Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Scientific Reports volume 14, Article number: 26213 (2024 ) Cite this article weighing machine cells
During the operation of load cell, heat is generated by the strain gauge and the electronics on the PCB board, which leads to temperature gradients within the sensor itself. These temperature gradients are unstable at different ambient temperatures. Compensation inaccuracies can also occur when compensating for sensor measurements at different temperatures This paper proposes a method to change the position of temperature compensation resistors to address errors caused by the temperature field effect of the strain gauge sensor itself. Without affecting the sensor’s strain measurement, the correctness of the proposed method is demonstrated through steady-state thermal simulation results in ANSYS and experimental results, effectively addressing errors caused by unstable temperature gradients during the operation of strain gauge sensors.
The load cell, also known as a force sensor, has the advantages such as high precision, high reliability, high sensitivity, high linearity, small size, and mature manufacturing technology1, and is widely used in robotics2,3, medicine4,5,6,7, agronomy8,9, vehicle science and other fields10.
However, during the production and use of load cells, many performance compensations are required, including hysteresis compensation11, creep compensation12, zero compensation13, and sensitivity improvement14,15. When the external temperature changes, the thermal expansion and contraction effects and residual stress will affect the performance of the load cell16. The performance of the load cell varies at different temperatures, which leads to the complexity of sensor compensation. According to OIML R60 regulations, as long as the performance compensation at -10℃, 20℃, and 40℃ is within the acceptable range, the performance compensation of the load cell is considered to be up to standard. Luo L et al.17 showed that within a certain temperature range, the output voltage, output linearity, and sensitivity of the load cell decrease with increasing temperature, and temperature compensation is performed on the sensor according to the principle of double Wheatstone bridge compensation. Dadasikandar K et al.18 studied the use of polysilicon as a piezoresistive material for measuring pressure in high-temperature environments, conducted relevant simulations from the perspective of materials, and improved the sensitivity and other performance of the sensor. Yi J H19 studied the factors affecting the thermal effects of load cells, including different strain gauge resistances, unequal lengths of Wheatstone bridges, and different TCRs (Temperature Coefficient of Resistance) of thermal compensation resistor nickel sheets. The results showed that it is impractical to include TCR differences in compensation, and proposed inserting a thermistor into one of the four bridge arms to compensate for zero drift caused by temperature. Yi J H et al.20 also proposed connecting a thermistor in series with the bridge circuit to compensate for the zero point of the Wheatstone bridge. The results showed that as long as the TCR of the temperature compensation resistor is greater than the TCR of the zero balance resistor, the two temperature output ratio differences obtained by the iterative method converge to zero. Du D L et al.21 analyzed the nonlinear relationship between the thermal resistance changes of different materials and expansion mismatch, and experimentally verified that the temperature characteristic curve is also nonlinear. Hui Chao Shi et al.22 proposed a temperature compensation method based on artificial neural networks (ANN) to reduce the additional temperature drift of the resonant frequency caused by electrothermal excitation and environmental temperature changes. Wang S et al.23 proposed a temperature compensation method based on backpropagation neural networks (BP-NN) and introduced genetic algorithms to improve BP neural networks, effectively improving temperature compensation. Based on the above literature, this paper studies the changes in the temperature gradient caused by the self-heating of the load cell, changes the position of the compensation resistor without affecting the strain of the elastomer, and provides a new idea for the temperature compensation of the load cell. Compared with the above compensation methods, this paper starts from the core of temperature compensation: the position of the compensation resistor, changes the position of the compensation resistor, and thus obtains more accurate compensation.
Section 2 of this paper introduces the heating of the load cell and the reasons for the formation of the temperature gradient, and verifies the feasibility of changing the position of the nickel sheet through finite element analysis. Section 3 uses the steady-state thermal module of ANSYS/Workbench to conduct thermal analysis of the load cell and obtains the position with the smallest temperature gradient difference. Section 4 conducts experiments on the sensor based on the finite element results of Sect. 3, and compares and analyzes the experimental data with the finite element analysis data. Section 5 summarizes and prospects the solutions to the thermal field effects of the load cell.
SG1, SG2, SG3 and SG4 in Fig. 1 correspond to four strain gauges, two compression sheets and two tension sheets respectively; The Ni is the nickel sheet used to compensate the TCR, and the P resistance is to prevent the nickel sheet from compensating over the parallel resistance.
In the actual operation of the load cell, the four strain gauge wire grid and strain gauge will heat up, and there is a certain distance between the nickel plate and the strain gauge, which will cause the local temperature of the strain gauge to be higher than that of the nickel plate, and there is a temperature difference between the two. the PCB board also generates heat, and some of the heat is transferred to the strain gauge and nickel sheet through the elastomer, which makes the temperature field inside the sensor very different. Under a certain temperature condition, the temperature gradient is constant, and we can correct the temperature gradient by compensation algorithm. However, when the temperature changes, the temperature gradient will change, and the compensation amount will also change to ensure the accuracy of compensation.
As shown in Fig. 2, the weighing sensor needs to be fixed with iron and bolts when it is tested before leaving the factory. The main components of the weighing sensor are: elastomer, strain gauge, PCB board (with electronic heating element), weld plate and weld cup for sealing.
Load cell assembly diagram and main parts explosion diagram.
As shown in Fig. 3, the sensor nickel plate is now attached at 1, and the distance from this position to the strain gauge is basically equal to that of 2, 3 and 4. It can be considered that the heating of the strain gauge has the same influence on the temperature of 1, 2, 3 and 4. However, the heat of the PCB board will also be conducted through the elastomer to the strain gauge and 1, 2, 3, 4. The voltage connected to the sensor is 5 V, and the current flows through the current acquisition module of the PCB board to the strain gauge. After calculation, the rated heating power of the integrated electronic components on the PCB board is 0.3 W, and the rated heating power of the strain gauge is 0.07 W. The distance from 1, 2, 3, 4 to the strain gauge is equal, so the heat generated by the strain gauge heating has the same effect on 1, 2, 3, 4, if only the strain gauge heating and the PCB board does not heat, then the temperature gradient generated by the nickel sheet attached to any position is basically constant, but the PCB board will generate heat in actual work. Moreover, the distance between the PCB board and the strain gauge 2 and 4 is equal, and the position of the nickel sheet is stuck at 1, and the distance between the PCB board and the PCB board is less than the distance between the strain gauge and the PCB board, which leads to the heat conduction of the PCB board to the strain gauge direction, the temperature impact on the 1 place will be greater than the impact on the strain gauge, which leads to the change of temperature gradient. The elastomer material of the sensor is stainless steel, its thermal conductivity is 20 W/(m.K), the specific heat capacity is 460 J/(kg.℃), and the density is 7850 kg/m3.
Schematic diagram of heat conduction between PCB board and strain gauge.
The component on the sensor PCB with the strain gauge as an internal heat source follows the heat conduction equation:
Where: T is temperature, t is time, α is the thermal diffusion coefficient, ∇ 2 is the Laplacian operator (which describes the change in temperature in space), q is the heat generated by the heat source, ρ is the density of the elastomer, and c is the specific heat capacity of the elastomer.
Convection is also considered, following Yelifu’s law and Newton’s law of cooling:
Where: \(\:a\) is the heat flux, \(\:\lambda\:\) is the thermal conductivity, ∇ T is the temperature gradient, h is the convective heat transfer coefficient, T is the surface temperature of the object, and \(\:{T}_{\infty\:}\) is the temperature at infinity (usually the ambient temperature).
The sensor used in the experiment is a steel sensor, except for the PCB board and strain gauge, all the other components are stainless steel, and the position of the PCB board is in the non-strain zone and away from the strain zone, according to the principle of Saint-Venant, we only consider the components in the strain zone, and in order to facilitate the calculation, the PCB board is also set as stainless steel during the finite element analysis. Since the thickness of the strain gauge is 20 μm, we directly treat the strain gauge as a surface. Therefore, all components are set to stainless steel, and its parameters are shown in Table 1.
Due to the different strain sizes at the four locations, the strain in the strain zone of the sensor may be affected under different conditions. Before changing the position of the nickel patch, the thermal coupling analysis of the sensor at full load is carried out to verify that changing the position of the nickel patch has little impact on the measurement performance of the sensor. The flow chart is shown in Fig. 4.
Flow chart of thermal coupling calculation.
The sensor elastomer model and nickel sheet model established by CREO were imported into ANSYS/Workbench for grid division. The thickness of the nickel sheet is about 0.1 mm. The nickel sheet is pasted on the substrate of the polymer material in the form of resistance wire, and then pasted on the elastomer of the sensor through a layer of glue. The main body of the nickel sheet is the polymer material of the substrate, and the resistance wire is only a small part, and the elastic modulus of the polymer material of the nickel sheet is 3GPa to 8GPa. In order to consider the maximum tolerance, the elastic modulus of the entire nickel sheet is set to 8GPa. After the nickel sheet is built, it is pasted at positions 1,2,3,4. Each area in the sensor forms a common node and the grid scale does not exceed 1:3. The grid division is shown in Fig. 5.
Binding contact is set between each cutting part. The measuring range of the sensor is 220Kg, so 2200 N force and fixed support are applied to it, and the axial direction is defined as the X axis, and the direction along the inner wall of the sensor is defined as the Z axis. X, Y and Z axes are shown in Fig. 6.
Schematic diagram of sensor constraints.
First, perform a steady-state thermal analysis, setting the initial temperature to 22 °C and the convection to horizontal cyclic convection. Calculate the heat generation through the power dissipation of the strain gauge and PCB. The thickness of the strain gauge can be neglected, and the surface heat generation of the strain gauge is set to 2.1875 × 10^-4 W/mm², while the volumetric heat generation of the PCB is set to 1 × 10^-3 W/mm³. The sensor’s elastic body material is stainless steel, with a thermal conductivity of 20 W/(m·K), specific heat capacity of 460 J/(kg·°C), and density of 7850 kg/m³. Perform steady-state thermal analysis on four sensor models with different nickel sheet attachment positions at ambient temperatures of 40 °C, 20 °C, and − 10 °C. After obtaining the results, couple them with the static module. The stress-strain cloud diagram is shown in Fig. 7.
Sensor full load strain cloud.
In Fig. 7, the circular area in the middle is the strain zone of the sensor. The measurement performance of the sensor is affected by the magnitude of its strain zone strain variable. According to the strain characteristics of the sensor, a path with an Angle of 45°from the level is taken in its strain zone, and the strain on the path is analyzed after the thermodynamic coupling results are calculated. The strain diagram of the path is shown in Fig. 8.
Figure 1 is the starting point of the path, 2 is the end point of the path. We extracted and compared the strain on the path of the sensor at different positions of the nickel sheet at three different temperatures, and the comparison results are shown in Fig. 9.
Path strain plots at different temperatures for each location.
It can be seen from Fig. 9 that the strain in the sensor strain zone is almost unchanged at each position under three different temperatures. In order to better compare the data, the strain on each path is averaged, as shown in Fig. 10.
Path-averaged strain plots at different temperatures for each location.
It can be clearly seen that when the nickel sheet is attached at different positions and the sensor is fully loaded at different temperatures, the average strain in the strain zone is almost equal, which will not affect the measurement performance of the sensor. For this reason, experimental verification is carried out in 4.1.
It can be seen from the thermodynamic coupling results that the measurement performance of the sensor will not be affected when the nickel sheet is attached to each position. Therefore, the temperature field of the sensor under different temperatures was analyzed, and the four nickel patch areas were mesh encrypted on the basis of Fig. 5 to calculate the temperature cloud image of the sensor under 40℃, 20℃ and − 10℃, as shown in Figs. 11, 12 and 13.
40 °C sensor operating temperature cloud.
20 °C sensor operating temperature cloud.
− 10 °C sensor operating temperature cloud.
The average temperature at the strain gauge and four nickel patch locations was derived, and the data were processed to obtain Table 2.
After obtaining the above data, the temperature difference between the four pre-selected nickel plates at different temperatures and the strain gauge is calculated. The calculated results are shown in Table 3.
In the table, △ 1, △ 2, △ 3, △ 4 are the difference between the average temperature of the strain gauge and the average temperature at 1, 2, 3, and 4 respectively. At different temperatures, the difference between 2 and 4 and the average temperature of the strain gauge is more stable than that at 1 and 3. The average value of each group of data is calculated. The average value is the predicted value of this group of data, and the root-mean-square error of each group of data is calculated.
Where:\(\:{P}_{i}\:\) is the actual temperature value,\(\:{O}_{i}\) is the predicted value, that is, the average value of this group of data, the root mean square error obtained is shown in Table 4.
The results of thermal analysis and later data processing show that the root-mean-square error of temperature difference between position 1 and position 3 is much larger than that between position 2 and position 4. This shows that when the nickel sheet is stuck at position 2 and position 4, the temperature difference between position 2 and position 4 and the strain gauge is stable when the ambient temperature changes, and the error generated by temperature compensation for the sensor will be very small, far less than position 1 and position 3, but the root mean square error at position 2 is slightly less than the root mean square error at position 4. Therefore, we choose position 2 as the best position for the experiment.
In Sect. 3.1, thermodynamic coupling strain analysis was performed on the four pre-selected patch positions of the weighing sensor. The results showed that no matter the nickel plate was attached at any position, the strain variable of the sensor was not significantly affected, which also indicated that the measurement performance of the sensor was not affected. In order to verify the results in Sect. 3, the position of the nickel sheet has no effect on the measurement performance of the sensor, so the nickel sheet is stuck at position 2 for the experiment, and then compared with the initial nickel sheet position 1. The initial position of the nickel sheet is shown in Fig. 14.
Schematic diagram of the position of the original nickel sheet.
Based on this idea, the following experiments are designed.
Experimental tools: 220 kg range weight sensor, static gravity machine, fixture for fixing the weight sensor, nickel sheet, and sampling equipment.
The nickel plate position is attached to the position 1 (original position), and then the weight sensor is fixed with a fixture, the force machine preloading, preheating for 30 min before loading to make its internal temperature field stable, the load force is the full load force of the sensor 2200 N, the purpose is to eliminate the installation stress of the weight sensor.
Apply a small force to the weighing sensor, the position of the force machine is fixed, record the data at this time and define it as zero. Then load to half of the range, record the load at this time, then load to full range, record the load at this time, and then unload to half of the range, record the load at this time, and finally completely unload, record the zero point after unloading again. Repeat three times and take the average value of each data.
Remove the nickel sheet from the original position of the sensor, paste it to position 2, and repeat steps 1 and 2.
The state of the force machine used in the experiment and the loading of the sensor is shown in Fig. 15.
Schematic diagram of force machine loading.
According to the data obtained in the experiment, the sensor lag value when the nickel plate is stuck in different positions is calculated, and the experimental data are shown in the following table. In addition, the data at position 1 is defined as the initial value, and the data at position 2 is compared with the data at position 1, and the data obtained is shown in Table 5.
In the above table, “ppm” represents one millionth of the unit. The lag value at position 2 is unchanged compared to position 1, and the full load value is reduced by 1.33 ppm, which has no impact on the performance of the sensor.
In Sect. 3.2, we conducted steady-state thermal analysis of the sensor, and the results of the obtained data show that the root-mean-square error of △ 2 and △ 4 is smaller than that of △ 1 and △ 3 and under different temperature conditions. As the distance between position 2 and position 4 and PCB board is equal, the finite element analysis results show that the average strain of position 4 is slightly less than that of position 2, so position 4 is tentatively the best position, so the pt1000 thermistor is affixed to position 1, position 3 and position 4 respectively. Since strain gauges are affixed to the position of strain gauge, the thermistor is affixed to the position close to the strain gauge. Its resistance is 1000Ω at 0℃, as shown in Fig. 16.
Schematic of pt1000 thermistor location.
After the pt1000 was pasted, in order to simulate the real working condition, it was sealed with a welding bowl and tested in a high and low temperature test chamber. The resistance of the pt1000 thermistor will also change with the change of temperature, and the change of temperature roughly presents a linear change, so as long as the resistance value of each thermistor under different temperature conditions is measured, and then according to the relationship between the pt1000 thermistor and temperature, the temperature at the position can be calculated. The experimental diagram of the temperature box is shown in Fig. 17.
Experimental diagram of the temperature box.
Based on this idea, the design of the experiment is as follows.
Experimental tools: 220 kg range weighing sensor, several pt1000 thermistors, high and low temperature test chamber, resistance meter, several copper wires.
Attach a pt1000 thermistor to the sensor at position 1, position 3 and position 4 and near the strain gauge, and connect each resistor to the resistance meter with copper wire, and the resistance meter is connected to the computer sampling number.
put the sensor into the high and low temperature test chamber, set the temperature of the chamber to 20℃, 6 h after the temperature in the chamber is stable, use the computer to collect data.
put the sensor into the high and low temperature test chamber, set the temperature of the chamber to 40℃, 6 h after the temperature in the chamber is stable, use the computer to collect data.
put the sensor into the high and low temperature test chamber, set the temperature of the chamber to -10℃, 6 h after the temperature in the chamber is stable, use the computer to collect data.
After collecting the data, average the temperature at each position and obtain the following Table 6.
After the average temperature of each position is obtained, we calculate the average temperature of each place through the relationship between pt1000 resistance and temperature. When − 200℃ < t < 0℃, the expression between its resistance and temperature is:
When 0℃< t < 850℃, the expression between the resistance and temperature is:
Where: is the resistance at temperature t, the resistance of pt1000 when R0 is 0℃, and its size is 1000Ω, A, B,C are the temperature coefficient, and its size is: A = 3.9083 × 10−3,B =-5.775 × 10−7,C =-4.183 × 10−12, t is the actual temperature at the position.
According to the above formula, we use the nonlinear solution function in Python and Scipy library: scipy.optimize.root function to calculate the temperature of the thermistor at each position at each temperature, and the obtained data are shown in Table 7.
After obtaining the above data, the temperature difference between the three pre-selected nickel plates at different temperatures and the strain gauge is calculated, and the calculated results are shown in the following Table 8.
In the table, △ 1, △ 3 and △ 4 are the difference between the average temperature of the strain gauge and the average temperature at 1, 3 and 4 respectively. At different temperatures, the difference between 2 and 4 and the average temperature of the strain gauge is more stable than that at 1 and 3. The average value of each group of data is calculated. The average value is the predicted value for that data set and the root-mean-square error is calculated for each data set as shown in Table 9.
The comparison between experimental data and finite element simulation results is shown in Fig. 18.
Root mean square error of temperature difference at each position with respect to the strain gauge at different temperatures.
The experimental results show that the root-mean-square error of the mean temperature difference between position 2 and strain gauge is the smallest, that is, the mean temperature difference between position 2 and strain gauge tends to be stable at different temperatures, which is consistent with the finite element simulation results.
CREO was used to model the sensor and other components. Through ANSYS/Workbench thermal coupling simulation analysis, changing the position of the nickel sheet had no impact on the measurement performance of the sensor. Then, through steady-state thermal analysis of each position, the optimal position of the patch was found, and the authenticity of the simulation was verified through experiments. It provides a theoretical basis for compensation of load cell. It is evident in the paper that:
because the experimental temperature of the sensor is -10℃, 20℃, 40℃. The span is 50 ℃, during this period, the Young’s modulus of the sensor elastomer material is almost constant, so at these temperatures, the deformation of the elastomer at full load is almost constant. At different temperatures, the nickel plates were attached to the four pre-selected positions, and the thermal coupling was carried out. The results showed that no matter where the nickel plates were attached, the measurement performance of the sensor was very small.
At -10℃, 20℃ and 40℃, through steady-state thermal analysis, we found that the temperature difference between the nickel sheet and the strain gauge was the most stable when it was stuck at position 2 or position 4, which was conducive to the compensation of the sensor.
The experiment verifies that the temperature difference between the nickel sheet and the strain gauge is the most stable when the nickel sheet is stuck at position 2, which is consistent with the simulation results.
This paper starts by changing the position of the nickel compensation resistor and, through a series of studies, identifies the optimal compensation position for the nickel compensation resistor in the load cell. This method is more direct and accurate compared to previous studies. In terms of cost, this method involves moving the nickel sheet from its initial position to another position without incurring additional costs, demonstrating the feasibility of this method from a cost perspective. This method aids in the temperature compensation of load cells, allowing them to be used in more precise fields.
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Doğan, B. K. & Karaçay, T. Experimental fault analysis of polymer hybrid bearing using accelerometer and load cell[J]. Tribol. Int. 185, 108498 (2023).
Abdeetedal, M. & Kermani, M. R. An open-source integration platform for multiple peripheral modules with Kuka robots[J]. CIRP J. Manufact. Sci. Technol. 27, 46–55 (2019).
Projoth, T. N. & Nanthakumar, P. Analysis and prediction of cutting force through lathe tool dynamometer in CNC turning process[J]. Mater Today Proc. 46, 4174–4179 (2021).
Clausen, I. & Sveen, O. Die separation and packaging of a surface micromachined piezoresistive pressure sensor[J]. Sens. Actuators A: Phys. 133 (2), 457–466 (2007).
Smreczak, M., Rubbert, L. & Baur, C. Design of a compliant load cell with adjustable stiffness[J]. Precis. Eng. 72, 259–271 (2021).
Ramachandran, P. et al. In vivo strain alterations in mandibular molars after root canal treatment procedures[J]. J. Endod. 46 (12), 1849–1855 (2020).
Montoro-Bombú, R. et al. Validity and reliability of a load cell sensor-based device for Assessment of the isometric mid-thigh pull Test[J]. Sensors 23 (13), 5832 (2023).
Byrne, D. T. et al. Sheep lameness detection from individual hoof load[J]. Comput. Electron. Agric. 158, 241–248 (2019).
Saedin, N. S., Muttalib, M. F. A. & Jusoh, M. F. Performance Evaluation of Bar Load Cell Sensing System for Soil Moisture Measurement[C]. J. Phys. Conf. Ser. 2550 (1), 012013 (2023) (IOP Publishing).
Jakati, R. S., Balavalad, K. B. & Sheeparamatti, B. G. ensitivity enhancement in piezoresistive micro-pressure sensor using perforated diaphragm[C]//2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE 396–399 (2017).
Zhuang, S. et al. Analysis of Return-to-Zero Error after the First Load of Load Cell[J]. Sensors 23 (21), 8712 (2023).
Bartel, T. W. & Yaniv, S. L. Creep and creep recovery response of load cells tested according to US and international evaluation procedures[J]. J. Res. Natl. Inst. Stand. Technol. 102 (3), 349 (1997).
Lin, H. et al. Zero-point fault detection of load cells in truck scale based on recursive principal component analysis and comprehensive evaluation method[J]. Measurement 159, 107706 (2020).
Kanekal, D. & Jindal, S. K. Prefabrication Analysis and Numerical Modeling of Freely Supported MEMS Piezoresistive Pressure Sensor Employing Square Shaped Silicon Diaphragm[C]//2023 IEEE 20th India Council International Conference (INDICON). IEEE, 1259–1264 (2023).
Sabhapandit E, Jindal S K, Kanekal D, et al. Numerical simulation of a striated piezoresistive MEMS pressure sensor on circular silicon diaphragm: a finite element method-based study[J]. Nano 18 (04), 2350023 (2023).
Kanekal D, Jindal S K. Investigation of MEMS piezoresistive pressure sensor with a freely supported rectangular silicon carbide diaphragm as a primary sensing element for altitudinal applications[J]. Silicon 15 (4), 1947–1959 (2023).
Luo, L. X., Xu, B. T. & ,Bi, H. L. Research on compensation method of temperature drift in pressure Sensor using double wheatstone-bridge Method[J]. Adv. Mater. Res. 1643 (459–459), 311–314 (2012).
Dadasikandar, K. et al. Formulation analysis and follow-up study of multi-turn configuration for performance enhancement of MEMS piezoresistive pressure sensor utilized for low-pressure measurement applications[J]. Sensor Review 44 (4), 462–476 (2024).
Yi, J. H. Temperature dependence of zero point in force transducers: material properties of strain gages causing such dependence[J]. Exp. Tech. 38 (5), 64–69 (2014).
Yi, H. J. & Kim, H. J. Temperature dependence of Zero Point in Force Transducers II: how to Account for the effects of a Zero Balancing. Resistor[J] Experimental Techniques 40 (1), 221–225 (2016).
Du, DL et al. Temp. Characteristic Platinum Piezoresistive Press. Sensor[J] Key Eng. Mater. 1304 (483–483), 180–184 (2011).
Huichao, S. & Wenlong, L. Temperature Compensation Methodology based on Artificial Neural Network in a MEMS Electro- Thermal Excitation Resonant Pressure Sensor[J]. J. Residuals Sci. Technol. 13(8), 1884–1893 (2016).
Wang, S. et al. Temperature compensation for MEMS resonant accelerometer based on genetic algorithm optimized backpropagation neural network[J]. Sens. Actuators A: Phys. 316, 112393 (2020).
College of Mechanical and Electrical Engineering, Hohai University, 213022, Changzhou, China
Shudong zhuang, wen yang, yuxiang zhou, ying zou, le zhang, miao tong & jinlong ma
Department of Physics, California San Diego University, San Diego, CA, 92127, USA
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
S.Z. was in charge of researching progress, conceived the innovation, guided the data analysis, and wrote the manuscript. W.Y. finished the theory exploring, calculated the simulation results, and contributed to the draft.Yuxiang Zhou . and Ying. Zou. explored the structure model, made an initial beginning work. C.L. and L.Z. collected the experimental conditions, and guided the literature review. M.T. and J.M. conceived the ideas and guided the researching progress. All authors have read and agreed to the published version of the manuscript.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Zhuang, S., Yang, W., Zhou, Y. et al. Temperature field analysis and compensation improvement of load cell. Sci Rep 14, 26213 (2024). https://doi.org/10.1038/s41598-024-76688-0
DOI: https://doi.org/10.1038/s41598-024-76688-0
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Scientific Reports (Sci Rep) ISSN 2045-2322 (online)
load cell sensor 100kg Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.