Pub Date : 2023-12-21DOI: 10.1088/1361-6501/ad180a
lv zequn, An Ning, Fan Cunbo, Chengzhi Liu, Zhenwei Li, Gao Jian, Guanyu Wen, Dong Xue
Satellite Laser Ranging (SLR) is a technology with the highest precision of single measurement of satellite radial distance, which is developing rapidly in the direction of long-distance, high-precision and automation. SLR autonomous observation task scheduling is an important step in realizing station automation, which needs to satisfy the principles of satellite tracking priority and maximization of observation revenue at the same time. In order to improve the automation and intelligence level of SLR system, based on the framework of ant colony optimization algorithm, this paper combines the dynamic optimization characteristics of ant colony optimization algorithm and the local optimization characteristics of greedy algorithm, introduces the maximum-minimum ant mechanism, and puts forward a scheduling scheme for SLR observation task based on greedy ant colony algorithm (GACA). The results show that compared to the current scheduling methods applied in practice. The results show that compared with the current scheduling method applied in practice, the number of observation satellites obtained from the GACA algorithm-based observation task planning for the SLR system has been improved by 37.4%, the total arc segment of satellite observation with higher priority has been extended by 36.47%, and the total observation gain has been increased by 42.39% in the same period of time. It effectively solves the problems of low efficiency, easy to miss stars and less stars in the observation process in manual scheduling, and provides a simple, practical, efficient and convenient observation task planning scheme for the establishment of an unmanned SLR system.
{"title":"Research on satellite laser ranging observation task scheduling method","authors":"lv zequn, An Ning, Fan Cunbo, Chengzhi Liu, Zhenwei Li, Gao Jian, Guanyu Wen, Dong Xue","doi":"10.1088/1361-6501/ad180a","DOIUrl":"https://doi.org/10.1088/1361-6501/ad180a","url":null,"abstract":"\u0000 Satellite Laser Ranging (SLR) is a technology with the highest precision of single measurement of satellite radial distance, which is developing rapidly in the direction of long-distance, high-precision and automation. SLR autonomous observation task scheduling is an important step in realizing station automation, which needs to satisfy the principles of satellite tracking priority and maximization of observation revenue at the same time. In order to improve the automation and intelligence level of SLR system, based on the framework of ant colony optimization algorithm, this paper combines the dynamic optimization characteristics of ant colony optimization algorithm and the local optimization characteristics of greedy algorithm, introduces the maximum-minimum ant mechanism, and puts forward a scheduling scheme for SLR observation task based on greedy ant colony algorithm (GACA). The results show that compared to the current scheduling methods applied in practice. The results show that compared with the current scheduling method applied in practice, the number of observation satellites obtained from the GACA algorithm-based observation task planning for the SLR system has been improved by 37.4%, the total arc segment of satellite observation with higher priority has been extended by 36.47%, and the total observation gain has been increased by 42.39% in the same period of time. It effectively solves the problems of low efficiency, easy to miss stars and less stars in the observation process in manual scheduling, and provides a simple, practical, efficient and convenient observation task planning scheme for the establishment of an unmanned SLR system.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"49 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949381","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 : 2023-12-21DOI: 10.1088/1361-6501/ad1817
Lianpo Wang
With the rapid development of binocular reconstruction, fringe projection profilometry, and time of flight (ToF), 3D imaging technology has been widely applied in the field of 3D measurement. However, due to limited measurement range and self-occlusion, point cloud registration methods are often used to obtain larger or more complete 3D contours. Although many scholars have proposed various point cloud registration methods, the accuracy and efficiency of point cloud registration still need to be further improved, especially for point clouds with different density or non-rigid transformation. Image registration technology based on image correlation has been developed for many years and has achieved great success in fields such as computer vision, photomechanics, and photogrammetry. Therefore, a simple and direct idea in this paper is to transform the point cloud registration problem into volume image correlation problem. By this, an efficient image registration method based on Fast Fourier transform and an inverse compositional Gaussian Newton (IC-GN) optimization method that only needs to calculate the Hessian matrix once can be introduced into the point cloud registration field, which can greatly improve the speed and accuracy of point cloud registration. Comparative experiments have shown that our method has doubled the accuracy and efficiency compared to the ICP method, and its practicality has also been verified in impeller reconstruction experiments.
{"title":"High-precision point cloud registration method based on volume image correlation","authors":"Lianpo Wang","doi":"10.1088/1361-6501/ad1817","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1817","url":null,"abstract":"\u0000 With the rapid development of binocular reconstruction, fringe projection profilometry, and time of flight (ToF), 3D imaging technology has been widely applied in the field of 3D measurement. However, due to limited measurement range and self-occlusion, point cloud registration methods are often used to obtain larger or more complete 3D contours. Although many scholars have proposed various point cloud registration methods, the accuracy and efficiency of point cloud registration still need to be further improved, especially for point clouds with different density or non-rigid transformation. Image registration technology based on image correlation has been developed for many years and has achieved great success in fields such as computer vision, photomechanics, and photogrammetry. Therefore, a simple and direct idea in this paper is to transform the point cloud registration problem into volume image correlation problem. By this, an efficient image registration method based on Fast Fourier transform and an inverse compositional Gaussian Newton (IC-GN) optimization method that only needs to calculate the Hessian matrix once can be introduced into the point cloud registration field, which can greatly improve the speed and accuracy of point cloud registration. Comparative experiments have shown that our method has doubled the accuracy and efficiency compared to the ICP method, and its practicality has also been verified in impeller reconstruction experiments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"63 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952247","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 : 2023-12-21DOI: 10.1088/1361-6501/ad180f
Peipei Dai, Sen Wang, Tianhe Xu, Nazi Wang, Min Li, Jianping Xing, F. Gao
Abstract Real-time kinematic (RTK) positioning is a commonly used technique in modern industry, which is limited by problems such as signal occlusion, attenuation, and multipath, especially in complex urban canyons. To maintain the consistency of centimeter-level accuracy, we adopt the ultra-wideband (UWB) enhanced BDS-3 RTK positioning algorithm. This paper proposed a semi-tightly coupled (STC) BDS-3 RTK/UWB integration positioning model. This model realizes the UWB and BDS-3 complement each other and integrate information in the position domain. Besides, height constraint is imposed on UWB positioning to mitigate the effect of poor positioning of UWB in height components. To verify the effectiveness of the above algorithm, we have compared and analyzed the positioning performance of the semi-tightly coupled BDS-3 RTK/UWB integration model and single BDS-3 RTK model in different occlusion environments. The positioning performance of static and kinematic of BDS-3 RTK/UWB STC based on different number of UWB anchors is further analyzed. The real-world experiment results show that the positioning accuracy of theproposed method can reach centimeter-level. Moreover, the proposed model can obtain more accurate positioning results than those of using single system, and it shows more obvious advantages, especially in the occlusion environment. In the occlusion environment, the RMS in the east, north, and up directions is improved from (0.629 m, 0.325 m, 1.160 m) of the BDS-3-only to (0.075 m, 0.074 m, 0.029 m). This study can provide a reference for the future development of high-precision, real-time, continuous positioning, navigation, and timing in complex urban environments.
{"title":"BDS-3 RTK/UWB semi-tightly coupled integrated positioning system in harsh environments","authors":"Peipei Dai, Sen Wang, Tianhe Xu, Nazi Wang, Min Li, Jianping Xing, F. Gao","doi":"10.1088/1361-6501/ad180f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad180f","url":null,"abstract":"\u0000 Abstract Real-time kinematic (RTK) positioning is a commonly used technique in modern industry, which is limited by problems such as signal occlusion, attenuation, and multipath, especially in complex urban canyons. To maintain the consistency of centimeter-level accuracy, we adopt the ultra-wideband (UWB) enhanced BDS-3 RTK positioning algorithm. This paper proposed a semi-tightly coupled (STC) BDS-3 RTK/UWB integration positioning model. This model realizes the UWB and BDS-3 complement each other and integrate information in the position domain. Besides, height constraint is imposed on UWB positioning to mitigate the effect of poor positioning of UWB in height components. To verify the effectiveness of the above algorithm, we have compared and analyzed the positioning performance of the semi-tightly coupled BDS-3 RTK/UWB integration model and single BDS-3 RTK model in different occlusion environments. The positioning performance of static and kinematic of BDS-3 RTK/UWB STC based on different number of UWB anchors is further analyzed. The real-world experiment results show that the positioning accuracy of theproposed method can reach centimeter-level. Moreover, the proposed model can obtain more accurate positioning results than those of using single system, and it shows more obvious advantages, especially in the occlusion environment. In the occlusion environment, the RMS in the east, north, and up directions is improved from (0.629 m, 0.325 m, 1.160 m) of the BDS-3-only to (0.075 m, 0.074 m, 0.029 m). This study can provide a reference for the future development of high-precision, real-time, continuous positioning, navigation, and timing in complex urban environments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"9 13","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949270","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 : 2023-12-21DOI: 10.1088/1361-6501/ad1811
Wenxing Zhang, Jianhong Yang, Xinyu Bo, Zhenkai Yang
Different fault types of rolling bearings correspond to different features, the classical deep learning models using a single attention mechanism (AM) has limitations in feature diversity capturing. Therefore, a novel dual attention mechanism network (DAMN) with self-attention (SA) and frequency channel attention (FCA) is proposed for rolling bearings fault diagnosis, in which the SA mechanism is used to capture global relationships between the input features and the fault types, and the FCA mechanism applies mutli-spectral attention to learn the local useful information among different input channels. Results of the ablation study of the effects of FCA blocks show that including a proper combination of multiple frequency components is helpful to achieve higher accuracy. Experiments on the diagnosis of rolling bearings with multiple fault types were carried out. Results show that compared with the current fault diagnosis models, the proposed DAMN has better comprehensive performance on diagnosis accuracy and model convergence speed. It is also demonstrated that the backbone of DAMN based on dual AM can achieve better performance than the backbone based on single AM.
{"title":"A dual attention mechanism network with self-attention and frequency channel attention for intelligent diagnosis of multiple rolling bearing fault types","authors":"Wenxing Zhang, Jianhong Yang, Xinyu Bo, Zhenkai Yang","doi":"10.1088/1361-6501/ad1811","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1811","url":null,"abstract":"\u0000 Different fault types of rolling bearings correspond to different features, the classical deep learning models using a single attention mechanism (AM) has limitations in feature diversity capturing. Therefore, a novel dual attention mechanism network (DAMN) with self-attention (SA) and frequency channel attention (FCA) is proposed for rolling bearings fault diagnosis, in which the SA mechanism is used to capture global relationships between the input features and the fault types, and the FCA mechanism applies mutli-spectral attention to learn the local useful information among different input channels. Results of the ablation study of the effects of FCA blocks show that including a proper combination of multiple frequency components is helpful to achieve higher accuracy. Experiments on the diagnosis of rolling bearings with multiple fault types were carried out. Results show that compared with the current fault diagnosis models, the proposed DAMN has better comprehensive performance on diagnosis accuracy and model convergence speed. It is also demonstrated that the backbone of DAMN based on dual AM can achieve better performance than the backbone based on single AM.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"28 8","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138948834","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 hard rocks in the stratum can pose safety risks and hinder the progress of urban underground tunnel construction using shield and jacking methods, thereby reducing construction efficiency and increasing construction costs. This paper utilizes wavelet scale energy spectrum, wavelet packet theory and statistical methods to conduct research on the detection of special geological formations such as hard rocks and voids, as well as the analysis of their signal time-frequency characteristics based on the ground-penetrating radar (GPR) technique. On the basis of calibrating the permittivity of different types of rock blocks, we established a forward model for detecting hard rocks and voids, and the simulated signals were analyzed in the time and frequency domains. Subsequently, laboratory experiments were conducted to perform GPR tests on different types of hard rocks in natural and water-saturated states and voids, to explore the time-frequency characteristics, frequency band energy variations, and statistical patterns of typical single-trace signals. The results show that the granite detection signal contains more low-frequency components, the sandstone detection signal contains more medium-low frequency components, while the limestone detection signal contains more medium-high frequency components in their natural state; the signal from the karst cave has relatively more low-frequency components than the signal from the empty cavity. The geometric shape of the rock has no influence on the dominant frequency and time-frequency distribution of its reflection signal. Generally, rocks with higher rebound values (hardness) also exhibit larger variance and standard deviation in frequency band energy. The research has important theoretical significance and practical value for the measurement and assessment of special geological features such as hard rocks and voids in urban underground trenchless construction.
{"title":"Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction","authors":"Liang Zhang, Sheng Zhang, Zongwei Deng, Tonghua Ling","doi":"10.1088/1361-6501/ad1806","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1806","url":null,"abstract":"\u0000 The hard rocks in the stratum can pose safety risks and hinder the progress of urban underground tunnel construction using shield and jacking methods, thereby reducing construction efficiency and increasing construction costs. This paper utilizes wavelet scale energy spectrum, wavelet packet theory and statistical methods to conduct research on the detection of special geological formations such as hard rocks and voids, as well as the analysis of their signal time-frequency characteristics based on the ground-penetrating radar (GPR) technique. On the basis of calibrating the permittivity of different types of rock blocks, we established a forward model for detecting hard rocks and voids, and the simulated signals were analyzed in the time and frequency domains. Subsequently, laboratory experiments were conducted to perform GPR tests on different types of hard rocks in natural and water-saturated states and voids, to explore the time-frequency characteristics, frequency band energy variations, and statistical patterns of typical single-trace signals. The results show that the granite detection signal contains more low-frequency components, the sandstone detection signal contains more medium-low frequency components, while the limestone detection signal contains more medium-high frequency components in their natural state; the signal from the karst cave has relatively more low-frequency components than the signal from the empty cavity. The geometric shape of the rock has no influence on the dominant frequency and time-frequency distribution of its reflection signal. Generally, rocks with higher rebound values (hardness) also exhibit larger variance and standard deviation in frequency band energy. The research has important theoretical significance and practical value for the measurement and assessment of special geological features such as hard rocks and voids in urban underground trenchless construction.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"36 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138948901","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 : 2023-12-21DOI: 10.1088/1361-6501/ad1810
Qin Sun, Xinbing Wang, D. Zuo
Droplet-based laser-produced plasma source shows enormous significance in extreme ultraviolet lithography, which places high demands on the stability of tin droplets. This paper presents a tin droplet target system including the tin droplet generator (DG), droplet diagnosis, and spatiotemporal synchronization of tin droplets and laser. Shadowgraph technology is used to determine the stability of tin droplets. The characteristics of the DG were analyzed, and the operation parameter maps are provided. By varying operating frequencies from 18.4 kHz to 49.3 kHz, the diameter and spacing of droplets can be adjusted in the ranges of 120~200 μm and 200~1100 μm respectively. Both theoretical calculations and experimental results show that tin droplets keep high stability when the operation parameters locate at the optimal range. The long-term lateral stability is also proven under a high degree of vacuum. Additionally, the application feasibility of the DG system is verified by the experiments of laser impact tin droplets.
{"title":"Characteristics of tin droplet target system for EUV source research","authors":"Qin Sun, Xinbing Wang, D. Zuo","doi":"10.1088/1361-6501/ad1810","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1810","url":null,"abstract":"\u0000 Droplet-based laser-produced plasma source shows enormous significance in extreme ultraviolet lithography, which places high demands on the stability of tin droplets. This paper presents a tin droplet target system including the tin droplet generator (DG), droplet diagnosis, and spatiotemporal synchronization of tin droplets and laser. Shadowgraph technology is used to determine the stability of tin droplets. The characteristics of the DG were analyzed, and the operation parameter maps are provided. By varying operating frequencies from 18.4 kHz to 49.3 kHz, the diameter and spacing of droplets can be adjusted in the ranges of 120~200 μm and 200~1100 μm respectively. Both theoretical calculations and experimental results show that tin droplets keep high stability when the operation parameters locate at the optimal range. The long-term lateral stability is also proven under a high degree of vacuum. Additionally, the application feasibility of the DG system is verified by the experiments of laser impact tin droplets.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"2 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953245","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 : 2023-12-21DOI: 10.1088/1361-6501/ad180c
Jianjun Zhao, Yuxin Zhang, Xiaozhong Du, Xiaoming Sun
Ultrasonic testing is a widely used non-destructive testing technique for precision forgings. However, assessing defects in ultrasonic B-scan images can be prone to errors, misses, and inefficiencies due to human judgment. To address these challenges, we propose a method based on deep learning to automate the evaluation of such images.We started by creating a dataset comprising 8,000 images, each measuring 224x224 pixels. These images were cropped from ultrasonic B-scan images of 7 specimens, each featuring different sizes and locations of holes and crack defects. We then used state-of-the-art deep learning models to benchmark the dataset and identified YOLOv5s as the best-performing baseline model for our study. To address the challenges of deploying deep learning models and the issue of small defects being easily confused with the background in ultrasonic B-scan images, we made lightweight improvements to the deep learning model. Additionally, we enhanced the quality of data labels through data cleaning. Our experiments show that our method achieved a precision of 97.8%, a recall of 98.1%, mAP@0.5 of 99.0%, and mAP@.5:.95 of 67.6%, with a frames per second (FPS) of 74.5. Furthermore, the number of model parameters was reduced by 43.2%, while maintaining high detection accuracy.Overall, our proposed method offers a significant improvement over the original model, making it a more reliable and efficient tool for automated defect assessment in ultrasonic B-scan images.
超声波检测是一种广泛应用于精密锻件的无损检测技术。然而，评估超声波 B 扫描图像中的缺陷可能容易出现错误、遗漏以及因人为判断而导致的低效。为了应对这些挑战，我们提出了一种基于深度学习的方法来自动评估此类图像。我们首先创建了一个由 8000 张图像组成的数据集，每张图像的尺寸为 224x224 像素。这些图像是从 7 个试样的超声波 B 扫描图像中裁剪出来的，每个试样都有不同大小和位置的孔洞和裂纹缺陷。然后，我们使用最先进的深度学习模型对数据集进行基准测试，并确定 YOLOv5s 为我们研究中表现最佳的基准模型。为了解决部署深度学习模型所面临的挑战，以及超声波 B 扫描图像中的小缺陷容易与背景混淆的问题，我们对深度学习模型进行了轻量级改进。此外，我们还通过数据清洗提高了数据标签的质量。实验结果表明，我们的方法实现了 97.8% 的精确度、98.1% 的召回率、99.0% 的 mAP@0.5 和 67.6% 的 mAP@.5:.95，每秒帧数（FPS）为 74.5。此外，模型参数的数量减少了 43.2%，同时保持了较高的检测精度。总体而言，我们提出的方法比原始模型有了显著的改进，使其成为超声波 B 扫描图像中自动缺陷评估的更可靠、更高效的工具。
{"title":"Automated Defect Detection in Precision Forging Ultrasonic Images Based on Deep Learning","authors":"Jianjun Zhao, Yuxin Zhang, Xiaozhong Du, Xiaoming Sun","doi":"10.1088/1361-6501/ad180c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad180c","url":null,"abstract":"\u0000 Ultrasonic testing is a widely used non-destructive testing technique for precision forgings. However, assessing defects in ultrasonic B-scan images can be prone to errors, misses, and inefficiencies due to human judgment. To address these challenges, we propose a method based on deep learning to automate the evaluation of such images.We started by creating a dataset comprising 8,000 images, each measuring 224x224 pixels. These images were cropped from ultrasonic B-scan images of 7 specimens, each featuring different sizes and locations of holes and crack defects. We then used state-of-the-art deep learning models to benchmark the dataset and identified YOLOv5s as the best-performing baseline model for our study. To address the challenges of deploying deep learning models and the issue of small defects being easily confused with the background in ultrasonic B-scan images, we made lightweight improvements to the deep learning model. Additionally, we enhanced the quality of data labels through data cleaning. Our experiments show that our method achieved a precision of 97.8%, a recall of 98.1%, mAP@0.5 of 99.0%, and mAP@.5:.95 of 67.6%, with a frames per second (FPS) of 74.5. Furthermore, the number of model parameters was reduced by 43.2%, while maintaining high detection accuracy.Overall, our proposed method offers a significant improvement over the original model, making it a more reliable and efficient tool for automated defect assessment in ultrasonic B-scan images.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"32 8","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949666","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 : 2023-12-21DOI: 10.1088/1361-6501/ad1804
yupeng shi, Li Bing, Li Lei, Tongkun Liu, Du Xiao, Xiang Wei
Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a CNN model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.
{"title":"Automatic non-contact grinding surface roughness measurement based on multi-focused sequence images and CNN","authors":"yupeng shi, Li Bing, Li Lei, Tongkun Liu, Du Xiao, Xiang Wei","doi":"10.1088/1361-6501/ad1804","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1804","url":null,"abstract":"\u0000 Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a CNN model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952944","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 : 2023-12-21DOI: 10.1088/1361-6501/ad1812
Yong Sang, Lianjie Liao, Tao Chen, Jiakuo Liu
The 6-degrees-of-freedom (6-DOF) platform has gained popularity in industry owing to its advantages, especially in indoor simulation experiment requiring 6-dimensional force environments. However, up to date, the measurement of such 6-dimensional forces is challenging due to complex structures, difficult decoupling, and high costs. In this study, an electric 6-DOF loading device designed by ourselves is presented, with an ability to load high precision displacement and force. To measure the 6-dimensional force of the device’s moving platform, this paper proposes a scheme involving embedding tension-compression sensors in each leg of the device and describes the mechanical model of the device. Then a novel method with a variable transformation matrix is proposed, complemented by a decoupling study that optimizes the variable transformation matrix. Finally, we validate the proposed method through an external force loading experiment. The results indicate that the error in Fy experiment is less than 2.7%, and the error in Fz experiment is less than 1.6%. The novel method exhibits high accuracy, easy installation, fast response, and low cost.
{"title":"A Novel 6-dimensional Force Measurement Based on the Electric 6-DOF Loading Device","authors":"Yong Sang, Lianjie Liao, Tao Chen, Jiakuo Liu","doi":"10.1088/1361-6501/ad1812","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1812","url":null,"abstract":"\u0000 The 6-degrees-of-freedom (6-DOF) platform has gained popularity in industry owing to its advantages, especially in indoor simulation experiment requiring 6-dimensional force environments. However, up to date, the measurement of such 6-dimensional forces is challenging due to complex structures, difficult decoupling, and high costs. In this study, an electric 6-DOF loading device designed by ourselves is presented, with an ability to load high precision displacement and force. To measure the 6-dimensional force of the device’s moving platform, this paper proposes a scheme involving embedding tension-compression sensors in each leg of the device and describes the mechanical model of the device. Then a novel method with a variable transformation matrix is proposed, complemented by a decoupling study that optimizes the variable transformation matrix. Finally, we validate the proposed method through an external force loading experiment. The results indicate that the error in Fy experiment is less than 2.7%, and the error in Fz experiment is less than 1.6%. The novel method exhibits high accuracy, easy installation, fast response, and low cost.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"66 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951210","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 : 2023-12-21DOI: 10.1088/1361-6501/ad1807
Jia Li, Jialong He, wanghao shen, Ma Cheng, Wang Jili, He Yuzhi
The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.
{"title":"Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background","authors":"Jia Li, Jialong He, wanghao shen, Ma Cheng, Wang Jili, He Yuzhi","doi":"10.1088/1361-6501/ad1807","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1807","url":null,"abstract":"\u0000 The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"37 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951609","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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