting in poor surface quality of parts. However, due to the signal data distribution discrepancy under different working conditions, addressing the problem of local degradation and low prediction accuracy in tool wear monitoring model is a significant challenge. This paper proposes an entropy criterion deep conditional domain adaptation network, which effectively exploits domain invariant features of the signals and enhances the stability of model training. Furthermore, a novel unsupervised optimization method based on tool wear distribution is proposed, which refines the monitoring results of data-driven models. This approach reduces misclassification of tool wear conditions resulting from defects in data-driven models and interference from the manufacturing process, thereby enhancing the accuracy of the monitoring model. The experimental results show that the hybrid method provides assurance for the accurate construction of tool wear monitoring model under different working conditions.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"2 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1088/1361-6501/ad16b9
Xinyu Chen, Qihao Ma, Zhuzhen He, Xiaoyu Sun, Yan Ren
Structured light measurement is widely used in welding seam tracking because of its high precision and robustness. For the narrow butt joint, the positioning method by reconstructing the weld contour is not suitable for the welding of the narrow butt joint because it is difficult for the laser stripe to produce obvious deformation when projected to the weld. In this study, high-quality images with laser stripes and narrow butt joints are captured by the improved structured light vision sensor, which is equipped with an auxiliary light source. A two-step processing framework, including semantic segmentation and groove positioning, is raised to locate the feature point of the narrow butt joint. Firstly, we design the strip pooling ENet (SP-ENet), a real-time network specifically designed to accurately segment narrow weld images. Our proposed network outperforms other classical segmentation networks in terms of segmentation accuracy and proves to be highly suitable for the detection of narrow butt joint welds. Secondly, a combining method of random sample consensus (RANSAC) and iterative fitting to calculate the sub-pixel coordinates of weld feature points accurately. Finally, a trajectory smoothing model based on the Kalman filter is proposed to reduce the trajectory jitter. The above methods were tested on a self-built robotic welding experimental platform. Experimental results show that the proposed method can be used for real-time detection and positioning of narrow butt joints. The positioning trajectory is smooth, with most positioning errors less than 2 pixels. The mean tracking error reaches 0.207 mm, which can meet the practical welding requirements.
{"title":"Real-time detection and localization method for weld seam of narrow butt joint based on semantic segmentation","authors":"Xinyu Chen, Qihao Ma, Zhuzhen He, Xiaoyu Sun, Yan Ren","doi":"10.1088/1361-6501/ad16b9","DOIUrl":"https://doi.org/10.1088/1361-6501/ad16b9","url":null,"abstract":"Structured light measurement is widely used in welding seam tracking because of its high precision and robustness. For the narrow butt joint, the positioning method by reconstructing the weld contour is not suitable for the welding of the narrow butt joint because it is difficult for the laser stripe to produce obvious deformation when projected to the weld. In this study, high-quality images with laser stripes and narrow butt joints are captured by the improved structured light vision sensor, which is equipped with an auxiliary light source. A two-step processing framework, including semantic segmentation and groove positioning, is raised to locate the feature point of the narrow butt joint. Firstly, we design the strip pooling ENet (SP-ENet), a real-time network specifically designed to accurately segment narrow weld images. Our proposed network outperforms other classical segmentation networks in terms of segmentation accuracy and proves to be highly suitable for the detection of narrow butt joint welds. Secondly, a combining method of random sample consensus (RANSAC) and iterative fitting to calculate the sub-pixel coordinates of weld feature points accurately. Finally, a trajectory smoothing model based on the Kalman filter is proposed to reduce the trajectory jitter. The above methods were tested on a self-built robotic welding experimental platform. Experimental results show that the proposed method can be used for real-time detection and positioning of narrow butt joints. The positioning trajectory is smooth, with most positioning errors less than 2 pixels. The mean tracking error reaches 0.207 mm, which can meet the practical welding requirements.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"48 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139174657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.
对旋转机械故障的调查已进行了深入研究。在探索的各种方法中，基于稀疏优化的技术已成为一种非常理想的方法。然而，在实际工业环境中，收集到的轴承信号往往包含因工作条件变化和负载突变而产生的随机影响成分。当机器发生故障时，很容易诱发和产生新的故障，从而导致复合故障。为应对这一挑战，本文提出了一种名为快速非线性交叉稀疏滤波（FNCr-SF）的新型多维盲解卷方法。FNCr-SF 旨在分离随机冲击干扰下的弱复合故障。该方法采用了多种预处理技术，包括 Z 分数归一化和非线性 sigmoid 激活函数，以放大复合故障的微弱特征，并将随机干扰的影响降至最低。此外，FNCr-SF 方法还能自适应分解故障成分，而无需事先了解或预处理。这种方法能有效减少随机干扰，准确检测轴承中的复合故障。实验和模拟信号验证了 FNCr-SF 方法在复合故障检测中的有效性，证明了它的高准确性和鲁棒性。
{"title":"Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis","authors":"Shunxiang Yao, Zongzhen Zhang, Baokun Han, Jinrui Wang, Jiansong Zheng","doi":"10.1088/1361-6501/ad166f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad166f","url":null,"abstract":"The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"349 17‐18","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-17DOI: 10.1088/1361-6501/ad166d
Guangyi Zhu, Xi Zeng, Zheng Gong, Zhuohan Gao, Renquan Ji, Yisen Zeng, Pei Wang, Congda Lu
Tool wear during robotic polishing affects material removal rates and surface roughness, leading to erratic and inconsistent polishing quality. Therefore, a method that can predict the tool state is needed to replace the robot end tool in time. In this paper, based on the cutting-edge neural ordinary differential equations (Neural ODE) and BP neural network optimization based on genetic algorithm (BP-GA), we propose a method to identify the tool state during robotic machining: firstly, a new training method of Neural ODE is proposed to avoid the model from falling into poor stationary points, and then on this basis, Neural ODE is utilized to predict the changes of vibration signals during robot machining; secondly, the predicted vibration signals of the tool are processed using variable modal decomposition method to extract the eigen kurtosis index and envelope entropy of the modal function as the vibration signal eigenvectors, and compare them with the traditional vibration signal eigenvectors. Finally, the predicted tool states were identified using BP-GA, and numerical experiments yielded an F1 score of 91.76% and an accuracy of 96.55% for model identification.
机器人抛光过程中的工具磨损会影响材料去除率和表面粗糙度，导致抛光质量不稳定和不一致。因此，需要一种能预测工具状态的方法来及时更换机器人终端工具。本文基于前沿的神经常微分方程（Neural ODE）和基于遗传算法的 BP 神经网络优化（BP-GA），提出了一种识别机器人加工过程中刀具状态的方法：首先，提出了一种新的神经 ODE 训练方法，以避免模型陷入较差的静止点，然后在此基础上利用神经 ODE 预测机器人加工过程中振动信号的变化；其次，利用变模态分解方法对预测的刀具振动信号进行处理，提取模态函数的特征峰度指数和包络熵作为振动信号特征向量，并与传统的振动信号特征向量进行比较。最后，利用 BP-GA 对预测的工具状态进行识别，数值实验结果表明，模型识别的 F1 得分为 91.76%，准确率为 96.55%。
{"title":"Monitoring robot machine tool sate via neural ODE and BP-GA","authors":"Guangyi Zhu, Xi Zeng, Zheng Gong, Zhuohan Gao, Renquan Ji, Yisen Zeng, Pei Wang, Congda Lu","doi":"10.1088/1361-6501/ad166d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad166d","url":null,"abstract":"Tool wear during robotic polishing affects material removal rates and surface roughness, leading to erratic and inconsistent polishing quality. Therefore, a method that can predict the tool state is needed to replace the robot end tool in time. In this paper, based on the cutting-edge neural ordinary differential equations (Neural ODE) and BP neural network optimization based on genetic algorithm (BP-GA), we propose a method to identify the tool state during robotic machining: firstly, a new training method of Neural ODE is proposed to avoid the model from falling into poor stationary points, and then on this basis, Neural ODE is utilized to predict the changes of vibration signals during robot machining; secondly, the predicted vibration signals of the tool are processed using variable modal decomposition method to extract the eigen kurtosis index and envelope entropy of the modal function as the vibration signal eigenvectors, and compare them with the traditional vibration signal eigenvectors. Finally, the predicted tool states were identified using BP-GA, and numerical experiments yielded an F1 score of 91.76% and an accuracy of 96.55% for model identification.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"4 12","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-17DOI: 10.1088/1361-6501/ad166c
Changying Guo, Qi Wang
Objective: In laser self-mixing interferometry displacement measurement, noise interference has a significant impact on the measurement results. To improve measurement accuracy, this paper proposes a filtering method that combines empirical mode decomposition (EMD) with wavelet thresholding. Method: First, the signal is decomposed into several intrinsic mode functions (IMFs) using EMD. Then, wavelet thresholding is applied to each IMF. Subsequently, the processed IMFs are reconstructed to achieve signal filtering. Finally, by integrating the principles of interpolation and fringe counting, the reconstructed displacement signal is recovered, realizing accurate displacement measurement. Result: This paper presents comprehensive simulation analyses and experimental validations for the proposed method. The accuracy of the displacement recovery is quantitatively evaluated using the absolute error and standard error, comparing the recovered displacement signal with the actual displacement. Conclusion: The experimental results demonstrate that the laser self-mixing interferometry displacement signal filtering method based on EMD and wavelet thresholding has high accuracy.
目的：在激光自混合干涉仪位移测量中，噪声干扰对测量结果有很大影响。为了提高测量精度，本文提出了一种结合经验模态分解（EMD）和小波阈值的滤波方法。方法：首先，使用 EMD 将信号分解为多个固有模式函数（IMF）。然后，对每个 IMF 进行小波阈值处理。随后，对处理过的 IMF 进行重构，以实现信号滤波。最后，结合插值和条纹计数原理，恢复重建的位移信号，实现精确的位移测量。结果本文对所提出的方法进行了全面的仿真分析和实验验证。通过将恢复的位移信号与实际位移进行比较，利用绝对误差和标准误差对位移恢复的准确性进行了定量评估。得出结论：实验结果表明，基于 EMD 和小波阈值的激光自混合干涉测量位移信号滤波方法具有很高的精度。
{"title":"Laser Self-mixing Interference Displacement Signal Filtering Method based on Empirical Mode Decomposition and Wavelet Threshold","authors":"Changying Guo, Qi Wang","doi":"10.1088/1361-6501/ad166c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad166c","url":null,"abstract":"\u0000 Objective: In laser self-mixing interferometry displacement measurement, noise interference has a significant impact on the measurement results. To improve measurement accuracy, this paper proposes a filtering method that combines empirical mode decomposition (EMD) with wavelet thresholding. Method: First, the signal is decomposed into several intrinsic mode functions (IMFs) using EMD. Then, wavelet thresholding is applied to each IMF. Subsequently, the processed IMFs are reconstructed to achieve signal filtering. Finally, by integrating the principles of interpolation and fringe counting, the reconstructed displacement signal is recovered, realizing accurate displacement measurement. Result: This paper presents comprehensive simulation analyses and experimental validations for the proposed method. The accuracy of the displacement recovery is quantitatively evaluated using the absolute error and standard error, comparing the recovered displacement signal with the actual displacement. Conclusion: The experimental results demonstrate that the laser self-mixing interferometry displacement signal filtering method based on EMD and wavelet thresholding has high accuracy.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"13 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-17DOI: 10.1088/1361-6501/ad166e
Quan Wang, Min Lei, Jun Zhang, Huan Wang, Xin Qi
Remote calibration (RC) is a new promising technology for electric power instrument calibration. However, due to the inevitable impact of external environmental changes and internal insulation aging during the transportation, installation, and measurement processes involved in RC, the metrological performance of relevant instruments may deteriorate. Therefore, quality control of electric power instruments during RC is of great significance. In this paper, a novel process control method for RC is proposed. First, from the physical characteristic perspective, an improved multiscale permutation entropy (IMPE) algorithm is designed to detect the complexity change point of the instrument system. Second, from the statistical characteristic perspective, a dynamic multivariable Hotelling's T 2 control chart (DMHTCC) is developed to detect the outliers within a time series measurement signal. Finally, a fusion scheme of IMPE and DMHTCC is presented to promote the validity and reliability of process control. The effectiveness of the proposed approach and its superiority over some traditional process control techniques is demonstrated through both simulative and experimental case studies.
远程校准（RC）是一项很有前途的电力仪器校准新技术。然而，由于远程校准在运输、安装和测量过程中不可避免地受到外部环境变化和内部绝缘老化的影响，相关仪器的计量性能可能会下降。因此，在遥控过程中对电力仪器进行质量控制意义重大。本文提出了一种新型的 RC 过程控制方法。首先，从物理特性角度出发，设计了一种改进的多尺度置换熵（IMPE）算法来检测仪表系统的复杂性变化点。其次，从统计特性的角度出发，开发了动态多变量霍特林 T 2 控制图（DMHTCC）来检测时间序列测量信号中的异常值。最后，提出了 IMPE 和 DMHTCC 的融合方案，以提高过程控制的有效性和可靠性。通过模拟和实验案例研究，证明了所提方法的有效性及其优于一些传统过程控制技术的优势。
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Pub Date : 2023-12-17DOI: 10.1088/1361-6501/ad1672
XinCai Xu, Diyang Gu, Shaohua Gao, Lei Sun, Xingyu Lu, Kaiwei Wang, Jian Bai
Quality inspection of injection molding products with intricate three-dimensional (3D) structures and diffuse reflection characteristics is a very important procedure in industrial production. However, the current inspection process for these products still heavily relies on visual inspection, which introduces various issues including low efficiency, and missing or false detection. While previous studies have utilized deep-learning methods in conjunction with specific optical sensors and imaging systems to detect defects, the intricate structure of injection molding products and the small magnitude of defects pose significant challenges in defect detection. To address these challenges, this paper proposes an inspection system based on Michelson interferometer capable of detecting and characterizing defects of injection molding products. Notably, by utilizing the modulation of light intensity and an improved image differencing approach, this inspection system is capable of detecting defects with a magnitude as small as 0.1 mm and achieving a remarkable detection accuracy exceeding 93% on self-made datasets without utilizing phase information. The effectiveness of our method is validated by comparison with mainstream deep-learning-based defect detection methods and visual inspection method.
{"title":"Back to Michelson Interferometer: a precise inspection system for industrial intricate structures defect detection","authors":"XinCai Xu, Diyang Gu, Shaohua Gao, Lei Sun, Xingyu Lu, Kaiwei Wang, Jian Bai","doi":"10.1088/1361-6501/ad1672","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1672","url":null,"abstract":"\u0000 Quality inspection of injection molding products with intricate three-dimensional (3D) structures and diffuse reflection characteristics is a very important procedure in industrial production. However, the current inspection process for these products still heavily relies on visual inspection, which introduces various issues including low efficiency, and missing or false detection. While previous studies have utilized deep-learning methods in conjunction with specific optical sensors and imaging systems to detect defects, the intricate structure of injection molding products and the small magnitude of defects pose significant challenges in defect detection. To address these challenges, this paper proposes an inspection system based on Michelson interferometer capable of detecting and characterizing defects of injection molding products. Notably, by utilizing the modulation of light intensity and an improved image differencing approach, this inspection system is capable of detecting defects with a magnitude as small as 0.1 mm and achieving a remarkable detection accuracy exceeding 93% on self-made datasets without utilizing phase information. The effectiveness of our method is validated by comparison with mainstream deep-learning-based defect detection methods and visual inspection method.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"23 28","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138965805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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