Temperature strain bimodal flexible sensor and preparation method thereof

Abstract

The invention relates to a temperature and strain bimodal flexible sensor and a preparation method thereof, and the sensor achieves the efficient detection of temperature and strain signals through the combination of an ink direct writing process and a screen printing process. The sensor is unique in design and comprises a rigid area substrate and a soft area substrate which are respectively provided with a thermal resistance type temperature sensor and a piezoresistive strain sensor, so that effective separation and accurate measurement of temperature and strain signals are ensured. A flexible material and an improved FA-BP neural network algorithm based on a firefly algorithm are adopted, so that the stability and the measurement precision in a variable environment are improved. In addition, the sensor can still work normally in a high-temperature environment, and is superior to a common flexible thin film material in the market. The sensor disclosed by the invention shows huge application potential in the fields of intelligent wearable equipment, health monitoring, industrial automation and the like, and a new solution is provided for realizing high-precision and high-stability temperature and strain monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of health monitoring sensors, and the present invention relates to a temperature-strain dual-mode flexible sensor and a preparation method thereof. Background Art

[0002] With the rapid progress of flexible electronics technology, flexible sensors have become key components in the fields of Internet of Things, wearable technology and industrial automation. These sensors can monitor multiple physical parameters in real time and maintain stable operation in diverse environments and forms, which significantly expands their application range.

[0003] However, the performance of traditional strain sensors is poor under high temperature conditions. The strain signals they measure are often coupled with temperature signals, making it difficult to meet the requirements of specific fields. Therefore, it is very important to develop a strain sensor that can maintain high accuracy and high sensitivity in high temperature environments and avoid being coupled with temperature signals.

[0004] Although some related technologies have been proposed, such as the flexible resistive strain sensor and its preparation method and application described in Chinese invention patent CN202310100867.3, its measurement results are easily affected by ambient temperature fluctuations, resulting in signal coupling problems. Chinese invention patent CN202210887892.6 proposes a static calibration device and method for thin-film strain gauges deposited in situ on the surface of turbine engine rotor blades, but its manufacturing process is complicated and the cost is high. Chinese invention patent CN202311746126.9 proposes a Brillouin optical time domain analysis temperature and strain decoupling method and system, but the complex signal processing algorithm required by the system increases the complexity and computational load of the system. Summary of the invention

[0005] The present invention discloses a temperature-strain dual-mode flexible sensor and a preparation method thereof. The sensor uses conductive materials, combines ink direct writing technology and screen printing technology to manufacture a flexible strain sensor that can decouple temperature. The sensor can independently measure strain and temperature signals, and optimizes the BP neural network algorithm using the Firefly Algorithm (FA). The FA-BP neural network algorithm is constructed to decouple the strain signal under high temperature, thereby overcoming the performance limitations of existing strain sensors in high temperature environments.

[0006] A temperature-strain dual-mode flexible sensor comprises a temperature-strain sensitive area and a signal processing circuit, wherein the temperature-strain sensitive area comprises a soft area substrate, a rigid area substrate and a soft area electrode are arranged on the soft area substrate, and a rigid area electrode is arranged on the rigid area substrate; the rigid area electrode is a thermal resistance temperature sensor, and the soft area electrode constitutes a piezoresistive strain sensor; the rigid area electrode and the soft area electrode are both electrically connected to the signal processing circuit; the signal processing circuit is used to collect the resistance value of the rigid area electrode and the resistance value of the soft area electrode, and calculate the temperature and strain value based on the resistance value of the rigid area electrode and the resistance value of the soft area electrode.

[0007] Furthermore, the signal processing circuit includes an electrically connected resistance calculation circuit and a single-chip microcomputer, wherein the resistance calculation circuit is used to collect the resistance value of the rigid area electrode and the resistance value of the soft area electrode, and the single-chip microcomputer is used to calculate the temperature value based on the collected resistance value of the rigid area electrode, and calculate the strain value based on the resistance value of the flexible area electrode.

[0008] Furthermore, a FA-BP neural network model is integrated in the single chip microcomputer for decoupling temperature and strain, and the FA-BP neural network model is obtained by the following steps: SA1. Place the temperature-strain sensitive area on a stretching machine, stretch the temperature-strain sensitive area with the stretching machine at different temperatures, and use the resistance calculation circuit in the signal processing circuit to collect the resistance values ​​of multiple groups of flexible area electrodes and the resistance values ​​of rigid area electrodes. Use a data acquisition instrument to measure the real temperature of the temperature-strain dual-mode flexible sensor, and use the stretching machine to measure the real strain of the temperature-strain sensitive area to obtain a training set and a test set. SA2, taking the resistance values ​​of the flexible area electrodes and the rigid area electrodes in the training set as input, and the real temperature and real strain as output, construct and train the BP neural network; and use the data of the test set for verification; SA3. Obtain the optimal weights and thresholds by the firefly algorithm, assign the optimal weights and thresholds to the BP neural network as its initial weights and thresholds, complete the construction of the FA-BP neural network model, calculate the hidden layer weights of the FA-BP neural network according to the optimal weights and thresholds, and obtain the FA-BP neural network model.

[0009] Further, the SA2 comprises the following steps: SA201, determine the BP neural network structure level and the activation function type as sigmoid function, set the maximum number of iterations, learning rate, initial hidden layer weights , determine the number of neurons in the input layer, the number of neurons in the output layer, the number of hidden layers, and the number of neurons in the hidden layer; SA202, recording the resistance value of the flexible region electrode and the resistance value of the rigid region electrode as input samples; SA203, in the forward propagation process, the predicted temperature and strain values ​​are calculated based on the input samples, activation function and hidden layer weights; SA204, calculate the error between the output value and the actual value; if the error is less than the preset threshold or reaches the maximum number of iterations, stop training and save the network weights; otherwise, use the error to update the hidden layer weights in the BP neural network, and iterate until the conditions are met.

[0010] Furthermore, the rigid region electrode is prepared from alumina powder.

[0011] A method for preparing a temperature-strain dual-mode flexible sensor comprises the following steps: S1. Adding aluminum oxide powder and an adhesion promoter to an organic solvent, wherein the mass of the aluminum oxide powder is 50wt%-70wt% of the organic solvent, and the mass of the adhesion promoter is 1wt%-3wt% of the organic solvent; the aluminum oxide powder is evenly dispersed to obtain an aluminum oxide composite ink; S2, printing the aluminum oxide composite ink on the soft region substrate by screen printing, and sintering the aluminum oxide composite ink to obtain the rigid region substrate; S3, adding nano-conductive ink to an electronic printer, printing a rigid region electrode on a rigid region substrate to obtain a rigid electrode layer, and printing a soft region electrode on a soft region substrate to obtain a soft electrode layer sensitive to strain temperature; sintering the rigid electrode layer and the soft electrode layer to stabilize the resistance values ​​of the rigid region electrode and the soft region electrode; S4. Connecting two ends of the rigid region electrode to the signal processing circuit with wires, and connecting two ends of the soft region electrode to the signal processing circuit with wires.

[0012] Furthermore, in step S2, the soft region substrate is a polyimide film resistant to 200°C.

[0013] Furthermore, in step S2, the sintering temperature ranges from 150°C to 200°C, and the sintering time ranges from 30min to 60min.

[0014] Furthermore, in step S3, the speed of printing the rigid region electrode is 5-15 mm / s, and the air pressure is 100-180 kPa.

[0015] Furthermore, in step S3, the sintering temperature ranges from 150°C to 200°C, and the sintering time ranges from 60 min to 90 min.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: (1) The silver-based temperature-strain dual-mode flexible sensor of the present invention realizes the effective separation of strain signals and temperature signals through the design of independent areas in its structure and the back-end data processing circuit.

[0017] The sensor uses independent rigid area substrate and soft area substrate structures to measure temperature and strain signals respectively. The electrodes on the rigid area substrate are used to detect temperature changes. The rigid area substrate has good bending properties, but good tensile properties. The electrodes on it can avoid the influence of strain and measure accurate temperature. The electrodes on the soft area substrate are used to detect strain changes. Since the strain signal of the electrode on the soft area substrate is affected by temperature, when measuring the strain signal, the accurate temperature measured by the rigid area electrode can be used to perform corresponding corrections and compensation to obtain an accurate strain signal.

[0018] Although BP neural network has performed well in many fields, its gradient descent method may lead to slow training speed and easy to fall into local optimal solution in decoupling control, especially for nonlinear, high-dimensional or complex systems. The firefly algorithm (FA) can effectively avoid the local optimal problem with its global search ability and adaptability to complex solution space. By introducing the firefly algorithm, the initial weights and thresholds of the BP neural network are optimized, the convergence speed and global optimization ability of the network are improved, and thus better performance is achieved in decoupling control. The FA-BP neural network algorithm combines the advantages of both, using the firefly algorithm to optimize the parameters of the BP neural network and enhance its generalization ability and robustness in decoupling control tasks. This combination not only accelerates the training process of the neural network, but also improves the accuracy and stability of decoupling control.

[0019] The single chip microcomputer in the signal processing circuit of the sensor has a built-in FA-BP neural network, and the FA-BP neural network algorithm is used to achieve decoupling of strain temperature, solving the problem that traditional sensors are easily disturbed by data in complex environments. Through this decoupling method combining hardware and software, the sensor of the present invention not only improves the measurement accuracy, but also expands its application range in various complex environments.

[0020] (2) The silver-based temperature-strain dual-mode flexible sensor of the present invention can be attached to a variety of surfaces. The use of flexible materials such as polyimide silver electrodes allows it to easily adapt to various complex surfaces and shapes and achieve accurate measurement. The use of ink direct writing technology also allows customized patterns and printing electrodes on a variety of surfaces. This adaptability allows the sensor to work stably under changing environmental conditions, reduces measurement errors caused by shape mismatch, and broadens the scope of application.

[0021] The method for preparing the silver-based temperature-strain dual-mode flexible sensor provided by the present invention combines the ink direct writing process and the screen printing process. By directly writing the conductive ink onto the flexible substrate, the complex steps of the traditional photolithography and etching process are avoided, thereby shortening the production cycle and reducing the manufacturing cost. This process not only improves production efficiency, but also further reduces the overall production cost by reducing material waste and complex process steps.

[0022] Furthermore, in the silver-based temperature-strain dual-modal flexible sensor prepared by the present invention, metal nanoparticles and alumina slurry are prepared on a high-temperature resistant flexible substrate and subjected to high-temperature sintering treatment. Therefore, the sensor has good temperature resistance and can work normally at 200°C. It has obvious advantages over common flexible films such as polyurethane, polydimethylsiloxane and polyethylene terephthalate. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 :A preparation flow chart of a temperature-strain dual-modal flexible sensor; Figure 2 : Structural explosion diagram of silver-based temperature-strain dual-mode flexible sensor; Figure 3 : Strain test results of silver-based temperature-strain dual-mode flexible sensor; Figure 4 :Temperature test results of silver-based temperature-strain dual-mode flexible sensor; Figure 5 :Flowchart of temperature-strain signal extraction of silver-based temperature-strain dual-modal flexible sensor; Figure 6 : Schematic diagram of temperature-strain decoupling model based on FA-BP neural network algorithm; Figure 7 : Strain test results of silver-based temperature-strain dual-mode flexible sensor at various temperatures; Figure 8 : The structure diagram of FA-BP neural network algorithm; Fig. 9 : Comparison chart of strain prediction and test set of FA-BP neural network algorithm; Fig.10 : Comparison chart of the effects of FA-BP neural network algorithm and BP neural network algorithm.

[0024] In the attached drawings: 1. rigid region substrate; 2. rigid region electrode; 3. flexible region substrate; 4. flexible region electrode; 5. first wire connection point; 6. second wire connection point; 7. third wire connection point; 8. fourth wire connection point. DETAILED DESCRIPTION

[0025] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more. In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Example 1 Reference Figure 2 and Figure 5A silver-based temperature-strain dual-mode flexible sensor includes a temperature-strain sensitive area and a signal processing circuit, wherein the signal processing circuit includes a resistance calculation circuit and a single-chip microcomputer. The temperature-strain sensitive area includes a rigid area substrate 1, a soft area substrate 3, a rigid area electrode 2 and a soft area electrode 4; wherein the soft area substrate 3 includes a first area and a second area, the rigid area substrate 1 is fixed to the first area, the rigid area electrode 2 is printed on the rigid area substrate 1 by an ink direct writing process, and the soft area electrode 4 is printed on the second area by an ink direct writing process. The rigid area electrode 2 is a thermal resistance temperature sensor, which is sensitive to temperature; the soft area electrode 4 constitutes a piezoresistive strain sensor, which is sensitive to strain. The rigid area electrode 2 and the soft area electrode 4 have four wire connections reserved in total, which are connected to the signal processing circuit through copper wires, namely: a first wire connection 5 at the first end of the rigid area electrode 2, a second wire connection 6 at the second end of the rigid area electrode 2, a third wire connection 7 at the first end of the soft area electrode 4, and a fourth wire connection 8 at the second end of the soft area electrode 4.

[0028] The first wire connection 5 and the second wire connection 6 are respectively led out with copper wires to connect to the signal processing circuit. The signal processing circuit includes an electrically connected resistance calculation circuit and a single chip microcomputer. The resistance calculation circuit collects the resistance value of the rigid region electrode 2. The single chip microcomputer calculates the temperature value according to the collected resistance value of the rigid region electrode 2. The resistance signal is respectively led out with copper wires at the third wire connection 7 and the fourth wire connection 8, and connected to the signal processing circuit. The resistance calculation circuit in the signal processing circuit collects the resistance value of the flexible region electrode 4. The single chip microcomputer in the signal processing circuit calculates the strain value according to the collected resistance value of the flexible region electrode 4.

[0029] Preferably, the single chip microcomputer in the signal processing circuit includes a communication module, and the strain value and the temperature value are sent to the host computer through the communication module.

[0030] The rigid region electrode 2 on the rigid region substrate 1 is specifically used to detect temperature changes, while the soft region electrode 4 on the soft region substrate 3 is used to detect strain changes. Since the strain signal of the soft region is affected by temperature, when measuring the strain signal, the temperature data measured by the rigid region electrode can be used to decouple the strain data. The rigid region substrate 1 is mainly made of aluminum oxide powder and has a rectangular structure. The rigid region electrode 2 on the rigid region substrate 1 is used to detect temperature changes. The rigid region substrate 1 has good bending properties, but good tensile properties. The rigid region electrode 2 thereon can avoid the influence of strain and only measure temperature changes.

[0031] The soft region substrate 3 is made of polyimide (PI) and has a rectangular structure. The soft region electrode 4 on the soft region substrate 3 is used to detect strain changes. Since the strain signal of the soft region electrode 4 is affected by temperature, the temperature data measured by the rigid region electrode 2 can be used to perform corresponding correction and compensation when measuring the strain signal.

[0032] The rigid region electrode 2 and the soft region electrode 4 both adopt a grid structure. Both the rigid region electrode 2 and the soft region electrode 4 are silver electrodes.

[0033] Reference Figure 8 In the single chip microcomputer of the signal processing circuit, the FA-BP neural network algorithm is integrated for the decoupling of temperature strain. The training steps based on the FA-BP neural network algorithm include: SA1. Place the temperature-strain sensitive area of ​​the silver-based temperature-strain dual-mode flexible sensor on a stretching machine. Use the stretching machine to stretch the temperature-strain sensitive area at different temperatures. Use the resistance calculation circuit in the signal processing circuit to collect the resistance values ​​of multiple groups of flexible regional electrodes 4 and rigid regional electrodes 2. Use the data acquisition instrument to measure the real temperature of the sensor using the thermocouple method. Use the stretching machine to measure the real strain of the temperature-strain sensitive area to obtain the training set and test set. The training set includes the resistance value of the flexible regional electrode 4 and the resistance value of the rigid regional electrode 2 and the corresponding temperature and strain; the test set includes the resistance value of the flexible regional electrode 4 and the resistance value of the rigid regional electrode 2 and the corresponding temperature and strain. Figure 7 As shown, it can be seen that the strain response test of the sensor at different temperatures is different.

[0034] SA2. Construct a BP neural network. Take the resistance value of the flexible region electrode 4 and the resistance value of the rigid region electrode 2 in the training set as input, and the real temperature and real strain as output, to construct and train the BP neural network. Use the data in the test set for verification.

[0035] SA3. Use the firefly algorithm to optimize the BP neural network and construct the FA-BP neural network model. Based on the principle that the greater the brightness, the greater the attraction to other fireflies, the firefly algorithm is used to obtain the optimal weights and thresholds, which are assigned to the BP neural network as its initial weights and thresholds to complete the construction of the FA-BP neural network model. The hidden layer weights of the FA-BP neural network are calculated based on the optimal weights and thresholds obtained by the firefly algorithm to obtain the FA-BP neural network model. Fig.10 As shown, it can be seen that the FA-BP neural network model converges faster and has better performance than the BP neural network.

[0036] SA4. The FA-BP neural network model is implanted into the single chip microcomputer of the signal processing circuit. The resistance calculation circuit of the signal processing circuit reads the resistance of the current flexible area electrode 4 and the resistance value of the rigid area electrode 2, and inputs them into the FA-BP neural network algorithm of the single chip microcomputer of the signal processing circuit to calculate the strain value and temperature value of the current sensor.

[0037] Step SA1 includes the following steps: SA101: Connect the microcontroller in the signal processing circuit of the silver-based temperature-strain dual-mode flexible sensor to the host computer, which is loaded with mobile phone applications and computer applications.

[0038] SA102: Place the silver-based temperature-strain dual-mode flexible sensor on a stretching machine, and connect the silver-based temperature-strain dual-mode flexible sensor to the resistance calculation circuit of the signal processing circuit.

[0039] SA103: The host computer transmits data collection commands to the single-chip microcomputer. At the same time, the temperature-strain sensitive area is stretched by a stretching machine at different temperatures. At this time, the data acquisition instrument uses the thermocouple method to measure the real temperature of the sensor. The real temperature is transmitted back to the host computer by the data acquisition instrument. The real strain of the temperature-strain sensitive area is measured by the stretching machine, and the real strain is transmitted back to the host computer by the stretching machine.

[0040] SA104: The single chip microcomputer receives the command, uses the resistance calculation circuit in the signal processing circuit to collect the resistance value of the flexible area electrode 4 and the resistance value of the rigid area electrode 2, and then transmits the collected two resistance data back to the host computer.

[0041] SA105: The host computer verifies and stores the received data, including the resistance value of the flexible region electrode 4, the resistance value of the rigid region electrode 2, the real temperature value and the real strain value. Then, the data collection command is issued again to continue data collection.

[0042] SA106: After the data collection process is completed, the host computer randomly divides the stored data into a training set and a test set in a ratio of 8:2. The data content includes the resistance value of the flexible area electrode 4, the resistance value of the rigid area electrode 2, the real temperature value and the real strain value. Figure 6 , including Next steps: SA201: Determine the BP neural network structure level and the activation function type as the sigmoid function, set the maximum number of iterations to 1000, set the learning rate η=0.1, and the initial hidden layer weights , determine the number of neurons in the input layer m, the number of neurons in the output layer n, the number of hidden layers p and the number of neurons in the hidden layer k, etc.

[0043] SA202: The resistance value of the flexible region electrode 4 and the resistance value of the rigid region electrode 2 are recorded as input samples as X.

[0044]

[0045] X is the BP neural network input value matrix. The resistance value calculation circuit in the representative signal processing circuit reads the resistance value of the flexible area electrode 4, The resistance value calculation circuit in the representative signal processing circuit reads the resistance value of the rigid region electrode 2 .

[0046] SA203: During the forward propagation process, the predicted temperature and strain values ​​are calculated based on the input samples, activation function and hidden layer weights.

[0047]

[0048] Input variable matrix for activation function, Input variables for the activation functions of neurons in the hidden layer of the neural network.

[0049]

[0050] is the activation function output value matrix, It is the output value of the activation function corresponding to each neuron in the hidden layer of the neural network.

[0051]

[0052] W is the hidden layer weight matrix, Representative Activation function input variables The first layer connected to the hidden layer Hidden layer weights.

[0053]

[0054] N is the output layer weight matrix, Representative Activation function output variables The first layer connected to the hidden layer Hidden layer weights.

[0055] From the above, we can know that:

[0056]

[0057] Then, the output of the final estimated temperature and strain is:

[0058] represents the strain value estimated by the trained BP neural network, Represents the temperature value estimated by the trained BP neural network.

[0059] SA204: Calculate the error between the output value and the actual value. If the error is less than the preset threshold or reaches the maximum number of iterations, stop training and save the network weights; otherwise, use the error to update the hidden layer weights in the BP neural network and iterate until the conditions are met.

[0060] The core of the temperature-strain decoupling model based on BP neural network is to adjust the hidden layer weights between each layer of neurons. The prediction value matrix output by BP neural network is , the actual value matrix is ​​recorded as Y, E is the loss function, and η is the learning rate constant.

[0061] Then we can calculate,

[0062]

[0063] in represents the true strain value collected in SA103, Represents the actual temperature value collected in SA103.

[0064] right Taking the derivation, we have

[0065] The activation function uses the sigmoid function:

[0066] Then we can get:

[0067]

[0068]

[0069] Therefore, the K+1 hidden layer weights :

[0070] Using K+1 hidden layer weights Calculate the prediction value matrix of the BP neural network output: , until the loss function E at the n+1th time is less than the preset threshold The calculation stops when BP neural network training is completed, and the hidden layer weights at this time are obtained. .like Fig. 9 As shown, the data of the test set is used for verification to obtain the accuracy of the BP neural network algorithm at this time.

[0071] The SA3 comprises the following steps: SA301: The basic idea of ​​the Firefly Algorithm (FA) is that there are random shining fireflies in a certain range, and these individuals will be attracted by the light emitted by other individuals. The brighter the light, the greater the attraction. The brighter the individuals are, the more they "gather" towards them. So the brightness of the fireflies is defined as:

[0072] Among them, I0 is the original luminous brightness of the firefly position, which is related to the objective function value. The higher its own luminous brightness, the higher the objective function value; γ is the luminous brightness absorption coefficient; r is the value of any two fireflies i and j at their positions x i and X j The distance between them is also called the Cartesian distance.

[0073] The calculation formula for r is as follows

[0074] in , are the spatial coordinates of firefly i and firefly j , The k-th dimension coordinate value of ; k=1, …, d; d is the dimension of the problem.

[0075] SA302: Since the attractiveness of fireflies is proportional to their brightness, we define the attractiveness β (r)

[0076] Where β0 is the attraction when r = 0.

[0077] SA303: Firefly i will move towards other fireflies j that are brighter than it. The movement pattern is

[0078] is the position of firefly i after it moves; α is the step size factor; rand is a random real number uniformly distributed in [0, 1]; the second term on the right side of the equation represents the attraction of fireflies to light intensity, and the third term is the random disturbance term.

[0079] SA304: Therefore, the position update equation of the brightest firefly is as follows

[0080] in, is the brightest firefly location; The updated position of the brightest firefly.

[0081] SA305: Set the firefly algorithm input matrix to X in SA202, and the output to the predicted temperature and predicted strain, and initialize the firefly algorithm population. Each firefly individual corresponds to the threshold and weight in the BP neural network. Calculate the firefly fitness As shown in formula (7):

[0082] Where: is the actual output value of all p-th training vectors, p = 1, …, N; is the predicted value of all training vectors; N is the number of training vectors; SA306: The optimal firefly individual is selected by changing the firefly position, firefly brightness and decision radius, and then the initial weights and thresholds are replaced to complete the construction of the FA-BP neural network.

[0083] The present invention provides a flexible temperature-strain dual-mode sensor which has a simple manufacturing method, high precision and high sensitivity. At the same time, a single-chip microcomputer in a signal processing circuit is integrated with a temperature-strain decoupling model based on a FA-BP neural network algorithm to calculate temperature and strain data, thereby providing a more reliable monitoring solution. The present invention also provides a method for preparing a silver-based temperature-strain dual-mode flexible sensor, which combines two 3D printing processes, namely, screen printing and ink direct writing. In the screen printing process, an alumina slurry is prepared, and a uniform and highly adhesive alumina film substrate is formed through precise printing and sintering steps. In the ink direct writing process, a highly conductive nanosilver ink is used to construct the structure of the silver-based strain sensor, and precise patterning of the silver conductive circuit is achieved by precisely controlling the ink spraying and drying process. The entire preparation process needs to be carried out under strictly controlled environmental conditions to ensure the performance and reliability of the sensor. The application of this comprehensive process enables the silver-based temperature-strain dual-mode flexible sensor of the present invention to adapt to various complex surfaces and shapes in structure, and to achieve high-precision and high-stability temperature and strain signal detection in function, providing new possibilities for the fields of smart wearable devices, health monitoring, and industrial automation.

[0084] Reference Figure 1, a method for preparing a silver-based temperature-strain dual-mode flexible sensor, comprising the following steps: S1. Prepare aluminum oxide powder composite ink, add aluminum oxide powder and adhesion promoter into organic solvent, the mass of aluminum oxide powder is 50wt%-70wt% of organic solvent, and the mass of adhesion promoter is 1wt%-3wt% of organic solvent, and disperse them evenly to obtain aluminum oxide composite ink; S2. Print the aluminum oxide composite ink on the soft region substrate using a screen printing process. The soft region substrate uses a flexible polymer such as polyimide (PI). Sinter the composite ink at a high temperature to obtain a rigid region substrate whose strain response is much smaller than that of the soft region substrate. S3. Add nanosilver conductive ink to the electronic printer to make patterned electrodes: set the printing speed to 5-15mm / s, and set the printing pressure to 100-180kPa. Appropriate printing parameters are very important. Printing speed that is too fast or printing pressure that is too low may cause the printed lines to break; printing speed that is too slow will cause the printing pressure to be too high, which may cause ink accumulation, and the printed lines to be too thick and overlapped. Print the rigid region electrode 2 on the rigid region substrate 1 to obtain a rigid electrode layer that is sensitive to temperature and insensitive to strain, and print the soft region electrode 4 on the soft region substrate 3 to obtain a soft electrode layer that is sensitive to strain temperature; sinter the rigid electrode layer and the soft electrode layer to stabilize the resistance of the rigid region electrode and the soft region electrode.

[0085] S4. After sintering and curing, the first wire connection 5 and the second wire connection 6 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The third wire connection 7 and the fourth wire connection 8 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The single-chip microcomputer in the signal processing circuit includes a communication module, and the communication module is used to send the strain value and the temperature value to the host computer.

[0086] The host computer can be a mobile phone, computer, tablet or other terminal.

[0087] In the S1, the adhesion promoter is ethyl cellulose.

[0088] In the above-mentioned S1, the organic solvent is a mixed solution of anhydrous ethanol, acetone, terpineol and dibutyl phthalate in a ratio of 3 parts, 2 parts, 1 part and 1 part.

[0089] In the above-mentioned S2, the soft region substrate is a polymer film resistant to 200°C.

[0090] In the above-mentioned S2, the printing process is screen printing.

[0091] In the above-mentioned S2, the sintering temperature ranges from 150°C to 200°C, and the sintering time ranges from 30 min to 60 min.

[0092] In the above-mentioned S3, the conductive ink is metal nano-conductive ink.

[0093] In the above-mentioned S3, the method for making the patterned electrode is an ink direct writing process.

[0094] In the above-mentioned S3, the sintering temperature ranges from 150°C to 200°C, and the sintering time ranges from 60 min to 90 min.

[0095] Example 2 A method for preparing a silver-based temperature-strain dual-mode flexible sensor comprises the following steps: S1. Prepare an aluminum oxide powder composite ink by adding aluminum oxide powder and an adhesion promoter to an organic solvent, wherein the weight of the aluminum oxide powder is 50 wt % of the organic solvent and the weight of the adhesion promoter is 1 wt % of the organic solvent, and disperse the powder evenly to obtain an aluminum oxide composite ink; S2, printing the aluminum oxide composite ink on the soft region substrate by screen printing, sintering the composite ink at high temperature, and obtaining a rigid region substrate 1 whose strain response is much smaller than that of the soft region substrate; S3. Add the conductive ink to the electronic printer to make a patterned electrode: set the printing speed to 5 mm / s, and set the printing pressure to 100 kPa. Print the rigid region electrode 2 on the rigid region substrate 1 to obtain a rigid electrode layer that is sensitive to temperature and insensitive to strain, and print the soft region electrode 4 on the soft region substrate 3 to obtain a soft electrode layer that is sensitive to strain temperature; sinter the rigid electrode layer and the soft electrode layer to stabilize the resistance of the rigid region electrode and the soft region electrode.

[0096] In the S1, the adhesion promoter is ethyl cellulose.

[0097] In the above-mentioned S1, the organic solvent is a mixed solution of anhydrous ethanol, acetone, terpineol and dibutyl phthalate in a ratio of 3 parts, 2 parts, 1 part and 1 part.

[0098] In the above-mentioned S2, the soft region substrate is a polymer film resistant to 200°C.

[0099] In the above-mentioned S2, the printing process is screen printing.

[0100] In the S2, the sintering temperature range is 150° C. and the sintering time is 30 min.

[0101] In the above-mentioned S3, the conductive ink is metal nano-conductive ink.

[0102] In the above-mentioned S3, the method for making the patterned electrode is an ink direct writing process.

[0103] In the above-mentioned S3, the sintering temperature range is 150° C. and the sintering time is 60 min.

[0104] S4. After sintering and curing, the first wire connection 5 and the second wire connection 6 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The third wire connection 7 and the fourth wire connection 8 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The single-chip microcomputer in the signal processing circuit includes a communication module, and the communication module is used to send the strain value and the temperature value to the host computer.

[0105] S5. Use FA-BP neural network algorithm to realize the decoupling of strain temperature, conduct learning, training and simulation of neural network in relevant software environment, and write relevant programs to achieve the goal.

[0106] like Figure 3 and Figure 4 As shown in the corresponding curve, a silver-based temperature-strain dual-mode flexible sensor prepared based on the above steps can obtain the temperature value by directly detecting the resistance change of the rigid area electrode, while the electrode on the soft area substrate is used to detect the strain change. Since the strain signal of the soft area is affected by temperature, the temperature data measured by the rigid area electrode can be used to perform corresponding correction and compensation when measuring the strain signal.

[0107] Example 3 A method for preparing a silver-based temperature-strain dual-mode flexible sensor comprises the following steps: S1. Prepare an aluminum oxide powder composite ink by adding aluminum oxide powder and an adhesion promoter to an organic solvent, wherein the weight of the aluminum oxide powder is 70wt% of the organic solvent and the weight of the adhesion promoter is 3wt% of the organic solvent, and disperse the powder evenly to obtain an aluminum oxide composite ink; S2, printing the aluminum oxide composite ink on the soft region substrate by screen printing, sintering the composite ink at high temperature, and obtaining a rigid region substrate 1 whose strain response is much smaller than that of the soft region substrate; S3. Add the conductive ink to the electronic printer to make a patterned electrode: set the printing speed to 15 mm / s, and set the printing pressure to 180 kPa. Print the rigid region electrode 2 on the rigid region substrate 1 to obtain a rigid electrode layer that is sensitive to temperature and insensitive to strain, and print the soft region electrode 4 on the soft region substrate 3 to obtain a soft electrode layer that is sensitive to strain temperature; sinter the rigid electrode layer and the soft electrode layer to stabilize the resistance of the rigid region electrode and the soft region electrode.

[0108] In the S1, the adhesion promoter is ethyl cellulose.

[0109] In the above-mentioned S1, the organic solvent is a mixed solution of anhydrous ethanol, acetone, terpineol and dibutyl phthalate in a ratio of 3 parts, 2 parts, 1 part and 1 part.

[0110] In the above-mentioned S2, the soft region substrate is a polymer film resistant to 200°C.

[0111] In the above-mentioned S2, the printing process is screen printing.

[0112] In the above-mentioned S2, the sintering temperature range is 200° C. and the sintering time is 60 min.

[0113] In the above-mentioned S3, the conductive ink is metal nano-conductive ink.

[0114] In the above-mentioned S3, the method for making the patterned electrode is an ink direct writing process.

[0115] In the above-mentioned S3, the sintering temperature range is 200° C. and the sintering time is 90 min.

[0116] S4. After sintering and curing, the first wire connection 5 and the second wire connection 6 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The third wire connection 7 and the fourth wire connection 8 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The single-chip microcomputer in the signal processing circuit includes a communication module, and the communication module is used to send the strain value and the temperature value to the host computer.

[0117] The host computer can be a mobile phone, computer, tablet or other terminal.

[0118] S5. Use FA-BP neural network algorithm to realize the decoupling of strain temperature, conduct learning, training and simulation of neural network in relevant software environment, and write relevant programs to achieve the goal.

[0119] like Figure 3 and Figure 4 As shown in the corresponding curve, a silver-based temperature-strain dual-mode flexible sensor prepared based on the above steps can obtain the temperature value by directly detecting the resistance change of the rigid area electrode, while the electrode on the soft area substrate is used to detect the strain change. Since the strain signal of the soft area is affected by temperature, the temperature data measured by the rigid area electrode can be used to perform corresponding correction and compensation when measuring the strain signal.

[0120] Example 4 A method for preparing a silver-based temperature-strain dual-mode flexible sensor comprises the following steps: S1. Prepare an aluminum oxide powder composite ink by adding aluminum oxide powder and an adhesion promoter to an organic solvent, wherein the weight of the aluminum oxide powder is 60wt% of the organic solvent and the weight of the adhesion promoter is 2wt% of the organic solvent, and disperse the powder evenly to obtain an aluminum oxide composite ink; S2, printing the aluminum oxide composite ink on the soft region substrate by screen printing, sintering the composite ink at high temperature, and obtaining a rigid region substrate 1 whose strain response is much smaller than that of the soft region substrate; S3. Add the conductive ink to the electronic printer to make a patterned electrode: set the printing speed to 10 mm / s, and set the printing pressure to 150 kPa. Print the rigid region electrode 2 on the rigid region substrate 1 to obtain a rigid electrode layer that is sensitive to temperature and insensitive to strain, and print the soft region electrode 4 on the soft region substrate 3 to obtain a soft electrode layer that is sensitive to strain temperature; sinter the rigid electrode layer and the soft electrode layer to stabilize the resistance of the rigid region electrode and the soft region electrode.

[0121] In the S1, the adhesion promoter is ethyl cellulose.

[0122] In the above-mentioned S1, the organic solvent is a mixed solution of anhydrous ethanol, acetone, terpineol and dibutyl phthalate in a ratio of 3 parts, 2 parts, 1 part and 1 part.

[0123] In the above-mentioned S2, the soft region substrate is a polymer film resistant to 200°C.

[0124] In the above-mentioned S2, the printing process is screen printing.

[0125] In the above-mentioned S2, the sintering temperature range is 180° C. and the sintering time is 50 min.

[0126] In the above-mentioned S3, the conductive ink is metal nano-conductive ink.

[0127] In the above-mentioned S3, the method for making the patterned electrode is an ink direct writing process.

[0128] In the above-mentioned S3, the sintering temperature range is 180° C. and the sintering time is 80 min.

[0129] S4. After sintering and curing, the first wire connection 5 and the second wire connection 6 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The third wire connection 7 and the fourth wire connection 8 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The single-chip microcomputer in the signal processing circuit includes a communication module, and the communication module is used to send the strain value and the temperature value to the host computer.

[0130] The host computer can be a mobile phone, computer, tablet or other terminal.

[0131] S5. Use FA-BP neural network algorithm to realize the decoupling of strain temperature, conduct learning, training and simulation of neural network in relevant software environment, and write relevant programs to achieve the goal.

[0132] like Figure 3 and Figure 4 As shown in the corresponding curve, a silver-based temperature-strain dual-mode flexible sensor prepared based on the above steps can obtain the temperature value by directly detecting the resistance change of the rigid area electrode, while the electrode on the soft area substrate is used to detect the strain change. Since the strain signal of the soft area is affected by temperature, the temperature data measured by the rigid area electrode can be used to perform corresponding correction and compensation when measuring the strain signal.

[0133] Example 5 A method for preparing a silver-based temperature-strain dual-mode flexible sensor comprises the following steps: S1. Prepare an aluminum oxide powder composite ink by adding aluminum oxide powder and an adhesion promoter to an organic solvent, wherein the weight of the aluminum oxide powder is 50 wt % of the organic solvent and the weight of the adhesion promoter is 3 wt % of the organic solvent, and disperse the powder evenly to obtain an aluminum oxide composite ink; S2, printing the aluminum oxide composite ink on the soft region substrate by screen printing, sintering the composite ink at high temperature, and obtaining a rigid region substrate 1 whose strain response is much smaller than that of the soft region substrate; S3. Add the conductive ink to the electronic printer to make a patterned electrode: set the printing speed to 10 mm / s, and set the printing pressure to 120 kPa. Print the rigid region electrode 2 on the rigid region substrate 1 to obtain a rigid electrode layer that is sensitive to temperature and insensitive to strain, and print the soft region electrode 4 on the soft region substrate 3 to obtain a soft electrode layer that is sensitive to strain temperature; sinter the rigid electrode layer and the soft electrode layer to stabilize the resistance of the rigid region electrode and the soft region electrode.

[0134] In the S1, the adhesion promoter is ethyl cellulose.

[0135] In the above-mentioned S1, the organic solvent is a mixed solution of anhydrous ethanol, acetone, terpineol and dibutyl phthalate in a ratio of 3 parts, 2 parts, 1 part and 1 part.

[0136] In the above-mentioned S2, the soft region substrate is a polymer film resistant to 200°C.

[0137] In the above-mentioned S2, the printing process is screen printing.

[0138] In the above-mentioned S2, the sintering temperature range is 180° C. and the sintering time is 50 min.

[0139] In the above-mentioned S3, the conductive ink is metal nano-conductive ink.

[0140] In the above-mentioned S3, the method for making the patterned electrode is an ink direct writing process.

[0141] In the above-mentioned S3, the sintering temperature range is 200° C. and the sintering time is 80 min.

[0142] S4. After sintering and curing, the first wire connection 5 and the second wire connection 6 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The third wire connection 7 and the fourth wire connection 8 are respectively led out with copper wires to obtain resistance signals, and connected to the signal processing circuit. The single-chip microcomputer in the signal processing circuit includes a communication module, and the communication module is used to send the strain value and the temperature value to the host computer.

[0143] The host computer can be a mobile phone, computer, tablet or other terminal.

[0144] S5. Use FA-BP neural network algorithm to realize the decoupling of strain temperature, conduct learning, training and simulation of neural network in relevant software environment, and write relevant programs to achieve the goal.

[0145] like Figure 3 and Figure 4 As shown in the corresponding curve, a silver-based temperature-strain dual-mode flexible sensor prepared based on the above steps can obtain the temperature value by directly detecting the resistance change of the rigid area electrode, while the electrode on the soft area substrate is used to detect the strain change. Since the strain signal of the soft area is affected by temperature, the temperature data measured by the rigid area electrode can be used to perform corresponding correction and compensation when measuring the strain signal.

[0146] In summary, the present invention aims to provide a flexible temperature-strain dual-modal sensor with a simple manufacturing method, high precision and high sensitivity, and to provide a more reliable monitoring solution.

[0147] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A temperature-strain dual-mode flexible sensor, characterized in that: The invention comprises a temperature-strain sensitive area and a signal processing circuit, wherein the temperature-strain sensitive area comprises a soft area substrate (3), a rigid area substrate (1) and a soft area electrode (4) are arranged on the soft area substrate (3), and a rigid area electrode (2) is arranged on the rigid area substrate (1); the rigid area electrode (2) is a thermal resistance temperature sensor, and the soft area electrode (4) constitutes a piezoresistive strain sensor; the rigid area electrode (2) and the soft area electrode (4) are both electrically connected to the signal processing circuit; the signal processing circuit is used to collect the resistance value of the rigid area electrode (2) and the resistance value of the soft area electrode (4), and calculate the temperature and strain value based on the resistance value of the rigid area electrode (2) and the resistance value of the soft area electrode (4).

2. The temperature-strain dual-mode flexible sensor according to claim 1, characterized in that: The signal processing circuit comprises an electrically connected resistance calculation circuit and a single chip microcomputer, wherein the resistance calculation circuit is used to collect the resistance value of the rigid region electrode (2) and the resistance value of the flexible region electrode (4), and the single chip microcomputer is used to calculate the temperature value according to the collected resistance value of the rigid region electrode (2), and calculate the strain value according to the resistance value of the flexible region electrode (4).

3. The temperature-strain dual-mode flexible sensor according to claim 2, characterized in that: The single chip microcomputer is integrated with a FA-BP neural network model for decoupling temperature and strain, and the FA-BP neural network model is obtained by the following steps: SA1, placing the temperature-strain sensitive area on a stretching machine, stretching the temperature-strain sensitive area with the stretching machine at different temperatures, and simultaneously collecting the resistance values ​​of multiple groups of flexible area electrodes (4) and the resistance values ​​of rigid area electrodes (2) with a resistance value calculation circuit in a signal processing circuit, measuring the real temperature of the temperature-strain dual-mode flexible sensor with a data acquisition instrument, and measuring the real strain of the temperature-strain sensitive area with the stretching machine, thereby obtaining a training set and a test set; SA2, using the resistance value of the flexible region electrode (4) and the resistance value of the rigid region electrode (2) in the training set as input, and the real temperature and the real strain as output, to construct and train a BP neural network; and using the data in the test set for verification; SA3. Obtain the optimal weights and thresholds by the firefly algorithm, assign the optimal weights and thresholds to the BP neural network as its initial weights and thresholds, complete the construction of the FA-BP neural network model, calculate the hidden layer weights of the FA-BP neural network according to the optimal weights and thresholds, and obtain the FA-BP neural network model.

4. The temperature-strain dual-mode flexible sensor according to claim 3, characterized in that: The SA2 comprises the following steps: SA201, determine the BP neural network structure level and the activation function type as sigmoid function, set the maximum number of iterations, learning rate, initial hidden layer weights , determine the number of neurons in the input layer, the number of neurons in the output layer, the number of hidden layers, and the number of neurons in the hidden layer; SA202, recording the resistance value of the flexible region electrode (4) and the resistance value of the rigid region electrode (2) as input samples; SA203, in the forward propagation process, the predicted temperature and strain values ​​are calculated based on the input samples, activation function and hidden layer weights; SA204, calculate the error between the output value and the actual value; if the error is less than the preset threshold or reaches the maximum number of iterations, stop training and save the network weights; otherwise, use the error to update the hidden layer weights in the BP neural network, and iterate until the conditions are met.

5. The temperature-strain dual-mode flexible sensor according to claim 1, characterized in that: The rigid region electrode (2) is made of aluminum oxide powder.

6. The method for preparing a temperature-strain dual-mode flexible sensor according to claim 1, characterized in that: The following steps are involved: S1. Adding aluminum oxide powder and an adhesion promoter to an organic solvent, wherein the mass of the aluminum oxide powder is 50wt%-70wt% of the organic solvent, and the mass of the adhesion promoter is 1wt%-3wt% of the organic solvent; the aluminum oxide powder is evenly dispersed to obtain an aluminum oxide composite ink; S2, printing the aluminum oxide composite ink on the soft region substrate (3) by screen printing, and sintering the aluminum oxide composite ink to obtain the rigid region substrate (1); S3, adding nano-conductive ink to an electronic printer, printing a rigid region electrode (2) on a rigid region substrate (1) to obtain a rigid electrode layer, and printing a soft region electrode (4) on a soft region substrate (3) to obtain a soft electrode layer that is sensitive to strain temperature; sintering the rigid electrode layer and the soft electrode layer to stabilize the resistance values ​​of the rigid region electrode (2) and the soft region electrode (4); S4, connecting the two ends of the rigid region electrode (2) to the signal processing circuit with wires, and connecting the two ends of the soft region electrode (4) to the signal processing circuit with wires.

7. The method for preparing a temperature-strain dual-mode flexible sensor according to claim 6, characterized in that: In the step S2, the soft region substrate (3) is a polyimide film resistant to 200°C.

8. The method for preparing a temperature-strain dual-mode flexible sensor according to claim 6, characterized in that: In the step S2, the sintering temperature ranges from 150°C to 200°C, and the sintering time ranges from 30min to 60min.

9. The method for preparing a temperature-strain dual-mode flexible sensor according to claim 6, characterized in that: In the step S3, the speed of printing the rigid region electrode (2) is 5-15 mm / s, and the air pressure is 100-180 kPa.

10. The method for preparing a temperature-strain dual-mode flexible sensor according to claim 6, characterized in that: In step S3, the sintering temperature ranges from 150° C. to 200° C., and the sintering time ranges from 60 min to 90 min.

Images