Sound absorbing stone material curtain wall with micro-holes

Abstract

The application provides a sound-absorbing stone curtain wall with micro holes, which comprises a frame, organic glass and a stone plate, the organic glass and the stone plate are both installed in the frame, a cavity is formed between the organic glass and the stone plate, the organic glass is arranged in the direction of the inner surface of a building to be decorated, and a plurality of micro holes are arranged on the organic glass. The aperture of the holes arranged on the organic glass is 0.5cm-1.5cm, and the hole distance is 1.2cm-5cm. The distance between the organic glass and the stone plate is 12mm. The surface of the stone plate opposite to the organic glass retains the texture of the stone plate. The surface of the stone plate opposite to the organic glass is provided with a large number of micro holes according to preset micro hole parameters, and a multi-objective optimization method is used to determine the micro hole parameters, so that the sound-absorbing decorative stone is obtained, and the technical problems that the prior art does not consider the stone texture, does not absorb sound and affects the indoor sound expansion clarity are solved.

Description

Technical Field

[0001] This invention belongs to the field of sound-absorbing stone technology, and more specifically, relates to a sound-absorbing stone curtain wall with micropores. Background Technology

[0002] As people place increasing emphasis on the quality of sound environment in their work and living spaces, the application of sound-absorbing materials in construction, automotive, and aerospace industries is becoming increasingly widespread. Building facades are a crucial component, affecting not only the building's aesthetic appearance but also its internal acoustic environment. Traditional building facades are mostly constructed from solid stone or other hard materials. Natural stone, as an excellent decorative material, combines beauty and durability, making it popular in many sound-sensitive public spaces. However, the acoustic performance of natural stone itself is often suboptimal, failing to meet people's needs for a good sound environment. This structure has poor sound absorption and isolation effects, easily causing acoustic problems such as echoes within the building. To address this issue, the industry has proposed various improvement solutions, such as using composite materials with certain sound-absorbing properties as the main material of the facade or installing specialized sound-absorbing devices within the facade. However, these solutions often have limitations, such as limited sound absorption effects, complex structures, and difficulties in installation and maintenance. In recent years, with the continuous advancement of materials science and technology, some new sound-absorbing materials and structures have been gradually developed and applied.

[0003] Currently, there are two main methods commonly used in the industry to improve the sound absorption performance of stone: one is to coat the stone surface with a sound-absorbing coating; the other is to add a porous structure to the stone surface. The former, by coating the stone surface with a sound-absorbing coating to form a sound-absorbing layer, can effectively improve the overall sound absorption performance, but at the same time, it changes the original appearance and texture of the stone, ruining the visual effect of natural stone. The latter utilizes a microporous structure to effectively absorb sound waves through damping and resonance effects, thereby effectively improving the acoustic environment of the building. However, simply drilling micropores directly into the stone will affect the aesthetic effect of the stone curtain wall. Summary of the Invention

[0004] In view of this, the present invention provides a sound-absorbing stone curtain wall with micropores, which can solve the technical problem that the existing technology of simply drilling micropores directly on stone will affect the aesthetic effect of the stone curtain wall.

[0005] This invention is implemented as follows:

[0006] This invention provides a sound-absorbing stone curtain wall with micro-perforations, comprising a frame, plexiglass, and stone slabs. Both the plexiglass and the stone slabs are installed within the frame, forming a cavity between them. The plexiglass is positioned on the inner surface of the building to be decorated, and it has multiple micro-perforations. The perforations have a diameter of 0.5cm to 1.5cm and a spacing of 1.2cm to 5cm.

[0007] Preferably, the acrylic glass has multiple perforations with a diameter of 0.5 cm and a spacing of 1.2 cm to 1.8 cm.

[0008] The frame has dimensions of 300mm × 300mm × 39mm, and the acrylic glass has a thickness of 6mm to 8mm. Preferably, the acrylic glass has a thickness of 6mm.

[0009] The distance between the plexiglass and the stone slab is 12mm, and the thickness of the stone slab is 25mm.

[0010] Furthermore, the surface of the stone slab opposite the plexiglass retains the stone slab's own texture.

[0011] Furthermore, the surface of the stone slab opposite the plexiglass has a large number of micropores formed according to preset micropore parameters. The step of determining the preset micropore parameters specifically includes:

[0012] S10. Based on the scenario where sound-absorbing stone needs to be installed, obtain the sound frequency distribution of the scenario.

[0013] S20. Obtain the physical and texture properties of the base stone to be produced;

[0014] S30. Establish a first mathematical model to simulate the influence of different micropore parameters on sound wave absorption performance, and establish an evaluation function of the sound frequency distribution and micropore parameters as the first objective function.

[0015] S40. Establish a second mathematical model that combines the distribution of micropores with the texture characteristics of stone, and establish an evaluation function for the fit between the distribution of micropores and texture, which serves as the second objective function.

[0016] S50. A multi-objective optimization algorithm is adopted to simultaneously optimize the first objective function and the second objective function to obtain the optimal trade-off solution of the two objective functions, which is used as the optimal micropore parameters.

[0017] S60. Select multiple basic stone samples, and prepare multiple experimental samples according to the optimal micropore parameters. Perform sound absorption tests and texture evaluations on each experimental sample to obtain actual sound absorption data and texture matching data.

[0018] S70. Compare the actual sound absorption data and texture fit data with the calculation results of the first mathematical model and the second mathematical model, correct the parameters of the first mathematical model and the second mathematical model, repeat steps S50 to S60 until the deviation between the model prediction and the measured result is within an acceptable range, and obtain the target micropore parameters.

[0019] The base stone is un-drilled stone, which is then drilled to form a stone slab.

[0020] Specifically, step S10 includes:

[0021] Step S101: Conduct on-site survey of the target scene. Using professional measurement equipment such as a sound level meter, measure the sound pressure level and spectrum distribution of the scene at different time periods to obtain sound frequency distribution data of the scene under different time and space conditions.

[0022] Step S102: Combining factors such as the usage function of the scene and the population density, predict the main noise sources and their frequency characteristics that may occur in the future use of the target scene. This is done by referring to the noise source analysis data of similar scenes and analyzing the usage characteristics of the target scene.

[0023] Step S103: Overlay the measured data and the predicted data to obtain the comprehensive sound frequency distribution of the target scene. Use statistical analysis methods to calculate the average value, variance and other characteristic parameters of the sound pressure level of each frequency band as the sound frequency distribution characteristics of the scene.

[0024] Specifically, step S20 includes:

[0025] Step S201: Conduct physical property tests on the basic stone sample to be produced, including measuring indicators such as density, porosity, elastic modulus, and thermal conductivity. Obtaining these data can provide necessary parameter inputs for the subsequent establishment of a sound absorption model.

[0026] Step S202: Evaluate the surface texture characteristics of the basic stone sample, including measuring indicators such as surface roughness, color, and pattern, and obtain these stone texture characteristic data to provide a basis for the subsequent establishment of a model that matches the micropore distribution.

[0027] Step S203: Organize and analyze the physical properties and texture features data obtained above, establish a parameter file for basic stone materials, including statistical analysis of various indicators, and determine the representative values ​​of various performance parameters.

[0028] Specifically, step S30 includes:

[0029] Step S301: Using numerical simulation methods such as the finite element method, establish a mathematical model describing the propagation and absorption of sound waves in porous stone. Consider the complex processes of sound wave reflection, scattering, and dissipation inside the stone, and couple it with micropore parameters (pore size, depth, distribution density, etc.). By numerically solving the model, the sound absorption performance under different combinations of micropore parameters can be predicted.

[0030] Step S302: Establish an objective function to quantify sound absorption performance, such as the average sound wave absorption coefficient, which represents the overall sound absorption level within the target sound frequency range.

[0031] Step S303: Use numerical optimization algorithms, such as genetic algorithms and particle swarm optimization, to perform multi-objective optimization of micropore parameters and find the combination of micropore parameters that can meet the best sound absorption performance in the target sound frequency range.

[0032] Specifically, step S40 includes:

[0033] Step S401: Analyze the influence of micropore distribution on the texture of stone surface. The presence of micropores will change the roughness, reflective properties, etc. of the stone surface, thus affecting the overall visual effect. It is necessary to establish a mathematical model to describe the relationship between micropore distribution and texture features.

[0034] Step S402: Define an evaluation function that characterizes the fit between micropore distribution and stone texture. This function can comprehensively consider multiple texture indicators such as surface roughness, color, and pattern to give an overall fit score, which serves as the second optimization objective function.

[0035] Step S403: Using image processing, machine learning and other techniques, establish the mapping relationship between micropore distribution parameters and texture fit. Through training with a large amount of experimental data, establish a model that can quickly predict the impact of micropore distribution on texture.

[0036] Specifically, step S50 includes:

[0037] Step S501: Based on the two objective functions established in steps S30 and S40, construct a multi-objective optimization problem. The first objective function is the acoustic absorption coefficient, and the second objective function is the texture fit score.

[0038] Step S502: Select a suitable multi-objective optimization algorithm, such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II) or the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), which can find the optimal trade-off solution among multiple objective functions and provide a set of Pareto optimal solutions.

[0039] Step S503: Through iterative optimization, find the optimal trade-off solution between the first objective function (sound absorption performance) and the second objective function (texture fit), which is the final optimal combination of micropore parameters.

[0040] Specifically, step S60 includes:

[0041] Step S601: Select several representative basic stone samples from the candidate raw materials to be made into sound-absorbing stone, covering different physical properties and texture features, to provide diversity for subsequent experimental verification.

[0042] Step S602: Based on the optimal micropore parameters obtained in step S50, a corresponding micropore array is manufactured on the surface of the selected base stone sample using processes such as laser drilling and chemical etching to ensure that the micropore parameters of each sample meet the optimization results.

[0043] Step S603: Conduct detailed testing on the manufactured experimental sample, including measuring its actual sound absorption performance indicators, such as the sound wave absorption coefficient, and evaluating its surface texture characteristics, such as roughness and color, and obtain these measured data.

[0044] Specifically, step S70 includes:

[0045] Step S701: Compare and analyze the measured sound absorption performance data of the experimental sample with the prediction results of the first mathematical model established in step S30, find the deviation between the two, and analyze the possible reasons for the deviation.

[0046] Step S702: Compare and analyze the surface texture evaluation data of the experimental sample with the prediction results of the second mathematical model established in step S40, analyze the differences between the two, and find out the possible reasons.

[0047] Step S703: Based on the above comparative analysis results, adjust the key parameters in the first and second mathematical models appropriately, such as material property parameters and micropore influence coefficients, and make the model prediction results as close as possible to the measured data through iterative optimization.

[0048] Step S704: After completing the model parameter correction, repeat the multi-objective optimization process of step S50 to obtain a more accurate optimal combination of micropore parameters.

[0049] The base stone is un-drilled stone, which is then drilled to form a stone slab.

[0050] Optionally, the density of the base stone is between 2400-2800 kg / m³. 3The porosity is between 0.5% and 3%, the elastic modulus is between 40 and 80 GPa, the thermal conductivity is between 1.5 and 3.5 W / (m·K), and the surface roughness Ra is between 0.2 and 1.5 μm.

[0051] Optionally, the target audio frequency range is 300-3000Hz, and the average sound absorption coefficient is greater than 0.65.

[0052] The acceptable range is defined as a deviation of less than 5%.

[0053] The physical properties include density, porosity, elastic modulus, and thermal conductivity.

[0054] The texture characteristics include surface roughness, color, and pattern.

[0055] The micropore parameters include pore arrangement, pore diameter, pore depth, and pore spacing.

[0056] The optimal solution of the first objective function is the combination of micropore parameters for the best sound absorption effect.

[0057] The multi-objective optimization algorithm is either the NSGA-II algorithm or the MOPSO algorithm.

[0058] The base stone is marble, granite, or sandstone.

[0059] Furthermore, the optimal solution to the first objective function can be obtained using a genetic algorithm or a particle swarm optimization algorithm.

[0060] Compared with existing technologies, the beneficial effects of the sound-absorbing stone curtain wall with micropores provided by this invention are:

[0061] 1. Establish a mathematical model for sound absorption performance and micropore parameters.

[0062] A mathematical model detailing the propagation and absorption of sound waves in microporous stone was established using numerical analysis methods such as finite element simulation. This model couples parameters such as the size and distribution density of the micropores with the sound absorption performance of the stone, enabling the prediction of sound absorption effects under different combinations of micropore parameters. Numerical optimization algorithms can then be used to find the micropore parameters that provide optimal sound absorption performance under given acoustic conditions.

[0063] 2. Establish a model for the correlation between micropore distribution and visual texture.

[0064] In addition to sound absorption performance, this invention also emphasizes maintaining the original decorative effect of the stone. By analyzing the influence of micropore distribution on the surface roughness and reflectivity of the stone, an evaluation function describing the fit between micropores and texture features was established. Using machine learning and other techniques, a mapping relationship between micropore parameters and texture fit was established. Using this texture fit evaluation as a second optimization objective, the optimal balance between sound absorption performance and visual effect can be found.

[0065] 3. Employing multi-objective optimization to design optimal micropore parameters

[0066] Based on the two mathematical models mentioned above, this invention employs a multi-objective optimization algorithm to simultaneously optimize both sound absorption performance and visual fit, yielding the optimal trade-off solution for these two indicators—the best combination of micropore parameters. This approach enhances the sound absorption of the stone while maintaining its original decorative properties.

[0067] Compared with existing technologies, the sound-absorbing stone curtain wall with micropores proposed in this invention has the following main advantages:

[0068] 1. Significantly improved sound absorption performance: By optimizing the design of micropore parameters, a sound absorption coefficient greater than 0.65 can be achieved within the target sound frequency range, which is a significant improvement compared to ordinary stone.

[0069] 2. Maintain good decorative effect: While improving sound absorption performance, it can maintain the original surface texture and color characteristics of the stone to the greatest extent to meet decorative requirements.

[0070] In summary, this invention solves the technical problem that simply drilling micro-holes directly into stone in the prior art would affect the aesthetic effect of the stone curtain wall. Attached Figure Description

[0071] Figure 1 This is a structural schematic diagram of a sound-absorbing stone curtain wall with micropores provided by the present invention.

[0072] Figure 2 This is a flowchart of the method for determining the micropore parameters involved in this invention.

[0073] The specific labels in the diagram indicate: 10, frame; 20, plexiglass; 30, stone slab. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0075] like Figure 1As shown, this invention provides a sound-absorbing stone curtain wall with micro-perforations, comprising a frame 10, acrylic glass 20, and stone slabs 30. Both the acrylic glass and the stone slabs are installed within the frame, forming a cavity between them. The acrylic glass is intended to be installed on the inner surface of the building to be decorated, and it has multiple micro-perforations. The perforations in the acrylic glass have a diameter of 0.5cm to 1.5cm and a spacing of 1.2cm to 5cm.

[0076] Preferably, the acrylic glass has multiple perforations with a diameter of 0.5 cm and a spacing of 1.2 cm to 1.8 cm.

[0077] The frame has dimensions of 300mm × 300mm × 39mm, and the acrylic glass has a thickness of 6mm to 8mm. Preferably, the acrylic glass has a thickness of 6mm.

[0078] The distance between the plexiglass and the stone slab is 12mm, and the thickness of the stone slab is 25mm.

[0079] Furthermore, the surface of the stone slab opposite the plexiglass retains the stone slab's own texture.

[0080] Furthermore, the surface of the stone slab opposite the plexiglass has a large number of micropores formed according to preset micropore parameters. The step of determining the preset micropore parameters specifically includes:

[0081] S10. Based on the scene where sound-absorbing stone needs to be installed, obtain the sound frequency distribution of the scene;

[0082] S20. Obtain the physical and texture properties of the base stone to be produced;

[0083] S30. Establish the first mathematical model to simulate the influence of different micropore parameters on the sound wave absorption performance, and establish an evaluation function of sound frequency distribution and micropore parameters as the first objective function;

[0084] S40. Establish a second mathematical model that combines the distribution of micropores with the texture characteristics of stone, and establish an evaluation function for the fit between the distribution of micropores and texture, which serves as the second objective function.

[0085] S50. A multi-objective optimization algorithm is adopted to simultaneously optimize the first objective function and the second objective function to obtain the optimal trade-off solution of the two objective functions, which is used as the optimal micropore parameters.

[0086] S60. Select multiple basic stone samples, and make multiple experimental samples according to the optimal micropore parameters. Perform sound absorption tests and texture evaluations on each experimental sample to obtain actual sound absorption data and texture matching data.

[0087] S70. Compare the actual sound absorption data and texture fit data with the calculation results of the first mathematical model and the second mathematical model, correct the parameters of the first mathematical model and the second mathematical model, repeat steps S50 to S60 until the deviation between the model prediction and the measured result is within an acceptable range, and obtain the target micropore parameters.

[0088] The specific implementation methods of the above steps are described in detail below:

[0089] Step S10: Based on the scene where sound-absorbing stone needs to be installed, obtain the sound frequency distribution of the scene.

[0090] To obtain the sound frequency distribution of the desired installation scenario for sound-absorbing stone, the following sub-steps can be used:

[0091] S101. Conduct on-site surveys of the target scene, using professional measuring equipment such as sound level meters to measure the sound pressure level and spectral distribution of the scene at different time periods. Through multiple measurements, obtain sound frequency distribution data of the scene under different time and spatial conditions.

[0092] S102. Based on factors such as the usage function and population density of the scenario, predict the main noise sources and their frequency characteristics that may occur in the target scenario during future use. This can be accomplished by referring to noise source analysis data from similar scenarios and analyzing the usage characteristics of the target scenario.

[0093] S103. Overlay the measured data with the predicted data to obtain the comprehensive sound frequency distribution of the target scene. Statistical analysis methods can be used to calculate the average value, variance, and other characteristic parameters of the sound pressure level in each frequency band, which can be used as the sound frequency distribution characteristics of the scene.

[0094] By following the steps above, comprehensive information on the sound frequency distribution of the target installation scenario can be obtained, providing a basis for subsequent optimization of micro-hole parameters. The purpose of this step is to understand the acoustic environment to be processed, providing fundamental data support for the subsequent optimization design.

[0095] Step S20: Obtain the physical and texture properties of the base stone to be produced.

[0096] To obtain the physical and textural properties of the base stone to be used, the following sub-steps can be performed:

[0097] S201. Conduct physical property tests on the base stone sample to be prepared, including measurements of indicators such as density, porosity, elastic modulus, and thermal conductivity. This can be accomplished using standard physical performance testing methods, such as density testing, compression testing, and thermal conductivity testing. Obtaining this data provides the necessary parameter input for subsequent sound absorption model development.

[0098] S202. Evaluate the surface texture characteristics of the base stone samples, including measurements of surface roughness, color, and pattern. This can be achieved using optical measuring instruments such as coordinate measuring machines and scanning electron microscopes to accurately measure and analyze the stone surface morphology. Obtaining this stone texture characteristic data provides a basis for subsequently establishing a model that matches the micropore distribution.

[0099] S203. Organize and analyze the acquired physical properties and texture features data to establish a parameter profile for the basic stone material. This includes statistical analysis of various indicators to determine representative values ​​for each performance parameter. This data will serve as input for subsequent modeling and experiments.

[0100] Through the above steps, a comprehensive understanding of the physical and surface properties of the base stone to be manufactured can be obtained, providing important parameter basis for subsequent sound absorption design optimization. The purpose of this step is to obtain basic characteristic data of the stone itself, as the basis for establishing a sound absorption model and evaluating micropore distribution.

[0101] Step S30: Establish the first mathematical model to simulate the influence of different micropore parameters on acoustic wave absorption performance.

[0102] To establish the first mathematical model and simulate the effect of different micropore parameters on acoustic wave absorption performance, the following sub-steps can be used:

[0103] S301. A mathematical model describing the propagation and absorption of sound waves in microporous stone is established using numerical simulation methods such as the finite element method. This model needs to consider the complex processes of sound wave reflection, scattering, and dissipation within the stone, and couple these processes with micropore parameters (pore size, depth, distribution density, etc.). By numerically solving this model, the sound absorption performance under different combinations of micropore parameters can be predicted.

[0104] S302. To optimize the micropore parameters, an objective function needs to be established to quantify the sound absorption performance. A commonly used objective function is the average sound absorption coefficient, which represents the overall sound absorption level within the target sound frequency range. Other indicators, such as the sound absorption coefficient for specific frequency bands, can also be considered.

[0105] S303. Employ numerical optimization algorithms, such as genetic algorithms and particle swarm optimization, to perform multi-objective optimization of the micropore parameters, seeking the combination of micropore parameters that can satisfy the optimal sound absorption performance within the target audio frequency range. The purpose of this step is to obtain the micropore parameters that can provide the best sound absorption effect under given acoustic environment conditions.

[0106] Through the above steps, a mathematical model capable of simulating the influence of micropore parameters on acoustic wave absorption performance was established, and the optimal combination of micropore parameters was obtained through optimization algorithms. This provides a theoretical basis for subsequent experimental design and sample fabrication.

[0107] Step S40: Establish a second mathematical model that combines the distribution of micropores with the texture characteristics of the stone.

[0108] To establish a second mathematical model that combines the distribution of micropores with the texture characteristics of the stone, the following sub-steps can be used:

[0109] S401. Analyze the impact of micropore distribution on the surface texture of stone. The presence of micropores alters the roughness and reflective properties of the stone surface, thus affecting the overall visual effect. A mathematical model describing the relationship between micropore distribution and texture features needs to be established.

[0110] S402. Define an evaluation function to characterize the fit between micropore distribution and stone texture. This function can comprehensively consider multiple texture indicators such as surface roughness, color, and pattern, and give an overall fit score. This scoring function will serve as the second optimization objective function.

[0111] S403. Using image processing, machine learning, and other techniques, a mapping relationship between micropore distribution parameters and texture fit is established. Through training with a large amount of experimental data, a model capable of quickly predicting the impact of micropore distribution on texture is built.

[0112] Through the above steps, a mathematical model was established that can predict the impact of micropore distribution on the surface texture of stone. This provides a basis for subsequent optimization that balances sound absorption performance and texture consistency.

[0113] Step S50: Employ a multi-objective optimization algorithm to simultaneously optimize the first objective function and the second objective function.

[0114] To employ a multi-objective optimization algorithm that simultaneously optimizes the first objective function (sound absorption performance) and the second objective function (texture fit), the following sub-steps can be used:

[0115] S501. Based on the two objective functions established in steps S30 and S40, construct a multi-objective optimization problem. The first objective function is the acoustic absorption coefficient, and the second objective function is the texture fit score.

[0116] S502. Select a suitable multi-objective optimization algorithm, such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II) or the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. These algorithms can find the optimal trade-off solution among multiple objective functions and provide a set of Pareto optimal solutions.

[0117] S503. Through iterative optimization, find the optimal trade-off between the first objective function (sound absorption performance) and the second objective function (texture fit). This solution is the final optimal combination of micropore parameters, which achieves a balance between sound absorption effect and texture aesthetics.

[0118] Through the above steps, a multi-objective optimization algorithm was used to simultaneously optimize both sound absorption performance and texture compatibility, resulting in optimal micropore parameters that balance both objectives. This provides a theoretical basis for subsequent experimental sample fabrication.

[0119] Step S60: Select multiple basic stone samples and prepare multiple experimental samples according to the optimal micropore parameters.

[0120] To select multiple base stone samples, and to prepare multiple experimental samples based on the optimal micropore parameters, the following sub-steps can be used:

[0121] S601. Select several representative base stone samples from the candidate raw materials to be used to produce sound-absorbing stone. These samples should cover different physical properties and texture characteristics to provide diversity for subsequent experimental verification.

[0122] S602. Based on the optimal micropore parameters obtained in step S50, including pore size, depth, and distribution density, a corresponding micropore array is fabricated on the surface of the selected base stone sample using processes such as laser drilling and chemical etching. Ensure that the micropore parameters of each sample conform to the optimization results.

[0123] S603. Conduct detailed testing on the manufactured experimental samples, including measuring their actual sound absorption performance indicators, such as the sound wave absorption coefficient, and evaluating their surface texture characteristics, such as roughness and color. Obtaining these measured data provides a basis for subsequent comparison and verification with the model prediction results.

[0124] Through the above steps, multiple experimental samples were manufactured based on the optimal micropore parameters obtained through theoretical optimization. These samples covered different types of base stone, providing necessary samples for subsequent experimental testing and model verification.

[0125] Step S70: Compare the actual sound absorption data and texture matching data with the calculation results of the first mathematical model and the second mathematical model, and correct the parameters of the first mathematical model and the second mathematical model.

[0126] To compare the actual sound absorption data and texture matching data with the calculation results of the first and second mathematical models, and to correct the parameters of the first and second mathematical models, the following sub-steps can be used:

[0127] S701. Compare and analyze the measured sound absorption performance data of the experimental sample obtained in step S60 with the prediction results of the first mathematical model established in step S30. Identify the deviation between the two and analyze the possible causes of the deviation, such as micropore parameter measurement error, inaccurate material property input, etc.

[0128] S702. Similarly, compare the surface texture evaluation data of the experimental samples with the prediction results of the second mathematical model established in step S40. Analyze the differences between the two and identify possible reasons, such as insufficient accuracy of the micropore distribution and texture relationship model.

[0129] S703. Based on the above comparative analysis results, appropriately adjust the key parameters in the first and second mathematical models, such as material property parameters and micropore influence coefficients. Through iterative optimization, make the model prediction results as close as possible to the measured data, reaching an acceptable deviation range.

[0130] S704. After completing the model parameter calibration, repeat the multi-objective optimization process of step S50 to obtain a more accurate optimal combination of micropore parameters.

[0131] Through the above steps, the experimental test data and mathematical model predictions were repeatedly compared and the parameters were optimized, making the model predictions closer to reality. This provides reliable theoretical guidance for subsequent sample manufacturing.

[0132] Through the above steps, based on the optimized micropore parameters, advanced processing technology is used to create the required micropore array on the stone surface, ultimately producing a sound-absorbing stone slab with excellent sound absorption performance and aesthetically pleasing texture. This step is the final step in the entire manufacturing process, transforming the initial theoretical design into a practical product.

[0133] To better understand this invention, the specific embodiments of this invention will be described in more detail below with reference to specific formulas. In step S10, it is necessary to obtain the sound frequency distribution information of the target installation scene. First, the target scene can be measured in the field using equipment such as a sound level meter to obtain sound pressure level data at different time periods and spatial locations. These measurement data are then organized into sound pressure level data. Where f represents frequency and t represents time. Indicates spatial location.

[0134] Then, considering the characteristics of the usage scenario, the main noise sources and their frequency characteristics are predicted. Assuming there are N main noise sources, the sound pressure level of each noise source can be expressed as L. p,i (f), i = 1, 2, ..., N. By superimposing the predicted noise source sound pressure level data with the measured data, the comprehensive sound frequency distribution of the target scene can be obtained:

[0135]

[0136] Where, L p (f) represents the overall sound pressure level of the target scene at frequency f. Through statistical analysis, the average sound pressure level μ for each frequency band can be calculated. p (f) and variance This serves as the audio frequency distribution characteristic of the scene.

[0137] In step S20, it is necessary to obtain the physical properties and texture characteristics of the base stone to be produced. For physical properties, indicators such as density ρ, porosity φ, elastic modulus E, and thermal conductivity k can be tested.

[0138] For texture features, surface roughness R can be measured. a Indicators such as color (C) and pattern (P) can be used. These indicators can be described mathematically; for example, surface roughness can be expressed as the arithmetic mean roughness (R). a It can be indicated that colors can be described using values ​​in RGB or HSV color spaces, and patterns can be characterized using methods such as Fourier transform or wavelet transform.

[0139] The above physical properties and texture features are organized into a parameter set Ω={ρ,φ,E,k,R} a ,C,P}, are used as input data for subsequent modeling and experiments.

[0140] In step S30, a mathematical model describing the propagation and absorption of sound waves in microporous stone needs to be established. Numerical simulation methods such as the finite element method can be used to establish the following sound wave propagation equation:

[0141]

[0142] Where p is the sound pressure, k c =ω / c is the complex wave number, ω is the angular frequency, and c is the speed of sound. This model needs to consider the effects of the micropore on sound wave reflection, scattering, and dissipation, which can be addressed by introducing a micropore effect factor α:

[0143] k c =ω / c0+iα

[0144] c0 represents the sound velocity in non-porous stone. By numerically solving this model, the sound absorption performance under different combinations of micropore parameters can be predicted.

[0145] To optimize micropore parameters, an average acoustic absorption coefficient is defined. As the objective function:

[0146]

[0147] Where [f1, f2] represents the target audio frequency range. Using optimization algorithms, such as genetic algorithms, the optimal frequency range can be determined. The maximized combination of micropore parameters Φ = {d, h, N, s}, where d is the pore diameter, h is the pore depth, N is the pore density, and s is the pore spacing.

[0148] In step S40, a relationship model between micropore distribution and stone texture characteristics needs to be established. The presence of micropores alters the surface roughness and reflectivity of the stone, thus affecting the overall visual effect. A texture fit evaluation function F can be used. t To quantify this impact:

[0149] F t =w1f1(R a )+w2f2(C)+w3f3(P)

[0150] Where f1, f2, and f3 are the mapping functions corresponding to roughness, color, and pattern, respectively, and w1, w2, and w3 are the corresponding weighting coefficients. The higher the value of this evaluation function, the better the micropore distribution matches the original stone texture.

[0151] Machine learning methods can be used to establish the relationship between micropore parameters Φ and texture fit F. t Mapping relationship between them:

[0152] F t =g(Φ)

[0153] Here, g(·) is a nonlinear mapping function that can be determined through training with a large amount of experimental data.

[0154] In step S50, a multi-objective optimization algorithm is required to simultaneously optimize both sound absorption performance and texture compatibility. The first objective function is defined as the average sound absorption coefficient. The second objective function is the texture fit degree F. t Construct a multi-objective optimization problem:

[0155]

[0156] stΦ={d,h,N,s}

[0157] This multi-objective optimization problem can be solved using methods such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II) or the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, to obtain a set of Pareto optimal solutions, which are the optimal trade-off solutions between the two objectives of sound absorption performance and texture fit.

[0158] In step S60, experimental samples need to be manufactured. First, representative basic stone samples are selected from the candidate raw materials, covering different physical properties and texture features, and denoted as the sample set Ω. s ={Ω1,Ω2,...,Ω m}

[0159] Then, based on the optimal micropore parameter Φ obtained in step S50... * ={d * ,h * N * ,s * A corresponding array of micropores is fabricated on the surface of each sample. For laser drilling, CNC machining equipment can be used, according to Φ * Precise control of micropore size and distribution. For chemical etching processes, this can be achieved by adjusting the etching time t. c and the concentration of the corrosive solution C c To achieve this.

[0160] Finally, the manufactured sound-absorbing stone samples were subjected to detailed testing, and their sound absorption coefficient α was measured. i Texture fit F t,i Record the actual test dataset {(α) i ,F t,i )}, i=1,2,...,m.

[0161] In step S70, it is necessary to compare the model prediction results with the measured data and optimize the model parameters. First, the measured sound absorption data {α} are compared with the measured data. i} and the model prediction results of step S30 Compare them and calculate the average relative error between them.

[0162]

[0163] Similarly, the measured texture fit {F} t,i} and the model prediction {F} in step S40 t,i Compare the results and calculate the average relative error.

[0164] If both of the above error indicators are less than the acceptable threshold ∈ 0, it indicates that the model prediction is accurate enough; otherwise, the model parameters need to be adjusted. Key parameters such as the micropore effect factor α, texture mapping functions f1, f2, f3, and weight coefficients w1, w2, w3 can be optimized using methods such as least squares to make the model prediction results more consistent with the measured data.

[0165] After optimization, repeat the multi-objective optimization process in step S50 to obtain a more accurate optimal micropore parameter Φ. * .

[0166] In step S80, sound-absorbing stone products need to be manufactured based on the optimized micropore parameters. For laser drilling, a laser beam diameter d can be used. L Power P L and scanning speed v L Process parameters are used to control the micropore size to satisfy d = d L h=P L / (v L k s ), where k s The absorption coefficient of the stone.

[0167] For chemical corrosion processes, the concentration C of the corrosion solution can be adjusted. c and corrosion time t c To control the micropore size, satisfying d = f1(C c ,t c ),h=f2(C c ,t c ), where f1 and f2 are empirical relational functions.

[0168] Finally, the manufactured sound-absorbing stone samples were subjected to detailed testing to measure their actual sound absorption coefficient and texture characteristics, and compared with the previous prediction results to ensure that the product performance met the requirements.

[0169] In summary, this method for determining the parameters of microperforations fully utilizes mathematical modeling and multi-objective optimization techniques to achieve an effective balance between sound absorption performance and visual aesthetics. Through iterative optimization via experimental verification and model calibration, the optimal micropore parameter design was finally obtained, and the required microporous structure was fabricated on the stone surface using advanced processing technology.

[0170] Specifically, the principle of this invention is:

[0171] 1. Mathematical Modeling of Sound Wave Propagation and Absorption

[0172] To accurately predict the sound absorption performance of stone under different combinations of micropore parameters, this invention employs numerical simulation methods such as the finite element method to establish a mathematical model that details the propagation and absorption process of sound waves in microporous stone. This model considers the complex influencing factors of micropores on sound wave reflection, scattering, and dissipation, coupling sound wave propagation characteristics with micropore parameters (such as pore size, depth, and distribution density). By numerically solving this model, the predicted sound absorption coefficients under different micropore design schemes can be obtained, providing a basis for optimizing micropore parameters.

[0173] 2. Modeling the relationship between micropore distribution and visual texture

[0174] In addition to improving sound absorption performance, this invention also focuses on maintaining the original decorative effect of the stone. The presence of micropores alters the surface roughness and reflective properties of the stone, thus affecting its visual texture characteristics. To quantitatively analyze this effect, this invention establishes a mathematical model to evaluate the relationship between micropore distribution and texture fit. This model comprehensively considers multiple texture indicators such as surface roughness, color, and pattern, providing an overall fit score as a second optimization objective. A mapping relationship between micropore parameters and texture fit is established using machine learning and other methods, providing a basis for optimizing the trade-off between sound absorption performance and visual effect.

[0175] 3. Multi-objective optimization design of optimal micropore parameters

[0176] Based on the two mathematical models mentioned above, this invention employs a multi-objective optimization algorithm, such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II), to simultaneously optimize both sound absorption performance and visual fit, obtaining the optimal trade-off solution for these two indicators. This improves the overall sound absorption effect of the stone while maintaining its original decorative properties. The key to multi-objective optimization lies in finding the Pareto optimal solution between these two often contradictory objectives, providing a set of optimal micropore parameter combinations that meet the needs of practical applications.

[0177] 4. Advanced processing technology for manufacturing microporous arrays

[0178] To precisely manufacture the designed micropore array on the stone surface, this invention employs advanced processing techniques such as laser drilling and chemical etching. These techniques allow for precise control of the micropore size and distribution, ensuring that the final product's performance meets design requirements. Laser drilling allows for precise control of the micropore diameter and depth by adjusting parameters such as laser beam diameter, power, and scanning speed. Chemical etching, on the other hand, controls the dimensional characteristics of the micropores by adjusting the concentration of the etching solution and the etching time. This advanced manufacturing process provides a reliable technical guarantee for achieving high-performance sound-absorbing stone products.

[0179] In summary, this invention fully leverages the potential of mathematical simulation, machine learning, and precision machining in key technical aspects such as sound wave propagation modeling, micropore and texture relationship analysis, multi-objective optimization design, and advanced manufacturing processes. It systematically resolves the contradiction between the two objectives of sound absorption performance and decorative effect in natural stone, providing effective technical support for promoting the development of high-performance sound-absorbing stone.

[0180] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the micropore parameters of a sound-absorbing stone curtain wall with micropores, wherein the sound-absorbing stone curtain wall with micropores includes a frame, plexiglass, and stone slabs, wherein the plexiglass and stone slabs are both installed within the frame, and a cavity is formed between the plexiglass and the stone slabs, wherein the plexiglass is positioned on the inner surface of the building to be decorated, and the surface of the stone slab opposite to the plexiglass has a large number of micropores formed according to the target micropore parameters; wherein, The base stone is un-drilled stone, and after drilling, a stone slab is formed. The method for determining the micropore parameters is characterized by: S10. Based on the scenario where sound-absorbing stone needs to be installed, obtain the sound frequency distribution of the scenario; S20. Obtain the physical properties and texture features of the base stone to be produced; the physical properties include density, porosity, elastic modulus, and thermal conductivity; the texture features include surface roughness, color, and pattern; organize and analyze the physical properties and texture feature data obtained above, establish a parameter file for the base stone, including statistical analysis of various indicators, and determine the representative values ​​of various performance parameters. S30. A first mathematical model describing the propagation and absorption of sound waves in microporous stone is established using the finite element method. The complex process of sound wave reflection, scattering, and dissipation inside the stone is considered and coupled with the micropore parameters. The first mathematical model is solved numerically to predict the sound absorption performance under different combinations of micropore parameters. An evaluation function of the sound frequency distribution and micropore parameters is established as the first objective function. S40. Establish a second mathematical model describing the relationship between micropore distribution and texture features. Combine the distribution of micropores with the texture features of stone to establish an evaluation function for the fit between micropore distribution and texture, which serves as the second objective function. S50. A multi-objective optimization algorithm is adopted to simultaneously optimize the first objective function and the second objective function to obtain the optimal trade-off solution of the two objective functions, which is used as the optimal micropore parameters. S60. Select multiple basic stone samples, and prepare multiple experimental samples according to the optimal micropore parameters. Perform sound absorption tests and texture evaluations on each experimental sample to obtain actual sound absorption data and texture matching data. S70. Compare the actual sound absorption data and texture matching data with the calculation results of the first mathematical model and the second mathematical model, correct the parameters of the first mathematical model and the second mathematical model, repeat steps S50 to S60 until the deviation between the model prediction and the measured result is within an acceptable range, and obtain the target micropore parameters.

2. The method for determining the micropore parameters of the sound-absorbing stone curtain wall with micropores according to claim 1, characterized in that, The dimensions of the frame are 300mm × 300mm × 39mm.

3. The method for determining the micropore parameters of the sound-absorbing stone curtain wall with micropores according to claim 1, characterized in that, The thickness of the plexiglass is 6mm to 8mm.

4. The method for determining the micropore parameters of the sound-absorbing stone curtain wall with micropores according to claim 1, characterized in that, The thickness of the plexiglass is 6 mm.

5. The method for determining the micropore parameters of the sound-absorbing stone curtain wall with micropores according to claim 1, characterized in that, The distance between the plexiglass and the stone slab is 12mm.

6. The method for determining the micropore parameters of the sound-absorbing stone curtain wall with micropores according to claim 1, characterized in that, The thickness of the stone slab is 25mm.

7. The method for determining the micropore parameters of the sound-absorbing stone curtain wall with micropores according to claim 1, characterized in that, The surface of the stone slab opposite the plexiglass retains the stone slab's own texture.

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