Preparation method and preparation device of non-standard screw with functional layer

Abstract

The present invention discloses a preparation method and a preparation device of a non-standard screw with a functional layer; the present invention belongs to the technical field of non-standard screws; it is a physical parameter that affects the thermal expansion characteristics of the screw and the thermal expansion characteristics of the thread modification solution, wherein the input variables include: a known and fixed thermal expansion coefficient SC of the screw, that is, the thermal expansion coefficient of the C / SiC material; the thermal expansion coefficient C of the thread modification solution on each sample i; it can be quickly calculated by a thermal expansion instrument or an exothermic method. The present invention uses a fuzzy control algorithm to accurately adjust the proportion of each component in the mixed solution, so that the thermal expansion coefficient of the thread modification solution can match the thermal expansion coefficient of the screw matrix, thereby improving the bonding strength and durability between the functional layer and the matrix. By accurately adjusting the proportion of the mixed solution by a fuzzy control algorithm, the number of adjustments can be reduced, thereby improving production efficiency, and production costs are also effectively controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of non-standard screws, in particular to the field of preparation of screws with fiber-reinforced functional layers, and in particular to a preparation method and a preparation device of non-standard screws with functional layers. Background Art

[0002] Screws with fiber-reinforced functional layers are non-standard screws made of fiber-reinforced composite materials. The characteristic is that high-performance composite materials are used as the functional layer on the surface of the screws. Therefore, compared with ordinary standardized screws, they have high strength, high rigidity, heat resistance, corrosion resistance and other characteristics.

[0003] Although the cost of screws with fiber-reinforced functional layers is relatively high, they are particularly suitable for special needs in high-end fields, especially in special environments that require corrosion resistance, fatigue resistance, electrical insulation, thermal insulation or non-magnetic properties, such as smart locks.

[0004] In the conventional technology, a Chinese invention patent (application number CN201210186721.7) discloses a process for preparing a screw having a fiber-reinforced functional layer, and the technical solution adopted is:

[0005] Step 1): Select the C / SiC blank plate with a relative density of 50% to 80% to process the thread and obtain the screw blank;

[0006] Step 2): impregnating, curing and cross-linking the screw blank with a thread modification solution, and performing thread processing with a diamond grinding wheel to obtain a semi-finished screw;

[0007] The thread modification solution is a mixed solution composed of epoxy resin, acetone and diethylenetriamine, and the mass ratio of the three is 1:1-2:0.2-0.8;

[0008] Step 3): Further densification and anti-oxidation treatment of the semi-finished screw to obtain the finished C / SiC composite screw.

[0009] The second step is the core of the prior art, which is to form a modified coating to increase the strength by impregnating, curing and cross-linking the surface of the blank.

[0010] However, this prior art uses only long fibers (i.e., "C / SiC") as the composite material matrix. Since the difference in thermal expansion coefficients between the composite material matrix and the modified coating (i.e., the functional layer formed by "impregnation, curing and cross-linking") is too large, the prepared modified coating has poor adhesion to the composite material matrix and is easily fallen off in a rapid cooling and heating application environment.

[0011] Therefore, there are some existing technologies that have improved it. For example, a Chinese invention patent (application number CN201410116104.9) discloses a process for preparing a screw with a fiber-reinforced functional layer. The improvement of the prior art solution lies in the above-mentioned "step 2". In short:

[0012] Step 2.1): By covering the rod surface of the screw blank with fine short fibers - the prior art is to form a surface layer reinforced by fine short fibers on the threaded part. These fine short fibers can better integrate with the composite matrix and improve the bonding between the coating and the matrix.

[0013] Step 2.2): Forming a long fiber reinforced screw core and a short fiber reinforced threaded portion - This prior art enables the short fiber to be tightly combined with the matrix material to form an integral reinforced structure. At the same time, the cold extruded threaded portion is integrally formed with the screw core, ensuring the connection strength and integrity between the threaded portion and the core.

[0014] Step 2.3): The short fibers are then pressed into the surface of the composite rod during the cold extrusion process, and the cold extruded threaded portion is integrally formed with the screw core. This prior art is essentially an approximation to make the thermal expansion coefficients between the composite matrix and the modified coating as close as possible. This is because the short fibers can better disperse and transfer thermal stress, reduce the internal stress caused by the mismatch of thermal expansion coefficients, and thus improve the bonding and durability of the coating and the matrix.

[0015] However, this prior art has the following technical problems that need to be solved urgently: although the introduction of short fibers helps to reduce the mismatch of thermal expansion coefficients, the introduction of excess media may still cause interface problems between the fibers and the matrix material. If the interface bonding is poor, it may still lead to performance degradation or peeling during use. At the same time, the introduction of excess media will further increase production costs, time and difficulty.

[0016] To this end, the present invention provides a method for preparing a non-standard screw with a functional layer and a preparation device thereof. Summary of the invention

[0017] In view of this, the present invention hopes to provide a method for preparing a non-standard screw with a functional layer and a preparation device thereof to solve or alleviate the following technical problems existing in the prior art:

[0018] In traditional technology, the introduction of short fibers and the use of one-piece molding processes will increase the complexity of manufacturing and still have the risk of poor interface bonding. If the thermal expansion coefficients between the composite substrate and the modified coating can be matched without introducing short fibers, it can not only solve the technical defects of poor bonding between the modified coating and the composite substrate in traditional technology, but also solve the technical defects of easy detachment in the application environment of rapid cooling and heating, and avoid the further increase in production cost, time and difficulty caused by the introduction of redundant media.

[0019] In the first aspect, a method for preparing a non-standard screw having a functional layer:

[0020] 1. Overview:

[0021] Considering that various factors such as external pressure and temperature in the screw production process will affect the thermal expansion coefficient of the mixed solution, the uncertainty is increased. Therefore, the present invention chooses to introduce a fuzzy control algorithm to process imprecise and ambiguous information. Based on the thermal expansion coefficient of the screw as a known and fixed quantity, because the mass ratio of the epoxy resin is a fixed value (1), the mass ratio of acetone has an adjustment range (1 to 2) and the mass ratio of diethylenetriamine also has an adjustment range (0.2 to 0.8). The fuzzy control algorithm will predictively calculate the adjustment amount of the mass ratio of acetone and diethylenetriamine, so that the thermal expansion coefficient of the thread modification solution composed of epoxy resin, acetone and diethylenetriamine is consistent with the thermal expansion coefficient of the screw.

[0022] (II) Technical solution:

[0023] Theoretically, the introduction of support vector machine algorithm or neural network algorithm should be the optimal solution of the present invention, but considering that the above algorithm will essentially regard it as a nonlinear regression problem when facing the above technical problems to be solved. And because of the special situation faced by the present invention, that is, in the production process of screws, there will be different external pressures, temperatures and other factors, resulting in changes in the thermal expansion coefficient of the mixed solution before and after contact with the screws. The introduction of this uncertainty makes the support vector machine algorithm or neural network algorithm less robust (unless a dynamic model is introduced, but it will lead to unsatisfactory computing costs and production efficiency); on the contrary, the fuzzy control algorithm that is good at handling such uncertain problems is the optimal solution in practical applications.

[0024] In the production line, when the C / SiC screw blanks are impregnated, cured and cross-linked with a thread modification solution (a mixed solution of epoxy resin, acetone and diethylenetriamine, with a mass ratio of 1:1 to 2:0.2 to 0.8), a plurality of screw blanks are randomly selected as samples according to the production batch (for example, one sample is selected from every production batch of 100 screws to reflect the process performance of the entire batch, and a total of five samples are selected so that each production batch can be compared with each other, or a single batch and a single sample are directly measured), and then the following steps S1 to S7 are performed.

[0025] 2.1 Step S1, obtain input and output variables:

[0026] are the physical parameters that affect the thermal expansion characteristics of screws and thread modification solutions, among which:

[0027] 1) Input variables include:

[0028] The thermal expansion coefficient SC of the known and fixed screw, i.e. the thermal expansion coefficient of the C / SiC material;

[0029] The thermal expansion coefficient C of the thread modification solution on each sample i can be quickly calculated using a thermal expansion instrument or an exothermic method.

[0030] Current mass ratio A of acetone; the value can be directly known based on the liquid feeding system.

[0031] The current mass ratio D of diethylenetriamine can be directly known based on the liquid feeding system.

[0032] Current temperature T; the value can be directly obtained based on the temperature sensor in the current production environment.

[0033] Current humidity H: Based on the humidity sensor in the current production environment, the value can be directly obtained.

[0034] 2) Output variables include:

[0035] The adjustment amount of acetone mass ratio A';

[0036] Adjustment amount D' of diethylenetriamine mass ratio.

[0037] 2.2 Step S2, fuzzification of input variables:

[0038] Convert the input variables into fuzzy sets. The fuzzy sets include: fuzzy set F1 of thermal expansion coefficient difference, fuzzy set F2 of temperature difference, fuzzy set F3 of humidity difference, and fuzzy set F4 of acetone and diethylenetriamine mass ratio difference;

[0039] 2.2.1 Step S200, fuzzy set F1 of thermal expansion coefficient difference:

[0040] The difference in thermal expansion coefficient is divided into fuzzy sets of negative large (NB), negative small (NS), zero (ZE), positive small (PS) and positive large (PB). The fuzzy set F1 of the difference in thermal expansion coefficient includes the following five bell-shaped functions:

[0041] 1) Zero (ZE):

[0042]

[0043] Among them, m ZE is the center point of the membership function, representing the typical value of "zero" difference; s ZE is the standard deviation, which determines the width of the membership function.

[0044] Following the same logic, we continue to construct the following four bell-shaped functions:

[0045] 2) Negative big (NB):

[0046]

[0047] Among them, c NB is a negative number, representing the center point of the negative large, s NB is the standard deviation.

[0048] 3) Negative Small (NS):

[0049]

[0050] Among them, m NS is the center point with the smallest negative value, and m NS <0, but higher than c NB Closer to 0, s NS is the standard deviation.

[0051] 4) Positive Small (PS):

[0052]

[0053] Among them, m PS is a positive small center point, and m PS >0,s PS is the standard deviation.

[0054] 5) Zhengda (PB):

[0055]

[0056] Among them, c PB is a positive number, representing the center point of the positive center, s PB is the standard deviation.

[0057] 2.2.2 Step S201, fuzzy set F2 of temperature difference:

[0058] The temperature difference is divided into fuzzy sets of low temperature (LT), medium temperature (MT) and high temperature (HT); the fuzzy set F2 of temperature difference includes the following three bell-shaped functions:

[0059] 1) Medium temperature (MT):

[0060]

[0061] Among them, m MT is the center point of medium temperature, s MT is the standard deviation.

[0062] Following the same logic, continue to construct the following two bell-shaped functions:

[0063] 2) Low Temperature (LT):

[0064]

[0065] Among them, m LT is the center point of low temperature, representing a lower temperature value, s LT is the standard deviation.

[0066] 3) High temperature (HT):

[0067]

[0068] Among them, m HT It is the center point of high temperature, representing a higher temperature value, s HT is the standard deviation.

[0069] 2.2.3 Step S202, fuzzy set F3 of humidity difference:

[0070] The humidity difference is divided into fuzzy sets of low humidity (LH), medium humidity (MH) and high humidity (HH); the fuzzy set F3 of humidity difference includes the following three bell-shaped functions:

[0071] 1) Take medium humidity (MH) as an example:

[0072]

[0073] Among them, m MH is the center point of medium humidity, s MH is the standard deviation.

[0074] Following the same logic, continue to construct the following two bell-shaped functions:

[0075] 2) Low humidity (LH):

[0076]

[0077] Among them, mLH It is the center point of low humidity, representing a lower humidity value, s LH is the standard deviation.

[0078] 3) High humidity (HH):

[0079]

[0080] Among them, m HH It is the center point of high humidity, representing a higher humidity value, s HH is the standard deviation.

[0081] 2.2.4 Step S203, fuzzy set F4 of the difference in mass ratio of acetone and diethylenetriamine:

[0082] The difference of the mass ratio of acetone to diethylenetriamine is divided into low, medium and high; that is, the difference of the mass ratio of acetone to diethylenetriamine includes a fuzzy set of "medium" of acetone mass ratio, "low" of acetone mass ratio, "high" of acetone mass ratio, "medium" of diethylenetriamine mass ratio, "low" of diethylenetriamine mass ratio and "high" of diethylenetriamine mass ratio;

[0083] The fuzzy set F4 of the difference in mass ratio of acetone and diethylenetriamine includes the following six bell-shaped functions:

[0084] 1) "Medium" acetone mass ratio:

[0085]

[0086] Among them, m A,Medium is the center point of the acetone mass ratio "medium", which is a value between 1 and 2; s A,Medium is the standard deviation.

[0087] 2) Acetone mass ratio is "low":

[0088]

[0089] Among them, m A,Low is the center point of the "low" acetone mass ratio, which is near 1 but less than 1, s A,Low is the standard deviation.

[0090] 3) Acetone mass ratio is "high":

[0091]

[0092] Among them, m A,High is the center point of the acetone mass ratio "high", a value between 1 and 2 but greater than 1, s A,High is the standard deviation.

[0093] 4) "Medium" mass ratio of diethylenetriamine:

[0094]

[0095] Among them, m D,Medium is the center point of the "medium" mass ratio of diethylenetriamine, which is a value between 0.2 and 0.8; s D,Medium is the standard deviation.

[0096] 5) Diethylenetriamine mass ratio is "low":

[0097]

[0098] Among them, m D,Low It is the center point of the "low" mass ratio of diethylenetriamine, which is located near 0.2 but slightly larger than 0.2. D,Low is the standard deviation.

[0099] 6) Diethylenetriamine mass ratio "high":

[0100]

[0101] Among them, m D,High is the center point of the "high" mass ratio of diethylenetriamine, which lies between 0.2 and 0.8 but less than 0.8, s D,High is the standard deviation.

[0102] 2.3 Step S3, establish fuzzy rules:

[0103] The fuzzy rule describes how to adjust the mass ratio of acetone and diethylenetriamine when the input variables are in a certain fuzzy set:

[0104] Rule 1) If the difference in thermal expansion coefficient is "positive", increase the mass ratio of acetone and reduce the mass ratio of diethylenetriamine. The expression that can be read by the fuzzy logic controller under this rule is:

[0105] If μ PB (ΔC) is high (i.e. the difference in thermal expansion coefficient is positive), then adjust the mass ratio of acetone and diethylenetriamine:

[0106] ΔA=k1·μ PB (ΔC);

[0107] ΔD=-k2·μ PB (ΔC);

[0108] Wherein, ΔA and ΔD are the adjustment amounts of the mass ratio of acetone and diethylenetriamine, respectively, and k1 and k2 are positive proportional coefficients.

[0109] Rule 2) If the difference in thermal expansion coefficient is "negatively large", the mass ratio of acetone is reduced and the mass ratio of diethylenetriamine is increased. The expression that can be read by the fuzzy logic controller under this rule is:

[0110] If μ NB (ΔC) is high (i.e. the difference in thermal expansion coefficient is large and negative), then adjust the mass ratio of acetone and diethylenetriamine:

[0111] ΔA=-k3·μ NB (ΔC);

[0112] ΔD=k4·μ NB (ΔC);

[0113] Among them, k3 and k4 are positive proportional coefficients.

[0114] Rule 3) If the sample i of the previous production batch -1 After the adjustment in step S6, if the difference in thermal expansion coefficients is inconsistent with rule 1 or rule 2, it is considered that the current temperature or humidity has exceeded a certain threshold, causing the thermal expansion coefficient C of the thread-modified solution to change beyond the expectations of rules 1 and 2, and the following rules are executed:

[0115] Rule 3.1) If the temperature T is high temperature (HT), the mass ratio A of acetone needs to be reduced, because high temperature may cause the solution to expand, and the thermal expansion coefficient needs to be balanced by adjusting the ratio. Similarly, if it is low temperature (LT), the mass ratio A of acetone needs to be increased. The expression that can be read by the fuzzy logic controller under this rule is:

[0116] If μ HT (T) is high (i.e., the temperature is high), then adjust the mass ratio of acetone:

[0117] ΔA=-k5·μ HT (T);

[0118] Among them, k5 is a positive proportionality coefficient.

[0119] If it is low temperature (μ LT (T) is high), then adjust the mass ratio of acetone:

[0120] ΔA=k6·μ LT (T);

[0121] Among them, k6 is a positive proportionality coefficient.

[0122] Rule 3.2) If the humidity H is high humidity (HH), the mass ratio D of diethylenetriamine needs to be increased because high humidity may affect the curing process of the solution. Similarly, if it is low humidity (LH), the mass ratio D of diethylenetriamine needs to be reduced. The expression that can be read by the fuzzy logic controller under this rule is:

[0123] If μ HH(H) is high (i.e., the humidity is high), then adjust the mass ratio of diethylenetriamine:

[0124] ΔD=k7·μ HH (H);

[0125] Among them, k7 is a positive proportionality coefficient.

[0126] If it is low humidity (μ LH (H) is high), then adjust the mass ratio of diethylenetriamine:

[0127] ΔD=-k8·μ LH (H);

[0128] Among them, k8 is a positive proportional coefficient.

[0129] Rule 4) If the sample i of the previous production batch -1 After adjustment in step S6 and application of rule 3, if the difference in thermal expansion coefficient still conflicts with rule 1 or rule 2, for example, the more the mass ratio of acetone increases, the more positive the difference in thermal expansion coefficient becomes, then the following rule is executed:

[0130] Rule 4.1) If the difference in thermal expansion coefficient is "positive", reduce the mass ratio of acetone and increase the mass ratio of diethylenetriamine;

[0131] Rule 4.2) If the difference in thermal expansion coefficient is "negative large", increase the mass ratio of acetone and decrease the mass ratio of diethylenetriamine.

[0132] The expressions in the above rules 4.1-4.2 that can be read by the fuzzy logic controller are similar to those in rules 1 and 2, except that the adjustment directions are completely opposite.

[0133] Rule 5) If the difference in thermal expansion coefficient of sample i of the previous production batch does not conflict with Rule 1 and Rule 2 after adjustment in step S6, then Rule 1 or Rule 2 is continued.

[0134] 2.4 Step S4, fuzzy reasoning and decision making:

[0135] According to the established fuzzy rules and the fuzzified values ​​of the input variables, the fuzzy rules are applied using the "min-max" composition method, and then the Mamdani fuzzy reasoning method is used to perform fuzzy reasoning to obtain the output variable y, which corresponds to the fuzzy set FS1 of the acetone mass ratio adjustment amount and the fuzzy set FS2 of the diethylenetriamine mass ratio adjustment amount. Step S4 specifically includes the following two sub-steps S400-S401.

[0136] 2.4.1 Step S400, applying fuzzy rules through the “min-max” synthesis method:

[0137] For each application of a fuzzy rule, the “min-max” synthesis method is used to calculate the degree of activation of the rule (i.e., the strength of the rule, i.e., a kind of weight); that is, for a given input and fuzzy rule, the matching degree between the premise part of the rule and the input is calculated by taking the minimum value of the fuzzy set membership.

[0138] Suppose there are n input variables x: x1, x2, ..., x n ; Each input variable x corresponds to a fuzzy set membership function μ Ai (x i ), where A i is the fuzzy set corresponding to the i-th input variable, i = 1, 2, ..., n.

[0139] At the same time, there are m fuzzy rules, each of which is in the form of "if x1 is A1 and x2 is A2 and ... and x n Yes A n , then the output y is B", where B is the fuzzy set of output variables. Then for the jth rule (j = 1, 2, ..., m), its activation degree D k The expression is:

[0140] D k =min(μ A1 (x1),μ A2 (x2),...,μ An (x n ));

[0141] Among them, k expresses the specific degree level;

[0142] 2.4.2 Step S401, use Mamdani fuzzy reasoning method to perform fuzzy reasoning:

[0143] Rule-based activation level D k , the membership of the output fuzzy set is calculated using the Mamdani fuzzy inference method.

[0144] That is, first, for each rule, according to the conclusion part and activation degree D of the rule, the fuzzy set of activation degree and conclusion part is subjected to "maximum" operation, and the membership degree of the output fuzzy set is calculated:

[0145]

[0146] Among them, μ Output (y) is the membership of the output fuzzy set, m is the total number of rules, and each rule has a fuzzy set C of the conclusion part. j , and its corresponding membership function is μ Cj (y), where y is the corresponding output variable.

[0147] This formula indicates that the degree to which the output variable y belongs to the final output fuzzy set is the maximum value of the degree to which y under all rules belongs to the respective conclusion part fuzzy sets.

[0148] If the output variable y corresponds to the adjustment amount A' of the acetone mass ratio, i.e. y1, then the membership degree μ of the output fuzzy set is calculated. Output (y1) = FS1;

[0149] If the output variable y corresponds to the adjustment amount D' of the mass ratio of diethylenetriamine, that is, y2, then the membership degree μ of the output fuzzy set is calculated. Output (y2) = FS2;

[0150] 2.4.3 (Optional) Step S402, calculate the interaction between fuzzy set FS1 and fuzzy set FS2:

[0151] The weighted average method can be a way to consider the interaction between two fuzzy sets. In this method, we can weight the membership of z according to the membership of FS1 and FS2:

[0152]

[0153] Among them, μ z (z) is the membership function on the output variable z. z can be a variable representing the output result. It does not directly correspond to the output variable y1 or y2 of FS1 or FS2, but a new variable that reflects the comprehensive impact of the interaction between FS1 and FS2. w1 and w2 are the weight factors of fuzzy set FS1 and fuzzy set FS2, respectively. They can be formulated according to the specific situation and subjective initiative; if there is no special requirement, both values ​​can be assigned to 0.5.

[0154] 2.5 Step S5, defuzzification:

[0155] Based on the obtained output variables y1 and y2, they are converted into specific numerical values ​​through the Center of Gravity (COG) method to obtain the specific numerical values ​​of the adjustment amount A' of the acetone mass ratio and the adjustment amount D' of the diethylenetriamine mass ratio.

[0156] 1) If the interaction is not considered in step S4:

[0157] 1.1) The specific value of the adjustment amount A' of the acetone mass ratio can be calculated by the following formula:

[0158]

[0159] 1.2) The specific value of the adjustment amount D' of the mass ratio of diethylenetriamine can be calculated by the following formula:

[0160]

[0161] In the above 1.1 to 1.2, μ fs1 (y1) is the membership function of the fuzzy set FS1 on the output variable y1 (the adjustment amount of the acetone mass ratio). fs2 (y2) is the membership function of the fuzzy set FS2 on the output variable y2 (the adjustment amount of the mass ratio of diethylenetriamine). The integrals of the two numerator parts represent the weighted integrals of the membership function and the output value, which are used to calculate the center of the output value. The integrals of the two denominators are used for normalization to ensure that the obtained center value is a reasonable output value.

[0162] If the membership functions are discrete, then these integrals can be approximated by summing them.

[0163] 2) If the interaction is considered in step S4 (ie, step S402 is used):

[0164] The specific values ​​of the adjustment amount A' of the acetone mass ratio and the adjustment amount D' of the diethylenetriamine mass ratio are obtained by converting them into specific values ​​through the Center of Gravity (COG) method:

[0165] 2.1) Adjustment amount A' for acetone mass ratio:

[0166]

[0167] 2.2) Adjustment amount D' for the mass ratio of diethylenetriamine:

[0168]

[0169] in, and is the partial derivative of the variable z with respect to y1 and y2, which is used to map the defuzzified result in z space back to the original y1 and y2 space.

[0170] 2.6 Step S6, implementation of adjustments:

[0171] According to the adjustment amount A' of the acetone mass ratio and the adjustment amount D' of the diethylenetriamine mass ratio obtained in step S5, the mass ratio of acetone to diethylenetriamine is adjusted, and a new mixed solution is prepared for use in the current production batch of screws.

[0172] 2.7 Step S7, preparing the finished product:

[0173] The prepared new mixed solution is used to impregnate, cure and cross-link the screw blank, and the thread is processed using a diamond grinding wheel to obtain a semi-finished screw. The semi-finished screw is then further densified and subjected to an anti-oxidation treatment to obtain a C / SiC composite screw with a functional layer.

[0174] In summary, the technical scheme of steps S1 to S7 shows that the core idea of ​​the fuzzy control algorithm used in the present invention is to use fuzzy sets and fuzzy rules to deal with uncertainty and nonlinear problems. It fuzzifies the input variables, performs fuzzy reasoning and defuzzification processes through fuzzy rules, and obtains a specific control strategy. The fuzzy control algorithm provided by the present invention can predict and adjust the mass ratio of acetone and diethylenetriamine so that the thermal expansion coefficient of the mixed solution is as consistent as possible with the thermal expansion coefficient of the screw.

[0175] Second aspect, a preparation device for non-standard screws with functional layers:

[0176] The invention comprises an immersion tank 1 for immersing a screw blank with a thread modification solution, and further comprises:

[0177] A controller 6, a memory connected to the controller 6, wherein program instructions are stored in the memory, and when the program instructions are executed by the controller 6, a specific value of the heat ratio of the thread modification solution as described above is calculated;

[0178] three groups of second containers 4 for storing epoxy resin, acetone and diethylenetriamine respectively, wherein the second containers 4 are connected to the first container 2;

[0179] Based on the specific values ​​of the heat ratio of the thread modification solution, each of the second containers 4 introduces epoxy resin, acetone and diethylenetriamine into the first container 2 according to the corresponding ratio, and prepares the new mixed solution;

[0180] The first container 2 can introduce the new mixed solution into the immersion tank 1 .

[0181] In one embodiment, each of the second containers 4 is connected to the first container 2 via a respective first solenoid valve 7 , and the first container 2 is connected to the second container 4 via a second solenoid valve 8 ; and the controller 6 controls the first solenoid valve 7 and the second solenoid valve 8 .

[0182] In one embodiment, the first container 2 is equipped with a stirring device 3 for mixing epoxy resin, acetone and diethylenetriamine.

[0183] In one embodiment, a sensor group 5 for detecting the ambient temperature and the ambient humidity is provided in the environment.

[0184] Compared with the prior art, the present invention has the following beneficial effects:

[0185] 1. Dual thermal expansion coefficient matching mechanism: The present invention uses a fuzzy control algorithm to accurately adjust the proportion of each component in the mixed solution, so that the thermal expansion coefficient of the thread-modified solution can match the thermal expansion coefficient of the screw matrix. This matching can greatly reduce the interfacial stress caused by temperature changes, thereby improving the bonding strength and durability between the functional layer and the matrix. At the same time, in the application environment of rapid cooling and heating, the traditional functional layer is prone to fall off due to thermal stress. Through the matching of thermal expansion coefficients, the technical solution of the present invention effectively reduces this thermal stress, so that the functional layer can still maintain good adhesion under extreme temperature changes, greatly improving the durability of non-standard screws.

[0186] 2. Optimize production efficiency and cost: The present invention uses a fuzzy control algorithm to accurately adjust the ratio of the mixed solution, which can reduce the number of adjustments, thereby improving production efficiency and effectively controlling production costs. The fuzzy control algorithm of the present invention can dynamically adjust the ratio of the mixed solution according to actual conditions under consideration of multiple factors, making the entire production process more stable and controllable. Even when external pressure, temperature and other factors change, the algorithm of the present invention can respond accordingly to maintain the stability of the production process.

[0187] 3. Improved adhesion and durability of the functional layer: Matching of thermal expansion coefficients helps reduce thermal stress between the functional layer and the substrate, thereby improving adhesion and durability of the functional layer. That is, even in an environment with large temperature changes, the functional layer of the non-standard screws produced by the technical solution of the present invention is less likely to crack or fall off than the non-standard screws produced by traditional technology, thereby extending the service life. And because the risk of functional layer falling off and cracking is reduced, the reliability of the product is significantly improved.

[0188] 4. Simplified manufacturing process: Compared with the need to introduce fine and short fiber filaments and the complexity of the one-piece molding process in traditional technology, the technical solution of the present invention achieves the matching of thermal expansion coefficients through intelligent and dynamic adjustment of the proportion of the mixed solution. It not only simplifies the manufacturing process, but also reduces the difficulty of operation and improves production efficiency. At the same time, the introduction of fine and short fiber filaments in traditional technology may increase the risk of poor interface bonding. The solution of the present invention significantly reduces the stress difference between the functional layer and the substrate by matching the thermal expansion coefficients, reduces the risk of poor interface bonding from the root, and enhances the bonding strength between the functional layer and the substrate.

[0189] 5. Optimization of green and low-carbon preparation process: The technical solution of the present invention fundamentally avoids the introduction of redundant media such as fine and short fibers in traditional technologies, which not only reduces the cost of raw materials and production costs, but also reduces waste and environmental pollution in the production process, which is in line with modern green and environmentally friendly production concepts. BRIEF DESCRIPTION OF THE DRAWINGS

[0190] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0191] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0192] Figure 2 It is a three-dimensional schematic diagram of the device assembly of the present invention from one perspective;

[0193] Figure 3 It is a three-dimensional schematic diagram of the device assembly of the present invention from another perspective;

[0194] Figure 4 It is a three-dimensional schematic diagram of the cooperation between the first container and the second container in the device of the present invention;

[0195] Figure 5 A schematic diagram of the coordinated use of the method and device of the present invention;

[0196] Figure 6 A schematic diagram of a method flow of an additional technical solution of Embodiment 4 of the present invention;

[0197] Figure 7 It is a line graph shown in the test example of the present invention.

[0198] Description of the drawings: 1. Immersion tank; 2. First container; 3. Stirring device; 301. Motor; 302. Stirring blade; 4. Second container; 5. Sensor group; 6. Controller; 7. First solenoid valve; 8. Second solenoid valve. DETAILED DESCRIPTION

[0199] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below;

[0200] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0201] Example 1: Please refer to Figure 1 and Figure 5 , this embodiment discloses a method for preparing a non-standard screw with a functional layer; in a production line, when a thread modification solution (a mixed solution composed of epoxy resin, acetone, and diethylenetriamine, with a mass ratio of 1:1 to 2:0.2 to 0.8) is used to impregnate, cure, and cross-link a C / SiC screw blank, a plurality of screw blanks are randomly selected as samples according to the production batch (for example, one sample is selected from every production batch of 100 screws to reflect the process performance of the entire batch, and a total of 5 samples are selected so that each production batch can be compared with each other, or a single batch and a single sample are directly measured), and then the following steps S1 to S7 are performed.

[0202] In this embodiment, regarding step S1, the input and output variables are obtained: the input and output variables are physical parameters that affect the thermal expansion characteristics of the screw and the thermal expansion characteristics of the thread modification solution;

[0203] Specifically, the input variables include:

[0204] 1) The thermal expansion coefficient SC of the known and fixed screw, i.e. the thermal expansion coefficient of the C / SiC material;

[0205] 2) The coefficient of thermal expansion C of the thread modification solution on each sample i; this can be quickly calculated using either a dilatometer or an exothermic method; knowing this coefficient helps us adjust the solution formulation to match the thermal expansion coefficient of the screw substrate, thereby reducing thermal stress.

[0206] 3) The current mass ratio A of acetone; the value can be directly obtained based on the liquid feeding system;

[0207] 4) The current mass ratio D of diethylenetriamine; the value can be directly obtained based on the liquid feeding system;

[0208] Mass ratio A and mass ratio D are the key component ratios in the thread modification solution, which directly affect the physical and chemical properties of the solution, including the thermal expansion coefficient. Through the liquid feeding system, we can directly obtain the real-time mass ratio of these two components, providing a basis for subsequent adjustments.

[0209] 5) Current temperature T: The temperature sensor based on the current production environment can directly obtain the value;

[0210] 6) Current humidity H: The humidity sensor based on the current production environment can directly obtain the value;

[0211] Specifically, the output variables include:

[0212] 1) Acetone mass ratio adjustment amount A': Based on the real-time monitoring value of the input variable and the preset target performance parameters (such as the matching degree of the thermal expansion coefficient), the amount of acetone mass ratio that needs to be adjusted is calculated through the fuzzy control algorithm. This adjustment amount is to make the thermal expansion coefficient of the thread modification solution closer to the thermal expansion coefficient of the screw matrix.

[0213] 2) Adjustment of the mass ratio of diethylenetriamine D'; Similar to the adjustment of the mass ratio of acetone, this output variable is also calculated by the fuzzy control algorithm based on the real-time monitored input variables and the preset target. Adjusting the mass ratio of diethylenetriamine can further fine-tune the performance of the thread modification solution to achieve the best thermal expansion coefficient match.

[0214] In this embodiment, regarding step S2, fuzzifying input variables: converting input variables into fuzzy sets. The fuzzy sets include: a fuzzy set F1 of thermal expansion coefficient difference, a fuzzy set F2 of temperature difference, a fuzzy set F3 of humidity difference, and a fuzzy set F4 of acetone and diethylenetriamine mass ratio difference. Step S2 specifically includes steps S200 to S203.

[0215] It should be pointed out that fuzzy sets are described by membership functions, which indicate the degree to which an element belongs to a certain set.

[0216] Specifically, step S200, the fuzzy set F1 of thermal expansion coefficient difference: the difference in thermal expansion coefficient is divided into a fuzzy set of negative large (NB), negative small (NS), zero (ZE), positive small (PS) and positive large (PB), and the fuzzy set F1 of thermal expansion coefficient difference includes the following five bell-shaped functions:

[0217] 1) Zero (ZE):

[0218]

[0219] Among them, m ZE is the center point of the membership function, representing the typical value of "zero" difference; s ZE is the standard deviation, which determines the width of the membership function.

[0220] It should be noted that this function describes the situation when the difference in thermal expansion coefficients is close to zero. The typical value of "zero" difference, that is, the situation where the thermal expansion coefficients match perfectly. Standard deviation s ZE Determines the width of this membership function, that is, the range within which the difference can be considered "zero". Following the same logic, continue to construct the following four bell-shaped functions:

[0221] 2) Negative big (NB):

[0222]

[0223] Among them, c NB is a negative number, representing the center point of the negative large, s NB is the standard deviation.

[0224] It should be noted that this function is used to describe the situation where the difference in thermal expansion coefficient is significantly small. NB is a negative number, indicating large negative differences. NB Determines the shape and range of this membership function.

[0225] 3) Negative Small (NS):

[0226]

[0227] Among them, m NS is the center point with the smallest negative value, and m NS <0, but higher than c NB Closer to 0, s NS is the standard deviation.

[0228] It should be noted that this function describes the case where the difference in thermal expansion coefficient is slightly smaller. NS It is a typical value of small negative value, which is closer to 0 than cNB, but still negative. NS Defines the width of this difference range.

[0229] 4) Positive Small (PS):

[0230]

[0231] Among them, m PS is a positive small center point, and m PS >0,s PS is the standard deviation.

[0232] It should be noted that this function is used to describe the situation where the difference in thermal expansion coefficient is slightly larger. The center point (mPS) is a typical value with a small positive value, indicating a slightly larger positive difference. Standard deviation s PS Determines the shape of this membership function.

[0233] 5) Zhengda (PB):

[0234]

[0235] Among them, c PB is a positive number, representing the center point of the positive center, sPB is the standard deviation.

[0236] It should be noted that this function describes the situation where the difference in thermal expansion coefficient is significantly large. The center point (cPB) is a positive number, indicating a large positive difference. Standard deviation s PB Defines the width of this difference range.

[0237] It can be understood that in the above S200, through these bell-shaped functions, we can map the differences in thermal expansion coefficients to the corresponding fuzzy subsets. The advantage of this is that the control system can make reasonable decisions and adjustments even in the face of imprecise or ambiguous input information. This fuzzy control method improves the flexibility and adaptability of the preparation process, making the preparation of the functional layer of non-standard screws more intelligent and precise. That is, by constructing a fuzzy set F1 of thermal expansion coefficient differences, we can more accurately describe and control the degree of thermal expansion coefficient matching between the screw matrix and the thread modification solution. This helps to reduce the interfacial stress caused by the mismatch of thermal expansion coefficients, thereby improving the bonding strength and durability between the functional layer and the matrix.

[0238] Specifically, in step S201, the fuzzy set F2 of temperature difference: the temperature difference is divided into the fuzzy sets of low temperature (LT), medium temperature (MT) and high temperature (HT); the fuzzy set F2 of temperature difference includes the following three bell-shaped functions:

[0239] 1) Medium temperature (MT):

[0240]

[0241] Among them, m MT is the center point of medium temperature, s MT is the standard deviation.

[0242] It should be noted that this function describes the situation when the temperature is in a moderate range. The center point m of the function MT Indicates typical values ​​for moderate temperatures. Standard deviation s MT Determines the width of this membership function, that is, the range of temperature that can be considered "medium temperature". Following the same logic, continue to construct the following two bell-shaped functions:

[0243] 2) Low Temperature (LT):

[0244]

[0245] Among them, m LT is the center point of low temperature, representing a lower temperature value, s LT is the standard deviation.

[0246] It should be pointed out that this function is used to describe the situation where the temperature is low. LT It is a representative value of low temperature, indicating a typical situation of low temperature. Standard deviation s LT The shape and range of this membership function are determined, reflecting the extent to which the temperature deviates from the moderate range to be considered "low temperature".

[0247] 3) High temperature (HT):

[0248]

[0249] Among them, m HT It is the center point of high temperature, representing a higher temperature value, s HT is the standard deviation.

[0250] It should be pointed out that this function describes the situation where the temperature is high. HT It is a representative value of high temperature, indicating a typical situation where the temperature is high. Standard deviation s HT The width of this temperature range is defined, i.e. how much above the moderate value a temperature is considered “hot”.

[0251] It can be understood that in the above step S201, through these bell-shaped functions, we can map the actual temperature value to the corresponding fuzzy subset. The advantage of this fuzzy control method is that it allows the system to make flexible adjustments under imprecise or changing temperature conditions. Because temperature is one of the key factors affecting material properties and coating quality, this is particularly important for the functional layer preparation process of non-standard screws. That is, by constructing the fuzzy set F2 of temperature differences, we can more accurately monitor and control the temperature conditions during the preparation process. This helps to ensure that the preparation process is carried out within the optimal temperature range, thereby improving the quality and performance of the functional layer. At the same time, the introduction of the fuzzy control algorithm also enhances the robustness and adaptability of the system, enabling the preparation process to better cope with various uncertainties and interference factors.

[0252] Specifically, step S202, the fuzzy set F3 of humidity difference: the humidity difference is divided into the fuzzy sets of low humidity (LH), medium humidity (MH) and high humidity (HH); the fuzzy set F3 of humidity difference includes the following three bell-shaped functions:

[0253] 1) Take medium humidity (MH) as an example:

[0254]

[0255] Among them, m MH is the center point of medium humidity, s MH is the standard deviation.

[0256] It should be noted that this function describes the situation when the humidity is in a moderate range. The center point m of the function MH Indicates the typical value of moderate humidity. Standard deviation s MH This determines the width of the membership function, that is, the range of humidity that can be considered "moderately humid". Following the same logic, continue to construct the following two bell-shaped functions:

[0257] 2) Low humidity (LH):

[0258]

[0259] Among them, m LH It is the center point of low humidity, representing a lower humidity value, s LH is the standard deviation.

[0260] It should be pointed out that this function is used to describe the situation of low humidity. LH It is a representative value of low humidity, indicating a typical situation with low humidity. Standard deviation s LH The shape and range of this membership function are determined, reflecting the extent to which humidity deviates from the moderate range and is considered "low humidity".

[0261] 3) High humidity (HH):

[0262]

[0263] Among them, m HH It is the center point of high humidity, representing a higher humidity value, s HH is the standard deviation.

[0264] It should be pointed out that this function describes the situation of high humidity. HH It is a representative value of high humidity, indicating a typical situation of high humidity. Standard deviation s HH The width of this humidity range is defined as how high above moderate humidity is considered “high humidity”.

[0265] It can be understood that in the above step S202, through these bell-shaped functions, we can determine the fuzzy subset to which the humidity value actually measured belongs. This fuzzy logic processing method allows the system to make reasonable adjustments and optimizations when the humidity conditions do not fully meet the standards. That is, by constructing the fuzzy set F3 of humidity differences, we can more accurately monitor and control the humidity conditions in the preparation process. It helps to ensure that the preparation environment is within the optimal humidity range, thereby improving the quality and stability of the functional layer. At the same time, the introduction of the fuzzy control method enhances the flexibility and fault tolerance of the preparation process, allowing the system to better adapt to the preparation requirements under different humidity conditions.

[0266] It is understandable that in the above steps S200 to S202, since the present embodiment adopts a fuzzy control algorithm, the corresponding parameter interval needs to be fuzzy processed. Take the "medium humidity", "low humidity" and "high humidity" of step S202 as an example: specific numerical ranges can be set for characteristics such as "medium humidity", "low humidity" and "high humidity" according to historical data, industry standards or the needs of actual production scenarios. "Medium humidity" can be defined within a moderate humidity range, while "low humidity" and "high humidity" are defined in areas below and above this range, respectively.

[0267] Specifically, step S203, fuzzy set F4 of the difference in mass ratio of acetone to diethylenetriamine: the difference in mass ratio of acetone to diethylenetriamine is divided into low, medium and high; that is, the difference in mass ratio of acetone to diethylenetriamine includes a fuzzy set of "medium" mass ratio of acetone, "low" mass ratio of acetone, "high" mass ratio of acetone, "medium" mass ratio of diethylenetriamine, "low" mass ratio of diethylenetriamine and "high" mass ratio of diethylenetriamine;

[0268] The fuzzy set F4 of the difference in mass ratio of acetone and diethylenetriamine includes the following six bell-shaped functions:

[0269] 1) "Medium" acetone mass ratio:

[0270]

[0271] Among them, m A,Medium is the center point of the acetone mass ratio "medium", which is a value between 1 and 2; s A,Medium is the standard deviation.

[0272] It should be pointed out that this function describes the situation when the acetone mass ratio is at a moderate level. A,Medium Represents a moderate acetone mass ratio, between 1 and 2. Standard deviation s A,Medium determines the width of this membership function, reflecting the range of acetone mass ratio that can be considered "medium".

[0273] 2) Acetone mass ratio is "low":

[0274]

[0275] Among them, m A,Low is the center point of the "low" acetone mass ratio, which is near 1 but less than 1, s A,Low is the standard deviation.

[0276] 3) Acetone mass ratio is "high":

[0277]

[0278] Among them, mA,High is the center point of the acetone mass ratio "high", a value between 1 and 2 but greater than 1, s A,High is the standard deviation.

[0279] It should be noted that in the above acetone mass ratio "low" and "high", these two functions are used to describe the situation where the acetone mass ratio is low and high, respectively. A,Low and m A,High Represent the typical values ​​of low and high acetone mass ratios, respectively. Standard deviation s A,Low and A,High Determines the shape and range of these two membership functions.

[0280] 4) "Medium" mass ratio of diethylenetriamine:

[0281]

[0282] Among them, m D,Medium is the center point of the "medium" mass ratio of diethylenetriamine, which is a value between 0.2 and 0.8; s D,Medium is the standard deviation.

[0283] 5) Diethylenetriamine mass ratio is "low":

[0284]

[0285] Among them, m D,Low It is the center point of the "low" mass ratio of diethylenetriamine, which is located near 0.2 but slightly larger than 0.2. D,Low is the standard deviation.

[0286] 6) Diethylenetriamine mass ratio "high":

[0287]

[0288] Among them, m D,High is the center point of the "high" mass ratio of diethylenetriamine, which lies between 0.2 and 0.8 but less than 0.8, s D,High is the standard deviation.

[0289] It should be noted that in the above-mentioned diethylenetriamine mass ratio of "medium", "low" and "high", these three functions are similar to the function of acetone mass ratio, but are for diethylenetriamine. Center point m D,Medium 、m D,Low and m D,High Corresponding to the different levels of mass ratio of diethylenetriamine. Standard deviation s D,Medium 、s D,Low and D,High Defines the width of each membership function.

[0290] It can be understood that in the above step S203, these bell-shaped functions can be used to determine the fuzzy subsets to which the acetone and diethylenetriamine mass ratios are actually measured. This method allows us to fine-tune the preparation process to achieve the best mass ratio when the mass ratio is not completely as expected. That is, by constructing the fuzzy set F4 of the difference in the mass ratio of acetone and diethylenetriamine, we can more accurately control the ratio of these two key chemicals. This helps to improve the stability and performance of the functional layer and ensure the repeatability and consistency of the preparation process. At the same time, the flexibility of the fuzzy control method enables the system to be optimized and adjusted under different mass ratio conditions to adapt to various preparation requirements.

[0291] It can be understood that in the above step S203, since the mass ratio of acetone and the mass ratio of diethylenetriamine have clear experimental data indicators (as described at the beginning of this embodiment); therefore, in step S203, it is necessary to clearly assign values ​​for a total of six types of "medium", "low" and "high".

[0292] Preferably, "middle" is located at the midpoint of the data interval, and "low" and "high" are the minimum and maximum values ​​of the data interval.

[0293] It is understandable that in steps S200 to S203, the concept of fuzzy sets is introduced to more accurately describe the fuzziness of these characteristics. Fuzzy subsets are defined by constructing a bell-shaped function (Gaussian function). The center point of the function represents the typical value of the characteristic, and the standard deviation determines the width of the membership function, that is, the range within which the characteristic can be regarded as belonging to a fuzzy subset. Furthermore, this embodiment adopts the form of a bell-shaped function, so that the center point of the bell-shaped function represents the typical value of the characteristic, so that the system can more accurately identify and control the target values ​​of key parameters (such as temperature, humidity, chemical quality ratio, etc.). Moreover, the standard deviation determines the width of the membership function, that is, the range within which the characteristic can be regarded as belonging to a fuzzy subset. This flexibility allows the system to maintain stable control when the parameters fluctuate slightly, without overly strict threshold settings, so as to better adapt to the uncertainty in the actual production process. At the same time, since the definition of the fuzzy subset has a certain range, the system has better fault tolerance for measurement errors or small operational errors. The risk of preparation failure caused by minor deviations is reduced, and the fault tolerance rate is also optimized.

[0294] It is understandable that through fuzzy logic, the system can dynamically adjust the control strategy according to real-time data. For example, when the humidity or chemical quality ratio deviates from the ideal value, the system can smoothly adjust the control parameters according to the membership function of the fuzzy set to achieve more optimized preparation conditions.

[0295] Furthermore, the embedded C language program of the above steps S200 to S203 is as follows:

[0296]

[0297]

[0298]

[0299] In the above program, two structures BellFunctionParams and FuzzySetS4 are used to store the parameters of the bell function and the parameters of the entire fuzzy set F4. The initializeFuzzySetS4 function is used to initialize the parameters of the fuzzy set F4. The bellFunction function is used to calculate the membership of a given input value in a bell function. The getFuzzySetS4 function encapsulates the logic of S200~S203. It accepts a pointer to the FuzzySetS4 structure and initializes the parameters of the structure. At the same time, the program only shows step S203, but the construction logic of steps S200~S202 is the same as step S203, so in order to simplify the space, it will not be shown in detail here.

[0300] In this embodiment, regarding step S3, a fuzzy rule is established: the fuzzy rule describes how to adjust the mass ratio of acetone and diethylenetriamine when the input variable is in a certain fuzzy set; the fuzzy rule of step S3 includes the following three rules and their corresponding fuzzy controller logic expressions:

[0301] Specifically, Rule 1: When the thermal expansion coefficient difference (ΔC) is positive, it means that the thermal expansion coefficient of the current mixed solution is higher than the ideal value. In order to reduce the thermal expansion coefficient, the system should increase the mass ratio of acetone (because acetone has a lower thermal expansion coefficient) and reduce the mass ratio of diethylenetriamine. This adjustment is made by the fuzzy logic controller according to the membership degree μ of ΔC. PB (ΔC) to achieve:

[0302] ΔA=k1·μ PB (ΔC);

[0303] ΔD=-k2·μ PB (ΔC);

[0304] Wherein, ΔA and ΔD are the adjustment amounts of the mass ratio of acetone and diethylenetriamine, respectively, and k1 and k2 are positive proportional coefficients.

[0305] Specifically, Rule 2: Contrary to Rule 1, if the difference in thermal expansion coefficient is "negative", it is necessary to reduce the mass ratio of acetone and increase the mass ratio of diethylenetriamine to increase the thermal expansion coefficient of the mixed solution. This adjustment is also based on the membership degree μ of ΔC NB (ΔC) to conduct:

[0306] ΔA=-k3·μ NB (ΔC);

[0307] ΔD=k4·μ NB (ΔC);

[0308] Among them, k3 and k4 are positive proportional coefficients.

[0309] Specifically, Rule 3: If sample i of the previous production batch -1 After the adjustment in step S6, if the difference in thermal expansion coefficients is inconsistent with rule 1 or rule 2, it is considered that the current temperature or humidity has exceeded a certain threshold, causing the thermal expansion coefficient C of the thread-modified solution to change beyond the expectations of rules 1 and 2, and the following rules are executed:

[0310] Rule 3.1) In a high temperature (HT) environment, considering the possibility of solution expansion, the mass ratio of acetone needs to be reduced to balance the thermal expansion coefficient; while in a low temperature (LT) environment, the mass ratio of acetone needs to be increased. This adjustment is based on the temperature membership μ HT (T) or μ LT (T) to:

[0311] If μ HT (T) is high (i.e., the temperature is high), then adjust the mass ratio of acetone:

[0312] ΔA=-k5·μ HT (T);

[0313] Among them, k5 is a positive proportionality coefficient.

[0314] If it is low temperature (μ LT (T) is high), then adjust the mass ratio of acetone:

[0315] ΔA=k6·μ LT (T);

[0316] Among them, k6 is a positive proportionality coefficient.

[0317] Rule 3.2) In a high humidity (HH) environment, the mass ratio of diethylenetriamine may need to be increased to cope with the effect of humidity on the solution curing process; in a low humidity (LH) environment, its mass ratio needs to be reduced. This adjustment is based on the humidity membership μ HH (H) or μ LH (H) to:

[0318] If μ HH (H) is high (i.e., the humidity is high), then adjust the mass ratio of diethylenetriamine:

[0319] ΔD=k7·μ HH(H);

[0320] Among them, k7 is a positive proportionality coefficient.

[0321] If it is low humidity (μ LH (H) is high), then adjust the mass ratio of diethylenetriamine:

[0322] ΔD=-k8·μ LH (H);

[0323] Among them, k8 is a positive proportional coefficient.

[0324] It is understandable that through fuzzy logic control, the system can flexibly adjust the mass ratio of acetone and diethylenetriamine according to actual conditions, so as to better adapt to different environmental conditions and material properties. And by dynamically adjusting the solution ratio, the thermal expansion coefficient of the functional layer can be more effectively controlled, thereby improving the performance and stability of non-standard screws. Moreover, fuzzy logic control has a certain tolerance to measurement errors and environmental changes, making the preparation process more robust and reliable. At the same time, by introducing a fuzzy logic controller, the preparation process can be automated and intelligently controlled, reducing manual intervention and improving production efficiency.

[0325] It should be noted that positive proportional coefficients k1-k8 are used to adjust the influence of input variables (such as thermal expansion coefficient difference, temperature, humidity, etc.) on output variables (such as the adjustment amount of acetone and diethylenetriamine mass ratio). These proportional coefficients actually represent the weights of different input variables on output variables. By adjusting these proportional coefficients, accurate control of the system can be achieved. For example, if it is desired that the thermal expansion coefficient difference has a greater influence on the adjustment amount of acetone mass ratio, the values ​​of k1 and k2 can be increased. However, its specific assignment needs to be subjectively assigned by the staff in the form of iteration during the execution of several rounds of the algorithm provided in this embodiment, and other optimization algorithms (such as genetic algorithm, particle swarm optimization, etc.) can also be introduced to automatically adjust the proportional coefficient.

[0326] Furthermore, in the above rules 1 to 3, the embedded C language program is as follows:

[0327]

[0328]

[0329]

[0330]

[0331] In the above program, the calculate_adjustments function receives the difference in thermal expansion coefficient (delta_C), temperature (T), and humidity (H) as input, and calculates the adjustment amount (delta_A and delta_D) of the mass ratio of acetone and diethylenetriamine. The function uses a hypothetical membership function to simulate the membership calculation in fuzzy logic and adjusts the mass ratio according to rules 1 to 3.

[0332] In this embodiment, regarding step S4, fuzzy reasoning and decision making: according to the established fuzzy rules and the fuzzified values ​​of the input variables, the fuzzy rules are applied using the "min-max" synthesis method, and then the Mamdani fuzzy reasoning method is used to perform fuzzy reasoning to obtain the output variable y, corresponding to the fuzzy set FS1 of the acetone mass ratio adjustment amount and the fuzzy set FS2 of the diethylenetriamine mass ratio adjustment amount. Step S4 specifically includes the following two sub-steps S400-S401.

[0333] Specifically, step S400, applies fuzzy rules through the "min-max" synthesis method: In the fuzzy logic system, the fuzzy rules of step S3 describe the relationship between input and output. Each fuzzy rule has a premise part and a conclusion part. The premise part describes the conditions that the input variables need to meet, and the conclusion part gives the fuzzy set of output variables when these conditions are met. The "min-max" synthesis method is a method for determining the degree of activation of fuzzy rules. The basic principle is that for a given input and fuzzy rule, the degree of match between the premise part of the rule and the input is determined by calculating the minimum value of the fuzzy set membership. This matching degree is the degree of activation of the rule, which can also be understood as the weight of the rule.

[0334] Furthermore, for each application of a fuzzy rule, the "min-max" synthesis method is used to calculate the activation degree of the rule (i.e., the strength of the rule, i.e., a kind of weight); that is, for a given input and fuzzy rule, the matching degree between the premise part of the rule and the input is calculated by taking the minimum value of the fuzzy set membership. That is, suppose there are n input variables x: x1, x2, ..., x n ; Each input variable x corresponds to a fuzzy set membership function μ Ai (x i ), where A i is the fuzzy set corresponding to the i-th input variable, i = 1, 2, ..., n. At the same time, there are m fuzzy rules, each of which is in the form of "if x1 is A1 and x2 is A2 and ... and x n Yes A n , then the output y is B", where B is the fuzzy set of output variables. Then for the jth rule (j = 1, 2, ..., m), its activation degree D k The expression is:

[0335] D k =min(μ A1 (x1),μ A2 (x2),...,μ An (x n ));

[0336] Among them, k expresses the specific degree level.

[0337] It can be understood that by applying fuzzy rules through the "min-max" synthesis method, the degree of activation of each rule can be determined more accurately. This accuracy helps the system to more accurately adjust the fuzzy set of output variables according to the input variables, thereby improving the accuracy of the preparation process. And because of the flexibility of fuzzy rules, the system can adapt to different input situations and adjust the output according to actual conditions. This flexibility enables the preparation process to better cope with various complex and changing environmental conditions. And as described in the bell-shaped function characteristics above, because the fuzzy logic system is insensitive to small changes in the input variables, it has good robustness. By applying fuzzy rules in conjunction with the "min-max" synthesis method, the robustness of the system can be further enhanced and the impact of external interference on the preparation process can be reduced.

[0338] Exemplarily, for the rule in step S3 "if the thermal expansion coefficient difference is PB, increase the mass ratio of acetone", in order to calculate the activation degree of this rule, two factors need to be considered: the matching degree of the current thermal expansion coefficient difference value with PB, and (if necessary) the matching degree of the current value of the acetone mass ratio with the fuzzy set A.

[0339] Then, by calculating the membership degree μ of the current thermal expansion coefficient difference ΔC to the PB fuzzy set PB (ΔC) is used to evaluate the matching degree. The higher the membership degree, the more the current thermal expansion coefficient difference conforms to the definition of PB, that is, the more the premise of the rule matches the actual situation. In layman's terms, the minimum membership degree of the difference between PB and the current thermal expansion coefficient is calculated as the activation degree of this rule:

[0340] D=min(μ PB (ΔC),μ A (A));

[0341] Where D is the degree of specific rule activation, μ PB (ΔC) is the degree of membership of the difference in thermal expansion coefficient to PB, μ A (A) is the degree of membership of the current value of the acetone mass ratio (if necessary).

[0342] This expression means that the degree of activation of the rule is determined by the factor that has the least match. In other words, even if the difference in thermal expansion coefficients is very well matched to PB (i.e. μ PB (ΔC) is high), if the current value of the acetone mass ratio matches its fuzzy set A poorly (i.e. μ PB (ΔC) is very low), then the activation level of the rule will also be limited. This calculation method ensures that the rule will be highly activated only when all relevant factors reach a certain matching level. This helps the system remain robust when dealing with complex and changing situations, and avoids the whole system from losing control due to the abnormality of a single factor.

[0343] Therefore, it can be understood that, as shown in the example above, the fuzzy logic control method provided in this embodiment is particularly suitable for systems that are difficult to establish accurate mathematical models for the functional layer preparation of non-standard screws. Through fuzzy control, a more flexible and intelligent control strategy can be achieved, which is also the characteristic of the fuzzy control algorithm.

[0344] Specifically, in step S401, fuzzy reasoning is performed using the Mamdani fuzzy reasoning method: based on the activation degree D of the rule k , the membership of the output fuzzy set is calculated using the Mamdani fuzzy reasoning method. The Mamdani fuzzy reasoning method is a rule-based reasoning method that uses fuzzy sets and fuzzy logic to simulate the human reasoning process. In this method, the activation degree (i.e., weight) of the rule and the conclusion part of the rule are combined to calculate the membership of the output fuzzy set.

[0345] That is, first, for each rule provided in step S3, according to the conclusion part and the activation degree D of the rule, the fuzzy set of the activation degree and the conclusion part is subjected to the "maximum" operation, and the membership degree of the output fuzzy set is calculated:

[0346]

[0347] Among them, μ Output (y) is the membership of the output fuzzy set, m is the total number of rules, and each rule has a fuzzy set C of the conclusion part. j , and its corresponding membership function is μ Cj (y), where y is the corresponding output variable.

[0348] This formula indicates that the degree to which the output variable y belongs to the final output fuzzy set is the maximum value of the degree to which y under all rules belongs to the respective conclusion part fuzzy sets.

[0349] If the output variable y corresponds to the adjustment amount A' of the acetone mass ratio, i.e. y1, then the membership degree μ of the output fuzzy set is calculated. Output(y1) = FS1;

[0350] If the output variable y corresponds to the adjustment amount D' of the mass ratio of diethylenetriamine, that is, y2, then the membership degree μ of the output fuzzy set is calculated. Output (y2) = FS2;

[0351] It should be noted that in step S401, the “conclusion part” and the “fuzzy set C of the conclusion part” j " refers to the output result or action part in the fuzzy rule, which describes the action that the system should take or the conclusion it should draw when the premise part of the rule is satisfied (that is, the input matches the fuzzy condition of the rule). This conclusion is usually not a definite value, but a fuzzy set, which indicates the range or degree of possible output results (the specific value needs to be realized through subsequent steps). For example, in the rule "If the difference in thermal expansion coefficient is large, increase the mass ratio of acetone", "increase the mass ratio of acetone" is the conclusion part. The associated fuzzy set C j It is "a small increase in the mass ratio of acetone", moderate or "a large increase in the mass ratio of acetone".

[0352] It can be understood that through the Mamdani fuzzy reasoning method, the system can flexibly reason based on the input fuzzy information and the activated rules to obtain outputs that adapt to the current situation. This flexibility enables the system to better cope with complex and changing environmental conditions. And because the fuzzy reasoning method allows a certain degree of fuzziness in input and output, the system is insensitive to small changes in input data. This robustness allows the system to maintain good performance when processing imprecise or noisy data. And by calculating the membership of the output fuzzy set, the system can accurately determine the adjustment amount of the mass ratio of acetone and diethylenetriamine. This accuracy helps to optimize the adjustment strategy of the preparation process, thereby improving product quality and production efficiency.

[0353] Furthermore, the embedded C language program of the above steps S400 to S401 is as follows, and the encapsulation functions provided in the above steps S2 and S3 need to be called:

[0354]

[0355]

[0356]

[0357]

[0358] In the above program, the membership function (bellFunction) is used to calculate the degree to which an element belongs to a fuzzy set (i.e., the membership). Then the input value (delta_C) is first fuzzified, that is, it is mapped to one or more fuzzy sets. The calculate_adjustments function defines a set of fuzzy rules, each of which has a premise part (i.e., the membership of the input value) and a conclusion part (i.e., the membership of the output action). In addition, the getFuzzySetS4 function in the program is used to initialize the parameters of the fuzzy set (i.e., the center point a and the width b), which usually need to be adjusted and optimized according to the actual application scenario. The calculate_adjustments function encapsulates the entire process of fuzzification, fuzzy reasoning, and defuzzification, and calculates the adjustment amount of the mass ratio of acetone and diethylenetriamine based on the input delta_C value.

[0359] In this embodiment, regarding step S5, defuzzification: after obtaining the output variables y1 (the adjustment amount of the acetone mass ratio) and y2 (the adjustment amount of the diethylenetriamine mass ratio) of the fuzzy logic system, we need to convert these fuzzy outputs into specific values ​​so as to make precise adjustments in actual production. The central average defuzzification method (COG) is used here.

[0360] Specifically, the specific value of the adjustment amount A' of the acetone mass ratio can be calculated by the following formula:

[0361]

[0362] Among them, μ fs1 (y1) is the membership function of the fuzzy set FS1 on the output variable y1. The numerator of this formula represents the membership function μ fs1 The weighted integral of (y1) and the output value y1 is used to calculate the "center of gravity" or center of the output value. The denominator is the integral of the membership function itself, which is used for normalization to ensure that the obtained A' is a reasonable output value.

[0363] Specifically, the specific value of the adjustment amount D' of the mass ratio of diethylenetriamine can be calculated by the following formula:

[0364]

[0365] Among them, μ fs2 (y2) is the membership function of the fuzzy set FS2 on the output variable y2. The structure of this formula is similar to the formula for calculating A', which also finds the center of the output value through weighted integration and normalization.

[0366] It can be understood that through the COG defuzzification method, we are able to convert the output of the fuzzy logic system into a specific numerical value. This method takes into account all possible output values ​​and their memberships to obtain a weighted average result, which is more accurate than a single membership maximum or minimum value. And because the COG method considers the contribution of all elements in the fuzzy set, it is less sensitive to noise and outliers. This makes the system more robust in the face of uncertainty and interference. In summary, through the central average defuzzification method (COG), we are able to effectively convert the output of fuzzy logic into a specific adjustment value, which is of great significance for guiding the intelligent preparation process of the functional layer of non-standard screws.

[0367] Furthermore, the embedded C language program of step S5 is as follows:

[0368]

[0369]

[0370] In the above program, the membership_y1 and membership_y2 arrays should be calculated by the fuzzy logic system based on the input variables. The centerOfGravityDefuzzification function implements the center average defuzzification algorithm. It traverses the membership array and calculates the weighted average, where the weight is the membership of the output value. This function returns the calculated center value. The centerOfGravityDefuzzification function is called twice in the main function to calculate the adjustment amount A_prime for the mass ratio of acetone and the adjustment amount D_prime for the mass ratio of diethylenetriamine respectively.

[0371] In this embodiment, with respect to step S6, an adjustment is implemented: the original mass ratio of acetone and diethylenetriamine is adjusted accordingly according to the adjustment amount A' of the mass ratio of acetone and the adjustment amount D' of the mass ratio of diethylenetriamine calculated in step S5. The purpose of this step is to optimize the composition of the mixed solution to ensure that its thermal expansion coefficient matches the thermal expansion coefficient of the screw material to be prepared. The process of adjusting the mass ratio involves reducing or increasing the amount of acetone or diethylenetriamine to achieve the required adjustment amounts A' and D'. After completing the adjustment, we prepare a new mixed solution, the composition ratio of which has been optimized and is more suitable for the preparation requirements of the current production batch of screws.

[0372] In this embodiment, regarding step S7, the finished product is prepared: first, the screw blank is impregnated with an optimized mixed solution so that the mixed solution can fully penetrate into the blank of the screw. Subsequently, curing and cross-linking treatments are performed to enhance the structural strength and stability of the material. Next, the cured material is threaded using a diamond grinding wheel to obtain a semi-finished screw. Due to its high hardness and wear resistance, the diamond grinding wheel can ensure precise processing and high-quality surface of the thread. Finally, the semi-finished screw is further densified and anti-oxidation treated. The densification process can further improve the density and mechanical properties of the material, while the anti-oxidation treatment can enhance the corrosion resistance of the screw and extend its service life.

[0373] In summary, the core idea of ​​the fuzzy control algorithm used in this embodiment is to use fuzzy sets and fuzzy rules to deal with uncertainty and nonlinear problems. In the process of preparing non-standard screws with functional layers, many factors such as the thermal expansion coefficient of the material and the composition ratio of the solution are nonlinear and difficult to quantify accurately. The fuzzy control algorithm fuzzifies the input variables, establishes a series of fuzzy rules for fuzzy reasoning, and derives a specific control strategy through the defuzzification process.

[0374] Specifically, in this embodiment, the fuzzy control algorithm is used to predict and adjust the mass ratio of acetone and diethylenetriamine. By continuously adjusting and optimizing the composition ratio of the mixed solution, the algorithm aims to make the thermal expansion coefficient of the mixed solution as consistent as possible with the thermal expansion coefficient of the screw, thereby improving the performance and service life of the screw.

[0375] Embodiment 2: Based on Embodiment 1, this embodiment further introduces two additional rules into step S3, so that the robustness of the overall fuzzy control logic system of Embodiment 1 is further optimized:

[0376] In this embodiment, regarding Rule 4, if the sample i of the previous production batch -1 After adjustment in step S6 and application of rule 3, if the difference in thermal expansion coefficient still conflicts with rule 1 or rule 2, for example, the more the mass ratio of acetone increases, the more positive the difference in thermal expansion coefficient becomes, then the following rule is executed:

[0377] In this embodiment, regarding Rule 4: After the samples of the previous production batch have been adjusted in step S6 and Rule 3 has been applied, if the difference in the thermal expansion coefficient still conflicts with Rule 1 or Rule 2, Rule 4 needs to be introduced for processing. The "contradiction" here means that after adjustment according to Rule 3, the difference in the thermal expansion coefficient has not been improved as expected, but instead a situation that is contrary to Rule 1 or Rule 2 has occurred. For example, after increasing the mass ratio of acetone, the difference in the thermal expansion coefficient has become larger (i.e., "positive"), which is inconsistent with the guiding principles of Rule 1 or Rule 2. Rules 4.1 and 4.2 are adjusted accordingly based on the specific manifestations of the difference in the thermal expansion coefficient:

[0378] Specifically, Rule 4.1: If the difference in thermal expansion coefficient is "positive", it means that there is too much acetone in the current mixing ratio, resulting in a too large thermal expansion coefficient. In order to reduce this difference, it is necessary to reduce the mass ratio of acetone and increase the mass ratio of diethylenetriamine to achieve a more balanced thermal expansion coefficient.

[0379] Specifically, Rule 4.2: If the difference in thermal expansion coefficient is "negative large", it means that there is too much diethylenetriamine at the current mixing ratio, resulting in a too small thermal expansion coefficient. To correct this situation, the mass ratio of acetone needs to be increased while the mass ratio of diethylenetriamine needs to be reduced.

[0380] The adjustment directions of these two rules are completely opposite to those of Rule 1 and Rule 2, because they deal with situations that are still contradictory after adjustment by Rule 3. In this way, the preparation system can self-correct when faced with abnormal situations, improving the flexibility and adaptability of the preparation process. At the same time, there is no need to introduce the step of analyzing the chemical properties of the raw materials, and the ideal effect can be relatively close to it by directly making reverse adjustments based on contradictory results.

[0381] In this embodiment, regarding Rule 5: after the samples of the previous production batch are adjusted in step S6, if the difference in the thermal expansion coefficient does not conflict with Rule 1 and Rule 2, that is, the adjusted thermal expansion coefficient meets expectations, then the current adjustment strategy should be maintained, that is, Rule 1 or Rule 2 should be continued to be applied. The advantage of doing so is that the stability and continuity of the preparation process can be maintained, and new uncertainties can be avoided from being introduced due to unnecessary adjustments.

[0382] It is understandable that after the introduction of rules 4 and 5, the preparation system can respond to various situations in the production process more flexibly and intelligently. Rule 4 enables the system to effectively self-correct when faced with contradictions that still occur after the adjustment of rule 3; while rule 5 ensures the stability and continuity of the preparation process when the adjustment is effective. The introduction of these rules not only improves the consistency and reliability of product quality, but also reduces the scrap rate and labor costs in the production process, thus bringing significant economic benefits to the enterprise.

[0383] Furthermore, the embedded C language program of this embodiment is as follows:

[0384]

[0385]

[0386]

[0387] Embodiment 3: Based on step S4 of embodiment 1, this embodiment further provides a solution that takes into account the interaction between fuzzy set FS1 and fuzzy set FS2. If the interaction between fuzzy set FS1 and fuzzy set FS2 can be considered, the nonlinear relationship in the system can be further accurately simulated, so that the fuzzy control algorithm of embodiment 1 can better handle the mutual influence between multiple inputs, thereby reducing errors and uncertainties.

[0388] In this embodiment, step S402 introduces a method to calculate the interaction between the fuzzy set FS1 (affecting the adjustment amount of the acetone mass ratio) and the fuzzy set FS2 (affecting the adjustment amount of the diethylenetriamine mass ratio).

[0389] Specifically, step S402 calculates the interaction between fuzzy set FS1 and fuzzy set FS2: Through the weighted average method, we can weight the membership of a new variable z according to the membership of FS1 and FS2. This new variable z reflects the comprehensive impact of the interaction between FS1 and FS2:

[0390]

[0391] Among them, μ z (z) is the membership function on the output variable z. z can be a variable representing the output result. It does not directly correspond to the output variable y1 or y2 of FS1 or FS2, but a new variable that reflects the comprehensive impact of the interaction between FS1 and FS2. w1 and w2 are the weight factors of fuzzy set FS1 and fuzzy set FS2, respectively. They are interval values ​​of [0,1] and can be determined according to the specific situation. If there is no special requirement, the value can be 0.5.

[0392] In this embodiment, after the interaction between fuzzy sets is considered in step S4, the defuzzification process in step S5 also needs to be adjusted accordingly. At this time, we no longer perform defuzzification directly in the y1 and y2 space, but in the z space, and then map the result back to the y1 and y2 space.

[0393] Specifically, the adjustment amount A' of the acetone mass ratio and the adjustment amount D' of the diethylenetriamine mass ratio are converted into specific values ​​through the Center of Gravity (COG) method:

[0394]

[0395] in, and is the partial derivative of variable z with respect to y1 and y2, which is used to map the defuzzified result in z space back to the original y1 and y2 space. Because the partial derivative represents the rate of change of another variable at a certain point when one variable changes, while keeping the other variables unchanged. And according to the above step S402, the functional relationship between z and y1 and y2 is known. Then the partial derivative It means the rate of change of z with y1 when y2 remains unchanged. It represents the rate of change of z as y2 changes while y1 remains unchanged.

[0396] In the defuzzification process of the fuzzy control of this embodiment, these two partial derivatives are used to map the defuzzification results in the z space back to the y1 and y2 spaces. By calculating the integral of the product of the weighted average (considering the membership function) and the partial derivatives, we can obtain the adjustment amounts A' and D' in the y1 and y2 spaces. This method ensures that the defuzzification results can correctly reflect the adjustment requirements of the original output variables y1 and y2.

[0397] It can be understood that the technical solution of this embodiment, when combined with the first embodiment, enables the fuzzy control algorithm of the first embodiment to take into account the interaction between fuzzy sets, and can more accurately simulate the nonlinear relationship in the complex system. It helps to improve the accuracy and response speed of the control system. And when multiple fuzzy sets jointly affect the output results, considering the interaction between them can make the system more robust and stable. This is because the interaction model can better handle the mutual influence between multiple inputs, thereby reducing errors and uncertainties.

[0398] Example 4: In Example 3, the method of assigning weight factors w1 and w2 is "to exercise subjective initiative according to specific circumstances; if there is no special requirement, both can be assigned a value of 0.5". However, this subjective assignment results in insufficient robustness of its technical solution, so this example further introduces an intelligent assignment method for weight factors w1 and w2; the assignment logic of the two is the same, so only weight factor w1 is used as an example for display:

[0399] See also Figure 6 , this embodiment introduces the DS evidence theory algorithm (Dempster / Shafer), which has the ability to process uncertain information; when it is used as an uncertain reasoning method in the assignment of the weight factor w1 in the technical solution, because the weight factor w1 is a correction to the fuzzy set FS1, and the fuzzy set FS1 corresponds to the acetone mass ratio adjustment amount A', the DS evidence theory algorithm needs to consider the adjustment information of the historical acetone mass ratio adjustment amount. Therefore, it includes the following steps S800~S802.

[0400] In this embodiment, regarding step S800, evidence is obtained: in the non-standard screw production line, after at least two rounds of preparation batches are executed (it should be noted that because historical trends are considered, it is recommended to change two rounds to more rounds in practice), evidence set A and evidence set B about the historical acetone mass ratio adjustment amount are collected:

[0401] A=[A1(h)',A3(h)',...,A N (h)'];

[0402] B=[A2(h)',A4(h)',...,A N (h)'];

[0403] Among them, A1(h)',A3(h)',...,A N (h)' is the acetone mass ratio adjustment amount predicted by step S5 in the odd historical time period, A2(h)', A4(h)', ..., A N (h)' is the acetone mass ratio adjustment amount predicted by step S5 in an even-numbered historical time period, and N is the total number of historical time periods.

[0404] It can be understood that in this step, the system collects data on the historical acetone mass ratio adjustment, which constitutes the basic evidence for our reasoning. The data sets are divided into odd and even time periods, namely evidence A and evidence B. This can more comprehensively consider the changing trends of different time periods and increase the diversity and reliability of the data.

[0405] In this embodiment, Dempster's combination rule is implemented in S801: for a given set of evidence A and a set of evidence B, Dempster's combination rule calculates a set called the combined confidence distribution Bel(C), and also calculates the combined uncertainty distribution Pl(C):

[0406] S8010, Merge trust allocation Bel (C):

[0407]

[0408] S8011. Combined uncertainty allocation Pl(C):

[0409]

[0410] Among them, A i and B i are a subset of evidence set A and evidence set B respectively; mass is the weight (if a certain time period is special, it is weighted, otherwise it is assigned a value of 0.5); represents the empty set;

[0411] Where C is the "possible set"; it represents the set of all possible results, i.e., the frame of discrimination. In step S801, the combined confidence assignment Bel(C) and uncertainty assignment Pl(C), C represents the set of all possible states we consider.

[0412] Specifically, C represents all possible ranges or states of the acetone mass ratio adjustment. When we update our belief in the acetone mass ratio adjustment based on historical data (evidence A and evidence B), we are actually updating the trust and uncertainty of each state in this possible set.

[0413] The belief distribution Bel(C) indicates the degree of our belief in a state or subset in set C, that is, based on the current evidence, how likely we think this state or subset is true. The uncertainty distribution Pl(C) indicates the degree of our uncertainty about a state or subset in set C, that is, how likely this state or subset is false. In Dempster's combination principle, we update our beliefs about each state in set C by combining the belief and uncertainty distribution of different evidences on set C. Doing so can help us more accurately predict the future acetone mass ratio adjustment, thereby improving the robustness and efficiency of the preparation process.

[0414] S8013. Obtain the joint trust function Bel(A∪B):

[0415]

[0416] Bel(A) and Pl(A) are the degrees of belief and uncertainty assignments for the set of evidence A;

[0417] Bel(B) and Pl(B) are the degrees of belief and uncertainty assignments for the set of evidence B;

[0418] (Pl(A) ∩ Pl(B)) is the uncertainty assignment for the intersection of evidence A and evidence B respectively.

[0419] Specifically, Bel(A) is a belief function that represents the degree of belief in proposition A. It measures the minimum degree of belief that we have in proposition A being true based on the current evidence. In other words, Bel(A) is the lower bound of the probability that we consider proposition A to be absolutely true. Pl(A) is a plausibility function that represents the degree of belief that proposition A is not false. It measures the maximum degree of belief that we have in proposition A not being false based on the current evidence. In other words, Pl(A) is the upper bound of the probability that we consider proposition A to be possibly true.

[0420] These two functions together define a belief interval [Bel(A), Pl(A)], which represents the range of uncertainty for proposition A. When Bel(A) = Pl(A), it means that the belief in proposition A is certain; while when Bel(A) < Pl(A), it means that there is a certain degree of uncertainty.

[0421] Specifically, Bel(B) and Pl(B) are similar to Bel(A) and Pl(A). Bel(B) and Pl(B) respectively represent the degrees of belief and uncertainty assignments for another piece of evidence B. These functions help us understand and quantify the degree of belief and uncertainty in a proposition based on evidence B.

[0422] Specifically, (Pl(A) ∩ Pl(B)) represents using Dempster's combination rule to combine the degrees of belief and uncertainty assignments of two pieces of evidence, thereby obtaining a new degree of belief and uncertainty assignment that comprehensively considers the two pieces of evidence. That is, it focuses on the degree to which they jointly support or oppose a certain proposition.

[0423] It can be understood that step S801 introduces the D-S evidence theory to handle this collected uncertain information. This theory can synthesize evidence from different sources (historical data in this case) through Dempster's combination rule to obtain a comprehensive degree of belief and uncertainty assignment. In the D-S evidence theory, we quantify the degree of belief and uncertainty in a certain proposition through the belief function Bel and the plausibility function Pl. By combining the degrees of belief and uncertainty assignments of different pieces of evidence, we can obtain a more comprehensive and accurate judgment.

[0424] In this embodiment, regarding S802, the joint trust function Bel(A∪B) is mapped to an interval value between [0,1] as the correction factor w1 through the sigmoid function:

[0425]

[0426] e is the base of natural logarithm. This method makes the assignment of w1 no longer static, but adjusted according to the dynamic changes of historical data, thus realizing intelligent and dynamic assignment.

[0427] In this embodiment, by comprehensively considering evidence from multiple sources, the DS evidence theory can reduce the uncertainty caused by a single data source and make the decision more accurate. At the same time, since the weight factor w1 is dynamically adjusted according to historical data, the system can better adapt to changes in actual conditions and improve the robustness of decision-making. In addition, the DS evidence theory allows us to continuously update the trust and uncertainty allocation based on new evidence, which enables the system to quickly adjust and maintain flexibility when facing new situations. At the same time, the dynamically adjusted weight factor w1 also enables the system to better reflect the current state and make timely adjustments.

[0428] It is understandable that the above technical solution of this embodiment is only shown for the weight factor w1; for the weight factor w2, it is only necessary to replace the evidence set A and the evidence set B in the above step S800 with the historical adjustment information of the adjustment amount D' of the mass ratio of diethylenetriamine.

[0429] Example 5: Please refer to Figure 2-4 Based on the above specific implementation, this embodiment further discloses a device for preparing a non-standard screw with a functional layer: the device includes an immersion tank 1 for immersing a screw blank with a thread modification solution, and also includes:

[0430] A controller 6 and a memory connected thereto, wherein the memory stores program instructions, and when the program instructions are executed by the controller 6, a specific value of the heat ratio of the thread modification solution as described in the above specific embodiment is calculated;

[0431] three groups of second containers 4 for storing epoxy resin, acetone and diethylenetriamine respectively, the second containers 4 being connected to the first containers 2;

[0432] Based on the specific values ​​of the heat ratio of the thread modification solution, each second container 4 introduces epoxy resin, acetone and diethylenetriamine into the first container 2 according to the corresponding ratio, and prepares a new mixed solution; the first container 2 can introduce the "new mixed solution" into the immersion tank 1.

[0433] Specifically, each second container 4 is connected to the first container 2 through its own first solenoid valve 7, and the first container 2 is connected to the second container 4 through the second solenoid valve 8; the controller 6 controls the first solenoid valve 7 and the second solenoid valve 8. That is, after the controller 6 calculates the adjustment amounts A' and D', it controls the first solenoid valve 7 to open in the form of an electrical signal, and by calculating the corresponding opening time or the solenoid valve itself feedback flow information, the respective ratios of the epoxy resin, acetone and diethylenetriamine reaching the first container 2 are achieved to meet the ratio of the "new mixed solution".

[0434] Specifically, the first container 2 is equipped with a stirring device 3 for mixing epoxy resin, acetone and diethylenetriamine. The stirring device 3 includes a motor 301 installed on the first container 2 and a stirring blade 302 driven to rotate by its output shaft. The stirring blade 302 drives the epoxy resin, acetone and diethylenetriamine in the first container 2 to stir and form a "new mixed solution".

[0435] Specifically, in the environment, a sensor group 5 for detecting the ambient temperature and the ambient humidity is provided. The sensor group 5 includes a temperature sensor and a humidity sensor, which are used to detect the "current temperature T" and the "current humidity H" respectively.

[0436] In this example, see Figure 5 After the controller 6 calculates the specific values ​​of the heat ratio of the thread modification solution (i.e., the adjustment amounts A' and D'), the predetermined ratio of epoxy resin, acetone and diethylenetriamine is introduced into the first container 2 by controlling the first solenoid valve 7, and then the stirring device 3 starts stirring and prepares a "new mixed solution"; then it is introduced into the immersion tank 1 based on the second solenoid valve 8 to realize the execution of the functional layer molding process of the screw.

[0437] It is understandable that the functional layer of the screw is not limited to the use of the dipping process; when the curing process and cross-linking process used in the conventional technology to form the functional layer of the screw require the use of a thread modification solution, the device and method for preparing the "new mixed solution" disclosed in this embodiment can be used to perform the corresponding operations.

[0438] Test example: This example is used to verify the above specific implementation method, that is, the dynamic control and correction effect of the above technical solution on the thermal expansion coefficient of the functional layer molding of non-standard screws.

[0439] (I) Experimental materials and methods:

[0440] 1.1 Test materials:

[0441] Two devices as disclosed in Example 5 and non-standard screws of the same specification produced by them.

[0442] 1.2 Preparation method:

[0443] Device for executing the preparation method (experimental group): non-standard screws are produced according to steps S1 to S8 disclosed in Examples 1 to 4.

[0444] Device without the preparation method (control group): The conventional method is used to produce non-standard screws, and the following preparation method is performed:

[0445] Step 1): Select the C / SiC blank plate with a relative density of 50% to 80% to process the thread and obtain the screw blank;

[0446] Step 2): impregnating, curing and cross-linking the screw blank with a thread modification solution, and processing the thread with a diamond grinding wheel to obtain a semi-finished screw; the thread modification solution is a mixed solution composed of epoxy resin, acetone and diethylenetriamine, and the mass ratio of the three is 1:1-2:0.2-0.8;

[0447] Step 3): Further densification and anti-oxidation treatment of the semi-finished screw to obtain the finished C / SiC composite screw.

[0448] 1.3 Thermal expansion test method:

[0449] This test uses the volume expansion method to achieve thermal expansion detection: the thermal expansion coefficient is calculated by measuring the volume change of the material at different temperatures. The device used is a volume expansion instrument, model CN61M / ZRPY-Ⅲ-1000 push rod glass bottle volume expansion instrument.

[0450] (II) Test environment and specifications:

[0451] 2.1 Environmental parameters:

[0452] The test is carried out in a constant temperature and humidity environment to ensure that the impact of ambient temperature and humidity on the test results is minimized. At the same time, in order to simulate the temperature changes that the screws may encounter in the actual use environment, the screws will be tested for temperature changes during the test, ranging from -40℃ to 100℃.

[0453] 2.2 Experimental specifications:

[0454] In order to ensure the validity and comparability of the test, the non-standard screws produced by the two devices should meet the following specifications:

[0455] 1) Screw material: the same base material and functional layer material.

[0456] 2) Screw size: key dimensions such as length and diameter should remain consistent.

[0457] 3) Preparation batch: Produced in the same batch to eliminate differences between production batches.

[0458] 4) Multiple batch preparation: Use two devices to prepare 20 batches of screws respectively. The number of screws in each batch should be the same to ensure sufficient sample volume for subsequent testing and comparison.

[0459] 5) Screw selection and functional layer stripping: One screw is randomly selected from each batch and shot blasted to ensure that the functional layer is completely stripped from the screw substrate without damaging the substrate. The stripped functional layer is ground into powder of the same mesh size by a pulverizer and then molded by a small mold of 5*2*1 (unit: mm) to form the sample to be tested.

[0460] 6) Preparation of test samples: Both the screw substrate and the peeled functional layer samples should be marked in detail, including batch, screw number and other information.

[0461] (III) Test steps:

[0462] The first step is sample preparation: a certain number of non-standard screws are selected from the experimental group and the control group as test samples.

[0463] Step 2: Initial measurement: Measure and record the initial length or volume of the selected sample at room temperature.

[0464] The third step is temperature change processing: place the samples in environments with different temperatures and record the length or volume changes of the samples at each temperature.

[0465] Step 4: Calculation of thermal expansion coefficient: Place the samples of the experimental group and the control group into the sample chamber of the volume expansion measuring instrument respectively, heat them according to the set temperature program, and record the volume change data.

[0466] Step 5, Data Storage and Analysis: All measured data should be recorded in detail and stored in a computer. Based on the thermal expansion coefficient of C / SiC material as the ideal parameter, record the percentage of deviation of the thermal expansion coefficient of the experimental group and the control group from that of the C / SiC material.

[0467] Step 6, multiple batch comparison: Repeat the above steps to test and compare multiple batches of screws. Observe and record the stability and consistency of the thermal expansion coefficient of the functional layer of the device that implements the S1-S8 preparation method in different batches. Compare the changes in the thermal expansion coefficient of the functional layer of another device that does not implement the S1-S8 preparation method in different batches. Based on the deviation percentage described in step 5, draw a line graph for different batches ( Figure 7 ).

[0468] 4. Results and Discussion

[0469] like Figure 7As shown, through multiple rounds of batch comparison tests, it is known that the device implementing the S1-S8 preparation method can continuously correct the thermal expansion coefficient of the functional layer during the preparation process, making it more consistent with the ideal value, with an error margin of ±6%. The other device that did not implement this method showed a large change in the thermal expansion coefficient in different batches, with a large degree of deviation from the expected value, and an error margin of ±21%. This result proves the effectiveness of the S1-S8 preparation method provided in Examples 1 to 4 in improving the stability and consistency of the thermal expansion coefficient of the functional layer of non-standard screws.

[0470] V. Conclusion:

[0471] This comparative experiment compared and analyzed the differences in thermal expansion performance of non-standard screws using the S1-S8 preparation method and those using the non-S1-S8 preparation method. The results show that the device using the S1-S8 preparation method can significantly improve the stability and consistency of the thermal expansion coefficient of the functional layer, providing useful exploration and practical experience for improving the preparation process of non-standard screws.

[0472] The above specific embodiments only express the embodiments of the relevant practical applications of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A method for preparing a non-standard screw with a functional layer, comprising: impregnating a screw blank with a thread modification solution to form a functional layer, characterized in that: Prior to the impregnation, the following steps are performed: S1, obtaining input variables and output variables, which are physical parameters affecting the thermal expansion characteristics of the screw and the thermal expansion characteristics of the thread modification solution; S2, fuzzifying input variables: converting the input variables into fuzzy sets; S3, establishing fuzzy rules: for planning how to adjust the heat ratio of the thread modification solution when the input variable is in a certain fuzzy set; S4, fuzzy reasoning and decision making: based on the fuzzified value of the input variable, the fuzzy rule is applied using the minimum-maximum synthesis method, and then the Mamdani fuzzy reasoning method is used to perform fuzzy reasoning, and a fuzzy set for adjusting the heat ratio of the thread modification solution is obtained to obtain an output variable y; S5, defuzzification: converting the output variable y into a specific numerical value for adjusting the heat ratio of the thread modification solution by a central average defuzzification method; During the impregnation, the following steps are performed: S6, implementing adjustment: preparing a new mixed solution according to the specific value obtained in S5 to be applied to the screws of the current production batch; In said S4, the obtained fuzzy set for adjusting the heat ratio of said thread modification solution includes: a fuzzy set FS1 of the acetone mass ratio adjustment amount and a fuzzy set FS2 of the diethylenetriamine mass ratio adjustment amount; In S4, the minimum-maximum synthesis method applies the fuzzy rules, and the fuzzy reasoning is achieved through the following two steps: S400, based on the n input variables x: x1, x2, ..., x n , each of the input variables x corresponds to a fuzzy set membership function μ Ai (x i ), where A i is the fuzzy set corresponding to the i-th input variable, i = 1, 2, ..., n; and m items are of the form "if x1 is A1 and x2 is A2 and ... and x n Yes A n , then the output y is the fuzzy rule of B", where B is the fuzzy set of output variables; For the jth rule (j=1,2,...,m), calculate the activation degree D k for: D k =min(μ A1 (x1),m A2 (x2),...,m An (x n )); Among them, k expresses the specific degree level; S401, rule-based activation level D k And each of the fuzzy rules has the activation degree D k Perform the maximization operation on the fuzzy set in the conclusion part and calculate the membership of the output fuzzy set: Among them, μ Output (y) is the membership of the output fuzzy set, m is the total number of rules, and each rule has a fuzzy set C of the conclusion part. j , and its corresponding membership function is μ Cj (y), where y is the corresponding output variable; S7, preparing the finished product: impregnating, curing and / or cross-linking the screw blank with the new mixed solution in S6, performing thread processing to obtain a semi-finished screw, and then densifying and anti-oxidation treatment the semi-finished screw to obtain a composite material screw with a functional layer.

2. The method for preparing non-standard screws according to claim 1, characterized in that: The thread modification solution is a mixed solution consisting of epoxy resin, acetone and diethylenetriamine; In S1, the input variables include: thermal expansion coefficient SC of the screw, thermal expansion coefficient C of the thread modification solution, current mass ratio A of acetone, current mass ratio D of diethylenetriamine, current temperature T and current humidity H; The output variables include an adjustment amount A' of the acetone mass ratio and an adjustment amount D' of the diethylenetriamine mass ratio.

3. The method for preparing non-standard screws according to claim 2, characterized in that: In S2, the fuzzy sets include: a fuzzy set F1 of thermal expansion coefficient differences, a fuzzy set F2 of temperature differences, a fuzzy set F3 of humidity differences, and a fuzzy set F4 of acetone and diethylenetriamine mass ratio differences.

4. The method for preparing non-standard screws according to claim 3, characterized in that: In said S2, said thermal expansion coefficient differences include a fuzzy set of negative large NB, negative small NS, zero ZE, positive small PS and positive large PB, and said fuzzy set F1 is composed of bell-shaped functions corresponding to these five differences; The temperature difference includes a fuzzy set of low temperature LT, medium temperature MT and high temperature HT, and the fuzzy set F2 is composed of bell-shaped functions corresponding to the three differences; The humidity difference includes a fuzzy set of low humidity LH, medium humidity MH and high humidity HH, and the fuzzy set F3 is composed of bell-shaped functions corresponding to the three differences; The differences in the mass ratios of acetone and diethylenetriamine include fuzzy sets of "medium" acetone mass ratio, "low" acetone mass ratio, "high" acetone mass ratio, "medium" diethylenetriamine mass ratio, "low" diethylenetriamine mass ratio and "high" diethylenetriamine mass ratio, and the fuzzy set F4 is composed of bell-shaped functions corresponding to these six differences.

5. The method for preparing non-standard screws according to claim 4, characterized in that: In S3, the heat ratio of the thread modification solution is adjusted by adjusting the mass ratio of acetone and diethylenetriamine; and the fuzzy rules include: Rule 1: If the difference in thermal expansion coefficient is "positive", increase the mass ratio of acetone and reduce the mass ratio of diethylenetriamine; Rule 2: If the difference in thermal expansion coefficient is "negatively large", reduce the mass ratio of acetone and increase the mass ratio of diethylenetriamine; Rule 3: If the difference in thermal expansion coefficient of the screws of the previous production batch after adjustment in S6 is inconsistent with Rule 1 or Rule 2, the following rules are executed: Rule 3.1: if the temperature T is the high temperature HT, the mass ratio A of the acetone is reduced; if the temperature is the low temperature LT, the mass ratio A of the acetone is increased; Rule 3.2: If the humidity H is the high humidity HH, the mass ratio D of the diethylenetriamine is increased; if the humidity H is the low humidity LH, the mass ratio D of the diethylenetriamine is reduced.

6. The method for preparing non-standard screws according to claim 4, characterized in that: In S401, if the output variable y corresponds to the adjustment amount A' of the acetone mass ratio, i.e. y1, the membership degree μ of the output fuzzy set is calculated. Output (y1) = FS1; If the output variable y corresponds to the adjustment amount D' of the mass ratio of diethylenetriamine, i.e. y2, then the membership degree μ of the output fuzzy set is calculated. Output (y2) = FS2.

7. The method for preparing non-standard screws according to claim 6, characterized in that: In S5, the specific values ​​are the specific values ​​of the adjustment amount A' of the acetone mass ratio and the adjustment amount D' of the diethylenetriamine mass ratio, and the two are obtained in the following ways: Among them, μ fs1 (y1) is the membership function of the fuzzy set FS1 on the output variable y1; μ fs2 (y2) is the membership function of the fuzzy set FS2 on the output variable y2.

8. A device for preparing a non-standard screw with a functional layer, comprising an immersion tank (1) for immersing a screw blank with a thread modification solution, characterized in that: Also includes, A controller (6), a memory connected to the controller (6), wherein program instructions are stored in the memory, and when the program instructions are executed by the controller (6), a specific value of the heat ratio of the thread modification solution according to any one of claims 1 to 7 is calculated; three groups of second containers (4) for storing epoxy resin, acetone and diethylenetriamine respectively, wherein the second containers (4) are connected to the first container (2); Based on the specific values ​​of the heat ratio of the thread modification solution, each of the second containers (4) introduces epoxy resin, acetone and diethylenetriamine into the first container (2) according to the corresponding ratio to prepare the new mixed solution; The first container (2) is capable of introducing the new mixed solution into the immersion tank (1).

9. The device for preparing non-standard screws according to claim 8, characterized in that: Each of the second containers (4) is connected to the first container (2) via a respective first solenoid valve (7), and the first container (2) is connected to the second container (4) via a second solenoid valve (8); The controller (6) controls the first solenoid valve (7) and the second solenoid valve (8).

Images