An Optimization Printing Method for Freeze-Thaw Sensitive Composite Materials Based on Artificial Intelligence

Abstract

The present invention provides an optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence, including: obtaining the performance parameters of rock and soil masses in alpine regions; designing specimens of freeze-thaw sensitive composite materials with various formulations and process parameters; conducting systematic mechanical tests and freeze-thaw cycle experiments on the specimens; establishing an intelligent database containing material compositions, printing process parameters, and material performance data; establishing a mapping relationship model between materials, processes, and performance, and training and optimizing the model; predicting the performance of freeze-thaw sensitive composite materials under different formulations and process parameters by the model, and recommending the optimal combination of printing parameters and composite material formulations; verifying the prediction accuracy of the recommended results and inversely optimizing the model; introducing real-time monitoring technology and developing an adaptive control strategy; constructing a comprehensive evaluation system and recommending the optimal printing scheme; the present invention can accurately reproduce the characteristics of special rock and soil masses in alpine regions, improve the printing quality and material performance stability, and provide an efficient and accurate solution for related fields.

Description

Technical Field

[0001] The present invention relates to the technical fields of special geotechnical materials and 3D printing technology in alpine regions, and particularly to an optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence. Background Art

[0002] The geotechnical materials in alpine regions, with their unique physical and mechanical properties, such as significantly low compressive strength, high permeability, and high sensitivity to freeze-thaw cycles, have brought unprecedented challenges to traditional construction and repair projects. Traditionally, the engineering treatment of such special geotechnical materials relies on on-site tests and empirical judgments, which are not only time-consuming and laborious, but also difficult to accurately simulate and predict the material behavior under actual working conditions. In recent years, with the rapid development of 3D printing technology, its application in the fields of civil engineering and materials science has shown great potential, providing unprecedented possibilities for customizing and constructing complex structures and high-performance materials. However, directly applying existing 3D printing technology to the simulation and repair of geotechnical materials in alpine regions still faces many problems: existing printing materials are difficult to reproduce the complex mechanical properties of special geotechnical materials such as moraine and freeze-thaw clay; the correlation mechanism between printing parameters and material properties is not clear; there is a lack of a dynamic parameter adjustment mechanism under freeze-thaw cycle conditions. In addition, existing technologies mostly use the manual trial-and-error method to adjust the formulation and process parameters of printing materials, which is not only inefficient, but also difficult to achieve precise control of performance. Therefore, developing a 3D printing material and method that can accurately match the characteristics of geotechnical materials in alpine regions and has high-efficiency and precise performance control capabilities has become an important research direction. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for preparing 3D printing materials that can accurately match the characteristics of geotechnical materials in alpine regions and has high-efficiency and precise control capabilities.

[0004] To achieve the above object, the present invention proposes an optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence, including the following steps:

[0005] S1: Obtain the performance data of geotechnical materials under different conditions in alpine regions;

[0006] S2: Based on the performance of geotechnical materials, design freeze-thaw sensitive composite material specimens with various formulations and process parameters, covering different proportions of base materials, reinforcing phases, and additives. Use advanced 3D printing technology to prepare specimens to ensure the accuracy and repeatability of the preparation process;

[0007] S3: Through systematic mechanical tests and freeze-thaw cycle experiments, obtain the performance data of each composite material specimen, and analyze the experimental results through image recognition and machine learning technologies;

[0008] S4: Establish an intelligent database containing material composition, printing process parameters, and material property data;

[0009] S5: Through the said database, establish a mapping relationship model between material - process - property, and train and optimize the model;

[0010] S6: Use the trained model to predict the properties of freeze - thaw sensitive composite materials under different formulations and process parameters, and according to the prediction results, use an optimization algorithm to recommend the optimal combination of printing parameters and composite material formulations;

[0011] S7: Print standardized specimens by 3D printing according to the recommended results, verify the prediction accuracy of the recommended results, and according to the verification results, perform reverse optimization on the model, iteratively adjust the recommended parameters, and improve the prediction accuracy;

[0012] S8: Introduce real - time monitoring technology and develop an adaptive control strategy;

[0013] S9: Construct a comprehensive evaluation system and use a multi - objective optimization algorithm to recommend the optimal printing scheme;

[0014] S10: Through multiple experiments and model adjustments, continuously optimize the prediction accuracy and recommendation ability of the model; and consider uncertain factors in the experimental process, such as material batch differences, equipment accuracy, etc., and optimize and improve the model through robustness testing;

[0015] S11: Connect the model to the 3D printing device in communication, realize the automatic adjustment of material formulations and process parameters during 3D printing to achieve the optimal performance, and regularly evaluate the system performance and accuracy, and perform necessary iterations and upgrades according to the evaluation results.

[0016] Further, step S1 is specifically: Collect the physical and mechanical parameters of various typical rock and soil masses in alpine regions. The physical and mechanical parameters of rock and soil masses include, but are not limited to, compressive strength, tensile strength, shear strength, permeability coefficient, and freeze - thaw cycle durability; Based on the influence of environmental factors on the properties of rock and soil masses, record the performance data under different conditions.

[0017] Further, in step S2, use a 3D printing device to design specimens of freeze - thaw sensitive composite materials with various formulations and process parameters. The formulations include different proportions of base materials, reinforcing phases, and additives.

[0018] Further, in step S3, the mechanical tests include compressive strength test, tensile strength test, and shear strength test. The failure mode and stress-strain curve are obtained and recorded to analyze the mechanical properties and failure mechanism of the specimen. By simulating the climatic conditions in alpine regions, multiple freeze-thaw cycle experiments are carried out on the specimen, and the performance changes of the specimen during the freeze-thaw cycle are obtained and recorded, including strength loss and volume change. Image recognition and machine learning techniques are used to automatically analyze the experimental results to improve the efficiency and accuracy of data processing.

[0019] Further, in steps S4 and S5, big data and cloud computing technologies are used to establish an intelligent database containing material composition, printing process parameters, and performance data. Through data mining and analysis, the similarities and differences between materials are identified to provide rich data support for intelligent algorithms. Clustering analysis and association rule mining methods are used to discover the potential relationships between material composition, printing process parameters, and performance. Through random forest or neural network algorithms, a mapping relationship model between materials-process-performance is learned and established from the database. The model is trained using cross-validation or grid search methods, and the model is optimized by adjusting algorithm parameters and increasing training data to improve the prediction accuracy and generalization ability of the model.

[0020] Further, in step S6, the predicted performance results include compressive strength, permeability coefficient, and elastic modulus. According to the prediction results, genetic algorithm and particle swarm optimization algorithm are used to recommend the optimal combination of printing parameters and composite material formula.

[0021] Further, step S7 is specifically as follows: Standard specimens are printed according to the recommended formula and process parameters, and mechanical tests and freeze-thaw cycle experiments are carried out. The experimental results are compared with the predicted results to verify the prediction accuracy and reliability of the recommendation system. According to the experimental results, the model is reversely optimized by adjusting the model parameters and recommendation strategies. Step S7 is iterative, and the model is reversely optimized by continuously verifying and adjusting the recommended parameters until the optimal highest prediction accuracy and recommendation effect are achieved.

[0022] Further, in step S8, during the 3D printing process, by introducing a real-time monitoring device, the key parameters of the printing material are monitored in real time, and an adaptive control strategy is developed to automatically adjust the printing process parameters according to the real-time monitoring data, such as key parameters such as the temperature and pressure of the printing material, to ensure the stability and consistency of the printing material. The real-time monitoring device includes an infrared thermal imager and a pressure sensor to monitor the key parameters such as the temperature and pressure of the printing material in real time.

[0023] Further, in step S9, the performance indicators in the comprehensive evaluation system include compressive strength, permeability coefficient, elastic modulus, and number of freeze-thaw cycles; the model uses a multi-objective optimization algorithm to comprehensively consider different performance indicators and find the optimal solution that balances multiple objectives through the multi-objective optimization algorithm, thereby recommending the optimal composite material formula and process parameters.

[0024] Further, step S11 is specifically: establishing an integrated framework and interface for the system to achieve seamless connection and data interaction with the 3D printing device; regularly evaluating the performance and accuracy of the system; according to the evaluation results and user requirements, making necessary iterations and upgrades to the system; at the same time, establishing a continuous technical support and maintenance mechanism to ensure the stable operation and continuous development of the system.

[0025] Compared with the prior art, the advantages of the present invention are as follows:

[0026] The present invention can accurately reproduce the complex mechanical properties of special rock and soil masses in alpine regions. Through multiple experiments and model optimization and adjustment, the correlation mechanism between printing parameters and material properties and the dynamic parameter adjustment mechanism under freeze-thaw cycle conditions are clarified, improving the printing quality and performance stability of composite materials. At the same time, artificial intelligence technology is used to adjust the formula and process parameters of printing materials, improving printing efficiency and accuracy, providing an efficient and accurate solution for the simulation, repair, and engineering application of rock and soil masses in alpine regions, and effectively filling the gap in the current technology in this field.

[0027] The present invention excavates the mapping relationship between material composition, printing process parameters, and material performance data through experiments, establishes a more comprehensive and accurate performance database, and at the same time, combines image recognition and machine learning technologies to automatically analyze experimental results, significantly improving the efficiency and accuracy of data processing.

[0028] By constructing a database and a model, the present invention can accurately predict the performance of composite materials under different formulas and process parameters, and also uses an optimization algorithm to recommend the optimal combination of printing parameters and composite material formulas, further improving the performance stability and consistency of composite materials.

[0029] By introducing real-time monitoring technology during the 3D printing process, the present invention can real-time monitor key parameters such as the temperature and pressure of printing materials, and automatically adjust process parameters such as printing speed, layer thickness, and temperature according to the real-time monitoring data. This adaptive control strategy ensures the stability and consistency of printing materials and improves printing quality.

[0030] By constructing a comprehensive evaluation system containing multiple performance indicators and using a multi-objective optimization algorithm to comprehensively consider different performance indicators and recommend the optimal composite material formula and process parameters, this comprehensive evaluation and optimization method makes the performance of composite materials more comprehensive and balanced. Description of the Drawings

[0031] Figure 1 FIG. 1 is a schematic flow chart of an optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence according to an embodiment of the present invention;

[0032] Figure 2 FIG. 2 is a schematic framework diagram of an optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence according to an embodiment of the present invention. Detailed Embodiments

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be further described below.

[0034] This embodiment provides an optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence, as Figure 1 shown, which includes the following steps:

[0035] Step 1: Obtain the rock and soil property data under different conditions in alpine regions, specifically:

[0036] 1.1: In alpine regions, collect representative rock and soil samples covering different types of geological structures and climatic conditions. The samples include various types such as sandstone, mudstone, frozen soil, and moraine soil to ensure the comprehensiveness and diversity of the data.

[0037] 1.2: Conduct systematic performance tests on the collected rock and soil samples. The test items include but are not limited to compressive strength, tensile strength, shear strength, permeability coefficient, elastic modulus, and freeze-thaw cycle durability. At the same time, considering the influence of environmental factors such as temperature and humidity on the properties of rock and soil, design and implement a series of experiments to record the performance data under different conditions.

[0038] 1.3: Clean, organize, and standardize the collected data, remove outliers and duplicate data, and ensure the accuracy and consistency of the data. Store the processed data in a dedicated database to provide a basis for subsequent analysis.

[0039] Step 2: Based on the rock and soil properties, design multiple formulations and process parameters for freeze-thaw sensitive composite material specimens, and use 3D printing technology to prepare specimens, specifically:

[0040] 2.1: Based on existing research and understanding of the composition of rock and soil, design multiple formulations of freeze-thaw sensitive composite materials; the formulations should cover different proportions of base materials (such as polymers, cement-based materials), reinforcing phases (such as fibers, particles), and additives (such as antifreeze agents, plasticizers). At the same time, considering the influence of different formulations on the printing process parameters, design a reasonable range of printing parameters.

[0041] 2.2: Prepare composite material specimens using a high-precision 3D printer according to the preset process parameters. During the preparation process, key parameters such as printing speed, layer thickness, and temperature should be strictly controlled to ensure the accuracy and repeatability of the specimens. At the same time, uniquely identify the prepared specimens for subsequent experiments and data analysis.

[0042] Step 3: Obtain the performance data of each composite material specimen through systematic mechanical tests and freeze-thaw cycle experiments, and analyze the experimental results through image recognition and machine learning techniques; specifically:

[0043] 3.1: Conduct systematic mechanical tests on the prepared specimens, including compressive strength tests, tensile strength tests, shear strength tests, etc., record the failure modes and stress-strain curves, and analyze the mechanical properties and failure mechanisms of the specimens.

[0044] 3.2: Simulate the climatic conditions in alpine regions and conduct multiple freeze-thaw cycle experiments on the specimens. Record the performance changes of the specimens during the freeze-thaw cycle, including strength loss, volume change, etc.

[0045] 3.3: Introduce image recognition and machine learning techniques to automatically analyze the experimental results and improve the efficiency and accuracy of data processing.

[0046] Step 4: Use big data and cloud computing technologies to establish an intelligent database containing material composition, printing process parameters, and material performance data; this database should support efficient data query, analysis, and mining functions, provide rich data support for subsequent intelligent algorithms, and facilitate the identification of similarities and differences between different materials through data mining and analysis techniques. In this embodiment, methods such as clustering analysis and association rule mining can be used to discover the potential relationships between material composition, printing process parameters, and performance, providing important references and bases for subsequent intelligent algorithms.

[0047] Step 5: Through the said database, establish a mapping relationship model between material-process-performance, and train and optimize the model, specifically:

[0048] 5.1: Select intelligent algorithms such as random forest and neural network, learn from the database and establish a mapping relationship model between material-process-performance; according to the complexity of the problem and the characteristics of the data, select appropriate algorithms and parameter configurations;

[0049] 5.2: Use methods such as cross-validation and grid search to train and optimize the model, and improve the prediction accuracy and generalization ability of the model by adjusting algorithm parameters, increasing training data, etc.; at the same time, evaluate and verify the performance of the model to ensure the reliability and stability of the model.

[0050] Step 6: Use the trained model to predict the performance of freeze-thaw sensitive composite materials under different formulations and process parameters, and based on the prediction results, use an optimization algorithm to recommend the optimal combination of printing parameters and composite material formulations; among them, the prediction results include multiple performance indicators, such as compressive strength, permeability coefficient, elastic modulus, etc.; according to the prediction results, use genetic algorithm and particle swarm optimization algorithm to recommend the optimal combination of printing parameters and composite material formulations. The recommendation system should comprehensively consider multiple performance indicators and actual application requirements to provide a reasonable recommendation scheme.

[0051] Step 7: Print standardized specimens by 3D printing according to the recommendation results, and verify the prediction accuracy of the recommendation results through mechanical tests and freeze-thaw cycle experiments. According to the verification results, reverse optimize the model to adjust the parameters. This step is iterative, specifically:

[0052] 7.1: Print standardized specimens according to the formulations and process parameters recommended in Step 6, conduct mechanical tests and freeze-thaw cycle experiments on the standardized specimens, and record the experimental results;

[0053] 7.2: Compare the experimental results with the prediction results to verify the prediction accuracy and reliability of the recommendation system. According to the verification results, conduct reverse optimization on the model, and improve the prediction accuracy and recommendation effectiveness by adjusting the model parameters and recommendation strategies.

[0054] 7.3: Repeat the above steps for continuous iterative optimization until satisfactory prediction accuracy and recommendation effects are achieved.

[0055] Step 8: Introduce real-time monitoring technology and develop an adaptive control strategy; specifically: during the 3D printing process, introduce real-time monitoring devices such as infrared thermal imagers and pressure sensors to monitor key parameters such as the temperature and pressure of the printing material in real time to ensure the stability and consistency of the printing material; develop an adaptive control strategy to automatically adjust process parameters such as printing speed, layer thickness, and temperature according to real-time monitoring data, and through a feedback control mechanism, achieve dynamic optimization and adaptive adjustment of the printing process.

[0056] Step 9: Build a comprehensive evaluation system and use a multi-objective optimization algorithm to recommend the optimal printing scheme; among them, the evaluation indicators in the comprehensive evaluation system include performance indicators such as compressive strength, permeability coefficient, elastic modulus, and number of freeze-thaw cycles, etc., to comprehensively reflect the performance characteristics of the composite material; this model uses a multi-objective optimization algorithm to comprehensively consider the trade-off relationships between different performance indicators, and solves for the optimal combination of composite material formulations and process parameters through an optimization algorithm, so as to recommend the optimal composite material formulations and process parameters to meet the needs of actual applications.

[0057] In this embodiment, as Figure 2As shown, an intelligent database is constructed by integrating experimental data, literature, and simulation results. The database covers material formulations (such as matrix / reinforcement ratios, additive types), process parameters (printing nozzle temperature, layer thickness, printing speed), and performance indicators (compressive strength, permeability coefficient, freeze-thaw cycle durability, or thermal stability), and has undergone data cleaning and standardization. Based on this database, a mapping model between printing parameters and material properties is established. As Figure 2 shown, in the model diagram, x represents the data set, y is the output result, and the intermediate decision tree branches together form a random forest model. By inputting the target mechanical parameters and combining with the multi-objective optimization algorithm of the random forest, with the goals of maximizing performance and minimizing cost, the optimal parameter combination is searched under process constraints, and a closed-loop optimization system is formed through experimental verification and feedback iteration, ultimately achieving the efficient design and intelligent manufacturing of composite materials.

[0058] Step 10: Through multiple experiments and model adjustments, continuously optimize the prediction accuracy and recommendation ability of the model; at the same time, consider the uncertain factors during the experiment, such as material batch differences, equipment accuracy, etc., analyze the impact of these factors on the model's prediction accuracy and recommendation effect, evaluate the stability and reliability of the model under different conditions through robustness testing, and optimize and improve the model according to the test results to enhance the robustness and adaptability of the model.

[0059] Step 11: Connect the model to the 3D printing device for communication, enabling the system to automatically adjust the material formulation and process parameters during 3D printing to achieve optimal performance, and regularly evaluate the system performance and accuracy, and perform necessary iterations and upgrades according to the evaluation results. Specifically: establish the integrated framework and interface of the system to achieve seamless connection and data interaction with the 3D printing device; regularly evaluate the performance and accuracy of the system; perform necessary iterations and upgrades on the system according to the evaluation results and user requirements; introduce new technologies and methods to improve the performance and competitiveness of the system. At the same time, establish a continuous technical support and maintenance mechanism to ensure the stable operation and continuous development of the system.

[0060] The above are only the preferred embodiments of the present invention and do not impose any limitations on the present invention. Any person skilled in the art, within the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification and other changes to the technical solution and technical content disclosed by the present invention, which are all within the content of the technical solution of the present invention and still fall within the protection scope of the present invention.

Claims

1. An optimized printing method for freeze-thaw sensitive composite materials based on artificial intelligence, characterized in that: The steps include: S1: Obtain rock and soil performance data under different conditions in high-altitude cold regions; S2: Design freeze-thaw sensitive composite material specimens with various formulations and process parameters based on the properties of the rock mass; S3: Obtain performance data of each composite material sample through systematic mechanical testing and freeze-thaw cycle experiments, and analyze the experimental results through image recognition and machine learning technology; S4: Establish an intelligent database containing material composition, printing process parameters and material performance data; S5: Establish a mapping relationship model between materials, processes and performance through the database, and train and optimize the model; S6: Use the trained model to predict the performance of freeze-thaw sensitive composite materials under different formulations and process parameters, and use the optimization algorithm to recommend the optimal printing parameter combination and composite material formulation based on the prediction results; S7: 3D print standardized samples according to the recommended results and verify the prediction accuracy of the recommended results. According to the verification results, reverse optimize the model and iteratively adjust the recommended parameters; S8: Introduce real-time monitoring technology and develop adaptive control strategies; S9: Build a comprehensive evaluation system and use multi-objective optimization algorithms to recommend the best printing solution; S10: Continuously optimize the prediction accuracy and recommendation ability of the model through multiple experiments and model adjustments; and optimize and improve the model through robustness testing; S11: Connect the model to the 3D printing equipment to enable the system to automatically adjust the material formula and process parameters, and regularly evaluate the system performance and accuracy.

2. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: In step S8, during the 3D printing process, a real-time monitoring device is introduced to monitor the key parameters of the printing material in real time, develop an adaptive control strategy, and automatically adjust the printing process parameters; the real-time monitoring device includes an infrared thermal imager and a pressure sensor.

3. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: In step S9, the performance indicators in the comprehensive evaluation system include compressive strength, permeability coefficient, elastic modulus and number of freeze-thaw cycles; the model uses a multi-objective optimization algorithm to comprehensively consider different performance indicators, thereby recommending the optimal composite material formula and process parameters.

4. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: Step S1 is specifically as follows: collecting the physical and mechanical parameters of various typical rock and soil bodies in high-altitude cold areas, the physical and mechanical parameters of rock and soil bodies include but are not limited to compressive strength, tensile strength, shear strength, permeability and freeze-thaw cycle durability; based on the influence of environmental factors on the performance of rock and soil bodies, recording performance data under different conditions.

5. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: In step S2, freeze-thaw sensitive composite material samples with various formulations and process parameters are designed using a 3D printing device, wherein the formulations include different proportions of a base material, a reinforcing phase, and an additive.

6. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: In step S3, the mechanical tests include compressive strength test, tensile strength test, and shear strength test, and the failure morphology and stress-strain curve are obtained and recorded to analyze the mechanical properties and failure mechanism of the sample; multiple freeze-thaw cycle experiments are carried out on the sample by simulating the climatic conditions in high-altitude cold areas to obtain and record the performance changes of the sample during the freeze-thaw cycle, including strength loss and volume change; and the experimental results are automatically analyzed using image recognition and machine learning technology.

7. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: In steps S4 and S5, cluster analysis and association rule mining methods are used to discover the potential relationship between material composition, printing process parameters and performance. A random forest or neural network algorithm is used to learn and establish a mapping relationship model between material, process and performance from the database. The model is trained using cross-validation or grid search methods, and the model is optimized by adjusting algorithm parameters and increasing training data.

8. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: In step S6, the predicted performance results include compressive strength, permeability coefficient, and elastic modulus; based on the predicted results, the genetic algorithm and particle swarm optimization algorithm are used to recommend the optimal printing parameter combination and composite material formula.

9. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: Step S7 is specifically as follows: printing standardized samples according to the recommended formula and process parameters, and conducting mechanical tests and freeze-thaw cycle experiments; comparing the experimental results with the predicted results to verify the prediction accuracy and reliability of the recommendation system; according to the experimental results, reversely optimizing the model by adjusting the model parameters and the recommendation strategy; step S7 is iteratively performed, and reversely optimizing the model by continuously verifying and adjusting the recommended parameters until the optimal prediction accuracy and recommendation effect are achieved.

10. The method for optimizing printing of freeze-thaw sensitive composite materials based on artificial intelligence according to claim 1, characterized in that: Step S11 is specifically as follows: establishing an integrated framework and interface for the system to achieve seamless connection and data interaction with 3D printing equipment; regularly evaluating the performance and accuracy of the system; iterating and upgrading the system based on the evaluation results and user needs; and at the same time, establishing a continuous technical support and maintenance mechanism to ensure the stable operation and sustainable development of the system.

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