A Novel White Light Interference Based AFM Head

Yang, Wenjun; Yang, Xiaojun; Lu, Wenlong; Yu, Nengguo; Chen, Liangzhou; Zhou, Liping; Chang, Suping

doi:10.1109/jlt.2016.2614542

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…Fiber optics are used in applications that require high precision and electromagnetic-interference immunity [116]. The principle underlying fiber optics is based on white-light interference [117], which can relate the absolute shifting of a signal emitted from a light source with any physical variable [118]. This type of sensor is used to measure deformation, temperature, material concentrations, acceleration, rotation, pressure, vibrations, and shifting.…”

Section: Fiber Opticsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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Section: Fiber Opticsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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“…The plunge cutting experiments generated five taper grooves on the 6H-SiC sample. The taper grooves were measured by a white light interferometer [ 52 ], and their surface morphologies were shown in Figure 11 . For each groove, the morphologies in the ductile-cut region and the brittle-cut region are significantly different from each other.…”

Section: Examination Of the Theory On 6h-sicmentioning

confidence: 99%

A Study of Mechanics in Brittle–Ductile Cutting Mode Transition

Xiao

Ren

2018

Micromachines

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This paper presents an investigation of the mechanism of the brittle–ductile cutting mode transition from the perspective of the mechanics. A mechanistic model is proposed to analyze the relationship between undeformed chip thickness, deformation, and stress levels in the elastic stage of the periodic chip formation process, regarding whether brittle or ductile mode deformation is to follow the elastic stage. It is revealed that, the distance of tool advancement required to induce the same level of compressive stress decreases with undeformed chip thickness, and thereby the tensile stress below and behind the tool decreases with undeformed chip thickness. As a result, the tensile stress becomes lower than the critical tensile stress for brittle fracture when the undeformed chip thickness becomes sufficiently small, enabling the brittle–ductile cutting mode transition. The finite element method is employed to verify the analysis of the mechanics on a typical brittle material 6H silicon carbide, and confirmed that the distance of the tool advancement required to induce the same level of compressive stress becomes smaller when the undeformed chip thickness decreases, and consequently smaller tensile stress is induced below and behind the tool. The critical undeformed chip thicknesses for brittle–ductile cutting mode transition are estimated according to the proposed mechanics, and are verified by plunge cutting experiments in a few crystal directions. This study should contribute to better understanding of the mechanism of brittle–ductile cutting mode transition and the ultra-precision machining of brittle materials.

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“…Extensive research has been conducted on developing various measurement instruments to fulfill a wide range of metrological needs [ 7 , 8 , 9 , 10 ], such as high precision coordinate measuring machines [ 7 , 8 ], micro topographical instruments [ 11 ], electron microscopy [ 12 ], optical interferometry [ 13 , 14 , 15 ], etc. Although these instruments are capable of performing accurate and efficient measurement at specific measurement range, few of them could realize high dynamic range multi-scale measurement with high efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement

Ren

Cheung

Xiao

2018

Sensors

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This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.

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A Novel White Light Interference Based AFM Head

Cited by 25 publications

References 25 publications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

A Study of Mechanics in Brittle–Ductile Cutting Mode Transition

Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement

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