Shape memory alloy actuator with strain gauge sensor and position estimation and its manufacturing method

Abstract

A shape memory actuator includes: a single shape memory alloy; a shape memory effect (SME) region of the alloy configured for actuation; a pseudoelastic (PE) region of the alloy configured as a sensor capable of position sensing; and a control system configured to control the actuator by controlling a current passing through at least the SME region based on sensor results from the PE region. A method of controlling a shape memory actuator includes: applying a predetermined current through the actuator; measuring a first resistance of the SME region; measuring a second resistance of the PE region; calculating an estimated position of the actuator based on the first and second resistances; and adapting the current applied to the actuator based on the estimated position. A method of manufacturing a shape memory actuator includes: laser processing; thermomechanical processing; and training the shape memory alloy.

Description

[0001] Citation of relevant applications

[0002] This document is a formal application based on and claims the benefit of provisional application 62 / 394,491, filed on 14 September 2016, which is incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a shape memory alloy actuator (including a strain gauge sensor and / or position estimation) and a method of manufacturing the actuator. Background Technology

[0004] Shape memory alloys (SMAs) are a class of materials that exhibit unique properties, including the shape memory effect (SME) and pseudoelasticity (PE). The first observation of SME performance occurred in 1932, achieved by Arne Olander using a cadmium-gold alloy. However, the name "shape memory alloy" wasn't applied to a group of materials exhibiting similar properties until after 1960. Many alloying compositions of shape memory alloys have been identified, including CuAlNi, TiNbFePt, and many more. However, the most widely used and commercially available SMA is NiTi, often referred to as Nitinol. NiTi offers several advantages over other SMAs, such as a high force-to-mass ratio, large recoverable strain, hyperelasticity, and biocompatibility.

[0005] SMAs are used in a variety of applications, such as biomedical vascular stents, motor vehicles, robotics, aerospace, and vibration absorption. However, some limitations may restrict the application of SMAs, including the difficulty in obtaining actuator position and / or strain feedback.

[0006] Thus, an improved actuator and manufacturing or production method are needed to overcome at least some of the problems of conventional SMA actuators. Summary of the Invention

[0007] According to one aspect of this document, a shape memory actuator is provided, comprising:

[0008] Single shape memory alloy;

[0009] The shape memory effect (SME) region of the individual shape memory alloy is configured for actuation;

[0010] The pseudoelastic (PE) region of the individual shape memory alloy is configured as a sensor capable of position sensing; and

[0011] The control system is configured to control the actuator by controlling the current passing through at least the SME region based on sensor results from the PE region.

[0012] In certain circumstances, the PE region can be configured as a strain gauge.

[0013] According to another aspect of this document, a method for controlling a shape memory actuator is provided, the method comprising:

[0014] A predetermined current is applied through the actuator by the control system;

[0015] Measure the first resistance of the shape memory effect (SME) region of the actuator;

[0016] Measure the second resistance of the pseudoelastic (PE) region of the actuator;

[0017] The control system calculates the estimated position of the actuator based on the first and second resistors;

[0018] The control system adapts the current applied to the actuator based on the estimated position.

[0019] According to another aspect of this document, a method for manufacturing a shape memory actuator is provided, the method comprising:

[0020] Laser processing of shape memory alloys to provide a shape memory effect (SME) region that differs from the existing pseudoelastic (PE) region at the phase transition temperature;

[0021] The laser-treated shape memory alloy is subjected to thermomechanical treatment; and

[0022] The thermomechanically treated shape memory alloy is trained.

[0023] In certain cases, the thermomechanical treatment may include: solution annealing the laser-treated shape memory alloy; work hardening the laser-treated shape memory alloy; and heat treatment the laser-treated shape memory alloy. In this case, the work hardening may include: drawing the laser-treated shape memory alloy through one or more dies; and periodically intermediate annealing the laser-treated shape memory alloy during the drawing process.

[0024] In another specific case, the training may include at least one of isothermal stress cycling or isothermal thermal cycling.

[0025] Other aspects and features will become apparent to those skilled in the art from the following description of some exemplary embodiments. Attached Figure Description

[0026] Embodiments will now be described by way of example only with reference to the accompanying drawings, in which:

[0027] Figure 1 This is a schematic diagram showing an embodiment of the actuator;

[0028] Figure 2 The experimental results shown demonstrate the effect of temperature on the pseudoelastic properties of the trained pseudoelastic NiTi wire.

[0029] Figure 3(a) shows the effect of laser power on the phase transition temperature of laser-treated NiTi, with note of the Ti-rich saturation region;

[0030] The DSC results shown in Figure 3(b) demonstrate the range and convertibility;

[0031] Figure 4 The schematic diagram shows an embodiment of a continuous laser processing structure suitable for mass production of actuators;

[0032] Figure 5(a) shows the base metal (BM) in the laser-treated NiTi lines.

[0033] Figure 5(b) illustrates the texture uniformity between the BM and LP regions after thermomechanical treatment;

[0034] Figure 6 shows the DSC results for (a) base metals (and PE), (b) after laser treatment, (c) after thermomechanical treatment, and (d) after training, illustrating the phase transition temperatures.

[0035] Figure 7(a) shows a schematic diagram of the operation of the test equipment;

[0036] Figure 7(b) is a photograph of the stand-alone portable testing equipment;

[0037] Figure 8 This is an electrical schematic diagram and embodiment of the SMA actuator drive circuit;

[0038] Figure 9 shows the data obtained from the circuit after applying the noise cancellation filter: (a) resistance during heating (martensite to austenite); (b) resistance during cooling (austenite to martensite); (c) position during heating; (d) position during cooling.

[0039] Figure 10 The diagram shows the phase transformation temperatures of the austenitic and martensitic phases in the SME section of the laser-treated SMA actuator relative to the applied stress, where the slopes of the martensitic and austenitic phase transformations are different.

[0040] Figure 11 The results show the heat capacity obtained from the DSC results of the actuator SME section under stress-free conditions and the stress-free heat capacity modeled based on the normal distribution function, illustrating the presence of the R phase in the DSC results (rather than the modeling results).

[0041] Figure 12 The graphs illustrating the results of the experiment (dashed line) and the SME model (solid line) show the position relative to resistance and position relative to temperature under different applied stresses;

[0042] Figure 13 The resistance of the PE is shown as a function of different stresses and temperatures;

[0043] Figure 14 The block diagram illustrates the conceptual structure of the location, temperature, and stress estimation algorithms;

[0044] Figure 15 The graph illustrating the position estimation results shows the estimated position (and position error) at different stress levels;

[0045] Figure 16 Another embodiment of the actuator is shown. Detailed Implementation

[0046] The following description generally relates to an improved actuator, including a strain gauge sensor, which can be used by a position estimation algorithm (which can be used by a control system to control the actuator position or directly control the force under dynamic and unknown stress levels). Typically, the actuator comprises two or more material components (regions) in a single-piece actuator line. Each of these components behaves differently at room temperature; one exhibits a shape memory effect (SME) for actuation, and another exhibits a pseudoelasticity (PE) effect to allow the use of a sensor (sometimes referred to as an embedded sensor because it is part of the actuator itself). Actuator fabrication includes laser processing, heat treatment, and cold working processes, followed by training (e.g., using iso-stress thermal cycling) to stabilize performance. The actuator may also include a model-based, externally sensorless position estimation algorithm that uses two resistance measurements from the two different material components. Until now, externally sensorless position estimation of SMA actuators under dynamically unknown applied stresses has been difficult or impossible to achieve due to system complexity and the number of unknown parameters. In the embodiments described herein, additional information obtained from embedded sensors is intended to address this problem. In particular, the proposed actuator is intended to be applicable to situations where the mechanical load is unknown in advance.

[0047] As mentioned earlier, the problems in conventional SMA actuators involve feedback, including determining position and strain sensing, which could allow for improved position control.

[0048] For example, position control of SMA actuators has been attempted using various control techniques and feedback signals. The most reliable feedback signal is typically a direct position measurement. However, position sensors can be very expensive and increase the complexity of the actuator assembly. For this reason, SMA is not very competitive compared to other actuation techniques such as piezoelectric and magnetic actuators. Sensorless sensing methods (e.g., using resistance (ER) as a feedback signal) have been considered for position control; however, in many studies, the applied stress is constant or known in advance and has a monotonic relationship with displacement, as occurs in springs.

[0049] The embodiments described herein are intended to provide an improved actuator that includes a strain sensor and a position sensor. An embodiment of actuator 100 is shown schematically. Figure 1 In this embodiment, the actuator 100 is formed by a single wire 105 having two sections with different alloy compositions. In this embodiment, the larger section 110 is advantageous for actuation (“Actuation Section” or “Actuation Part” or “Shape Memory Effect (SME) Section” or “SME Part”), while the smaller section 115 is used for sensing force (“Stress Sensing Section” or “Stress Sensing Part” or “Pseudoelastic (PE) Section” or “PE Part”). However, the size ratio of the actuation section to the stress sensing section can vary depending on the application / parameters required for the specific application of the actuator. Therefore, both actuation and stress sensing can be achieved using a single SMA wire. The stress sensing section 115 is configured to have a phase transition temperature lower than the expected operating temperature of the actuation section 110, thereby allowing the stress sensing section 115 to exhibit pseudoelastic (PE) properties. The effect of temperature on PE performance is illustrated in... Figure 2 In this actuator, the applied stress is configured to remain below the pseudo-elastic plateau at any given temperature. This helps ensure that operation is within the elastic deformation range of the austenitic phase, thus remaining relatively constant and linear at different temperatures. The actuation zone 110 is configured to have SME performance to be actuated (moved) by a phase transformation temperature higher than the initial operating temperature and the temperature of the PE zone 115.

[0050] like Figure 1 As shown, the actuator may also include an electrical contact 120 for applying current or for sensing. For this actuator, two electrical configurations can be used overall. One configuration allows current to pass through the PE and SME sections 115, 110, thereby heating these sections; the second configuration allows most of the current to pass through only the SME section 110. Both configurations are functional; however, the latter configuration is described in the embodiments herein.

[0051] The manufacture or production of actuators typically involves laser processing to adjust the composition (i.e., to form suitable SME and PE regions 110, 115), followed by thermomechanical treatment to achieve the desired mechanical properties.

[0052] 1.1. Laser processing

[0053] Pulsed laser processing of SMAs has been shown to alter their composition to add additional "memory," that is, to provide regions with different phase transition temperatures, and thus affect properties related to PE (sensing) or SME (actuation). For further details on laser processing of SMAs, see PCT Patent Publication No. WO2011014962 (PCT Application No. PCT / CA2010 / 001219), the contents of which are incorporated herein by reference. Examples of this method / process can alter the local functional properties of SMAs because these properties are sensitive to alloy composition. Even minute changes (e.g., 0.01% atomic percentage) can alter the functional properties of SMAs, such as phase transition temperature and pseudoelastic stress. This technology opens the way for single-wire manufacturing, where different regions possess unique thermomechanical and electromechanical properties (e.g., as previously described).

[0054] Figure 3(a) illustrates the effect of 5ms laser pulse power on the NiTi phase transition temperature, and Figure 3(b) is a differential scanning calorimetry (DSC) curve showing the range and convertibility of the laser treatment. It can be shown that the laser power has a direct impact on the amount of Ni evaporated, which can control the performance of different regions of the line by controlling the power amount. In addition to laser pulse power, laser pulse duration and laser spot overlap also affect performance, as described in the PCT patent publication. For example, each pulse can overlap with the previous pulse by 60% to provide a cured line. Figure 5(a) shows a magnified photograph of a NiTi line after laser treatment (LP), illustrating the original base metal (BM) and the region after laser treatment (LP). Figure 5(b) shows the same line after further processing.

[0055] Before laser treatment, BM lines can be cleaned by removing any impurities from their surface using ethanol and acetone (or similar substances). The BM lines then undergo laser treatment, for example, as... Figure 4The computer-controlled system 200 shown operates in a continuous manner. System 200 may include: a wire feed roller 205; a wire manipulation roller 210; a pulsed laser 215; and a control system 220 (including a processor 225). Typically, due to the properties of SMA materials, only the areas requiring SME performance need to be processed. To reduce or prevent oxidation, the wire may be processed in an argon chamber or the like during the process. For the actuator embodiment described herein, a 1000W, 5ms pulse is used. Further details of the system and method for laser processing are described in U.S. Patent Publication No. US20170165532, which is incorporated herein by reference. Figures 6(a) and (b) show the DSC results for BM and LP wires. The DSC results show the phase transition temperature and reveal changes in material composition.

[0056] 1.2. Thermomechanical / Chemical Treatment

[0057] Thermomechanical treatment of the laser-treated wire allows for further construction of the actuator's final microstructure and performance. It is important to note that various heat treatments also affect the phase transition temperature and mechanical properties of the SMA. Laser treatment alters the wire's microstructure; therefore, after laser treatment, the wire can be solution annealed, for example, at 1000°C for 1 hour. The wire can also be drawn through one or more dies to refine the crystal structure and induce dislocations through work hardening. In this embodiment, the wire drawing process reduces the wire diameter from an initial 460 micrometers to 250 micrometers. To prevent over-work hardening and breakage, the wire can be annealed intermittently after every three dies, at 600°C for 15 minutes. Once the wire drawing step is complete, the final heat treatment can be performed, in this case at 480°C for 2 hours. At this stage of thermomechanical treatment, the SMA actuator has the PE and SME sections as described above. Figure 6(c) shows the phase transition temperature after thermochemical treatment, and Figure 5 shows the drawn actuator wire. As shown in the diagram, after online stretching, there is no longer a visible boundary between the BM and LP regions. The actuator performance is then stabilized through the training process.

[0058] Typically, different types of training processes exist: isothermal stress cycling, isothermal thermal cycling, or a combination of thermal and stress cycling. The training process is believed to induce orientation-preferred crystals in the material's microstructure. For the actuator embodiment described herein, approximately 1000 isothermal thermal cycles were performed to train the SMA actuator. After training, the wire diameter decreased from 250 μm to approximately 226 μm. The difference in phase transition temperature before and after training is visible in Figures 6(c) and 6(d).

[0059] Most existing publications in the field of SMA utilize commercially available NiTi SMA, known as Flexinol. However, due to differences in the alloy composition and thermochemical history of the SMA used, at least some mechanical and electrical properties of the proposed actuators may appear different from those in existing literature, even if the overall performance is similar. In the experiments conducted, linear properties are typically determined experimentally.

[0060] 2. Test equipment and systems

[0061] The test system was designed to characterize the electromechanical properties of SMA actuators. Figures 7(a) and 7(b) show schematic diagrams of operation and images of an embodiment of the test system 300. System 300 is equipped with a torque-controlled servo motor (not shown) to apply dynamic loads to the actuator line; however, in the test, only a static weight 305 is used. The actuator line 105 is clamped at each end between two stainless steel plates (not shown). The weight is attached to the base plate. The line 105 is restricted to vertical movement only using linear keyway bearings (not shown) (torsional movement is limited). Even with good lubrication of the linear bearings, friction exists within the bearings. Thus, even with a constant weight 305, the actual stress applied to the line is not constant. In reality, the stress applied to the line is a combination of acceleration, friction, and gravity (weight 305). System 300 is enclosed in a plastic environment (not shown) to prevent the influence of chaotic random airflow in an uncontrolled environment on the convection coefficient of the line 105. The testing system is also equipped with sensors, including a high-precision incremental optical position encoder 310, a strain gauge / load sensor 315, and a precision ambient temperature sensor (not shown). An electrical connector 320 is used to connect the voltage on the line 105 and sense the current / voltage in the line 105.

[0062] 2.1 Current driver and measurement circuit

[0063] The heating of the wire is achieved via Joule heating. A variable and controlled power supply 325 is used to control the wire temperature and subsequently the actuator position. Since the goal is to estimate the actuator position using two resistance measurements in a sensorless manner, the control circuit must be able to measure both resistances very accurately. An embodiment of the control circuit 400 (including a controlled current source 405) is shown. Figure 8 middle.

[0064] In this example, the current source 405 is implemented using a high-gain Darlington bipolar junction NPN transistor 410. Transistor 410 is positioned relative to the electrical load (actuator) 100 in a sunken configuration. A current-sensing shunt resistor 415 on the underside measures the current and feeds it back to the negative input of the differential amplifier 420 (connected to the transistor). A digital-to-analog converter (DAC) (16-bit in this case) 425 is connected to the positive input of the differential amplifier 420 and serves as a reference (command) current signal. This hardware feedback loop operates at 5MHz and is configured to keep the command current constant as the electrical load (actuator resistance) changes.

[0065] In this example, the resistance is calculated by measuring the current through the load and the corresponding voltage drop (as shown in Equations 1 and 2). The measured current comes from a shunt resistor as described above. The two voltage drops on the PE and SME sections are measured using the high common-mode rejection ratio (CMRR) of a 140dB differential programmable gain amplifier (PGA) 430. CMRR is useful when measuring very small differential voltages (such as in the case of the actuator PE section) because, generally speaking, a higher CMRR results in a better signal-to-noise ratio. The gain of the PGA 430 is selected via a microcontroller (MC) 435 using the Serial Peripheral Interface (SPI) protocol 400. High amplification gain is used for more sensitive measurements, such as the voltage on the PE section. Similar to the gain, the PGA 430 includes a multiplexer and has eight inputs, each pair of which can be selected for differential measurement, also via SPI serial communication. To obtain higher effective resolution from the ADC, a technique called oversampling is used. Oversampling performs a fast, continuous analog-to-digital conversion and averages the converted values, thus presenting a trade-off between resolution and conversion speed. The output of PGA 430 passes through a second analog-to-digital converter (ADC) 445, and the converted digital value is sent to MC 435. Furthermore, to obtain not only accurate but also precise voltage measurements, the ADC can use a precise voltage reference, and the conversion can be calibrated for offset and gain errors using an auxiliary precision voltmeter.

[0066]

[0067]

[0068] The control circuit 400 also obtains other measurement data from the test system 300, such as the actual stress using the strain gauge sensor 315, the position of the actuator using the high-resolution incremental optical encoder 310, the ambient temperature sensor 450, and the input voltage 455 to the system 300.

[0069] As can be seen from Equations 1 and 2, the lower the current, the higher the noise in the calculated resistance. To filter out potential noise, the measured current and voltage are initially filtered through a median filter and a moving average filter. The MC435 is connected to a computer / processor 455, using an RS232 serial interface in this case. All measured data (including time) is sent to the computer 455 for recording. The MC 435 operates at a frequency of 200 Hz, and filtering and signal processing are performed in real time on the microcontroller. The location and resistance of the SME region under different applied stresses are shown in Figure 9.

[0070] 2.2 Electrical Connection

[0071] As previously described, each end of the actuator wire is clamped between two stainless steel plates; subsequently, the plates are connected to the end of a ring, which forms an electrical connection between the current source circuit and the actuator wire. For the purposes of this document, the intermediate sensing probe is connected only via a temporary electrical connection. To calculate the true resistance of the actuator (rather than the electrical connections and wiring), the resistance from the circuit to the connection is measured to be 0.32 ohms using a 4-wire resistance measurement technique.

[0072] 3. Electromechanical properties and modeling

[0073] A thermal model of the actuator line was developed to determine temperature-dependent material properties. These properties, once determined, were then used in a phenomenological model to describe the behavior of the PE and SME regions.

[0074] 3.1. Simulated Temperature

[0075] The resistivity of SME and PE can depend on the wire temperature. The wire temperature can be measured using thermocouples, thermal cameras, or the like. However, due to the small wire diameter (226 μm) and the desire to improve accuracy, the actuator temperature is simulated in these embodiments. Given the measured resistance, elongation, input current, and ambient temperature, a simulation is performed using the PDE toolbox library in MATLAB based on the basic heat transfer parabola PDE (Equation 3). This method of using mathematical heat transfer models to extrapolate the temperature of the SMA actuator wire has been used in other studies. However, most of these studies simplify the problem using simple lumped capacitance methods and involve materials with constant composition. Using more complex simulations, as described below, provides more accurate results, especially since the actuator wire in this embodiment comprises more than one material composition with different thermal properties.

[0076]

[0077] The actuator line is considered to be a cylinder; therefore, it is symmetrical about its length axis. Equation 3 can be described in cylindrical coordinates, as shown in Equation 4.

[0078]

[0079] Joule heating is modeled as internal heat generation. Assuming the radius is constant and the change in thermal conductivity is minimal Formula 4 then simplifies to:

[0080]

[0081] Where r, z, ρ, C, k, T, I, R, and L represent: radial direction, length direction, density, heat capacity, thermal conductivity, temperature, current, resistance, and length, respectively. Current, resistance, and line length are provided from experimental results to the simulation. Heat capacity, obtained from DSC measurements, is a function of temperature to represent the phase transition under stress-free conditions. The phase transition temperature of NiTi is a function of applied stress and increases with increasing applied stress. The increase in phase transition temperature is assumed to be essentially linear with respect to stress. When modeling SMA actuators, especially when dynamic loading is involved, this change in phase transition temperature should be considered as large as possible and can be modeled as follows:

[0082] M s，f (σ)=C M σ+M* s，f (6)

[0083] A s，f (σ)=C A σ+A * s，f

[0084] constant C A and C M M is obtained empirically based on experiments conducted under steady-state conditions. s and A s Through experiments, M was obtained. f and A f They are used in parallel respectively. Figure 10 The experimental data show the relationship between phase transition temperature and stress.

[0085] The heat capacities of the PE and SME regions are different. Due to the phase change, the change in heat capacity of the SME relative to temperature results in latent heat of phase change; however, the PE does not undergo a phase change, therefore its heat capacity coefficient is assumed to be constant. The heat capacity in the simulation is defined by the following piecewise relationship:

[0086]

[0087] The stress-dependent heat capacity of the SME portion is modeled based on a normal distribution characteristic function, as shown in Equations 8 and 9. Here, the middle portion of the curve represents the average of the phase transition start and end temperatures, and the standard deviation is 1 / 6 of the difference in phase transition temperatures at which 95% phase transition occurs. The results of the heat capacity model are shown in... Figure 11 middle.

[0088]

[0089]

[0090] Because the PE and SME regions have two different material compositions, their conductivity is also different. Consequently, the thermal conductivity of martensite and austenite is also different, making the thermal conductivity of the SME region dependent on its phase transformation. Equation 10 describes the thermal conductivity as a weighted series sum of the thermal conductivity of austenite and martensite. Equation 11 is a piecewise constraint on the thermal conductivity in the simulation.

[0091] k S ME (ξ)=(1-ξ)k A +ξk M (10)

[0092]

[0093] For simulation purposes, the martensitic transformation fraction is taken as the ratio of elongation to maximum elongation under a specific stress.

[0094] The actuator line is cooled by thermal convection and thermal radiation relative to the surrounding environment. Thermal radiation is neglected, and thermal convection is implemented as the boundary condition in Equation 13. The thermal convection coefficient of the cylindrical line has been studied by others. Recently, the effect of the angle of the line relative to the horizontal direction on the convection coefficient has also been studied and is shown in Equation 12:

[0095]

[0096] Where g is the gravitational constant, R c Z is the gas constant of air, D is the compressibility factor of air, and P is the diameter of the line. r Here, P is the Prandtl number, P is the air pressure, μ is the dynamic viscosity of air, and k is the thermal conductivity of air. A, B, and n are empirical constants based on the line angle. Since the line temperature is not uniform along its radius and length, the average temperature is considered for the PE and SME regions. The simulation was run multiple times for different applied stresses to the actuator.

[0097] 3.2. SME Modeling and Performance

[0098] Various methods exist for modeling SME performance, such as micromechanical and thermodynamic modeling based on crystal structure and fundamental physical laws. However, these models can be complex and difficult to define; therefore, a macroscopic phenomenological approach is chosen for the purposes of the embodiments described herein. This type of modeling is common for actuation and control purposes and can be performed in two main categories: machine learning and numerical methods or mathematical functions. Any of these modeling methods can be applied to the proposed actuator design.

[0099] Equations 13 and 14 are a set of functions that phenomenologically model the phase transition behavior by calculating the martensitic phase fraction based on empirical results. Other S-shaped functions with slightly different curvatures (cosine and sine, error function, arctangent function) have also been used to model SMA phase transitions.

[0100]

[0101]

[0102] The phase transition conditions are given by formulas 15 and 16.

[0103]

[0104]

[0105] Due to variations in the material properties of the manufactured actuator and the presence of the R phase (such as... Figure 11 The presence of (obvious) and other metallurgical phenomena (such as mild bidirectional shape memory effect) necessitates an additional linear correction to Equation 13 to provide better agreement with experimental results. This may not be necessary in all embodiments of the actuator.

[0106] The resistance of SME is represented by a series of resistors consisting of the martensitic and austenitic portions of the added resistance, relative to the martensitic phase fraction, as shown in Equation 17. Furthermore, the series model also has phenomenological significance because the phase transformation propagation begins at the outer end of the line and acts inward in a manner consistent with the phase fraction.

[0107] R S ME =(1-ξ)R A +ξR M (17)

[0108] The resistivity of austenite and martensite is modeled as a linear function of stress and temperature, a relationship empirically obtained from a set of steady-state tests. Equation 18 shows the linear relationship. The constant R... o A,M R T A,M R σ A,M These are curve fitting parameters, obtained based on empirical data, as shown in Figure 9.

[0109]

[0110] Similar to resistance, SME's plasticity model is also a function of phase fractions. However, through logic similar to that of the resistance model, the elasticity model is added as two parallel elastic components.

[0111]

[0112] The classical constitutive model of SME is given below:

[0113]

[0114] θ SME It represents thermal expansion and is also a function of phase transition.

[0115] θ S ME =(1+ξ)θ A +ξθ M (twenty one)

[0116] The stress component Ω of the phase transformation is expressed in Equation 22; where It is the maximum recoverable strain.

[0117]

[0118] SME modeling results under different applied stresses show Figure 12 The data were simulated and compared with empirical data. It should be noted that the temperatures were simulated, not measured. For the austenite-to-martensite phase transformation, the model is closer to experimental results due to material-related reasons mentioned herein.

[0119] 3.3. PE Performance and Modeling

[0120] Unlike the SME portion of the actuator, the PE portion does not undergo a phase transformation (because the applied stress is assumed to be below the pseudo-elastic plateau stress), thus actuation occurs only in the elastic region. Therefore, the maximum stress applied to this actuator design should be below the pseudo-elastic plateau at any given temperature. For this reason, its behavior is very similar to that of a conventional elastic alloy. Thus, the resistance of the elastic region of the PE portion depends linearly on stress and temperature, such as... Figure 13 The experimental data is shown in the figure.

[0121]

[0122] This linear relationship of PE elasticity can be modeled in the same way as the SME martensitic and austenitic resistance expressed in Equation 18. Therefore, since there is no phase transformation (hysteresis), a clear relationship between the temperature of the PE region and the stress applied to the actuator can be obtained. Test resistance measurements of the PE region relative to different stresses and temperatures are shown... Figure 13 middle.

[0123]

[0124] 3.4. Controlled Actuation Range

[0125] The actuation range of an SMA actuator typically depends on the stress applied to the line. Generally, only the elongation of the line due to phase transformation and thermal expansion (not due to material elasticity) can be controlled by Joule heating. For example, if the SME portion is entirely in the austenitic phase and the stress increases, the actuator position becomes solely a function of the applied stress (and negligible thermal expansion), and cannot be controlled by changes in line temperature. Therefore, these limitations on range and stress level must be considered for different applications. The total length of the proposed actuator (actuator position) is expressed by the following formula.

[0126] L = ε PE L PE +ε S ME L SME (25)

[0127] 4. Location estimation algorithm

[0128] An embodiment of the Position Estimation Algorithm (PEA) has been developed based on the empirical model described in the previous section. The purpose of this section is to: outline the method of estimating position by measuring two resistors (R0, R0, as previously proposed)... PE and R SME The algorithm for estimating the position of the SMA actuator line is as follows. This embodiment of PEA is based on the following assumptions: both PE and SME are under the same stress and thermal environmental conditions (e.g., ambient temperature and convection).

[0129] Since the PE region operates only within its elastic zone, there is no phase fraction formula, and the stress-temperature relationship can be directly obtained from Equation 23. However, as Figure 13 As shown, the effect of temperature is greater than that of stress. Therefore, to obtain the applied stress, both the temperature and resistance of the PE need to be known. Equation 26 is a simplified lumped capacitance heat transfer function that calculates the temperature of the PE region in real time and is part of the PEA. It is based on the previous temperature of the SME region, the surrounding temperature, the heat capacity of the PE, the resistance of the PE, the thermal conductivity, and the current passing through the PE region. The distance between the center of the PE section and the SME section is represented by L*. PE It refers to the surface area of ​​the PE portion. Because the properties of the PE region are more constant due to the absence of a phase change, the temperature of the PE is easier to calculate online compared to SME.

[0130]

[0131] At each time point, Equation 13 or 14 is used to calculate based on the memory-related constants ξa and ξb, depending on the direction of the phase transition. The calculated martensitic phase fraction formula is then inserted into the resistance model in Equation 17. By measuring the resistance and using Equation 17, the relationship between SME temperature and stress can be obtained for the specific data point. Thus, the temperature of the SME region at the current time can be calculated using the stress obtained from the PE region.

[0132] The results can be considered as estimated temperatures and stresses on the actuator line. These estimated parameters can now be interpolated back into the SME and PE models explained in previous sections to obtain a fully analytical state of the actuator. Therefore, using the models and estimated stresses and temperatures, the position (length) of the actuator line can be estimated under different applied stresses. Furthermore, the estimated stresses can be directly used in force control systems or the like. Examples of PEA are summarized in... Figure 14 In the block diagram shown. As Figure 14 As shown, the various formulas are used to determine the length / position of the actuator.

[0133] Figure 15 The applied current, estimated and measured position, position error, and measured stress are displayed. Initially, an open-loop steady-state current of 0.34 A is supplied to the actuator. Subsequently, the actuator contracts and reaches the steady-state position. During this stage, additional weight is added to increase the stress applied to the actuator wire. As the weight is added, the wire begins to elongate. Finally, a current of 0.6 A is supplied to the wire to completely transform it into austenite. As can be seen from the presented results, the PEA tends to closely match the actual position. In this experiment, a position error of 160 μm was achieved, which is approximately 4% of the total present actuation under maximum present stress.

[0134] The method in this embodiment typically relies on an accurate, experience-based mathematical model for the PE and SME zones of the line. Therefore, any discrepancy between the model and reality can lead to errors in the estimated parameters (e.g., location). However, parameter identification, artificial intelligence, and adaptive techniques can be used to enhance the material and environmental performance used in the model. Further adjustments to various models and algorithms should yield even better results.

[0135] This document presents a novel SMA actuator design with an embedded strain gauge sensor, incorporating two different material compositions within a single actuator line; and discusses an operation and manufacturing method. An electronic circuit board is designed to provide a linear current source power supply for the actuator and to measure resistance, such as high-side resistance. Furthermore, a model-based position estimation algorithm is developed based on the proposed actuator design.

[0136] Various examples of actuators

[0137] Example 1: SMA actuator with embedded sensor

[0138] Actuator design: (e.g.) Figure 1 (as shown)

[0139] The SMA actuator line comprises two sections of different material compositions along its length in a single piece of wire. One material composition section serves as the actuator, and the other as an embedded sensor. Thus, this design integrates sensing and actuation into a single device. Actuation can be due to pseudoelastic actuation or shape memory effects.

[0140] Location estimation:

[0141] The position and force of the actuator are estimated using two resistance measurements from two different regions of the actuator, as described above. These resistance measurements are then fed into a model-based and / or machine learning estimation algorithm.

[0142] Example 2: Single spring-biased SMA actuator and inductive position controller

[0143] Actuator design: (e.g.) Figure 16 (as shown)

[0144] The SMA actuator line comprises two sections of different material composition along its length in a single piece of line. One material composition section serves as the actuator, while the other material composition section is formed in a spring shape and serves as a sensor and bias force.

[0145] Location estimation:

[0146] The position and force of the wire are calculated using inductance measurements of the spring portion of the wire. The inductance of the spring varies with its pitch. The greater the elongation, the lower the inductance. Inductance measurements can be performed using, for example, three different methods: rise time; frequency counting using an LC resonant circuit; and frequency response amplitude measurement using a high-pass peak detection circuit. The inductance is then mapped to the position, and the calculated position is used in the control algorithm to control the position. In addition to inductance, the actuator resistance can also be measured (as previously described) and can help determine the phase transition state.

[0147] Example 3: Continuously variable Ni content linearizes SMA performance.

[0148] Based on the Fourier series principle, any monotonically continuous function can be obtained (or approximated) by summing an infinite (or finite) series of trigonometric functions. The same principle can be applied to laser-processed SMA lines to manipulate the effective performance of actuators for different applications. It is known that the laser pulse power and application time affect the Ni composition of the line (i.e., the amount of evaporated Ni). Therefore, by controlling the laser pulse, the amount of Ni can be controlled, thus controlling the thermomechanical and electromechanical properties of the processed region. By aggregating multiple small sections with different properties, the final effective performance can be formed and optimized for a specific application. For example, different sections of the line can have different amounts of Ni content to linearize the mechanical or electrical properties for passive and active applications, thereby making the actuator more controllable.

[0149] Example 4: Magnetic vibration induction cooling of individual SMA actuators in a bundle

[0150] Based on the principles of electromagnetism, current-carrying wires with the same current direction attract each other, while those with opposite current directions repel each other. The same principle can be applied to bundled SMA actuator wires. By switching the direction and amplitude of the current at a frequency, the bundled wires will attract and repel each other, thus also causing vibration at that frequency. This vibration creates effective force convection on the wires, causing them to cool at a rate much faster than free convection. The vibration frequency can be selected to be outside the range of human hearing.

[0151] The above embodiments can also be combined to form other actuator designs. Some applications of the above embodiments include, but are not limited to: exoskeletons, haptic feedback, adaptive seating (backrests and lumbar support), virtual reality and rehabilitation gloves, wearables, robotics, motor vehicles (actuators, valves, and the like), biomedical devices and prostheses (stents, actuators, end effectors, and the like), aerospace engineering (deformable wings, unmanned aerial vehicles (UAVs)), and various other applications.

[0152] While this disclosure has been illustrated and described herein with reference to various embodiments and specific examples thereof, it will be apparent to those skilled in the art that the elements of each embodiment may be combined in other ways to form further embodiments, and that other embodiments and examples may perform similar functions and / or achieve similar results. All such equivalent embodiments and examples are within the spirit and scope of this disclosure as defined by the claims. For example, the principles and ideas in this document are believed to be applicable to other shape memory materials, including shape memory plastics or the like.

[0153] In the preceding description, several details have been set forth for illustrative purposes to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that not all of these specific details may be necessary. In other instances, well-known structures may be shown in block diagram form to avoid obscuring understanding. For example, no specific details are provided regarding whether elements in the embodiments described herein are implemented as software routines or computer-readable code (executed by a processor) or as hardware circuitry, firmware, or a combination thereof.

Claims

1. A shape memory actuator, comprising: Single shape memory alloy; The shape memory effect (SME) region of the individual shape memory alloy is configured for actuation; The pseudoelastic (PE) region of the single shape memory alloy is configured as a sensor capable of position sensing. The PE region is configured to have a phase change temperature lower than the expected operating temperature of the SME region.

2. The shape memory actuator according to claim 1, wherein, The PE region is configured as a strain gauge.

3. The shape memory actuator of claim 1, further comprising a control system configured to control the actuator by controlling the current passing through at least the SME region based on sensor results from the PE region.

4. A method for manufacturing a shape memory actuator, the method comprising: Laser processing is used to process shape memory alloys having existing pseudoelastic (PE) regions to provide shape memory effect (SME) regions with phase transition temperatures different from those of the existing pseudoelastic (PE) regions, which are lower than the expected operating temperature of the SME regions. The laser-treated shape memory alloy is subjected to thermomechanical treatment; and The thermomechanically treated shape memory alloy is trained.

5. The method according to claim 4, wherein, The thermomechanical treatment includes: The laser-treated shape memory alloy is then subjected to solution annealing. The laser-treated shape memory alloy is then work-hardened. The laser-treated shape memory alloy is then subjected to heat treatment.

6. The method according to claim 5, wherein, The work hardening includes: The laser-treated shape memory alloy is drawn through one or more molds; During the drawing process, the laser-treated shape memory alloy is subjected to periodic intermediate annealing.

7. The method according to claim 4, wherein, The training includes: At least one of isothermal stress cycle or isothermal thermal cycle.

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