Pub Date : 2025-03-03DOI: 10.1016/j.measurement.2025.117172
Tingyu Li , Biao Shen , Dalong Chen , Bihao Guo , Yao Huang , Tonghui Shi , Qingze Yu , Kai Wu , Bingjia Xiao
The magnetic diagnostic system provides critical input signals for plasma feedback control, which is vital for the safe and stable operation of tokamak devices. Considering system cost and engineering constraints related to installation, optimizing the placement of magnetic sensors to minimize the sensor count is essential. In this study, a sensor layout optimization algorithm based on an improved minimal-redundancy-maximal-relevance criterion is proposed. This improved criterion takes into account both the distance and measurement direction differences between sensors in the redundancy assessment, addressing the issue of dense sensor placement in localized regions, often observed in existing studies. In addition, binary search is employed to identify the minimum number of sensors required, significantly speeding up the optimization process. This algorithm is applied to the design of magnetic sensor layout for a next-generation fusion energy experimental device. The optimized layout consists of only 13 pick-up coils and 16 flux loops. In contrast, a widely used uniform layout requires approximately 50 sensors to achieve the same reconstruction accuracy, demonstrating the effectiveness and practicality of the proposed optimization algorithm.
{"title":"A method for optimizing the layout of magnetic sensors in tokamaks based on improved minimal-redundancy-maximal-relevance criterion","authors":"Tingyu Li , Biao Shen , Dalong Chen , Bihao Guo , Yao Huang , Tonghui Shi , Qingze Yu , Kai Wu , Bingjia Xiao","doi":"10.1016/j.measurement.2025.117172","DOIUrl":"10.1016/j.measurement.2025.117172","url":null,"abstract":"<div><div>The magnetic diagnostic system provides critical input signals for plasma feedback control, which is vital for the safe and stable operation of tokamak devices. Considering system cost and engineering constraints related to installation, optimizing the placement of magnetic sensors to minimize the sensor count is essential. In this study, a sensor layout optimization algorithm based on an improved minimal-redundancy-maximal-relevance criterion is proposed. This improved criterion takes into account both the distance and measurement direction differences between sensors in the redundancy assessment, addressing the issue of dense sensor placement in localized regions, often observed in existing studies. In addition, binary search is employed to identify the minimum number of sensors required, significantly speeding up the optimization process. This algorithm is applied to the design of magnetic sensor layout for a next-generation fusion energy experimental device. The optimized layout consists of only 13 pick-up coils and 16 flux loops. In contrast, a widely used uniform layout requires approximately 50 sensors to achieve the same reconstruction accuracy, demonstrating the effectiveness and practicality of the proposed optimization algorithm.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117172"},"PeriodicalIF":5.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-03DOI: 10.1016/j.measurement.2025.117097
Melanie Schaller , Mathis Kruse , Antonio Ortega , Marius Lindauer , Bodo Rosenhahn
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training overestimates performance by inadequately accounting for drift. This is primarily because typical cross-validation techniques allow data instances to appear in both training and testing sets, thereby distorting the accuracy of the predictive evaluation. As a result, these models are unable to precisely predict future drift effects, compromising their ability to generalize and adapt to evolving data conditions. This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift. By employing strategies such as data balancing, meta-learning, automated ensemble learning, hyperparameter optimization, feature selection, and boosting, our AutoML-DC (Drift Compensation) model significantly improves classification performance against sensor drift. AutoML-DC further adapts effectively to varying drift severities.
{"title":"AutoML for multi-class anomaly compensation of sensor drift","authors":"Melanie Schaller , Mathis Kruse , Antonio Ortega , Marius Lindauer , Bodo Rosenhahn","doi":"10.1016/j.measurement.2025.117097","DOIUrl":"10.1016/j.measurement.2025.117097","url":null,"abstract":"<div><div>Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training overestimates performance by inadequately accounting for drift. This is primarily because typical cross-validation techniques allow data instances to appear in both training and testing sets, thereby distorting the accuracy of the predictive evaluation. As a result, these models are unable to precisely predict future drift effects, compromising their ability to generalize and adapt to evolving data conditions. This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift. By employing strategies such as data balancing, meta-learning, automated ensemble learning, hyperparameter optimization, feature selection, and boosting, our AutoML-DC (Drift Compensation) model significantly improves classification performance against sensor drift. AutoML-DC further adapts effectively to varying drift severities.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117097"},"PeriodicalIF":5.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.measurement.2025.117117
Yan Li, Sheng Bao
This paper investigates the anisotropic magnetic memory signal of low-carbon steel produced by wire-arc directed energy deposition (wire-arc DED) using tensile tests. Two rectangular specimens with different printing directions were tested. The residual magnetic field on the specimen surfaces during two loading stages was measured using a TSC-PC-16 magnetometer. The study reveals that material stress history significantly affects magnetic memory signals, with clear anisotropy between transversal and longitudinal specimens. The normal magnetic memory signal is less influenced by surface roughness and better reflects applied stress. Magnetic characteristic parameters are defined to quantify anisotropy and exhibit a quadratic relationship with load, enabling load level evaluation. This research highlights the potential for identifying printing direction and evaluating surface roughness in wire-arc DED components through magnetic memory signals, contributing to non-destructive testing of additive manufacturing steel.
{"title":"Anisotropic magnetic memory signal of low-carbon steel fabricated by wire-arc directed energy deposition","authors":"Yan Li, Sheng Bao","doi":"10.1016/j.measurement.2025.117117","DOIUrl":"10.1016/j.measurement.2025.117117","url":null,"abstract":"<div><div>This paper investigates the anisotropic magnetic memory signal of low-carbon steel produced by wire-arc directed energy deposition (wire-arc DED) using tensile tests. Two rectangular specimens with different printing directions were tested. The residual magnetic field on the specimen surfaces during two loading stages was measured using a TSC-PC-16 magnetometer. The study reveals that material stress history significantly affects magnetic memory signals, with clear anisotropy between transversal and longitudinal specimens. The normal magnetic memory signal is less influenced by surface roughness and better reflects applied stress. Magnetic characteristic parameters are defined to quantify anisotropy and exhibit a quadratic relationship with load, enabling load level evaluation. This research highlights the potential for identifying printing direction and evaluating surface roughness in wire-arc DED components through magnetic memory signals, contributing to non-destructive testing of additive manufacturing steel.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117117"},"PeriodicalIF":5.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-01DOI: 10.1016/j.measurement.2025.116968
Griffani Megiyanto Rahmatullah , Shanq-Jang Ruan , Lieber Po-Hung Li
Lipreading is one of the techniques that can enhance speech perception. However, there are still limited studies of lipreading research focusing on low-resource languages, such as Indonesian. In this study, we introduce an instrument designed to generate lipreading datasets using CC BY video data available on YouTube called Lipreading Information Resource Assembler-Generator (LIRA-Gen). Using this instrument, we present the first Indonesian language lipreading dataset (IDLRW) containing over 48,000 videos with 100-word categories spoken by various persons in natural conditions. Also, we developed a deep learning architecture consisting of an Advanced Residual Network (ARN) using ResNet-34 incorporated with a Channel Spatial Attention (CSA) module, improved sequence modeling by fusing Bi-Gru with Mamba (BGM), an integrated word decision module, and fine-tuned hyperparameter. Our measurement shows that it reaches an accuracy of 60.51% on the IDLRW dataset and outperforms state-of-the-art lipreading models from another dataset even without implementing an additional learning strategy.
唇读是能够增强语音感知能力的技术之一。然而，针对印尼语等低资源语言的唇读研究仍然有限。在本研究中，我们介绍了一种利用 YouTube 上的 CC BY 视频数据生成唇读数据集的工具，名为 "唇读信息资源汇编生成器"（LIRA-Gen）。利用该工具，我们生成了第一个印尼语唇读数据集（IDLRW），其中包含超过 48,000 个视频，由不同的人在自然条件下说出 100 个单词类别。此外，我们还开发了一种深度学习架构，该架构由使用 ResNet-34 的高级残差网络（ARN）和通道空间注意（CSA）模块组成，通过融合 Bi-Gru 和 Mamba（BGM）改进了序列建模，集成了单词判定模块和微调超参数。我们的测量结果表明，它在 IDLRW 数据集上的准确率达到了 60.51%，即使不采用额外的学习策略，也超过了另一个数据集上最先进的读唇模型。
{"title":"Recognizing Indonesian words based on visual cues of lip movement using deep learning","authors":"Griffani Megiyanto Rahmatullah , Shanq-Jang Ruan , Lieber Po-Hung Li","doi":"10.1016/j.measurement.2025.116968","DOIUrl":"10.1016/j.measurement.2025.116968","url":null,"abstract":"<div><div>Lipreading is one of the techniques that can enhance speech perception. However, there are still limited studies of lipreading research focusing on low-resource languages, such as Indonesian. In this study, we introduce an instrument designed to generate lipreading datasets using CC BY video data available on YouTube called Lipreading Information Resource Assembler-Generator (LIRA-Gen). Using this instrument, we present the first Indonesian language lipreading dataset (IDLRW) containing over 48,000 videos with 100-word categories spoken by various persons in natural conditions. Also, we developed a deep learning architecture consisting of an Advanced Residual Network (ARN) using ResNet-34 incorporated with a Channel Spatial Attention (CSA) module, improved sequence modeling by fusing Bi-Gru with Mamba (BGM), an integrated word decision module, and fine-tuned hyperparameter. Our measurement shows that it reaches an accuracy of 60.51% on the IDLRW dataset and outperforms state-of-the-art lipreading models from another dataset even without implementing an additional learning strategy.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 116968"},"PeriodicalIF":5.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-01DOI: 10.1016/j.measurement.2025.117075
Yu Zhao , Jie Meng , Peng Ye , Aijun Chen , Wuhuang Huang , Duyu Qiu , Qinchuan Zhang , Kuojun Yang
The prevalent architecture for wide-band data acquisition (DAQ) systems is the time-interleaving (TI) sampling architecture. However, addressing the frequency response mismatch (FRM) and frequency response distortion (FRD) errors caused by the analog front-end circuit is crucial for accurate sampling in this architecture. This paper models the wide-band DAQ system based on the TI architecture as a periodic time-varying (PTV) system. It proposes a joint-PTV compensation (J-PTVC) method for both FRM and FRD errors. The compensation of FRM and FRD errors involves designing a digital PTV filter in the digital back end and an improved FFT convolution architecture for the PTV filter system. Furthermore, this paper constructs a TI base DAQ system with a sampling rate of 40 GSPS using 4 ADCs with a sampling rate of 10 GSPS and implements the proposed improved FFT convolution architecture in field programmable gate array (FPGA). After the joint compensation, the spurious-free dynamic range (SFDR) of the system increases from 24.4 dB to 51.94 dB, effective number of bits (ENOB) increases from 3.16 bits to 6.34 bits, the magnitude-frequency response flatness after compensation reaches 0.25 dB, and the step response rise time also decreases from 65 ps to 52.5 ps with 25.6 ps fast-edge signal input.
{"title":"General digital background compensation strategy for wide-band time-interleaved data acquisition system based on periodically time-varying filters","authors":"Yu Zhao , Jie Meng , Peng Ye , Aijun Chen , Wuhuang Huang , Duyu Qiu , Qinchuan Zhang , Kuojun Yang","doi":"10.1016/j.measurement.2025.117075","DOIUrl":"10.1016/j.measurement.2025.117075","url":null,"abstract":"<div><div>The prevalent architecture for wide-band data acquisition (DAQ) systems is the time-interleaving (TI) sampling architecture. However, addressing the frequency response mismatch (FRM) and frequency response distortion (FRD) errors caused by the analog front-end circuit is crucial for accurate sampling in this architecture. This paper models the wide-band DAQ system based on the TI architecture as a periodic time-varying (PTV) system. It proposes a joint-PTV compensation (J-PTVC) method for both FRM and FRD errors. The compensation of FRM and FRD errors involves designing a digital PTV filter in the digital back end and an improved FFT convolution architecture for the PTV filter system. Furthermore, this paper constructs a TI base DAQ system with a sampling rate of 40 GSPS using 4 ADCs with a sampling rate of 10 GSPS and implements the proposed improved FFT convolution architecture in field programmable gate array (FPGA). After the joint compensation, the spurious-free dynamic range (SFDR) of the system increases from 24.4 dB to 51.94 dB, effective number of bits (ENOB) increases from 3.16 bits to 6.34 bits, the magnitude-frequency response flatness after compensation reaches 0.25 dB, and the step response rise time also decreases from 65 ps to 52.5 ps with 25.6 ps fast-edge signal input.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117075"},"PeriodicalIF":5.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-01DOI: 10.1016/j.measurement.2025.117061
Zhi Li , Kaige Xue , Junfeng Chen , Xin Peng
Soft sensing technology has been widely applied in quality prediction for refining production. The refining process involves multiple strongly coupled subsystems, resulting in process variables exhibiting localized patterns with different properties. Existing soft sensing methods often struggle to extract multi-attribute features from such complex coupled process data effectively. This paper proposes a multi-attribute channel hybrid neural network, integrated with a novel quality-driven learning mechanism to enhance the quality relevance of feature representation. Specifically, a multi-attribute channel feature learning module is designed to capture local spatio-temporal dependencies from process variables with varying properties, and the outputs from each channel are integrated to obtain a global feature representation of the variables. Additionally, a quality information extraction module is developed to account for fluctuations in quality data. This module employs wavelet transforms to analyze the overall trends and detailed information of quality variables, enabling the long short-term memory network to more effectively capture dynamic historical information. Finally, a feature interaction mechanism is introduced, where the results of the quality information extraction guide the feature learning of process variables to obtain more quality-relevant feature representations. The performance evaluation on two typical refining processes demonstrates the superiority of the proposed method compared to other modeling methods.
{"title":"A quality-driven multi-attribute channel hybrid neural network for soft sensing in refining processes","authors":"Zhi Li , Kaige Xue , Junfeng Chen , Xin Peng","doi":"10.1016/j.measurement.2025.117061","DOIUrl":"10.1016/j.measurement.2025.117061","url":null,"abstract":"<div><div>Soft sensing technology has been widely applied in quality prediction for refining production. The refining process involves multiple strongly coupled subsystems, resulting in process variables exhibiting localized patterns with different properties. Existing soft sensing methods often struggle to extract multi-attribute features from such complex coupled process data effectively. This paper proposes a multi-attribute channel hybrid neural network, integrated with a novel quality-driven learning mechanism to enhance the quality relevance of feature representation. Specifically, a multi-attribute channel feature learning module is designed to capture local spatio-temporal dependencies from process variables with varying properties, and the outputs from each channel are integrated to obtain a global feature representation of the variables. Additionally, a quality information extraction module is developed to account for fluctuations in quality data. This module employs wavelet transforms to analyze the overall trends and detailed information of quality variables, enabling the long short-term memory network to more effectively capture dynamic historical information. Finally, a feature interaction mechanism is introduced, where the results of the quality information extraction guide the feature learning of process variables to obtain more quality-relevant feature representations. The performance evaluation on two typical refining processes demonstrates the superiority of the proposed method compared to other modeling methods.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117061"},"PeriodicalIF":5.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-03-01DOI: 10.1016/j.measurement.2025.117096
Veli Bakırcıoğlu , Nihat Çabuk , Hossein B. Jond , Mete Kalyoncu
Hydraulic-actuated legs for quadruped robots excel in producing high force and offering precise control. Although the overall efficiency of hydraulic servo systems can be limited by pump and valve losses, the local mechanical efficiency from the actuator to the leg mechanism can be relatively high. This study introduces an optimization driven methodology for designing and validating robotic leg mechanisms using evolutionary algorithms. By solving three distinct optimization problems, the study addresses trajectory tracking accuracy and force transmission efficiency. The resulting design was experimentally validated, demonstrating reliable motion reproduction with minimal deviation and achieving a force transmission efficiency of 94%. These results demonstrate the feasibility of translating optimization outcomes into high-performing physical prototypes, providing a robust framework for future robotic mechanism development.
{"title":"Optimization-driven design and experimental validation of a hydraulic robot leg mechanism","authors":"Veli Bakırcıoğlu , Nihat Çabuk , Hossein B. Jond , Mete Kalyoncu","doi":"10.1016/j.measurement.2025.117096","DOIUrl":"10.1016/j.measurement.2025.117096","url":null,"abstract":"<div><div>Hydraulic-actuated legs for quadruped robots excel in producing high force and offering precise control. Although the overall efficiency of hydraulic servo systems can be limited by pump and valve losses, the local mechanical efficiency from the actuator to the leg mechanism can be relatively high. This study introduces an optimization driven methodology for designing and validating robotic leg mechanisms using evolutionary algorithms. By solving three distinct optimization problems, the study addresses trajectory tracking accuracy and force transmission efficiency. The resulting design was experimentally validated, demonstrating reliable motion reproduction with minimal deviation and achieving a force transmission efficiency of 94%. These results demonstrate the feasibility of translating optimization outcomes into high-performing physical prototypes, providing a robust framework for future robotic mechanism development.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117096"},"PeriodicalIF":5.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-28DOI: 10.1016/j.measurement.2025.117165
Zhi-hai He , Wen-qiang Zhai , Jin-yan Shi , Di-ping Hong , Jia-bing Mao , Hui Rong
Nanoindentation is an effective method for characterizing nanoscale features of cement-based materials. However, the limitations of the statistical grid approach used for nanoindentation make it difficult to accurately quantify the relatively small interfacial transition zone (ITZ). In order to identify a method suitable for obtaining nanomechanical characteristics of narrow ITZ, the characteristics of ITZ between steel fiber (SF) and ultra-high performance concrete (UHPC) matrix were characterized using nanoindentation and nanoscratch. The results of nanoindentation test show that the elastic modulus of the area other than the SF is basically the same, which makes it impossible to identify the ITZ between the UHPC matrix and SF, even though the maximum thickness of the ITZ can reach 2.9 um. More importantly, nanoscratch identifies all ITZs even though their minimum thickness is 0.75 μm, benefiting from the more continuous information they acquire. Compared to the hardness and elastic modulus obtained through nanoindentation, nanoscratch obtains additional fracture toughness and is more stable in assessing the thickness of the ITZ. In addition, the substitution of a portion of cement or silica fume in UHPC with coral powder (CP) changes the thickness and nanomechanical properties of the ITZ, which is effectively identified by nanoscratch technology. Compared to the control group, 5 % cement was substituted by CP making the scratch hardness and fracture toughness of SF/matrix ITZ increased by about 38 % and 13.2 %, respectively, and the thickness of ITZ was reduced by about 64 %. This research provides a reliable basis for the accurate identification of narrow ITZ in cement-based materials, which provides guidance for the regulation of ITZ performance.
{"title":"Characterizing the interfacial transition zone in ultra-high performance concrete: Comparison of multiple nanoscale characterization methods","authors":"Zhi-hai He , Wen-qiang Zhai , Jin-yan Shi , Di-ping Hong , Jia-bing Mao , Hui Rong","doi":"10.1016/j.measurement.2025.117165","DOIUrl":"10.1016/j.measurement.2025.117165","url":null,"abstract":"<div><div>Nanoindentation is an effective method for characterizing nanoscale features of cement-based materials. However, the limitations of the statistical grid approach used for nanoindentation make it difficult to accurately quantify the relatively small interfacial transition zone (ITZ). In order to identify a method suitable for obtaining nanomechanical characteristics of narrow ITZ, the characteristics of ITZ between steel fiber (SF) and ultra-high performance concrete (UHPC) matrix were characterized using nanoindentation and nanoscratch. The results of nanoindentation test show that the elastic modulus of the area other than the SF is basically the same, which makes it impossible to identify the ITZ between the UHPC matrix and SF, even though the maximum thickness of the ITZ can reach 2.9 um. More importantly, nanoscratch identifies all ITZs even though their minimum thickness is 0.75 μm, benefiting from the more continuous information they acquire. Compared to the hardness and elastic modulus obtained through nanoindentation, nanoscratch obtains additional fracture toughness and is more stable in assessing the thickness of the ITZ. In addition, the substitution of a portion of cement or silica fume in UHPC with coral powder (CP) changes the thickness and nanomechanical properties of the ITZ, which is effectively identified by nanoscratch technology. Compared to the control group, 5 % cement was substituted by CP making the scratch hardness and fracture toughness of SF/matrix ITZ increased by about 38 % and 13.2 %, respectively, and the thickness of ITZ was reduced by about 64 %. This research provides a reliable basis for the accurate identification of narrow ITZ in cement-based materials, which provides guidance for the regulation of ITZ performance.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117165"},"PeriodicalIF":5.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-28DOI: 10.1016/j.measurement.2025.117138
Tianchen Cao , Dongbo Wu , Huiling Li , Xueping Liu , Hui Wang
The wear conditions of honeycomb sealing rings in aerospace engines are often complex. Traditional human operations based on sample paste exhibit poor adaptability and are inefficient. This paper proposes an automated detection method for the geometric features of wear marks on honeycomb sealing structures based on depth ratio features. Adaptive identification and quantification of cellular wear through point cloud data analysis. First, the point cloud data is cropped, followed by a least-squares fit iterative method to compute a reference line at a specified cross-section, which serves as a standard for computing the width and depth of wear marks while denoising the point cloud data. Subsequently, N-neighborhood sets and the depth ratio features within these sets are introduced, transforming the task of detecting wear marks’ start and end points into a peak detection problem. An improved automatic multiscale-based peak detection (AMPD) algorithm with a masking mechanism is utilized to determine the extent of each wear mark. Finally, the geometric features are calculated for each wear mark. Experimental results demonstrate that the proposed method can robustly identify wear areas with varying depths and distributions, measurement time reduced by more than 90%, and fulfilling the requirements for identifying and measuring honeycomb wear marks.
{"title":"Automatic detection on wear features of aero-engine honeycomb sealing ring","authors":"Tianchen Cao , Dongbo Wu , Huiling Li , Xueping Liu , Hui Wang","doi":"10.1016/j.measurement.2025.117138","DOIUrl":"10.1016/j.measurement.2025.117138","url":null,"abstract":"<div><div>The wear conditions of honeycomb sealing rings in aerospace engines are often complex. Traditional human operations based on sample paste exhibit poor adaptability and are inefficient. This paper proposes an automated detection method for the geometric features of wear marks on honeycomb sealing structures based on depth ratio features. Adaptive identification and quantification of cellular wear through point cloud data analysis. First, the point cloud data is cropped, followed by a least-squares fit iterative method to compute a reference line at a specified cross-section, which serves as a standard for computing the width and depth of wear marks while denoising the point cloud data. Subsequently, N-neighborhood sets and the depth ratio features within these sets are introduced, transforming the task of detecting wear marks’ start and end points into a peak detection problem. An improved automatic multiscale-based peak detection (AMPD) algorithm with a masking mechanism is utilized to determine the extent of each wear mark. Finally, the geometric features are calculated for each wear mark. Experimental results demonstrate that the proposed method can robustly identify wear areas with varying depths and distributions, measurement time reduced by more than 90%, and fulfilling the requirements for identifying and measuring honeycomb wear marks.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"250 ","pages":"Article 117138"},"PeriodicalIF":5.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-28DOI: 10.1016/j.measurement.2025.117163
Dexin Gao , Muzi Zhang , Qingquan Sun , Jiali Wu , Zhigang Qiao , Wenyue Gao , Juan Su , Xin Liu , Chi Wu
The oceans cover approximately 71 % of the Earth’s surface and serve as the largest heat reservoir globally, playing a crucial role in climate regulation. Ocean temperature, as a key hydrological parameter, influences other hydrological measurements and has profound implications for the climate system. Therefore, long-term precise monitoring of seawater temperature is essential. This study proposes a multi-dimensional stability assessment strategy and conducts stability tests on NTC thermistors widely used in marine temperature measurements. The 20 samples tested were sourced from four different manufacturers. The experimental results show that approximately 140 h of annealing treatment significantly reduced the sensor drift rate, allowing the sensors to reach a stable state more quickly. In 240 thermal shock tests, the drift of glass-encapsulated sensors was below 1 mK, while the drift of epoxy-resin-encapsulated sensors remained below 10 mK. In constant temperature environments, sensor drift exhibited a segmented linear pattern, with a faster initial rate that then stabilized. Although calibration methods affected measurement accuracy, they had no significant impact on drift characteristics. Quantitative analysis based on the Arrhenius model indicated that temperature accelerates sensor aging within the range of 10 ∼ 35 ℃, while the influence is minimal below 10 ℃, with an activation energy of 0.06 eV, only 1/5 of that in the mid-to-high temperature range. This study establishes a systematic experimental framework, providing theoretical support for the selection, pre-treatment, long-term stability assessment, and optimization of high-precision temperature sensors, and offers reliable data assurance for long-term observations in marine and climate research.
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