Manufacturing method for sensitive core of six-dimensional pressure sensor of robot based on glass micro-melting

Abstract

The invention relates to the field of intelligent sensors, and discloses a method for manufacturing a sensitive core of a six-dimensional pressure sensor of a robot based on glass micro-melting, which comprises the following steps of: calculating the deflection of a flat diaphragm corresponding to a glass micro-melting structure so as to analyze the structural strain characteristic of the sensitive core under the action of pressure; calculating the gas viscosity coefficient of the sensitive core body under the dynamic load condition, and calculating the pressed film damping amount of the sensitive core body; performing a temperature cycle test on the sensitive core, collecting thermal zero drift data in the test process, and analyzing the temperature drift stability of the sensitive core; carrying out calibration test on the sensitive core body to obtain non-linearity data, and evaluating the measurement precision of the sensitive core body; and performing optimization processing on the sensitive core body manufacturing process to obtain an optimization result. The manufacturing process stability of the sensitive core body of the six-dimensional pressure sensor of the robot can be improved.

Description

Technical Field

[0001] This invention relates to a method for manufacturing a sensing core for a six-dimensional pressure sensor for robots based on glass micro-melting, belonging to the field of intelligent sensors. Background Technology

[0002] The sensing core is the core sensing component of a robot's six-dimensional pressure sensor, directly determining the sensor's detection accuracy for three-dimensional force and three-dimensional torque. It is widely used in scenarios such as precision grasping of industrial robotic arms, human-machine interaction of collaborative robots, and motion control of bionic robots. The core needs to have micron-level structural precision, fast dynamic response, and wide temperature range stability. Glass micro-melting process, due to its combination of high bonding sealing and low thermal stress characteristics, has become a key technology for manufacturing the sensing core.

[0003] However, the current manufacturing of the sensing core for six-dimensional pressure sensors in robots uses photolithography in MEMS silicon-based processes. This involves a key step of transferring the designed sensitive structure pattern to a silicon wafer: first, uniformly coating photoresist on the silicon wafer surface; then, using a photolithography machine, precisely exposing the pattern on the mask onto the photoresist layer; removing the unexposed photoresist with a developing solution; and finally, using the residual photoresist as a mask, combining dry or wet etching to form a micron-scale sensitive structure on the silicon wafer, thus building the basic structure for subsequent processes such as ion implantation and bonding. However, this method does not consider the core performance indicators of the chip, such as strain characteristics, pressure film damping quantization, and temperature stability, which leads to a reduction in the performance of the sensing core. Summary of the Invention

[0004] This invention provides a method for manufacturing a sensing core for a six-dimensional robot pressure sensor based on glass micro-melting, the main purpose of which is to improve the process stability of manufacturing the sensing core for a six-dimensional robot pressure sensor.

[0005] To achieve the above objectives, the present invention provides a method for manufacturing a sensing core for a six-dimensional robotic pressure sensor based on glass micro-fusion, comprising: A glass micro-fusion structure for the sensing core of a six-dimensional pressure sensor for a robot was fabricated, and the deflection of the flat diaphragm corresponding to the glass micro-fusion structure was calculated to analyze the structural strain characteristics of the sensing core under pressure. Calculate the gas viscosity coefficient of the sensitive core under dynamic load conditions, and calculate the pressure film damping of the sensitive core based on the gas viscosity coefficient; Temperature cycling tests were performed on the sensitive core, and thermal zero-point drift data was collected during the test. Based on the thermal zero-point drift data, the temperature drift stability of the sensitive core was analyzed. The sensitive core is calibrated to obtain nonlinearity data, and the measurement accuracy of the sensitive core is evaluated based on the nonlinearity data. By combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy, the manufacturing process of the sensitive core is optimized to obtain the optimized result.

[0006] Optionally, calculating the flat diaphragm deflection corresponding to the glass micro-fusion structure includes: Obtain the diaphragm geometric parameters and external load conditions of the glass micro-fusion structure; The deflection calculation elements and the measured pressure value are determined from the geometric parameters and load conditions. Query the theoretical reference value corresponding to the deflection calculation element; Analyze the structural response corresponding to the deflection calculation elements; By combining the structural responsiveness, the measured pressure value, and the theoretical reference value, the flat diaphragm deflection corresponding to the glass micro-fusion structure is calculated.

[0007] Optionally, the analysis of the structural response corresponding to the deflection calculation elements includes: The deflection calculation elements are subjected to feature decomposition to obtain element attribute representations; The correlation effect analysis of the attribute representations of the elements is performed to obtain the interaction effects between the elements; Based on the interaction effects between the elements, a dependency graph of the deflection calculation elements is constructed to obtain the topological relationship of the influence of the elements, and historical test data of the elements corresponding to the deflection calculation elements are collected. By combining the influence of the elements on the topological relationship and the historical test data of the elements, the structural response corresponding to the deflection calculation elements is analyzed.

[0008] Optionally, calculating the flat diaphragm deflection corresponding to the glass micro-fusion structure to analyze the structural strain characteristics of the sensitive core under pressure includes: The deflection distribution of the flat diaphragm was verified to obtain effective deflection data; Strain-related features are extracted from the effective deflection data, and the strain-related features are normalized to obtain standard strain features; The stress field is reconstructed from the standard strain characteristics to obtain the reconstructed distributed stress; The strain tensor of the reconstructed distributed stress is calculated to obtain the full-field distributed strain; Key regions are identified in the full-field distributed strain to obtain the critical strain region; Based on the critical strain region, the structural strain characteristics of the sensitive core are analyzed.

[0009] Optionally, calculating the gas viscosity coefficient of the sensitive core under dynamic load conditions includes: The environmental parameters of the sensitive core under dynamic load conditions are collected and processed to obtain effective environmental parameters; Gas state characteristics are extracted from the effective environmental parameters; The gas state characteristics are filtered for key features to obtain core state characteristics; The core state features are subjected to state classification processing to obtain gas state categories; Analyze the gas flow characteristics corresponding to the gas state categories; Based on the gas flow characteristics, the gas viscosity coefficient of the sensitive core under dynamic load conditions is calculated.

[0010] Optionally, the key feature screening of the gas state characteristics to obtain core state characteristics includes: Calculate the variance contribution rate corresponding to the gas state characteristics; Based on the variance contribution rate, the feature discrimination degree corresponding to the gas state feature is determined; Analyze the feature redundancy between the gas state characteristics; By combining the feature redundancy and the feature distinguishability, key features of the gas state features are screened to obtain core state features.

[0011] Optionally, calculating the pressure damping of the sensitive core based on the gas viscosity coefficient includes: Extract the structural dimension parameters of the sensitive core, and determine the corresponding geometric feature values ​​of the sensitive core based on the structural dimension parameters; Query the environmental pressure conditions of the sensitive core and measure the environmental pressure value under the environmental pressure conditions; The pressure damping of the sensitive core is calculated by combining the geometric characteristic value, the gas viscosity coefficient, and the ambient pressure value.

[0012] Optionally, analyzing the temperature drift stability of the sensitive core based on the thermal zero-point drift data includes: The thermal zero-point drift data is subjected to anomaly removal processing to obtain purified drift data; The purification drift data is divided into temperature ranges to obtain segmented drift data; Analyze the drift trend characteristics in the segmented drift data; Based on the drift trend characteristics, the temperature drift coefficient of the sensitive core is calculated; Based on the temperature drift coefficient, the temperature drift stability of the sensitive core is analyzed.

[0013] Optionally, calculating the temperature drift coefficient of the sensitive core based on the drift trend characteristics includes: Identify the temperature drift range and stable operating range in the drift trend characteristics; Extract the temperature changes corresponding to the temperature drift range and the stable operating range respectively to obtain the drift change and the stable change. Based on the stable change amount, calculate the theoretical operating threshold of the sensitive core within the stable operating range; Based on the theoretical operating threshold, the drift change is mapped to the stable operating range to obtain the actual operating threshold; Calculate the sensitivity coefficient of the sensitive core within the temperature drift range based on the actual working threshold. Based on the sensitivity coefficient, the temperature drift coefficient corresponding to the sensitive core is obtained.

[0014] Optionally, evaluating the measurement accuracy of the sensitive core based on the nonlinearity data includes: Based on the nonlinearity data, an error distribution map corresponding to the sensitive core is constructed; Based on the error distribution map, the accuracy feature vector corresponding to the sensitive core is statistically analyzed; Obtain the application scenario corresponding to the sensitive core, and evaluate the measurement accuracy of the sensitive core based on the application scenario and the accuracy feature vector.

[0015] To address the aforementioned problems, this invention also provides a robotic six-dimensional pressure sensor core manufacturing system based on glass micro-melting, the system comprising: The structural strain analysis module is used to prepare the glass micro-fusion structure of the sensing core of the robot's six-dimensional pressure sensor, calculate the flat diaphragm deflection corresponding to the glass micro-fusion structure, and analyze the structural strain characteristics of the sensing core under pressure. The pressure film damping calculation module is used to calculate the air viscosity coefficient of the sensitive core under dynamic load conditions, and to calculate the pressure film damping of the sensitive core based on the air viscosity coefficient. The temperature drift stability analysis module is used to perform temperature cycling tests on the sensitive core, collect thermal zero-point drift data during the test, and analyze the temperature drift stability of the sensitive core based on the thermal zero-point drift data. The measurement accuracy evaluation module is used to perform calibration tests on the sensitive core, obtain nonlinearity data, and evaluate the measurement accuracy of the sensitive core based on the nonlinearity data. The process optimization module is used to combine the structural strain characteristics, the pressure film damping amount, the temperature drift stability, and the measurement accuracy to perform optimization processing on the manufacturing process of the sensitive core and obtain optimization results.

[0016] Compared to the problems described in the background art, this invention, by calculating the deflection of the flat diaphragm corresponding to the glass micro-fusion structure, can grasp the actual deformation law of the glass micro-fusion structure under loads such as pressure and temperature, providing a quantitative basis for the subsequent analysis of the structural strain characteristics. This invention, by calculating the gas viscosity coefficient of the sensitive core under dynamic load conditions, can understand the viscous characteristics of the gas surrounding the sensitive core in dynamic working conditions, providing basic parameters for analyzing the pressure-film damping effect. Furthermore, this invention, by analyzing the temperature drift stability of the sensitive core based on the thermal zero-point drift data, can obtain the amplitude and variation law of the zero-point offset of the sensitive core under different temperature conditions, improving... To further improve the accuracy of the temperature drift performance evaluation of the sensitive core, this invention calibrates the sensitive core to obtain nonlinearity data, which quantifies the deviation between the sensor output and the ideal linear response. Based on this nonlinearity data, the measurement accuracy of the sensitive core is evaluated, effectively assessing the sensor's performance indicators and improving the reliability of the measurement data. Finally, by combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy, this invention optimizes the manufacturing process of the sensitive core, enhancing the comprehensiveness and accuracy of the sensitive core performance optimization, thereby improving the stability of the sensitive core's manufacturing process. Therefore, the glass micro-melting-based robotic six-dimensional pressure sensor sensitive core manufacturing method provided by this invention can improve the process stability of robotic six-dimensional pressure sensor sensitive core manufacturing. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart illustrating a method for manufacturing a sensing core of a six-dimensional robot pressure sensor based on glass micro-melting, according to an embodiment of the present invention. Figure 2 A schematic diagram of the optimized processing flow of the manufacturing method of the sensing core of a robot six-dimensional pressure sensor based on glass micro-melting provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a robot six-dimensional pressure sensor sensing core manufacturing system based on glass micro-melting, provided in an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This application provides a method for manufacturing a sensing core for a robotic six-dimensional pressure sensor based on glass micro-fusion. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for manufacturing a robotic six-dimensional pressure sensor based on glass micro-fusion can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a method for manufacturing a robotic six-dimensional pressure sensor core based on glass micro-fusion according to an embodiment of the present invention. In this embodiment, the method for manufacturing a robotic six-dimensional pressure sensor core based on glass micro-fusion includes: S1. Prepare a glass micro-fusion structure for the sensitive core of a six-dimensional pressure sensor for a robot, and calculate the deflection of the flat diaphragm corresponding to the glass micro-fusion structure to analyze the structural strain characteristics of the sensitive core under pressure.

[0022] This invention, by calculating the deflection of the flat diaphragm corresponding to the glass micro-fusion structure, can grasp the actual deformation law of the glass micro-fusion structure under loads such as pressure and temperature, providing a quantitative basis for the subsequent analysis of the structural strain characteristics. The glass micro-fusion structure is a microstructure, such as a diaphragm or beam, prepared from the sensitive core of a six-dimensional pressure sensor for robots using a glass micro-fusion process. The flat diaphragm deflection is the bending deformation of the flat diaphragm under pressure, such as maximum deflection or average deflection. Furthermore, the glass micro-fusion structure of the sensitive core of a six-dimensional pressure sensor for robots can be prepared by combining a glass micro-fusion bonding machine with a precision flat diaphragm electrolytic polishing device.

[0023] As an embodiment of the present invention, the calculation of the flat diaphragm deflection corresponding to the glass micro-fusion structure includes: Obtain the diaphragm geometric parameters and external load conditions of the glass micro-fusion structure; The deflection calculation elements and the measured pressure value are determined from the geometric parameters and load conditions. Query the theoretical reference value corresponding to the deflection calculation element; Analyze the structural response corresponding to the deflection calculation elements; By combining the structural responsiveness, the measured pressure value, and the theoretical reference value, the flat diaphragm deflection corresponding to the glass micro-fusion structure is calculated.

[0024] The diaphragm geometric parameters include diaphragm radius, thickness, and location of the observation point; the external load condition is the pressure distribution acting on the diaphragm surface; the deflection calculation elements are key physical quantities affecting deflection calculation, such as pressure intensity, material elastic constants, and geometric dimensions; the measured pressure value is the specific pressure data of the deflection calculation elements obtained through experimental measurement or simulation; the theoretical reference value is the benchmark value of each deflection calculation element determined based on material mechanics theory or design specifications; and the structural response degree characterizes the contribution of each calculation element to deflection.

[0025] Furthermore, the acquisition of the diaphragm geometric parameters and external load conditions can be achieved by using optical measurement technology combined with load sensors to ensure data accuracy; the determination of the deflection calculation elements and measured pressure values ​​can be completed through parametric modeling and simulation analysis, and the core variables and their current values ​​can be selected from the dataset; the theoretical reference values ​​can be obtained from the Internet through human-computer interaction.

[0026] Furthermore, as an embodiment of the present invention, the analysis of the structural response degree corresponding to the deflection calculation element includes: The deflection calculation elements are subjected to feature decomposition to obtain element attribute representations; The correlation effect analysis of the attribute representations of the elements is performed to obtain the interaction effects between the elements; Based on the interaction effects between the elements, a dependency graph of the deflection calculation elements is constructed to obtain the topological relationship of the influence of the elements, and historical test data of the elements corresponding to the deflection calculation elements are collected. By combining the influence of the elements on the topological relationship and the historical test data of the elements, the structural response corresponding to the deflection calculation elements is analyzed.

[0027] The element attribute representation is a set of representations describing the intrinsic characteristics of each deflection calculation element through a mathematical model, including the stress-strain relationship, boundary condition constraints, and failure threshold of the element; the interaction effect between elements is an indicator that quantifies the degree of mutual influence among the calculation elements; the dependency graph is a graphical structure of the dependency relationship between elements established based on the interaction effect; the element influence topology is a topological framework that uses network theory to depict the interaction pattern of calculation elements; and the historical test data of elements is the performance evolution data accumulated by the deflection calculation elements in previous tests, including the element's durability record, deformation history, and environmental adaptation.

[0028] Furthermore, numerical simulation methods can be used to perform feature decomposition on the deflection calculation elements, extracting dominant parameters to constitute element attribute representations; covariance analysis can be used to analyze the correlation effects of the element attribute representations, and Pearson correlation coefficient can be applied to determine the degree of correlation between elements; based on the interaction effects between elements, a dependency graph can be constructed using graph theory tools, and a community detection algorithm can be used to generate the element influence topology; combining the element influence topology and historical test data of elements, the structural response corresponding to the deflection calculation elements can be evaluated through node importance analysis and multivariate variance analysis.

[0029] Furthermore, as another embodiment of the present invention, the calculation of the flat diaphragm deflection corresponding to the glass micro-fusion structure using the following formula, combining the structural responsiveness, the measured pressure value, and the theoretical reference value, includes: in, This represents the flat diaphragm deflection corresponding to the glass micro-fusion structure, where A represents the measured pressure value. B represents the material's Poisson's ratio in the theoretical reference value, B represents the material's elastic modulus in the theoretical reference value, and D represents the diaphragm thickness in the theoretical reference value. The radius of the membrane in the glass micro-fusion structure is represented by , and F represents the radius of the membrane at any point in the glass micro-fusion structure.

[0030] This invention analyzes the structural strain characteristics of the sensitive core under pressure by calculating the flat diaphragm deflection corresponding to the glass micro-fusion structure. This allows for understanding the deformation behavior and stress distribution of the sensitive core under pressure load, avoiding excessive strain or failure of the structure under pressure, which would affect the measurement accuracy and reliability of the sensor. The structural strain characteristics are the manifestation of the internal strain change of the sensitive core under pressure, such as strain distribution and strain concentration.

[0031] As an embodiment of the present invention, the step of calculating the flat diaphragm deflection corresponding to the glass micro-fusion structure to analyze the structural strain characteristics of the sensitive core under pressure includes: The deflection distribution of the flat diaphragm was verified to obtain effective deflection data; Strain-related features are extracted from the effective deflection data, and the strain-related features are normalized to obtain standard strain features; The stress field is reconstructed from the standard strain characteristics to obtain the reconstructed distributed stress; The strain tensor of the reconstructed distributed stress is calculated to obtain the full-field distributed strain; Key regions are identified in the full-field distributed strain to obtain the critical strain region; Based on the critical strain region, the structural strain characteristics of the sensitive core are analyzed.

[0032] The effective deflection data is a reliable deflection dataset that has been verified for measurement accuracy and calibrated for boundary conditions. The strain-related features are a set of parameters closely related to strain analysis derived from the deflection data, including curvature distribution, slope change rate, and deformation gradient. The standard strain features are strain feature data that eliminates the influence of dimensions and unifies the benchmark. The reconstructed stress distribution is a stress field obtained by inverting the deflection data through mechanical constitutive relations. The full-field strain distribution is a complete strain field calculated based on the stress distribution using Hooke's law. The critical strain region is a local region with high risk or special mechanical behavior identified from the full-field strain distribution.

[0033] Furthermore, the deflection distribution of the flat diaphragm can be verified using laser interferometry to ensure the accuracy and completeness of the data. Differential geometry algorithms can be used to extract strain-related features from the effective deflection data, and key parameters can be obtained by calculating surface curvature and deformation gradient. The strain-related features are normalized using the Z-score normalization method to eliminate differences in magnitude between features. The stress field is reconstructed based on Kirchhoff's thin-plate theory, and the reconstructed stress distribution is obtained by solving for bending moment and shear force distributions. The plane stress assumption and generalized Hooke's law are used to strain-tensile the reconstructed stress distribution. The strain distribution, including principal strain and shear strain, is calculated and derived. The strain energy density threshold method is used to identify key regions of the strain distribution, locating strain concentration areas and potential failure areas to obtain the critical strain region. Based on the critical strain region, the structural strain characteristics of the sensitive core are analyzed. First, the strain concentration areas are identified by analyzing the distribution pattern of the critical strain region. Then, the material yield risk is determined by evaluating the maximum principal strain value. Combined with strain gradient analysis, the crack initiation location is predicted. Finally, the overall structural integrity and reliability of the sensitive core are evaluated by comprehensively considering the strain states of each region, thus comprehensively characterizing the structural strain characteristics under pressure.

[0034] S2. Calculate the gas viscosity coefficient of the sensitive core under dynamic load conditions, and calculate the pressure film damping of the sensitive core based on the gas viscosity coefficient.

[0035] This invention calculates the gas viscosity coefficient of the sensitive core under dynamic load conditions to understand the viscous characteristics of the surrounding gas under dynamic working conditions, providing basic parameters for analyzing the pressure film damping effect. The dynamic load conditions refer to the load state that the sensitive core is subjected to over time, including periodic pressure, instantaneous impact force, etc. The sensitive core is the core sensing component in a microelectromechanical system (MEMS) used to detect changes in external physical quantities. The gas viscosity coefficient characterizes the viscous characteristics of the working gas under dynamic load and reflects the intensity of the interaction between the gas and the sensitive structure.

[0036] As an embodiment of the present invention, the calculation of the gas viscosity coefficient of the sensitive core under dynamic load conditions includes: The environmental parameters of the sensitive core under dynamic load conditions are collected and processed to obtain effective environmental parameters; Gas state characteristics are extracted from the effective environmental parameters; The gas state characteristics are filtered for key features to obtain core state characteristics; The core state features are subjected to state classification processing to obtain gas state categories; Analyze the gas flow characteristics corresponding to the gas state categories; Based on the gas flow characteristics, the gas viscosity coefficient of the sensitive core under dynamic load conditions is calculated.

[0037] The effective environmental parameters are a set of parameters such as temperature, humidity, and gas component concentration after data quality assessment and outlier removal; the gas state characteristics are a set of multi-dimensional parameters reflecting the physical state of the gas, including key parameters such as gas density, dynamic viscosity, sound velocity, and specific heat ratio; the core state characteristics are the subset of features that have the most significant impact on the gas viscosity coefficient after feature importance assessment; the gas state categories are gas state groups divided according to the similarity of physical properties; and the gas flow characteristics are key indicators characterizing the flow behavior of gas around the sensitive core, including velocity distribution, shear rate, and boundary layer thickness.

[0038] Furthermore, the environmental parameters of the sensitive core under dynamic load conditions can be collected and processed using a multi-channel data acquisition system. Abnormal data points can be identified and eliminated using the interquartile range method combined with sliding window technology to obtain valid environmental parameters. Gas state characteristics can be extracted from these valid environmental parameters using physical parameter calculation models, such as calculating gas density using the ideal gas law model and calculating the molecular mean free path model to calculate the collision distance between gas molecules. The core state characteristics can be classified using a Gaussian mixture model, dividing them into different categories based on the probabilities calculated by the Gaussian mixture model. The gas flow characteristics corresponding to the gas state categories can be analyzed using fluid dynamics simulation, such as establishing flow field distribution models under different state categories to analyze the corresponding gas flow characteristics. Based on these gas flow characteristics, the gas viscosity coefficient of the sensitive core under dynamic load conditions can be calculated using empirical formulas combined with numerical lookup tables. For example, based on the correspondence between shear rate and viscosity, an accurate gas viscosity coefficient value can be obtained through interpolation.

[0039] Furthermore, as an optional embodiment of the present invention, the key feature screening of the gas state characteristics to obtain core state characteristics includes: Calculate the variance contribution rate corresponding to the gas state characteristics; Based on the variance contribution rate, the feature discrimination degree corresponding to the gas state feature is determined; Analyze the feature redundancy between the gas state characteristics; By combining the feature redundancy and the feature distinguishability, key features of the gas state features are screened to obtain core state features.

[0040] The variance contribution rate is the proportion of a single gas state feature that explains the overall gas state variation, reflecting the total amount of information carried by the feature; the feature discrimination is the ability of a feature to distinguish different gas states based on the variance contribution rate, and the higher the value, the more effectively the feature can distinguish the differences in gas states; the feature redundancy is an indicator that measures the degree of information overlap between features of different gas states, and the higher the redundancy, the more duplicate information carried by the features.

[0041] Furthermore, variance contribution rate of each gas state feature can be calculated through analysis of variance. For example, when the variance contribution rate of a feature reaches 15% or more, it can be preliminarily determined that it carries important information. The level of feature discrimination can be determined by setting a discrimination threshold. Redundancy can be analyzed by calculating the Pearson correlation coefficient between features. If the absolute value of the correlation coefficient is greater than 0.8, it is determined to be a highly redundant feature. When combining the two for screening, the top 50% of features in terms of discrimination can be retained first, and then features with redundancy exceeding the threshold with other features can be removed. For example, in a feature set containing gas density, molecular mean free path, and viscosity coefficient, if the redundancy of density and viscosity coefficient reaches 0.85 and viscosity coefficient has a higher discrimination, then viscosity coefficient is retained and density is removed, ultimately forming the core state features.

[0042] This invention calculates the pressure film damping of the sensitive core based on the gas viscosity coefficient, thereby understanding the damping characteristics of the sensitive core in a gaseous medium environment. This provides data support for the subsequent optimization of the manufacturing process of the sensitive core. The pressure film damping is the magnitude of the damping that hinders the vibration of the sensitive core in a gaseous medium due to the viscosity of the gas film.

[0043] As an embodiment of the present invention, calculating the pressure damping of the sensitive core based on the gas viscosity coefficient includes: Extract the structural dimension parameters of the sensitive core, and determine the corresponding geometric feature values ​​of the sensitive core based on the structural dimension parameters; Query the environmental pressure conditions of the sensitive core and measure the environmental pressure value under the environmental pressure conditions; The pressure damping of the sensitive core is calculated by combining the geometric characteristic value, the gas viscosity coefficient, and the ambient pressure value.

[0044] The structural dimension parameters are a set of parameters describing the geometric characteristics of the sensitive core; the geometric characteristic values ​​are key geometric quantities extracted from the structural dimension parameters for calculating the pressure membrane damping, such as the core length, width, thickness, and distance from the substrate; and the environmental pressure value is the gas pressure value in the working environment of the sensitive core.

[0045] Furthermore, the structural dimension parameters of the sensitive core can be extracted using a three-dimensional coordinate measuring machine; based on the structural dimension parameters, the geometric feature values ​​corresponding to the sensitive core can be determined using a geometric feature extraction algorithm; the environmental pressure conditions of the sensitive core can be queried from the process database, and the environmental pressure values ​​of the environmental pressure conditions can be measured using a high-precision pressure sensor.

[0046] Furthermore, as another embodiment of the present invention, the pressure damping of the sensitive core is calculated by combining the geometric characteristic value, the gas viscosity coefficient, and the ambient pressure value: Where C represents the pressure damping of the sensitive core. Indicates the air viscosity coefficient. Let represent the sensitive core length in the geometric eigenvalues, g represent the sensitive core width in the geometric eigenvalues, b represent the gap distance in the geometric eigenvalues, p and r represent positive integers, and q represent the width-to-length ratio. The value represents the vibration frequency, and N represents the ambient pressure value.

[0047] S3. Perform temperature cycling tests on the sensitive core and collect thermal zero-point drift data during the test. Analyze the temperature drift stability of the sensitive core based on the thermal zero-point drift data.

[0048] This invention analyzes the temperature drift stability of the sensitive core based on the thermal zero-point drift data, thereby obtaining the amplitude and variation law of the zero-point offset of the sensitive core under different temperature conditions, improving the accuracy of the temperature drift performance evaluation of the sensitive core. The thermal zero-point drift data refers to the measurement data sequence of the output signal of the sensitive core changing with temperature under zero input conditions during temperature cycling testing; the temperature drift stability refers to the degree to which the output of the sensitive core is affected by temperature and its repeatability characteristics, reflecting the working reliability of the sensitive core in variable temperature environments. Furthermore, the sensitive core can be subjected to temperature cycling testing using a high and low temperature humidity test chamber; and the thermal zero-point drift data during the testing process can be collected using a multi-channel high-precision data acquisition instrument.

[0049] As an embodiment of the present invention, the step of analyzing the temperature drift stability of the sensitive core based on the thermal zero-point drift data includes: The thermal zero-point drift data is subjected to anomaly removal processing to obtain purified drift data; The purification drift data is divided into temperature ranges to obtain segmented drift data; Analyze the drift trend characteristics in the segmented drift data; Based on the drift trend characteristics, the temperature drift coefficient of the sensitive core is calculated; Based on the temperature drift coefficient, the temperature drift stability of the sensitive core is analyzed.

[0050] The purification drift data refers to the reliable data obtained after removing outliers from the thermal zero-point drift data, such as removing extreme drift values ​​that exceed reasonable physical ranges. The segmented drift data refers to the drift dataset corresponding to each interval after the purification drift data is divided according to a preset temperature range, such as the typical operating temperature range of the sensitive core. The drift trend characteristics are the regular characteristics of the drift amount changing with temperature reflected in the segmented drift data, such as the linear correlation between drift amount and temperature, the change range of drift rate, etc. The temperature drift coefficient is calculated based on the drift trend characteristics and reflects the average change value of the zero-point drift amount of the sensitive core under a unit temperature change. It is the core indicator for quantifying temperature drift stability.

[0051] Furthermore, the thermal zero-point drift data can be processed for outlier detection using the 3σ criterion method, removing extreme data that deviate from the mean by more than three times the standard deviation, thus obtaining purification drift data. The purification drift data can be segmented into temperature intervals of 10℃ or 20℃ based on the actual operating temperature range of the sensitive core, resulting in segmented drift data. Linear regression analysis can be used to fit the segmented drift data, obtaining the slope of the drift amount as a function of temperature, and simultaneously calculating the standard deviation of the fitting residuals to analyze the drift trend characteristics in the segmented drift data. When analyzing the temperature drift stability of the sensitive core based on the temperature drift coefficient, the temperature drift coefficient can be compared with a preset stability threshold. If the temperature drift coefficient is less than the threshold and the fluctuation of the coefficient in each interval is within the allowable range, the temperature drift stability is considered good. If it exceeds the threshold or the interval fluctuation is too large, the stability is considered substandard, and the abnormal temperature interval is located.

[0052] Furthermore, as an optional embodiment of the present invention, calculating the temperature drift coefficient of the sensitive core based on the drift trend characteristics includes: Identify the temperature drift range and stable operating range in the drift trend characteristics; Extract the temperature changes corresponding to the temperature drift range and the stable operating range respectively to obtain the drift change and the stable change. Based on the stable change amount, calculate the theoretical operating threshold of the sensitive core within the stable operating range; Based on the theoretical operating threshold, the drift change is mapped to the stable operating range to obtain the actual operating threshold; Calculate the sensitivity coefficient of the sensitive core within the temperature drift range based on the actual working threshold. Based on the sensitivity coefficient, the temperature drift coefficient corresponding to the sensitive core is obtained.

[0053] Wherein, the temperature drift range is the temperature range in which the output signal of the sensitive core deviates significantly with temperature changes; the stable operating range is the normal operating temperature range in which the output signal of the sensitive core is basically unaffected by temperature changes; the drift change and the stable change are the changes in the output signal of the sensitive core with unit temperature change within the temperature drift range and the stable operating range, respectively; the theoretical operating threshold is the maximum allowable output deviation of the sensitive core under ideal conditions, calculated based on the stable change; the actual operating threshold is the effective output boundary that the sensitive core can actually maintain within the temperature drift range after correction by the drift change; the sensitivity coefficient characterizes the sensitivity of the output signal of the sensitive core to temperature changes within the temperature drift range; and the temperature drift coefficient is a comprehensive index used to quantify the overall temperature drift characteristics of the sensitive core.

[0054] Furthermore, the temperature drift range and stable operating range can be identified by calculating the absolute value of the slope of the drift amount in each temperature segment of the drift trend characteristics. When the absolute value of the slope is greater than a preset slope threshold, it is determined to be a temperature drift range; otherwise, it is a stable operating range. The stable change amount can be obtained by extracting the temperature extreme values ​​within the stable operating range and calculating the difference. The theoretical operating threshold can be determined by adding three times the standard deviation to the mean of the drift amount within the stable operating range. The drift change amount can be mapped to the stable operating range through a linear transformation formula, i.e., actual operating threshold = theoretical operating threshold × (drift change amount / stable change amount) × calibration coefficient (the calibration coefficient is preset based on the core material characteristics). The sensitivity coefficient can be obtained by calculating the ratio of the change amount of the actual operating threshold within the temperature drift range to the corresponding temperature change amount. The temperature drift coefficient can be obtained by weighting the sensitivity coefficient with the proportion of the temperature drift range within the entire operating temperature range of the core, with higher proportions of the range being assigned higher weights.

[0055] S4. Perform calibration tests on the sensitive core to obtain nonlinearity data, and evaluate the measurement accuracy of the sensitive core based on the nonlinearity data.

[0056] This invention obtains nonlinearity data by calibrating the sensitive core, which quantifies the deviation between the sensor output and the ideal linear response. Based on this nonlinearity data, the measurement accuracy of the sensitive core is evaluated, effectively assessing the sensor's performance indicators and improving the reliability of the measurement data. The nonlinearity data refers to the maximum percentage deviation between the actual value output by the sensitive core and the theoretical linear value during calibration testing. The measurement accuracy is the degree of consistency between the measurement result and the true value of the sensitive core within its full-scale range. Furthermore, the calibration test of the sensitive core can be achieved through a multi-point calibration method combined with least squares fitting. First, a series of known standard input signals are applied, and the output response of the sensitive core is recorded. Then, nonlinearity data is calculated through linear regression analysis to evaluate the measurement accuracy of the sensitive core.

[0057] As an embodiment of the present invention, the evaluation of the measurement accuracy of the sensitive core based on the nonlinearity data includes: Based on the nonlinearity data, an error distribution map corresponding to the sensitive core is constructed; Based on the error distribution map, the accuracy feature vector corresponding to the sensitive core is statistically analyzed; Obtain the application scenario corresponding to the sensitive core, and evaluate the measurement accuracy of the sensitive core based on the application scenario and the accuracy feature vector.

[0058] The error distribution map is a visual graph representing the deviation between the actual output value and the ideal linear response value of each test point of the sensitive core during calibration testing. The accuracy feature vector is a set of multi-dimensional indicators extracted from the error distribution map to quantify the performance of the sensitive core, including nonlinear error, repeatability error, hysteresis error, and temperature drift coefficient. The application scenario is a set of parameters such as the environmental conditions, measurement range, and accuracy requirements for the expected operation of the sensitive core.

[0059] Furthermore, the nonlinearity data can be processed using residual analysis combined with least squares fitting to construct an error distribution map. Specifically, the standard input values ​​in the calibration test are used as the x-axis, and the difference between the actual output value and the theoretical linear value is used as the y-axis. A scatter plot is drawn and connected to form an error curve, thus forming the error distribution map. The error distribution map can be analyzed using descriptive statistical methods combined with professional sensor performance indicators to statistically analyze the accuracy characteristic vector corresponding to the sensitive core. For example, the nonlinear error can be obtained by calculating the maximum positive and negative deviations from the error distribution map, the repeatability error can be obtained by calculating the standard deviation through multiple cyclic tests, and the hysteresis error can be obtained by obtaining the maximum difference through forward and reverse tests. The temperature influence coefficient is calculated by testing results at different temperatures to obtain the temperature drift coefficient, which together constitutes the accuracy feature vector. The accuracy feature vector can be comprehensively evaluated by combining multi-criteria decision analysis methods with application scenario requirements to assess the measurement accuracy of the sensitive core. First, the specific parameters of the application scenario are obtained, such as an accuracy level of 0.5 for industrial process control and an accuracy level of 0.2 for medical monitoring. Then, each index in the accuracy feature vector is matched with the corresponding scenario requirements, and a comprehensive score is calculated using a weighted scoring method. Finally, based on the score range, it is determined whether the sensitive core meets the accuracy requirements of the scenario or its accuracy level is classified, thereby completing the evaluation of measurement accuracy.

[0060] S5. Combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy, optimize the manufacturing process of the sensitive core to obtain the optimized result.

[0061] This invention optimizes the manufacturing process of the sensitive core by combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy. This improves the comprehensiveness and accuracy of sensitive core performance optimization, thereby enhancing the stability of the sensitive core manufacturing process. Furthermore, by combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy to optimize the manufacturing process of the sensitive core, the following steps are taken: First, analyze the key deformation modes in the structural strain characteristics to identify the structural support elements that need strengthening; then analyze the... The damping distribution characteristics in the pressure film damping amount are identified to clarify the core factors affecting dynamic response performance. Then, the temperature sensitivity coefficient in the temperature drift stability is evaluated to identify areas for improvement in temperature compensation design. Finally, by comprehensively considering various error indicators in the measurement accuracy, an improvement scheme for material selection, a structural design optimization strategy, and key points for adjusting process parameters are formulated to form a complete optimized manufacturing process scheme for the sensitive core. Finally, based on this scheme, the manufacturing process of the sensitive core is optimized to obtain the optimized results. Specifically, for a more intuitive understanding of the optimization process of the manufacturing method for the sensitive core of a robot six-dimensional pressure sensor based on glass micro-melting in this application, please refer to [reference needed]. Figure 2 The diagram shown is an optimized processing flow of the manufacturing method for the sensing core of a six-dimensional robot pressure sensor based on glass micro-melting provided by this invention. It should be noted that in this invention... Figure 2 The flowchart presented is only for the optimization process of the manufacturing method of the sensing core of the robot six-dimensional pressure sensor based on glass micro-melting, and is not limited to the optimization process of the manufacturing method of the sensing core of the robot six-dimensional pressure sensor based on glass micro-melting in different actual application scenarios.

[0062] Compared to the problems described in the background art, this invention, by calculating the deflection of the flat diaphragm corresponding to the glass micro-fusion structure, can grasp the actual deformation law of the glass micro-fusion structure under loads such as pressure and temperature, providing a quantitative basis for the subsequent analysis of the structural strain characteristics. This invention, by calculating the gas viscosity coefficient of the sensitive core under dynamic load conditions, can understand the viscous characteristics of the gas surrounding the sensitive core in dynamic working conditions, providing basic parameters for analyzing the pressure-film damping effect. Furthermore, this invention, by analyzing the temperature drift stability of the sensitive core based on the thermal zero-point drift data, can obtain the amplitude and variation law of the zero-point offset of the sensitive core under different temperature conditions, improving... To further improve the accuracy of the temperature drift performance evaluation of the sensitive core, this invention calibrates the sensitive core to obtain nonlinearity data, which quantifies the deviation between the sensor output and the ideal linear response. Based on this nonlinearity data, the measurement accuracy of the sensitive core is evaluated, effectively assessing the sensor's performance indicators and improving the reliability of the measurement data. Finally, by combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy, this invention optimizes the manufacturing process of the sensitive core, enhancing the comprehensiveness and accuracy of the sensitive core performance optimization, thereby improving the stability of the sensitive core's manufacturing process. Therefore, the glass micro-melting-based robotic six-dimensional pressure sensor sensitive core manufacturing method provided by this invention can improve the process stability of robotic six-dimensional pressure sensor sensitive core manufacturing.

[0063] like Figure 3 The diagram shown is a functional block diagram of the robotic six-dimensional pressure sensor sensing core manufacturing system based on glass micro-melting according to the present invention.

[0064] The glass micro-fusion-based robotic six-dimensional pressure sensor core manufacturing system 200 described in this invention can be installed in an electronic device. Depending on the functions implemented, the glass micro-fusion-based robotic six-dimensional pressure sensor core manufacturing system may include a structural strain analysis module 201, a pressure film damping calculation module 202, a temperature drift stability analysis module 203, a measurement accuracy evaluation module 204, and a process optimization module 205. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0065] In this embodiment of the invention, the functions of each module / unit are as follows: The structural strain analysis module 201 is used to prepare the glass micro-fusion structure of the sensitive core of the robot six-dimensional pressure sensor, calculate the flat diaphragm deflection corresponding to the glass micro-fusion structure, and analyze the structural strain characteristics of the sensitive core under pressure. The pressure film damping calculation module 202 is used to calculate the air viscosity coefficient of the sensitive core under dynamic load conditions, and calculate the pressure film damping amount of the sensitive core based on the air viscosity coefficient. The temperature drift stability analysis module 203 is used to perform temperature cycling tests on the sensitive core, collect thermal zero-point drift data during the test, and analyze the temperature drift stability of the sensitive core based on the thermal zero-point drift data. The measurement accuracy evaluation module 204 is used to perform calibration tests on the sensitive core, obtain nonlinearity data, and evaluate the measurement accuracy of the sensitive core based on the nonlinearity data. The process optimization module 205 is used to combine the structural strain characteristics, the pressure film damping amount, the temperature drift stability and the measurement accuracy to perform optimization processing on the manufacturing process of the sensitive core and obtain optimization results.

[0066] In detail, the modules in the robotic six-dimensional pressure sensor sensitive core manufacturing system 200 based on glass micro-melting described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The same technical means are used in the manufacturing method of the sensitive core of the robot six-dimensional pressure sensor based on glass micro-melting described in the article, and it can produce the same technical effect, so it will not be repeated here.

[0067] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0068] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for manufacturing the sensing core of a six-dimensional robotic pressure sensor based on glass micro-melting, characterized in that, The method includes: A glass micro-fusion structure for the sensing core of a six-dimensional pressure sensor for a robot was fabricated, and the deflection of the flat diaphragm corresponding to the glass micro-fusion structure was calculated to analyze the structural strain characteristics of the sensing core under pressure. Calculate the gas viscosity coefficient of the sensitive core under dynamic load conditions, and calculate the pressure film damping of the sensitive core based on the gas viscosity coefficient; Temperature cycling tests were performed on the sensitive core, and thermal zero-point drift data was collected during the test. Based on the thermal zero-point drift data, the temperature drift stability of the sensitive core was analyzed. The sensitive core is calibrated to obtain nonlinearity data, and the measurement accuracy of the sensitive core is evaluated based on the nonlinearity data. By combining the structural strain characteristics, the pressure film damping, the temperature drift stability, and the measurement accuracy, the manufacturing process of the sensitive core is optimized to obtain the optimized result.

2. The method for manufacturing the sensing core of a robot six-dimensional pressure sensor based on glass micro-melting as described in claim 1, characterized in that, The calculation of the flat diaphragm deflection corresponding to the glass micro-fusion structure includes: Obtain the diaphragm geometric parameters and external load conditions of the glass micro-fusion structure; The deflection calculation elements and the measured pressure value are determined from the geometric parameters and load conditions. Query the theoretical reference value corresponding to the deflection calculation element; Analyze the structural response corresponding to the deflection calculation elements; By combining the structural responsiveness, the measured pressure value, and the theoretical reference value, the flat diaphragm deflection corresponding to the glass micro-fusion structure is calculated.

3. The method for manufacturing the sensing core of a robot six-dimensional pressure sensor based on glass micro-melting as described in claim 2, characterized in that, The analysis of the structural response corresponding to the deflection calculation elements includes: The deflection calculation elements are subjected to feature decomposition to obtain element attribute representations; The correlation effect analysis of the attribute representations of the elements is performed to obtain the interaction effects between the elements; Based on the interaction effects between the elements, a dependency graph of the deflection calculation elements is constructed to obtain the topological relationship of the influence of the elements, and historical test data of the elements corresponding to the deflection calculation elements are collected. By combining the influence of the elements on the topological relationship and the historical test data of the elements, the structural response corresponding to the deflection calculation elements is analyzed.

4. The method for manufacturing the sensing core of a six-dimensional robot pressure sensor based on glass micro-melting as described in claim 1, characterized in that, The calculation of the flat diaphragm deflection corresponding to the glass micro-fusion structure, in order to analyze the structural strain characteristics of the sensitive core under pressure, includes: The deflection distribution of the flat diaphragm was verified to obtain effective deflection data; Strain-related features are extracted from the effective deflection data, and the strain-related features are normalized to obtain standard strain features; The stress field is reconstructed from the standard strain characteristics to obtain the reconstructed distributed stress; The strain tensor of the reconstructed distributed stress is calculated to obtain the full-field distributed strain; Key regions are identified in the full-field distributed strain to obtain the critical strain region; Based on the critical strain region, the structural strain characteristics of the sensitive core are analyzed.

5. The method for manufacturing the sensing core of a robot six-dimensional pressure sensor based on glass micro-melting as described in claim 1, characterized in that, The calculation of the gas viscosity coefficient of the sensitive core under dynamic load conditions includes: The environmental parameters of the sensitive core under dynamic load conditions are collected and processed to obtain effective environmental parameters; Gas state characteristics are extracted from the effective environmental parameters; The gas state characteristics are filtered for key features to obtain core state characteristics; The core state features are subjected to state classification processing to obtain gas state categories; Analyze the gas flow characteristics corresponding to the gas state categories; Based on the gas flow characteristics, the gas viscosity coefficient of the sensitive core under dynamic load conditions is calculated.

6. The method for manufacturing the sensing core of a robot six-dimensional pressure sensor based on glass micro-melting as described in claim 5, characterized in that, The key feature screening of the gas state characteristics to obtain core state characteristics includes: Calculate the variance contribution rate corresponding to the gas state characteristics; Based on the variance contribution rate, the feature discrimination degree corresponding to the gas state feature is determined; Analyze the feature redundancy between the gas state characteristics; By combining the feature redundancy and the feature distinguishability, key features of the gas state features are screened to obtain core state features.

7. The method for manufacturing the sensing core of a six-dimensional robot pressure sensor based on glass micro-melting as described in claim 1, characterized in that, The calculation of the pressure damping of the sensitive core based on the gas viscosity coefficient includes: Extract the structural dimension parameters of the sensitive core, and determine the corresponding geometric feature values ​​of the sensitive core based on the structural dimension parameters; Query the environmental pressure conditions of the sensitive core and measure the environmental pressure value under the environmental pressure conditions; The pressure damping of the sensitive core is calculated by combining the geometric characteristic value, the gas viscosity coefficient, and the ambient pressure value.

8. The method for manufacturing the sensing core of a six-dimensional robot pressure sensor based on glass micro-melting as described in claim 1, characterized in that, The analysis of the temperature drift stability of the sensitive core based on the thermal zero-point drift data includes: The thermal zero-point drift data is subjected to anomaly removal processing to obtain purified drift data; The purification drift data is divided into temperature ranges to obtain segmented drift data; Analyze the drift trend characteristics in the segmented drift data; Based on the drift trend characteristics, the temperature drift coefficient of the sensitive core is calculated; Based on the temperature drift coefficient, the temperature drift stability of the sensitive core is analyzed.

9. The method for manufacturing the sensing core of a six-dimensional robot pressure sensor based on glass micro-melting as described in claim 8, characterized in that, The calculation of the temperature drift coefficient of the sensitive core based on the drift trend characteristics includes: Identify the temperature drift range and stable operating range in the drift trend characteristics; Extract the temperature changes corresponding to the temperature drift range and the stable operating range respectively to obtain the drift change and the stable change. Based on the stable change amount, calculate the theoretical operating threshold of the sensitive core within the stable operating range; Based on the theoretical operating threshold, the drift change is mapped to the stable operating range to obtain the actual operating threshold; Calculate the sensitivity coefficient of the sensitive core within the temperature drift range based on the actual working threshold. Based on the sensitivity coefficient, the temperature drift coefficient corresponding to the sensitive core is obtained.

10. The method for manufacturing the sensing core of a six-dimensional robot pressure sensor based on glass micro-melting as described in claim 1, characterized in that, The evaluation of the measurement accuracy of the sensitive core based on the nonlinearity data includes: Based on the nonlinearity data, an error distribution map corresponding to the sensitive core is constructed; Based on the error distribution map, the accuracy feature vector corresponding to the sensitive core is statistically analyzed; Obtain the application scenario corresponding to the sensitive core, and evaluate the measurement accuracy of the sensitive core based on the application scenario and the accuracy feature vector.

Images