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Physical contact and traditional sensitive structure Physical contact and traditional pressure-sensitive structures typically do not operate well in harsh environments. This paper proposes a high-temperature pressure measurement system for wireless passive pressure sensors on the basis of inductively coupled LC resonant circuits.

This paper begins with a general introduction to the high-temperature pressure measurement system, which consists of a reader antenna inductively coupled to the sensor circuit, a readout unit and a heat insulation unit. The design and fabrication of the proposed measurement system are then described in detail.

A wireless passive pressure sensor without an air channel is fabricated using high-temperature co-fired ceramics (HTCC) technology and its signal is measured by the designed measurement system. The designed heat insulation unit keeps the reader antenna in a safe environment of 159.5°C when the passive sensor is located in a 900°C high-temperature zone continuously for 0.5 h. The proposed system can effectively detect the sensor’s resonance frequency variation in a high bandwidth from 1 to 100 MHz with a frequency resolution of 0.006 MHz, tested from room temperature to 500°C for 30 min.

Expensive and bulky equipment (impedance analyzers or network analyzers) restrict the use of the readout method outside the laboratory environment. This paper shows that a novel readout circuit can replace the laboratory equipment to demodulate the measured pressure by extracting the various sensors’ resonant frequency. The proposed measurement system realizes automatic and continuous pressure monitoring in a high-temperature environment with a coupled distance of 2.5 cm. The research finding is meaningful for the measurement of passive pressure sensors under a wide temperature range.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

Large size of aircraft assembly tooling structure and complex measurement environment exist. The laid enhanced reference points (ERS) are subject to a combination of nonuniform temperature fields and measurement errors, resulting in increased measurement registration errors. In view of the nonuniform temperature field and measurement errors affecting the ERS point registration problem, the purpose of this paper is to propose a neural network-based ERS point registration compensation method for large-size measurement fields under a nonuniform temperature field.

The approach is to collect ERS point information and temperature data, normalize the collected data to complete the data structure design and complete the construction of the neural network prediction model by data training. The data learning is performed to complete the prediction model construction, and the prediction model is used to complete the compensation analysis of ERS points. Finally, the algorithm is verified through experiments and engineering practice.

Experimental results show that the proposed neural network-based ERS point prediction and compensation method for nonuniform temperature fields effectively predicts ERS point deformation under nonuniform temperature fields compared with the conventional method. After the compensation analysis, the registration error is effectively reduced to improve registration accuracy. Reducing the combined effect of environmental nonuniform temperature field and measurement error has apparent advantages.

The method reduces the registration error caused by combining a nonuniform temperature field and measurement error. It can be used for aircraft assembly site prediction and registration error compensation analysis, which is essential to improve measurement accuracy further.

The size of the aircraft tooling structure is huge, and the ambient temperature is difficult to maintain a constant state. Aiming at the influence of current temperature, this paper aims to propose a compensation method for registration error of large-scale measurement fields based on multi-temperature sensors.

In this method, an enhanced reference points (ERS)–temperature regression model is constructed from ERS and temperature data. The ERS offsets compensation model is established by solving the offset through the regression model, and the ERS offset compensation analysis is carried out.

The experimental results show that the proposed registration error compensation algorithm has obvious advantages over traditional methods in reducing the influence of ambient temperature and improving the measurement accuracy by reducing the registration error.

This method reduces registration error caused by the influence of ambient temperature and is used for aircraft measurements in different temperature environments.

At present, using the finite element method is difficult to efficiently and accurately construct the temperature field of mass concrete based on temperature measurement points. Thus, there is a need to propose a method for improvement.

This study developed an implicit finite element method that digitally constructs the temperature field of mass concrete based on temperature measurement data. That is, in the proposed method of this paper, the temperature of the measuring point is also one of the boundary conditions, which real-time corrects the calculation error.

In this method, during the digital construction of the temperature field, the computed temperature approaches the actual measured value at the point of measurement with increasing iteration steps. Using this method and sufficient temperature measurement data, the errors in calculation conditions (such as the boundary conditions, the initial casting temperature and material parameters) can be automatically corrected during the iterative computation process.

This new method can improve calculation accuracy and allows the digitally constructed temperature field to converge to its true value with sufficient measurement data.

The purpose of this study is to deal largely with the influence of temperature variation on the measurement accuracy of transit-time ultrasonic flowmeter.

The causes of measurement error due to temperature are qualitatively and quantitatively analyzed, and a mathematical model is established. The experimental data are processed and analyzed, and the temperature compensation coefficient of flow measurement is obtained.

The experimental results show that the flow measurement results by temperature compensation are helpful in improving the measurement accuracy of the ultrasonic flowmeter.

This study has certain application value, which can provide theoretical support for the design of high-precision ultrasonic flowmeters and design guidance.

It is worth emphasizing that there are few research studies on the influence factors of temperature. This paper focuses on the influence of the temperature change on the flowmeter that is modeled, and the high precision flow parameter test system is designed based on the established model.

This paper aims to solve the typical thermal airflow sensor's high power consumption and integration difficulties, based on the FS5 thermal element and constant temperature measurement method, a flow sensor is developed with high measurement accuracy, low power consumption, small size, low cost and easy system integration.

A small wind tunnel was used to test and assess the sensor's measurement range, reaction time, stability, repeatability, measurement accuracy and multi-temperature calibration was performed in the temperature range of −10°C to 30°C. The effect of ambient temperature on the sensor's measurement data is investigated, and the coefficient correction method of power function was investigated to implement the sensor's software temperature compensation function.

The results show that the sensor is stable and repeatable, the output voltage has a power function relationship with the airflow rate, the flow rate measurement range is 0–18 m/s, the response time is less than 3 s, the measurement accuracy at high flow rates is within 0.4 m/s and the temperature-corrected airflow rate measurement error is less than 5%. Setting the temperature calibration interval to 2°C and 5°C has the same temperature compensation effect, reducing the sensor's calibration effort significantly.

This paper demonstrates that a thermostatic method is used to construct a thermal wind speed sensor that delivers accurate measurements in the wind speed measuring range of 0–18 m/s under test conditions. In addition, the sensor's performance is evaluated, and calibration tests for a wide range of temperatures are done. Finally, based on the power function correction method, a temperature compensation algorithm is proposed.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

This paper aims to considered the problem of identification of temperature-dependent thermal conductivity in the nonstationary, nonlinear heat equation. To describe the heat transfer in the furnace charge occupied by a homogeneous porous material, the heat equation is formulated. The inverse problem consists in finding the heat conductivity parameter, which depends on the temperature, from the measurements of the temperature in fixed points of the material.

A numerical method based on the finite-difference scheme and the least squares approach for numerical solution of the direct and inverse problems has been recently developed.

The influence of different numerical scheme parameters on the accuracy of the identified conductivity coefficient is studied. The results of the experiment carried out on real measurements are presented. Their results confirm the ones obtained earlier by using other methods.

Novelty is in a new, easy way to identify thermal conductivity by known temperature measurements. This method is based on special finite-difference scheme, which gives a resolvable system of algebraic equations. The results sensitivity on changes in the method parameters was studies. The algorithms of identification in the case of a purely mathematical experiment and in the case of real measurements, their differences and the practical details are presented.

Quantitative understanding of the temperatures, gradients and heating/cooling rates in and around the melt pool in laser powder bed fusion (L-PBF) is essential for simulation, monitoring and controls development. The research presented here aims to detail experiment design and preliminary results of high speed, high magnification, in-situ thermographic monitoring setup on a commercial L-PBF system designed to capture temperatures and dynamic process phenomena.

A custom door with angled viewport was designed for a commercial L-PBF system which allows close access of an infrared camera. Preliminary finite element simulations provided size, speed and scale requirements to design camera and optics setup to capture melt pool region temperatures at high magnification and frame rate speed. A custom thermal calibration allowed maximum measurable temperature range of 500°C to 1,025°C. Raw thermographic image data were converted to temperature assuming an emissivity of 0.5. Quantitative temperature results are provided with qualitative observations with discussion regarding the inherent challenges to future thermographic measurements and process monitoring.

Isotherms around the melt pool change in size depending on the relative location of the laser spot with respect to the stripe edges. Locations near the edges of a stripe are cooled to lower temperatures than the center of a stripe. Temperature gradients are highly localized because of rough or powdery surface. At a specific location, temperatures rise from below the measurable temperature range to above (<550°C to >1100°C) within two frames (<1.11 m/s). Particle ejection is a notable phenomenon with measured ejection speeds >11.7 m/s.

Several works are detailed in the Introduction of this paper that detail high-speed visible imaging (not thermal imaging) of custom or commercial LBPF processes, and lower-speed thermographic measurements for defect detection. However, no work could be found that provides calibrated, high-speed temperature data from a melt-pool monitoring configuration on a commercial L-PBF system. In addition, the paper elucidates several sources of measurement uncertainty (e.g. calibration, emissivity and time and spatial resolution), describes inherent measurement challenges based on observations of the thermal images and discusses on the implications to model validation and process monitoring and control.