Uncertainty evaluation for dynamic identification of a micro contact probe based on the signal transmission chain analysis method

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

Roadmap on signal processing for next generation measurement systems

Iakovidis

Ooi

Kuang

et al. 2021

“…In order to fully understand the open-loop dynamic characteristics of the generator, it needs to be dynamically identified. The generator is dynamically identified by the adaptive recursive least-squares (ARLS) method [19]. The experimental setup is shown in figure 8.…”

Section: Dynamic Identificationmentioning

confidence: 99%

“…To improve the accuracy of 2D vibration calibration, a linear vibration generator is a suitable approach. Du et al applied a high-stiffness flexible mechanism to improve the carrying capacity of a 2D micro-vibration generator, and the motion range of their vibration generator was 19.53 × 19.07 µm [7][8][9]. The driving displacement loss of the piezoelectric actuator was approximately 50%, and the motion range was small.…”

Section: Introductionmentioning

confidence: 99%

Development of a high-precision two-dimensional coplanar micro-vibration generator

Lei¹,

Li²,

Jiang³

et al. 2021

Self Cite

In this study, we propose a high-precision two-dimensional (2D) coplanar micro-vibration generator with a flexure-decoupling mechanism. The generator is composed of a 2D symmetrical coplanar platform and a slider pair. The 2D symmetrical coplanar platform is connected to the base through six compound basic parallelogram modules, which are orthogonally arranged to implement motion decoupling in the X and Y directions. A vertical support with a slider pair is used to guarantee strict planar motion under heavy loads. The generator is driven by two piezoelectric actuators. The generator is dynamically identified through the adaptive least-square method to obtain its dynamic characteristics, and experiments are conducted to validate the performance of the developed generator. Notably, the developed generator has the capability of translational motion strokes of 48.71 μm and 49.10 μm in the X and Y directions, respectively, and the maximum coupling error is approximately 0.78%. The motion resolution of the generator is 1.3 nm. The experimental results also show that the developed generator has good load capacity and trajectory tracking capabilities.

“…On the other hand, the optimization function is at least C2-continuous, so the symbol ||•|| should be removed by squaring the distance. The objective function is shown as (8).…”

Section: Fine-calibration Processmentioning

confidence: 99%

Modeling and sphere constrained calibration of a vision coordinate measurement machine

Huang¹,

Yan²,

Zhang³

et al. 2021

A new vision coordinate measurement machine (v-CMM) is proposed by integrating a binocular stereo vision tracking device and a 1D distance sensor to measure the surface. Using a standard sphere, the calibration process is carried out in two steps. Fewer points on the sphere are measured and the linear calibration model is used to calculate the approximated transformation. More points are collected and the radius-difference based optimization model is used to calculate the accurate transformation. Using the Monte Carlo method, the calibration uncertainty is analyzed by deriving the relationship between calibration uncertainty and tracking random errors. The effectiveness of the calibration algorithm is verified by analyzing the situation where the center error and tracking error mix. Two experiments are carried out: One is the calibration experiment, which will obtain the structure parameters of v-CMM with uncertainty. In the experiment, the measured runout error of the sphere is 0.024 mm, which is able to evaluate the accuracy of the calibration process. The other experiment is that a part with three cylindrical bars is measured. Key sizes of the part are calculated, containing bars radiuses and the distances between them. The experimental results show that after calibrating, the measuring precision of v-CMM is highly improved. The v-CMM is suitable in specific scenarios, like transparent media surface measuring.