A method for monitoring thermocouple arrangement of autoclave process of large composite components

Abstract

The application discloses a kind of large composite component autoclave process monitoring thermocouple arrangement and control methods, method includes the following steps: obtaining the temperature field data of whole curing process curve of part in autoclave;Obtain the low-temperature score sum and high-temperature score sum of each node;Obtain high-temperature score set and low-temperature score set;Screen out high-score low-temperature node and high-score high-temperature node;Obtain the node after optimization;According to the position and quantity of the node after optimization, arrange and control thermocouple.The application locks key monitoring point by unified scoring logic and dynamic weight mechanism, avoids the problems of part replacement failure and difficulty in capturing real temperature difference of experience scheme, overcomes the contingency and instability of single feature point tracking, effectively reduces process abnormal misjudgment and missed judgment risk, ensures accurate coverage of temperature key area while avoiding redundant arrangement. Ensure that monitoring target is always focused on temperature key area, avoid monitoring failure caused by feature point position distortion.

Description

Technical Field

[0001] This invention relates to the field of composite material processing and molding technology, and in particular, to a method for monitoring thermocouple deployment in a large composite component autoclave process. Background Technology

[0002] Large composite material components, with their superior properties such as high specific strength, high specific modulus, and corrosion resistance, have been widely used in high-end equipment fields such as aerospace and rail transportation. Autoclave curing is one of the core technologies for preparing large, high-performance composite material components. During this process, the uniformity of temperature distribution within the component has a decisive impact on key quality indicators such as mechanical properties and internal defects. Therefore, accurate and efficient temperature monitoring of the component during autoclave curing, and real-time acquisition of internal temperature data (especially leading and lagging point data that reflect temperature extremes), is not only a core basis for evaluating the quality of the curing process but also a key means to achieve dynamic optimization of process parameters and ensure product quality stability.

[0003] Currently, in temperature monitoring during the autoclave curing process of composite material components, the placement of thermocouples mainly relies on two existing technologies: one is the traditional layout method based on engineering experience, where technicians typically place thermocouples in areas prone to temperature differences, such as the thickness direction and edges of the component, based on heat transfer experience, to achieve preliminary monitoring of temperature changes; the other is a method that combines numerical simulation to track the trajectory of a single characteristic point of instantaneous temperature extreme values ​​to place thermocouples. The single characteristic point is the point with the highest or lowest instantaneous temperature. By obtaining the trajectory change of the single characteristic point through numerical simulation, thermocouples are placed to cover the area of ​​its trajectory change, thereby monitoring the temperature difference of the entire workpiece.

[0004] Empirical rule-based control methods lack a systematic analysis of the dynamic distribution of the component's temperature field, and the rationality of their control schemes highly depends on the personal experience of technical personnel. When the structural form, size specifications, or curing process parameters of the component change, the original experience-based control schemes often become inapplicable. They not only fail to accurately capture the true temperature difference inside the component, but may also lead to misjudgments or omissions of process anomalies due to monitoring blind spots, seriously affecting the reliability of process quality assessment. Although the method of arranging thermocouples by tracking the trajectory of a single characteristic point of instantaneous temperature extreme values ​​using numerical simulation introduces numerical simulation technology, this method has inherent randomness and instability. It is easily affected by simulation or small disturbances that cause trajectory changes, leading to monitoring failure. Furthermore, it ignores the overall dynamic evolution of the temperature field during the curing process, making the thermocouple control scheme lack sufficient robustness. If the trajectory changes frequently, a greater number of thermocouples will be required. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for thermocouple deployment and control in the process monitoring of large composite component autoclaves.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for thermocouple deployment for monitoring the process of large composite components in an autoclave includes the following steps: S1, obtaining temperature field data of the entire curing process curve of the part inside the autoclave; S2, according to a temperature scoring model, performing low-temperature and high-temperature scoring on all moments of each node of the part, and obtaining the sum of low-temperature and high-temperature scores for each node; S3, obtaining a high-temperature score set and a low-temperature score set based on the sum of low-temperature and high-temperature scores for all nodes; S4, selecting high-scoring low-temperature nodes and high-scoring high-temperature nodes from the high-temperature and low-temperature score sets; S5, optimizing the high-scoring low-temperature and high-scoring high-temperature nodes using an optimization algorithm to obtain the optimized nodes.

[0008] Furthermore, the temperature scoring model is as follows:

[0009] ;

[0010] ;

[0011] For the first The node at the th High temperature rating at any given time; For the first The node at the th Low temperature score at any given time; For the first The node at the th The baseline score for high temperature at any given moment; For the first The node at the th The baseline score at low temperature at any given time; For the first Dynamic weighting of scores at any given moment.

[0012] Furthermore, the sum of the low-temperature scores and the sum of the high-temperature scores are calculated according to the following formula:

[0013] ;

[0014] ;

[0015] For the first The sum of the high temperature scores of each node. For the first The sum of the low temperature scores of each node; This represents the total number of moments.

[0016] Furthermore, the high-temperature basic component and low temperature basic fraction The following steps are used to calculate S21: Statistical analysis of all node temperatures at time t yields the temperatures of all N nodes at time t. quantiles and quantiles S22, Compare the first The node at the th Temperature of a moment and , :like Then the time node Included in high temperature node set ,like Then the time node Included in the low-temperature node set S23, the high-temperature basic score is calculated according to the following formula. and low temperature basic fraction :

[0017] ;

[0018] ;

[0019] in For the preset area percentage, The preset threshold; This is the first nonlinear amplification factor. This is the second nonlinear amplification factor. Let be the maximum value of the temperature at all nodes at time t. Let be the minimum temperature of all nodes at time t. The maximum difference in node temperature at time t.

[0020] Furthermore, the dynamic weighting of the scoring It is obtained by calculation using the following formula:

[0021] ;

[0022] The rate of temperature change at time t is the normalized preset process temperature curve. For normalization , Let be the standard deviation of the temperature of all nodes at time t; It is the global temperature standard deviation of all nodes at all times. These are the first adjustable coefficient, the second adjustable coefficient, and the third adjustable coefficient, respectively.

[0023] Furthermore, the optimization algorithm is a particle swarm optimization algorithm.

[0024] Further, step S4 specifically includes: sorting the high temperature score set from high to low, and taking the top A nodes as high-scoring high temperature nodes; sorting the low temperature score set from high to low, and taking the top A nodes as high-scoring low temperature nodes.

[0025] Furthermore, The value range is [5, 10].

[0026] Furthermore, and The value range is greater than or equal to 1.

[0027] Furthermore, it also includes step S6: generating a node deployment strategy based on the optimized node positions and quantities, and displaying it through the terminal.

[0028] The present invention has the following beneficial effects:

[0029] By acquiring complete temperature field data of the entire curing process curve of the part through S1, the reliance on subjective judgment in traditional experience-based control is replaced, enabling a systematic understanding of the dynamic changes in the temperature field throughout the entire process, including heating and cooling. Combined with the temperature scoring model in S2, which performs bidirectional quantitative scoring of each node at all times for both low and high temperatures, the degree to which each node deviates from the ideal temperature at different process stages can be accurately located. By using a unified scoring logic to pinpoint key monitoring points, the system avoids the problems of component replacement failures and difficulty in capturing true temperature differences associated with experience-based solutions, effectively reducing the risk of misjudgments and omissions in process anomalies. By constructing high-temperature and low-temperature score sets through S3-S4 and selecting high-scoring nodes, we avoid simply following these single feature points to place thermocouples. Due to their randomness and instability, thermocouples are prone to monitoring distortion caused by simulation or minor disturbances, and the overall dynamic evolution of the temperature field during the curing process is ignored. This ensures that the monitoring target always focuses on the key temperature area, avoiding monitoring failure caused by feature point position distortion. The optimization algorithm in S5 further optimizes the high-scoring nodes, so that the deployment scheme determined in S6 covers the core risk points of high and low temperatures. This significantly improves the adaptability of the deployment scheme to complex curing conditions, avoids the limitations of single feature points, strengthens the robustness and stability of the deployment, and ensures the stability of temperature difference monitoring. The precise deployment and acquisition of full-cycle temperature data provide a reliable basis for process quality assessment. Technicians can monitor the changes in extreme temperature values ​​such as leading and lagging points in real time, and adjust the heating parameters accordingly to improve process efficiency and optimize process temperature to avoid defects such as porosity and delamination caused by uneven curing due to excessive temperature differences. At the same time, the number of thermocouples determined by scientific scoring and optimization reduces redundant deployment while ensuring monitoring accuracy, thereby reducing monitoring costs and providing strong technical support for the high-quality and large-scale production of large composite components.

[0030] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0031] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0032] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0033] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0035] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0036] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0037] Please refer to Figure 1 The present invention provides a preferred embodiment of a method for monitoring thermocouples in a large composite component autoclave process, comprising steps S1, S2, S3, S4, S5 and S6.

[0038] S1, obtain the temperature field data of the entire curing process curve of the part inside the autoclave. Specifically, this can be achieved through CFD simulation, which comprehensively considers the convective heat transfer, heat conduction, and heat release during curing between the gas flow inside the autoclave and the solid components such as the composite material, mold, and vacuum bag, to obtain the temperature field data of the entire curing process curve of the part inside the autoclave. This allows us to obtain the temperature data of each node of the part at each time point. , , for node index, ,for That is, the entire part has N nodes and the entire process time is divided into M time nodes.

[0039] S2, based on the temperature scoring model, perform low-temperature and high-temperature scoring on each node of the part at all times, and obtain the sum of the low-temperature score and the sum of the high-temperature score for each node.

[0040] S3. Based on the sum of the low temperature scores and the sum of the high temperature scores of all nodes, obtain the high temperature score set and the low temperature score set.

[0041] S4, select high-scoring low-temperature nodes and high-scoring high-temperature nodes from the high-temperature score set and the low-temperature score set.

[0042] S5. Based on the optimization algorithm, optimize the high-scoring low-temperature nodes and the high-scoring high-temperature nodes to obtain the optimized nodes.

[0043] This invention provides a method for thermocouple deployment in the process monitoring of large composite component autoclaves. Through S1, it acquires complete temperature field data of the entire curing process curve of the part, replacing the reliance on subjective judgment in traditional experience-based deployment. This method allows for a systematic understanding of the dynamic changes in the temperature field throughout the entire process, including heating and cooling. Combined with the temperature scoring model in S2, it performs bidirectional quantitative scoring of each node at all times, both at low and high temperatures, accurately pinpointing the degree to which each node deviates from the ideal temperature at different process stages. By using a unified scoring logic to lock in key monitoring points, it avoids the problems of component replacement failure and difficulty in capturing true temperature differences associated with experience-based solutions, effectively reducing the risk of misjudgment and missed detection of process anomalies. By constructing high-temperature and low-temperature score sets and selecting high-scoring nodes through S3-S4, it is ensured that the monitoring target always focuses on the key temperature area, avoiding the situation where simply following these single feature points to place thermocouples is prone to monitoring distortion due to the randomness and instability of these points, and ignoring the overall dynamic evolution of the temperature field during the curing process. This ensures that the monitoring target always focuses on the key temperature area, avoiding monitoring failure caused by the distortion of feature point positions. The optimization algorithm in S5 further optimizes the high-scoring nodes, so that the deployment scheme determined in S6 covers the core risk points of high and low temperatures, significantly improving the adaptability of the deployment scheme to complex curing conditions, avoiding the limitations of single feature points, strengthening the robustness and stability of the deployment, and ensuring the stability of temperature difference monitoring. The precise deployment and acquisition of full-cycle temperature data provide a reliable basis for process quality assessment. Technicians can monitor the changes in extreme temperature values ​​such as leading and lagging points in real time, and adjust the heating parameters accordingly to improve process efficiency and optimize process temperature to avoid defects such as porosity and delamination caused by uneven curing due to excessive temperature differences. At the same time, the number of thermocouples determined by scientific scoring and optimization reduces redundant deployment while ensuring monitoring accuracy, thereby reducing monitoring costs and providing strong technical support for the high-quality and large-scale production of large composite components.

[0044] Understandably, this also includes step S6: generating a node deployment strategy based on the optimized node positions and quantities, and displaying it on the terminal. Thermocouples can be deployed according to the displayed node deployment strategy (including node positions and quantities), thus providing a scientific decision-making reference for thermocouple deployment.

[0045] In a specific embodiment of the present invention, the temperature scoring model is as follows:

[0046] ;

[0047] ;

[0048] For the first The node at the th High temperature rating at any given time; For the first The node at the th Low temperature score at any given time; For the first The node at the th The baseline score for high temperature at any given moment; For the first The node at the th The baseline score at low temperature at any given time; For the first Dynamic weighting of scores at any given moment.

[0049] The temperature scoring model constructs high-temperature and low-temperature scores as products of dynamic weights and a base score, respectively, achieving quantitative assessment and dynamic adaptation of temperature deviation. On one hand, it uses the high-temperature base score... and low temperature basic fraction It accurately characterizes the temperature extreme value deviation characteristics of a single node at a specific time, avoiding fuzzy judgments of temperature anomalies; on the other hand, it introduces dynamic weights for scoring. This allows the scoring results to respond to temperature changes at different stages of the process, solving the problem that traditional static assessments cannot adapt to the dynamic temperature field throughout the curing cycle. It ensures the accuracy of node temperature assessments and achieves dynamic alignment with the process, providing a scientific and quantitative basis for subsequent selection of key monitoring nodes and improving the targeted nature of thermocouple deployment.

[0050] In a specific embodiment of the present invention, the sum of the low-temperature scores and the sum of the high-temperature scores are calculated according to the following formula:

[0051] ;

[0052] ;

[0053] For the first The sum of the high temperature scores of each node. For the first The sum of the low temperature scores of each node; The total number of time points is usually divided into the following segments: At each moment, the interval between adjacent moments is the same, thus enabling multi-point, full-time analysis of the entire process cycle.

[0054] The high-temperature score and low-temperature score of a single node over the entire process cycle (M time points) are summed using a summation formula to obtain the total high-temperature score for that node. Sum of low temperature scores This method enables the assessment of the cumulative effects of node temperature anomalies. Compared to existing technologies that focus only on a single moment or a single feature point, this calculation method comprehensively reflects the cumulative degree of temperature deviation throughout the entire heating, holding, and cooling phases of a node, avoiding the problem of ignoring potential risks throughout the entire cycle due to normal temperatures at local moments. Simultaneously, the sum of scores provides a unified quantitative standard for ranking the temperature risk of all nodes, ensuring the objectivity of subsequent high-scoring node selection and further improving the reliability of the deployment plan.

[0055] In a specific embodiment of the present invention, high-temperature basic components and low temperature basic fraction The following steps are used to calculate:

[0056] S21, Statistically analyze the temperatures of all nodes at time t to obtain the temperatures of all N nodes at time t. quantiles and quantiles .

[0057] S22, Comparison The node at the th Temperature of a moment and , :like Then the time node Included in high temperature node set ,like Then the time node Included in the low-temperature node set ;

[0058] S23, the high-temperature baseline score is calculated according to the following formula. and low temperature basic fraction :

[0059] ;

[0060] .

[0061] This means that if one of the conditions is true, the base score is zero.

[0062] in For a preset area percentage, for example, The value range is [5, 10]. The value of quantile can be adjusted according to the complexity of the part's geometry and manufacturing requirements. Based on the definition of quantiles, it ensures that the most extreme nodes of a specified proportion can always be captured under any temperature distribution pattern, exhibiting high versatility and robustness. For example... If the value range is 5, then This means that the temperature of all N nodes is at the 95th percentile, that is, at that moment... The temperature value at the 95th position is the temperature value after all the node temperature values ​​are arranged in ascending order. The preset threshold is a very small positive number (e.g.) (to prevent the denominator from being zero); This is the first nonlinear amplification factor. This is the second nonlinear amplification factor. and The range of values ​​for is greater than or equal to 1, that is... Used to enhance the most extreme nodes (i.e., those closest to the target node). or The score contribution of the node makes it easier to select in subsequent optimizations.

[0063] Let be the maximum value of the temperature at all nodes at time t. Let be the minimum temperature of all nodes at time t. The maximum difference in nodal temperature at time t is... and The difference.

[0064] The base score is not simply based on whether a node is high-temperature or low-temperature, but rather considers the temperature difference between the node and its endpoints, as well as the maximum temperature difference, to quantify the "extreme temperature" of the node at time t. The calculation process for high-temperature and low-temperature base scores utilizes a three-level logic—quantile statistics, node set classification, and nonlinear formula calculation—to accurately define temperature anomaly nodes and amplify the identification of deviations. The first step involves… quantiles and The first step involved dividing the high-temperature and low-temperature node sets by quantiles, ensuring the rationality of the anomaly node selection. The second step clarified the affiliation of anomaly nodes through set classification, providing a basis for targeted scoring. The third step involved using nonlinear amplification coefficients... , and space temperature difference The scoring formula is designed to highlight the weight of extreme temperature nodes while avoiding the ineffective interference of small temperature differences. (Time score is 0). This solves the problems of vague identification of abnormal nodes and insufficient quantification of deviation in traditional assessments, making the scoring results more consistent with the extreme characteristics of the actual temperature field, and providing more accurate support for the location of key monitoring points.

[0065] In a specific embodiment of the present invention, the scoring dynamic weight It is obtained by calculation using the following formula:

[0066] .

[0067] This is the rate of temperature change at time t of the normalized preset process temperature curve. This term has a higher value during the heating / cooling phase and is almost zero during the holding phase. The temperature at time t of the preset process temperature curve; For normalization This is used to evaluate the severity of the internal temperature difference of the workpiece at time t; Let be the standard deviation of the temperature of all nodes at time t; It is the global temperature standard deviation of all nodes at all times. This is the ratio of the standard deviation of the current time step to the global standard deviation. This value is used to assess the importance of the stage. The larger the value, the greater the temperature difference at the current time step, indicating that this time step needs to be monitored more closely. These are the first adjustable coefficient, the second adjustable coefficient, and the third adjustable coefficient, respectively, used to balance the contributions of "the severity of process changes", "the significance of spatial temperature difference", and "the dispersion of temperature field".

[0068] Dynamic weights for scoring are constructed using multi-dimensional parameters. This achieves comprehensive adaptation to process characteristics, spatial temperature difference, and temperature dispersion. The dynamic weighting integrates three core parameters: normalized process temperature change rate (reflecting heating / cooling rates), normalized spatial temperature difference (reflecting temperature uniformity), and the proportion of temperature standard deviation (reflecting overall temperature dispersion). These three parameters are combined and adjusted using an adjustable coefficient. , , It flexibly adapts to different process requirements. The scoring weights at different times can be dynamically adjusted according to the process status. For example, the weight increases when the temperature change rate is high during the heating phase, and the weight increases when there is a large temperature difference in the space, ensuring focused attention on key process stages and areas with abnormal temperatures. It eliminates the reliance on manual stage-by-stage judgment and avoids the problem of scores being "homogenized" or "diluted" due to long holding periods. It ensures focused attention on stages with significant temperature changes. Compared to traditional fixed-weight assessments, this dynamic weighting mechanism significantly improves the relevance and adaptability of the scoring results, enabling subsequent node selection to better focus on core temperature risk points in the curing process, further strengthening the robustness of the control scheme.

[0069] In a specific embodiment of this invention, the optimization algorithm is a particle swarm optimization algorithm. Particle swarm optimization has advantages such as strong global optimization capability, fast convergence speed, and simple parameter settings, enabling it to efficiently handle the spatial layout optimization problem of high-scoring low-temperature nodes and high-scoring high-temperature nodes. Compared to traditional optimization algorithms (such as enumeration methods and greedy algorithms), particle swarm optimization can quickly find the optimal solution that balances coverage of core risk points and rational spatial distribution within a multi-node candidate set, avoiding duplicate coverage or omission of key nodes and reducing the deployment of redundant thermocouples. While ensuring comprehensive monitoring, it achieves reasonable control of the number of thermocouples, reduces monitoring costs, and improves the engineering practicality of the deployment scheme.

[0070] In a specific embodiment of the present invention, step S4 specifically includes:

[0071] Sort the high temperature score set from high to low, and select the top A nodes as high-scoring high temperature nodes.

[0072] The low-temperature score set is sorted from highest to lowest score, and the top A nodes are selected as high-scoring low-temperature nodes. A is the preset number of nodes to be selected.

[0073] By sorting the high-temperature and low-temperature score sets separately, the top A nodes are selected as high-scoring nodes. Compared to traditional subjective screening or screening methods without clear standards, this sorting and screening mechanism ensures the objectivity of high-scoring nodes and avoids the omission of key nodes or the selection of invalid nodes due to human factors. Furthermore, by adjusting the value of A, the number of selected nodes can be flexibly adjusted according to actual monitoring needs, balancing monitoring accuracy and cost. This provides a reliable set of candidate nodes for the efficient operation of subsequent algorithm optimization, ensuring the consistency and effectiveness of the deployment plan.

[0074] This invention also proposes a thermocouple deployment system for monitoring the process of large composite component autoclaves, comprising: a data acquisition module for acquiring temperature field data of the entire curing process curve of the part inside the autoclave; a scoring module for performing low-temperature and high-temperature scoring on all moments of each node of the part according to a temperature scoring model, obtaining the sum of low-temperature scores and the sum of high-temperature scores for each node; a set establishment module for obtaining a high-temperature score set and a low-temperature score set based on the sum of low-temperature scores and the sum of high-temperature scores for all nodes; a screening module for screening high-scoring low-temperature nodes and high-scoring high-temperature nodes from the high-temperature score set and the low-temperature score set, providing a good optimization dataset for the optimization module; and an optimization module for optimizing the high-scoring low-temperature nodes and high-scoring high-temperature nodes according to an optimization algorithm, the optimization goal being to minimize the maximum deviation between the thermocouple-monitored temperature and the actual extreme temperature of the component throughout the entire process cycle, obtaining the optimized nodes.

[0075] This invention also proposes a thermocouple deployment system for monitoring the process of large composite component autoclaves, comprising: a data acquisition module for acquiring temperature field data of the entire curing process curve of the part inside the autoclave; a scoring module for performing low-temperature and high-temperature scoring on all moments of each node of the part according to a temperature scoring model, and obtaining the sum of low-temperature scores and the sum of high-temperature scores for each node; a set establishment module for obtaining a high-temperature score set and a low-temperature score set based on the sum of low-temperature scores and the sum of high-temperature scores for all nodes; a filtering module for filtering high-scoring low-temperature nodes and high-scoring high-temperature nodes from the high-temperature score set and the low-temperature score set; and an optimization module for optimizing the high-scoring low-temperature nodes and high-scoring high-temperature nodes according to an optimization algorithm, and obtaining the optimized nodes.

[0076] The deployment system achieves fully automated and standardized processing from temperature field data acquisition to deployment plan output. The data acquisition module ensures the integrity of the temperature field data, the scoring module enables quantitative assessment of node temperatures, the aggregation and filtering module completes precise selection of key nodes, and the optimization module outputs the optimal deployment plan. This improves the efficiency and stability of deployment work and provides reliable system support for temperature monitoring in the mass production of large composite components.

[0077] The present invention also proposes an electronic device, including a processor and a memory, wherein the memory is used to store program code and transmit the program code to the processor; the processor is used to execute a method for monitoring thermocouple deployment in a large composite component autoclave process according to instructions in the program code.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring thermocouples in a large composite component autoclave process, characterized in that, Includes the following steps: S1, obtain the temperature field data of the entire curing process curve of the parts in the autoclave; S2, based on the temperature scoring model, perform low-temperature and high-temperature scoring on all moments of each node of the part, and obtain the sum of low-temperature scores and the sum of high-temperature scores for each node; S3. Based on the sum of the low temperature scores and the sum of the high temperature scores of all nodes, obtain the high temperature score set and the low temperature score set. S4, select high-scoring low-temperature nodes and high-scoring high-temperature nodes from the high-temperature score set and the low-temperature score set; S5. Based on the optimization algorithm, optimize the high-scoring low-temperature nodes and the high-scoring high-temperature nodes to obtain the optimized nodes.

2. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 1, characterized in that, The temperature scoring model is as follows: ； ； For the first The node at the th High temperature rating at any given time; For the first The node at the th Low temperature score at any given time; For the first The node at the th The baseline score for high temperature at any given moment; For the first The node at the th The baseline score at low temperature at any given time; For the first Dynamic weighting of scores at any given moment.

3. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 2, characterized in that, The sum of the low-temperature scores and the sum of the high-temperature scores are calculated according to the following formula: ； ； For the first The sum of the high temperature scores of each node. For the first The sum of the low temperature scores of each node; This represents the total number of moments.

4. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 2, characterized in that, The high-temperature basic component and low temperature basic fraction The following steps are used to calculate: S21, Statistically analyze the temperatures of all nodes at time t to obtain the temperatures of all N nodes at time t. quantiles and quantiles ; S22, Comparison The node at the th Temperature of a moment and , :like Then the time node Included in high temperature node set ,like Then the time node Included in the low-temperature node set ; S23, the high-temperature baseline score is calculated according to the following formula. and low temperature basic fraction : ； ； in For the preset area percentage, The preset threshold; This is the first nonlinear amplification factor. This is the second nonlinear amplification factor. Let be the maximum value of the temperature at all nodes at time t. Let be the minimum temperature of all nodes at time t. The maximum difference in node temperature at time t.

5. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 4, characterized in that, The dynamic weight of the score It is obtained by calculation using the following formula: ； The rate of temperature change at time t is the normalized preset process temperature curve. For normalization , Let be the standard deviation of the temperature of all nodes at time t; It is the global temperature standard deviation of all nodes at all times. These are the first adjustable coefficient, the second adjustable coefficient, and the third adjustable coefficient, respectively.

6. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 4, characterized in that, The value range is [5, 10].

7. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 4, characterized in that, and The value range is greater than or equal to 1.

8. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 1, characterized in that, The optimization algorithm is the particle swarm optimization algorithm.

9. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 1, characterized in that, Step S4 specifically includes: Sort the high temperature score set from high to low, and select the top A nodes as high-scoring high temperature nodes. The low-temperature score set is sorted from high to low, and the top A nodes are selected as high-scoring low-temperature nodes.

10. The method for monitoring thermocouples in the autoclave process of large composite components according to claim 1, characterized in that, It also includes step S6: generating a node deployment strategy based on the optimized node location and number, and displaying it through the terminal.

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