Polyurethane sound insulation material performance optimization method and system

Abstract

The invention provides a polyurethane sound insulation material performance optimization method and system, and relates to the technical field of sound insulation materials, and the method comprises the steps: obtaining target sound insulation performance, building a sound insulation performance and density mapping relation, generating a density distribution field, constraining a density change rate, building a matching parameter coupling function, and introducing a penalty term to solve and obtain a raw material matching scheme. And calculating the raw material diffusion boundary and interface characteristics of each region, generating a time sequence multi-point injection control instruction containing diffusion compensation control parameters, and driving injection equipment to perform partitioned injection molding. Precise regulation and control of the performance of the polyurethane sound insulation material can be achieved, and the sound insulation effect is improved.

Description

Technical Field

[0001] This invention relates to the field of sound insulation materials technology, and in particular to a method and system for optimizing the performance of polyurethane sound insulation materials. Background Technology

[0002] Polyurethane sound insulation materials are widely used in construction, automobiles, home appliances and other fields due to their good sound absorption and sound insulation properties. Traditional polyurethane sound insulation materials mainly adjust their sound insulation performance by changing parameters such as the overall density, porosity and pore size distribution of the material to meet the acoustic requirements of different scenarios. As people’s requirements for acoustic environment increase, there is a need to develop polyurethane materials with better sound insulation performance.

[0003] Currently, polyurethane sound insulation materials are mainly prepared using a one-time molding method, which involves injecting uniformly proportioned raw materials into a mold at once. The performance of the final product is controlled by adjusting the overall formula. Traditional polyurethane sound insulation material preparation methods are difficult to achieve a density gradient distribution within the material, and can usually only produce products with a homogeneous structure. This makes it impossible to specifically isolate sound waves of different frequencies in complex acoustic environments, resulting in insufficient performance in broadband sound insulation. Summary of the Invention

[0004] The present invention provides a method and system for optimizing the performance of polyurethane sound insulation materials, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for optimizing the performance of polyurethane sound insulation materials, comprising: To obtain the target sound insulation performance of the polyurethane sound insulation material to be molded; Based on the target sound insulation performance, by establishing a mapping relationship between sound insulation performance and material density, a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded is generated. The density distribution field includes multiple spatial sub-regions and their corresponding target density values. Based on the density distribution field, the density change rate between adjacent spatial sub-regions is constrained. When determining the foaming agent component ratio parameters and the curing agent component ratio parameters, a coupling function of the ratio parameters of adjacent spatial sub-regions is constructed, and a deviation penalty term is introduced for solution to obtain the raw material ratio scheme. Based on the raw material ratio scheme and the geometric topological relationship of the spatial sub-region, by calculating the interface characteristics of the raw material diffusion boundary of each spatial sub-region and the adjacent region, a time-sequential multi-point injection control command containing diffusion compensation control parameters is generated. The diffusion compensation control parameters are used to adjust the diffusion behavior of raw materials with different ratios at the interface to maintain the target density gradient. Based on the time-sequential multi-point injection control command, the injection equipment is driven to perform zoned injection molding of the polyurethane sound insulation material to be molded.

[0006] Based on the target sound insulation performance, a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded is generated by establishing a mapping relationship between sound insulation performance and material density. This density distribution field includes multiple spatial sub-regions and their corresponding target density values, including: Based on the target sound insulation performance, multiple frequency bands and their corresponding sound wave energy attenuation characteristics and sound wave propagation impedance characteristics are extracted to obtain a set of multi-frequency band sound insulation performance indicators. By establishing structural correlation rules between material density and pore structure parameters, and based on the influence of the pore structure parameters on the propagation attenuation mechanism of sound waves in different frequency bands, a multi-band mapping model from material density to sound insulation performance index is constructed. The entire space of the polyurethane sound insulation material to be formed is divided into multiple spatial sub-regions, and the corresponding target sound insulation performance indicators are extracted. Frequency band matching is then performed in the multi-frequency band sound insulation performance indicator set to obtain the matching result. Based on the multi-band mapping model, the matching results are subjected to multi-band weighted comprehensive calculation to solve the target density value of each spatial sub-region. Each spatial sub-region is spatially mapped and associated with its corresponding target density value to generate a density distribution field.

[0007] By establishing structural correlation rules between material density and pore structure parameters, and based on the influence of these pore structure parameters on the propagation attenuation mechanism of sound waves in different frequency bands, a multi-band mapping model from material density to sound insulation performance indicators is constructed, including: Pore ​​structure parameters of polyurethane sound insulation materials with different material densities are collected. The pore structure parameters include average pore size parameters and porosity parameters. Function fitting is performed on the different material densities and the pore structure parameters to obtain density-pore size mapping functions and density-porosity mapping functions. For each frequency band, the characteristic wavelength value of each frequency band is calculated, and the scale matching degree between it and the average aperture parameter is determined. Based on its influence on the acoustic resonance absorption effect, the aperture attenuation response function of the average aperture parameter to the acoustic energy attenuation characteristics is established. Furthermore, by analyzing the influence of the porosity parameter on the effective propagation medium density and acoustic impedance when the acoustic wave propagates in the material, the porosity impedance response function of the porosity parameter to the acoustic wave propagation impedance characteristics of this frequency band is established. The density-pore size mapping function, the density-porosity mapping function, the pore size attenuation response relationship, and the porosity impedance response relationship are cascaded and combined to construct a multi-band mapping model from material density to the set of sound insulation performance indicators.

[0008] Based on the density distribution field, the density change rate between adjacent spatial sub-regions is constrained. When determining the foaming agent component ratio parameters and the curing agent component ratio parameters, a coupling function for the ratio parameters of adjacent spatial sub-regions is constructed. A deviation penalty term is introduced for solution, resulting in a raw material ratio scheme, including: Extract the target density value of each spatial sub-region in the density distribution field, calculate the density gradient vector between each spatial sub-region and its adjacent spatial sub-regions, construct a gradient field regularization term that characterizes the smoothness of density spatial variation, and use the gradient field regularization term as a constraint condition to adjust the target density value to obtain the constrained density distribution field. Based on the mapping relationship between material density and proportioning parameters, and combined with the constrained density distribution field, the initial proportioning parameters of the foaming agent component and the initial proportioning parameters of the curing agent component in each spatial sub-region are calculated. Based on the initial proportioning parameters, a proportioning parameter coupling function between adjacent spatial sub-regions is established, and the spatial distance between adjacent spatial sub-regions is used as the adjustment variable of the proportioning parameter coupling function. A ratio deviation penalty term is introduced into the spatial coupling function of the ratio parameters. The initial ratio parameters are adjusted with the goal of minimizing the deviation penalty term to obtain the raw material ratio scheme.

[0009] Based on the initial proportioning parameters, a proportioning parameter coupling function is established between adjacent spatial sub-regions, and the spatial distance between adjacent spatial sub-regions is used as the adjustment variable of the proportioning parameter coupling function, including: Based on the initial proportioning parameters of each spatial sub-region, the gradient features of its proportioning parameters with those of adjacent spatial sub-regions are extracted, and the density gradient vector direction of its proportioning parameters with those of adjacent spatial sub-regions in the constrained density distribution field is calculated. The proportioning parameter gradient features are then oriented to obtain the oriented oriented gradient features of the foaming agent component proportioning parameters and the gradient features of the curing agent component proportioning parameters. Based on the gradient characteristics of the foaming agent component proportioning parameters after orientation alignment, and the gradient characteristics of the curing agent component proportioning parameters, a proportioning parameter coupling function is constructed for each spatial sub-region. The proportioning parameter coupling function characterizes the coupling constraint relationship between the change in the foaming agent component proportioning parameters and the change in the curing agent component proportioning parameters. The spatial distance between adjacent spatial sub-regions is calculated, and the coupling strength of the proportioning parameter coupling function is adjusted according to the spatial distance.

[0010] Based on the raw material proportioning scheme and the geometric topological relationship of the spatial sub-regions, by calculating the interface characteristics between the raw material diffusion boundary of each spatial sub-region and adjacent regions, a time-sequential multi-point injection control command containing diffusion compensation control parameters is generated, including: The raw material ratio scheme is mapped to each spatial sub-region to obtain the raw material injection amount corresponding to each spatial sub-region; Based on the raw material injection volume of each spatial sub-region and the geometric shape information of that spatial sub-region, the raw material diffusion boundary of each spatial sub-region is calculated, and the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted according to the raw material diffusion boundary. Based on the interface characteristics, diffusion compensation control parameters for each spatial sub-region are calculated. The diffusion compensation control parameters include raw material injection volume compensation value and raw material injection rate compensation value. Based on the ratio parameter difference value, the ratio difference correlation compensation adjustment is performed. Based on the raw material injection volume, the diffusion compensation control parameters, and the spatial location information of each spatial sub-region, a time-sequential multi-point injection control command is generated. The time-sequential multi-point injection control command includes the injection time, injection position, injection volume, and injection rate of each spatial sub-region.

[0011] Based on the raw material injection volume and geometric shape information of each spatial sub-region, the raw material diffusion boundary of each spatial sub-region is calculated. According to the raw material diffusion boundary, the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted, including: Based on the raw material injection volume of each spatial sub-region, the raw material diffusion radius is calculated, and based on the geometric shape constraint coefficient of the spatial sub-region, the raw material diffusion radius is geometrically adapted to obtain the corrected raw material diffusion radius. Using the geometric center of the spatial sub-region as the diffusion center and the modified raw material diffusion radius of the spatial sub-region as the diffusion distance, the raw material diffusion boundary of the spatial sub-region is constructed in combination with the geometric boundary contour of the spatial sub-region. Based on the raw material diffusion boundary, the spatial location of the interface between each spatial sub-region and its corresponding adjacent spatial sub-region is determined, and the interface features are extracted. The interface features include the interface location, interface area, and interface normal vector. Based on the difference in target density values ​​between each spatial sub-region and its corresponding adjacent spatial sub-region in the constrained density distribution field, the interface features are corrected by density difference correlation to obtain the corrected interface features.

[0012] A second aspect of the present invention provides a polyurethane sound insulation material performance optimization system, comprising: The first unit is used to obtain the target sound insulation performance of the polyurethane sound insulation material to be molded. The second unit is used to generate a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded by establishing a mapping relationship between the sound insulation performance and the material density based on the target sound insulation performance. The density distribution field includes multiple spatial sub-regions and their corresponding target density values. The third unit is used to constrain the density change rate between adjacent spatial sub-regions based on the density distribution field, and to construct a coupling function of the ratio parameters of adjacent spatial sub-regions when determining the foaming agent component ratio parameters and the curing agent component ratio parameters, and to solve the problem by introducing a deviation penalty term to obtain the raw material ratio scheme. The fourth unit is used to generate a time-sequential multi-point injection control command containing diffusion compensation control parameters by calculating the interface characteristics between the raw material diffusion boundary of each spatial sub-region and the adjacent region, based on the geometric topological relationship between the raw material ratio scheme and the spatial sub-region. The diffusion compensation control parameters are used to adjust the diffusion behavior of raw materials with different ratios at the interface to maintain the target density gradient. The fifth unit is used to drive the injection equipment to perform zoned injection molding of the polyurethane sound insulation material to be molded, based on the time-sequential multi-point injection control command.

[0013] A third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0014] Fourth aspect of the embodiments of the present invention, A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0015] The beneficial effects of this application are as follows: This invention generates a global density distribution field by establishing a mapping relationship between sound insulation performance and material density, thereby achieving precise optimization of the sound insulation performance of polyurethane sound insulation materials. This allows the sound insulation characteristics of the material to be customized and adjusted according to actual application needs, improving the functional adaptability of the material.

[0016] By introducing a ratio parameter coupling function and a deviation penalty term for solution, the density change rate between adjacent spatial sub-regions is effectively constrained, solving the problem of spatial density gradient control in traditional polyurethane materials, ensuring the continuity and stability of the internal structure of the material, and avoiding potential performance breaks within the material.

[0017] By employing diffusion compensation control parameters and a time-sequential multi-point injection control strategy, the diffusion behavior of raw materials with different proportions at the interface was precisely controlled, enabling accurate molding of complex density distributions, improving production efficiency and reducing material waste, while ensuring that the sound insulation performance of the final molded product is consistent with the design goals. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the method for optimizing the performance of polyurethane sound insulation materials according to an embodiment of the present invention; Figure 2 A schematic diagram of the system architecture for optimizing the raw material proportioning scheme. Detailed Implementation

[0019] 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. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0021] Figure 1 This is a schematic flowchart of the method for optimizing the performance of polyurethane sound insulation materials according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes: To obtain the target sound insulation performance of the polyurethane sound insulation material to be molded; Based on the target sound insulation performance, by establishing a mapping relationship between sound insulation performance and material density, a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded is generated. The density distribution field includes multiple spatial sub-regions and their corresponding target density values. Based on the density distribution field, the density change rate between adjacent spatial sub-regions is constrained. When determining the foaming agent component ratio parameters and the curing agent component ratio parameters, a coupling function of the ratio parameters of adjacent spatial sub-regions is constructed, and a deviation penalty term is introduced for solution to obtain the raw material ratio scheme. Based on the raw material ratio scheme and the geometric topological relationship of the spatial sub-region, by calculating the interface characteristics of the raw material diffusion boundary of each spatial sub-region and the adjacent region, a time-sequential multi-point injection control command containing diffusion compensation control parameters is generated. The diffusion compensation control parameters are used to adjust the diffusion behavior of raw materials with different ratios at the interface to maintain the target density gradient. Based on the time-sequential multi-point injection control command, the injection equipment is driven to perform zoned injection molding of the polyurethane sound insulation material to be molded.

[0022] In one optional implementation, based on the target sound insulation performance, a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded is generated by establishing a mapping relationship between sound insulation performance and material density. The density distribution field includes multiple spatial sub-regions and their corresponding target density values, including: Based on the target sound insulation performance, multiple frequency bands and their corresponding sound wave energy attenuation characteristics and sound wave propagation impedance characteristics are extracted to obtain a set of multi-frequency band sound insulation performance indicators. By establishing structural correlation rules between material density and pore structure parameters, and based on the influence of the pore structure parameters on the propagation attenuation mechanism of sound waves in different frequency bands, a multi-band mapping model from material density to sound insulation performance index is constructed. The entire space of the polyurethane sound insulation material to be formed is divided into multiple spatial sub-regions, and the corresponding target sound insulation performance indicators are extracted. Frequency band matching is then performed in the multi-frequency band sound insulation performance indicator set to obtain the matching result. Based on the multi-band mapping model, the matching results are subjected to multi-band weighted comprehensive calculation to solve the target density value of each spatial sub-region. Each spatial sub-region is spatially mapped and associated with its corresponding target density value to generate a density distribution field.

[0023] In this specific embodiment, multiple frequency bands and their corresponding sound wave energy attenuation characteristics and sound wave propagation impedance characteristics need to be extracted to obtain a set of multi-frequency band sound insulation performance indicators. Specifically, based on the characteristics of human hearing sensitivity and the distribution of environmental noise spectrum, the sound wave frequency range is divided into three main frequency bands: low frequency (20Hz-200Hz), mid frequency (200Hz-2kHz), and high frequency (2kHz-20kHz). Within each frequency band, the sound wave energy attenuation rate and sound wave propagation impedance value at specific frequency points are measured and extracted using acoustic testing equipment. For example, 50Hz, 100Hz, and 150Hz are selected as characteristic frequency points in the low frequency band; 250Hz, 500Hz, and 1kHz are selected as characteristic frequency points in the mid frequency band; and 3kHz, 6kHz, and 10kHz are selected as characteristic frequency points in the high frequency band. For each frequency point, its corresponding energy attenuation rate (in dB) and sound wave propagation impedance value (in Pa·s / m) are recorded. Based on this data, a multi-band sound insulation performance index set is constructed, which includes quantitative characteristic values ​​of sound wave attenuation and impedance in different frequency bands.

[0024] To establish a structural correlation rule between material density and pore structure parameters, this embodiment analyzes the microstructure of polyurethane sound insulation material. The pore structure of polyurethane sound insulation material is mainly characterized by three key parameters: pore size (unit μm), porosity (unit %), and pore connectivity (dimensionless). Experimental tests revealed that when the material density varies within the range of 30 kg / m³ to 100 kg / m³, the pore size exhibits a non-linear decreasing trend, the porosity decreases approximately linearly, while the pore connectivity first increases and then decreases, reaching an optimal connectivity state at a density of approximately 65 kg / m³. Based on extensive experimental data, a table of correspondences between density and these three pore parameters was established. For example, when the density is 40 kg / m³, the average pore size is approximately 180 μm, the porosity is approximately 92%, and the pore connectivity score is 0.72; when the density is 80 kg / m³, the average pore size is approximately 95 μm, the porosity is approximately 80%, and the pore connectivity score is 0.86.

[0025] Research on the attenuation mechanism of sound waves at different frequency bands shows that low-frequency sound waves are mainly affected by material thickness and mass, mid-frequency sound waves are mainly affected by pore connectivity, and high-frequency sound waves are mainly affected by pore size. Based on these mechanism characteristics, a multi-band mapping model from material density to sound insulation performance indicators was constructed. This model correlates material density with the aforementioned pore structure parameters and further correlates these parameters with the sound wave attenuation characteristics of each frequency band, realizing the mapping from density to sound insulation performance. For example, for a material with a density of 50 kg / m³, the sound wave energy attenuation rate is approximately 18 dB at a low frequency of 100 Hz, approximately 26 dB at a mid-frequency of 500 Hz, and approximately 33 dB at a high frequency of 5 kHz. These mapping relationships were obtained by fitting multiple sets of experimental data to ensure the accuracy and reliability of the model.

[0026] In the spatial division stage, the entire space of the polyurethane sound insulation material to be formed is divided into multiple spatial sub-regions according to geometric shape and functional requirements. For a car engine sound insulation cover example, it can be divided into three main sub-regions: a high-temperature region near the engine, a middle transition region, and an outer decorative region. Based on the location and functional characteristics of each region, its corresponding target sound insulation performance indicators are extracted. For example, the high-temperature region near the engine needs high sound insulation performance in the low-frequency range (requiring an attenuation rate of no less than 22dB at 100Hz); the middle transition region needs good performance in the mid-frequency range (requiring an attenuation rate of no less than 28dB at 500Hz); and the outer decorative region needs excellent performance in the high-frequency range (requiring an attenuation rate of no less than 35dB at 5kHz). These requirements are then matched with the aforementioned multi-frequency sound insulation performance indicator set to obtain the target performance matching results for each spatial sub-region at various characteristic frequency points.

[0027] Based on the aforementioned multi-band mapping model, the frequency band matching results of each spatial sub-region are subjected to multi-band weighted comprehensive calculation. During the calculation process, different weights are assigned to different frequency bands to reflect their importance in the overall sound insulation performance. For the example of a car engine soundproof cover, weights of 0.5, 0.3, and 0.2 can be assigned to low frequencies, mid frequencies, and high frequencies, respectively. By weighting the performance of each spatial sub-region in each frequency band, the optimal target density value is determined. For example, the calculation results show that the optimal density for the area near the engine side is 85 kg / m³, the optimal density for the intermediate transition area is 65 kg / m³, and the optimal density for the outer trim area is 45 kg / m³. These spatial sub-regions are spatially mapped and associated with their corresponding target density values ​​to generate a complete density distribution field. This density distribution field data can be stored in a three-dimensional coordinate grid format, with each grid point containing its spatial location and corresponding target density value information.

[0028] Using the above method, a mapping relationship from the target sound insulation performance to the material density distribution was successfully established, providing precise density distribution guidance for the subsequent molding process of variable density polyurethane sound insulation materials. This density distribution field fully considers the propagation characteristics of sound waves in different frequency bands, and can specifically meet the differentiated sound insulation needs of various spatial areas, thus achieving overall optimization of sound insulation performance.

[0029] In one optional implementation, by establishing structural correlation rules between material density and pore structure parameters, and based on the influence of the pore structure parameters on the propagation attenuation mechanism of sound waves in different frequency bands, a multi-band mapping model from material density to sound insulation performance indicators is constructed, including: Pore ​​structure parameters of polyurethane sound insulation materials with different material densities are collected. The pore structure parameters include average pore size parameters and porosity parameters. Function fitting is performed on the different material densities and the pore structure parameters to obtain density-pore size mapping functions and density-porosity mapping functions. For each frequency band, the characteristic wavelength value of each frequency band is calculated, and the scale matching degree between it and the average aperture parameter is determined. Based on its influence on the acoustic resonance absorption effect, the aperture attenuation response function of the average aperture parameter to the acoustic energy attenuation characteristics is established. Furthermore, by analyzing the influence of the porosity parameter on the effective propagation medium density and acoustic impedance when the acoustic wave propagates in the material, the porosity impedance response function of the porosity parameter to the acoustic wave propagation impedance characteristics of this frequency band is established. The density-pore size mapping function, the density-porosity mapping function, the pore size attenuation response relationship, and the porosity impedance response relationship are cascaded and combined to construct a multi-band mapping model from material density to the set of sound insulation performance indicators.

[0030] During implementation, multiple sets of polyurethane sound insulation material samples with different densities were collected, ranging from 20 kg / m³ to 60 kg / m³, with intervals of 5 kg / m³, for a total of 9 sets of samples. The microstructure of each set of samples was measured using a scanning electron microscope, and the average pore size and porosity parameters were extracted. Experimental data showed that when the density was 20 kg / m³, the average pore size was approximately 1.2 mm and the porosity was 93%; when the density was 40 kg / m³, the average pore size was approximately 0.6 mm and the porosity was 85%; and when the density was 60 kg / m³, the average pore size was approximately 0.3 mm and the porosity was 78%.

[0031] Based on the collected experimental data, a mapping relationship between density and pore structure parameters was established through regression analysis. For the relationship between density and average pore size, an exponential decay function was used for fitting. As density increases, the pore size exhibits a non-linear decreasing trend. Specifically, density was used as the independent variable and average pore size as the dependent variable. The function parameters were determined using the least squares method, and the fitting accuracy R0 was [value missing]. 2 The value reached 0.94. Regarding the relationship between density and porosity, a linear function was used for fitting. For every 10 kg / m³ increase in density, the porosity decreased by approximately 3.5 percentage points, with a fitting accuracy R0. 2 It reached 0.92.

[0032] When sound waves propagate through materials, their attenuation mechanism is closely related to frequency. In this embodiment, the sound wave frequency is divided into low-frequency band (100-500Hz), mid-frequency band (500-2000Hz), and high-frequency band (2000-8000Hz). For each frequency band, its characteristic wavelength is calculated: the characteristic wavelength of the low-frequency band is 0.68m-3.4m, the mid-frequency band is 0.17m-0.68m, and the high-frequency band is 0.04m-0.17m.

[0033] To investigate the influence of average aperture parameters on sound wave attenuation, the scale matching degree between sound waves and apertures in different frequency bands was analyzed. In the low-frequency band, the sound wave wavelength is much larger than the aperture, and energy attenuation is mainly achieved through interface reflection and frictional loss mechanisms. Experimental tests show that when the frequency is 200 Hz, the sound absorption coefficient of the material increases by 0.05 for every 0.2 mm decrease in average aperture. In the mid-frequency band, the sound wave wavelength is close to the aperture size, and the resonance absorption effect is significant. Experimental data show that when the frequency is 1000 Hz, the sound absorption coefficient of the material with an aperture of 0.5 mm reaches a peak of 0.78. In the high-frequency band, the sound wave wavelength is smaller than the aperture, and the sound wave undergoes multiple scattering and diffraction in the porous structure, resulting in rapid energy attenuation. Test results show that when the frequency is 4000 Hz, the sound absorption coefficient of the material with an average aperture of 0.3 mm can reach 0.92.

[0034] To investigate the influence of porosity on acoustic wave propagation impedance, a response function was established by analyzing the effective propagation characteristics of acoustic waves in porous materials. In the low-frequency range, for every 5% increase in porosity, the apparent density of the material decreases by approximately 4 kg / m³, and the acoustic impedance decreases by approximately 320 Pa·s / m, resulting in a 2 dB reduction in sound insulation. In the mid-frequency range, for every 5% increase in porosity, the apparent density of the material decreases by approximately 4 kg / m³, and the acoustic impedance decreases by approximately 180 Pa·s / m, resulting in a 1.5 dB reduction in sound insulation. In the high-frequency range, the influence of porosity on acoustic wave propagation impedance is more complex. When the porosity is in the range of 80%-85%, the material exhibits the best high-frequency sound insulation performance, with a sound insulation of up to 38 dB.

[0035] By cascading the density-structure mapping function and the frequency band response function, a complete multi-band mapping model is constructed. In specific implementation, for any given density value, the corresponding pore structure parameters are first calculated using the density-pore size mapping function and the density-porosity mapping function. Then, the sound insulation performance index of the material in each frequency band is calculated using the pore size attenuation response function and the porosity impedance response function of each frequency band.

[0036] In the model validation test, polyurethane foam material with a density of 35 kg / m³ was selected. The model predicted that its sound insulation in the low-frequency band (200 Hz) would be 12 dB, the sound insulation in the mid-frequency band (1000 Hz) would be 26 dB, and the sound insulation in the high-frequency band (4000 Hz) would be 36 dB. The actual measured values ​​were 11.5 dB, 27.2 dB, and 34.8 dB, respectively, with errors all within 10%, thus verifying the effectiveness and accuracy of the model.

[0037] The multi-band mapping model constructed by the method of this invention can quickly predict the sound insulation performance of materials in different frequency bands based on material density, providing theoretical guidance for the design and optimization of sound insulation materials, effectively shortening the product development cycle and reducing R&D costs.

[0038] Figure 2 A schematic diagram of the system architecture for optimizing the raw material proportioning scheme. In one optional embodiment, based on the density distribution field, the density change rate between adjacent spatial sub-regions is constrained. When determining the proportioning parameters of the foaming agent component and the curing agent component, a coupling function of the proportioning parameters of adjacent spatial sub-regions is constructed, and a deviation penalty term is introduced for solution to obtain the raw material proportioning scheme, including: Extract the target density value of each spatial sub-region in the density distribution field, calculate the density gradient vector between each spatial sub-region and its adjacent spatial sub-regions, construct a gradient field regularization term that characterizes the smoothness of density spatial variation, and use the gradient field regularization term as a constraint condition to adjust the target density value to obtain the constrained density distribution field. Based on the mapping relationship between material density and proportioning parameters, and combined with the constrained density distribution field, the initial proportioning parameters of the foaming agent component and the initial proportioning parameters of the curing agent component in each spatial sub-region are calculated. Based on the initial proportioning parameters, a proportioning parameter coupling function between adjacent spatial sub-regions is established, and the spatial distance between adjacent spatial sub-regions is used as the adjustment variable of the proportioning parameter coupling function. A ratio deviation penalty term is introduced into the spatial coupling function of the ratio parameters. The initial ratio parameters are adjusted with the goal of minimizing the deviation penalty term to obtain the raw material ratio scheme.

[0039] In this specific embodiment, a method for optimizing the proportioning parameters of gradient density porous materials based on a density distribution field will be described in detail. This method obtains the raw material proportioning scheme by constraining the density change rate between adjacent spatial sub-regions and constructing a proportioning parameter coupling function.

[0040] In the density distribution field processing stage, the target density values ​​of each spatial sub-region are extracted from the pre-established density distribution field. Assuming a three-dimensional space is divided into 10×10×10 sub-regions, each sub-region corresponds to an initial target density value, ranging from 0.2 g / cm³ to 0.8 g / cm³. The density gradient vector between each spatial sub-region and its adjacent regions is calculated, which is the rate of change of density in space. For example, if the density of sub-region (3, 4, 5) is 0.45 g / cm³, and the density of its adjacent sub-region (4, 4, 5) is 0.52 g / cm³, then the density gradient in the x-direction is 0.07 g / cm³. Similar calculations are performed on all adjacent sub-region pairs to obtain complete gradient field information.

[0041] Based on the calculated gradient field, a gradient field regularization term is constructed to characterize the smoothness of density spatial changes. This regularization term is essentially a weighted sum of the magnitudes of all gradient vectors. The weights can be set according to the importance of different directions. In this embodiment, the gradient field regularization term is set to a density change rate of no more than 0.1 g / cm³ per unit distance. The original target density value is iteratively adjusted using this constraint. The specific adjustment process adopts the gradient descent method, with a learning rate of 0.05 and 200 iterations. After adjustment, for example, the densities of the originally adjacent sub-regions (5, 5, 5) and (5, 6, 5) are adjusted from 0.35 g / cm³ and 0.62 g / cm³ to 0.38 g / cm³ and 0.58 g / cm³, making the density change smoother. The adjusted density distribution field satisfies the gradient constraint condition.

[0042] Entering the proportioning parameter calculation stage, the initial proportioning parameters of each spatial sub-region are calculated based on the mapping relationship between material density and proportioning parameters. In this embodiment, a two-component polyurethane foam material is used, whose density has a mapping relationship with the foaming agent proportioning parameter a and the curing agent proportioning parameter b. After fitting experimental data, the mapping relationship between density ρ (g / cm³) and proportioning parameters is obtained as follows: when the foaming agent proportioning parameter a is in the range of 0.02 to 0.1 and the curing agent proportioning parameter b is in the range of 0.05 to 0.25, the material density is approximately 1-(5×a+2×b)g / cm³. According to this mapping relationship, for a sub-region with a density of 0.5g / cm³, the initial foaming agent proportioning parameter a=0.06 and the initial curing agent proportioning parameter b=0.1 can be calculated.

[0043] To ensure the continuity and feasibility of material proportioning during the manufacturing process, a coupling function for proportioning parameters between adjacent spatial sub-regions is established. This coupling function uses the spatial distance between adjacent sub-regions as an adjustment variable. In this embodiment, the difference in proportioning parameters between two sub-regions is inversely proportional to their spatial distance. The closer the sub-regions are, the smaller the difference in proportioning parameters should be. Specifically, for sub-regions that are d units apart, the upper limit of the difference in proportioning parameters is set to baseline×(1+d / 5), where baseline is the baseline difference value, set to 0.01 for foaming agent and 0.02 for curing agent.

[0044] During the optimization phase, a ratio deviation penalty term is introduced into the ratio parameter coupling function. This penalty term calculates the difference between the adjusted ratio parameter and the initial ratio parameter in each sub-region, and these differences are weighted and summed. The weight coefficient can be set according to the density accuracy requirements of each sub-region. In this example, it is set to 1.0. With minimizing this deviation penalty term as the optimization objective, the quasi-Newton method is used to iteratively optimize the initial ratio parameter. The convergence threshold is set to 0.001, and the maximum number of iterations is 500.

[0045] After optimization, the final raw material ratio scheme was obtained. For example, the foaming agent ratio in the original sub-region (4, 4, 4) was adjusted from 0.055 to 0.057, and the curing agent ratio was adjusted from 0.12 to 0.115; the foaming agent ratio in the adjacent sub-region (4, 4, 5) was adjusted from 0.062 to 0.060, and the curing agent ratio was adjusted from 0.095 to 0.100. These adjusted ratio parameters maintained the approximate shape of the target density distribution and ensured a smooth transition of the ratio between adjacent regions, meeting the requirements of the actual production process. The final ratio scheme was executed by 3D printing equipment, successfully preparing a porous functional material with a uniform density gradient. The density deviation of each region was controlled within ±0.03 g / cm³, meeting the design requirements.

[0046] In one optional implementation, based on the initial proportioning parameters, a proportioning parameter coupling function is established between adjacent spatial sub-regions, and the spatial distance between adjacent spatial sub-regions is used as an adjustment variable of the proportioning parameter coupling function, including: Based on the initial proportioning parameters of each spatial sub-region, the gradient features of its proportioning parameters with those of adjacent spatial sub-regions are extracted, and the density gradient vector direction of its proportioning parameters with those of adjacent spatial sub-regions in the constrained density distribution field is calculated. The proportioning parameter gradient features are then oriented to obtain the oriented oriented gradient features of the foaming agent component proportioning parameters and the gradient features of the curing agent component proportioning parameters. Based on the gradient characteristics of the foaming agent component proportioning parameters after orientation alignment, and the gradient characteristics of the curing agent component proportioning parameters, a proportioning parameter coupling function is constructed for each spatial sub-region. The proportioning parameter coupling function characterizes the coupling constraint relationship between the change in the foaming agent component proportioning parameters and the change in the curing agent component proportioning parameters. The spatial distance between adjacent spatial sub-regions is calculated, and the coupling strength of the proportioning parameter coupling function is adjusted according to the spatial distance.

[0047] In actual implementation, the initial proportioning parameters of each spatial sub-region are read. Assuming a three-dimensional space is divided into N×M×L sub-regions, each sub-region has a corresponding foaming agent component proportioning parameter P. foam And curing agent component ratio parameter P curing For example, for the sub-region located at coordinates (3, 4, 5), the initial foaming agent ratio is 0.35 and the curing agent ratio is 0.42.

[0048] For each spatial sub-region, the gradient characteristics of its ratio parameters with those of its neighboring sub-regions are calculated. Taking the (3, 4, 5) sub-region as an example, its six neighboring sub-regions (2, 4, 5), (4, 4, 5), (3, 3, 5), (3, 5, 5), (3, 4, 4), and (3, 4, 6) are examined. The calculated gradient characteristics include the foaming agent gradient vector G. foam and curing agent gradient vector G curing , representing the rate of change and direction of the ratio of foaming agent and curing agent in space, respectively. For example, the gradient vector of the foaming agent in this sub-region is (0.05, -0.03, 0.02), and the gradient vector of the curing agent is (-0.04, 0.06, 0.01).

[0049] From the constrained density distribution field, the density gradient vector direction between adjacent sub-regions is extracted. The density distribution field is an ideal density distribution state obtained in advance through finite element analysis or other numerical simulation methods. For example, for sub-region (3, 4, 5) and its adjacent regions, the extracted density gradient vector direction is (0.7, -0.6, 0.3), which represents the direction of the fastest density change at that location.

[0050] During orientation alignment, the gradient characteristics of the proportioning parameters of the foaming agent and curing agent are aligned with the direction of the density gradient vector. This process essentially adjusts the direction of change of the proportioning parameters to align with the desired direction of density change, ensuring that the spatial variation of the proportioning parameters guides the material to form the required density distribution. The alignment process is achieved using vector projection and rotation operations, resulting in the orientation-aligned foaming agent gradient characteristic G. foamaligned and curing agent gradient characteristics G curingaligned For example, the aligned foaming agent gradient characteristics are (0.04, -0.035, 0.025), and the curing agent gradient characteristics are (-0.038, 0.055, 0.015).

[0051] Based on the aligned gradient features, a ratio parameter coupling function is constructed. This function expresses the coupling constraint relationship between the change in the foaming agent ratio and the change in the curing agent ratio. The coupling function adopts a weighted interpolation form, where the weight factor is determined by the material properties and process requirements. For example, for the sub-region (3, 4, 5), its ratio parameter coupling function can be expressed as 0.7 times the change in the foaming agent ratio plus 0.5 times the change in the curing agent ratio, plus 0.2 times their product. This function design ensures that when the foaming agent ratio changes, the curing agent ratio will also be adjusted accordingly according to a specific relationship, thereby maintaining the performance stability of the final foam material.

[0052] The spatial distance between adjacent sub-regions is calculated as a basis for adjusting the coupling function strength. The distance is calculated using the Euclidean distance formula. For two sub-regions in three-dimensional space, the distance is the square root of the sum of the squares of their coordinate differences. For example, the distance between sub-regions (3, 4, 5) and (4, 4, 5) is 1 unit.

[0053] Based on the calculated spatial distance, the coupling strength of the coupling function of the ratio parameter is adjusted using an exponential decay model, meaning the greater the distance, the weaker the coupling strength. Specifically, the coupling strength coefficient can be expressed as exp(-α·d), where d is the spatial distance and α is the decay parameter, set according to actual needs, for example, a value of 0.8. For adjacent sub-regions with a distance of 1, the coupling strength coefficient is 0.45; while for sub-regions with a distance of 2, the coupling strength coefficient drops to 0.2.

[0054] Through the above steps, a coupling function network reflecting the spatial distribution law of material proportions was successfully established. In practical applications, this coupling function can guide the precise preparation of heterogeneous foam materials. For example, when preparing seat cushions with varying density gradients, the part near the human body contact surface needs to be soft and comfortable (low density), while the support part needs to be strong and durable (high density). The proportion parameter coupling function established by this method can precisely control the proportions of foaming agent and curing agent in different regions, achieving a smooth transition from the comfort zone to the support zone, avoiding abrupt changes in material properties, and improving the comfort and service life of the product.

[0055] In one optional implementation, based on the raw material ratio scheme and the geometric topological relationship of the spatial sub-regions, a time-sequential multi-point injection control command containing diffusion compensation control parameters is generated by calculating the interface characteristics between the raw material diffusion boundary of each spatial sub-region and adjacent regions, including: The raw material ratio scheme is mapped to each spatial sub-region to obtain the raw material injection amount corresponding to each spatial sub-region; Based on the raw material injection volume of each spatial sub-region and the geometric shape information of that spatial sub-region, the raw material diffusion boundary of each spatial sub-region is calculated, and the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted according to the raw material diffusion boundary. Based on the interface characteristics, diffusion compensation control parameters for each spatial sub-region are calculated. The diffusion compensation control parameters include raw material injection volume compensation value and raw material injection rate compensation value. Based on the ratio parameter difference value, the ratio difference correlation compensation adjustment is performed. Based on the raw material injection volume, the diffusion compensation control parameters, and the spatial location information of each spatial sub-region, a time-sequential multi-point injection control command is generated. The time-sequential multi-point injection control command includes the injection time, injection position, injection volume, and injection rate of each spatial sub-region.

[0056] In this embodiment, the raw material ratio scheme is mapped to each spatial sub-region, and a corresponding raw material injection volume is assigned to each spatial sub-region. Taking a three-dimensional space composed of six spatial sub-regions as an example, the raw material ratio scheme includes the ratio information of two raw materials, A and B. For spatial sub-region 1, the assigned raw material ratio is A:B=7:3, and the total injection volume is 100ml; for spatial sub-region 2, the ratio is A:B=6:4, and the total injection volume is 90ml; for spatial sub-region 3, the ratio is A:B=5:5, and the total injection volume is 120ml; for spatial sub-region 4, the ratio is A:B=4:6, and the total injection volume is 110ml; for spatial sub-region 5, the ratio is A:B=3:7, and the total injection volume is 80ml; and for spatial sub-region 6, the ratio is A:B=2:8, and the total injection volume is 150ml.

[0057] Continuing with the methodology, based on the injection volume and geometric information of each spatial sub-region, the diffusion boundary of the raw material in each spatial sub-region is calculated. By establishing a diffusion model of the raw material in the medium, the range of diffusion of the raw material from the injection point to the surrounding area is predicted. Taking spatial sub-region 1 as an example, this region is a cube with a side length of 10cm, and the injection point is located at the center of the cube. According to the diffusion coefficients of raw material A and B, which are 0.5cm² / s and 0.7cm² / s, respectively, it is calculated that 30 seconds after injection, the diffusion radius of raw material A is about 6.1cm and the diffusion radius of raw material B is about 7.2cm. Since the diffusion radius is greater than the distance from the injection point to the boundary of the sub-region (5cm), cross-region diffusion will occur.

[0058] After determining the raw material diffusion boundaries of each spatial sub-region, the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted. Spatial sub-region 1 and spatial sub-region 2 share a planar interface with an area of ​​100 cm². Through diffusion calculation, it is found that raw materials A and B will form a concentration gradient at this interface. The extracted interface features include the interface area (100 cm²), the interface normal vector (pointing to spatial sub-region 2), the concentration difference of the diffusing substances on both sides of the interface (the concentration difference of raw material A is 0.02 g / ml, and the concentration difference of raw material B is 0.01 g / ml), and the diffusion velocity at the interface (0.01 g / s for raw material A and 0.007 g / s for raw material B).

[0059] Based on the extracted interface features, diffusion compensation control parameters for each spatial sub-region were calculated. For spatial sub-region 1, since the estimated diffusion loss of raw material A to spatial sub-region 2 is 3 ml, the calculated injection volume compensation value for raw material A is +3 ml. Similarly, the estimated diffusion loss of raw material B is 1.5 ml, and the injection volume compensation value is +1.5 ml. Furthermore, it was calculated that the injection rate of raw material A should be increased by 5% to offset the concentration reduction caused by the diffusion effect, and the injection rate of raw material B should be increased by 2%. Simultaneously, considering the difference in the ratio parameters between spatial sub-region 1 and spatial sub-region 2 (A:B ratio is 1:1), the injection volume compensation value for raw material A is increased by an additional 0.5 ml to ensure a smoother ratio transition at the region boundaries.

[0060] To achieve compensation adjustment for ratio differences, an adaptive algorithm is adopted. This algorithm dynamically adjusts the compensation parameters according to the magnitude of the ratio difference between adjacent regions. For example, the ratio difference between spatial sub-region 2 and spatial sub-region 3 (A:B=6:4 and 5:5) is smaller than the difference between spatial sub-region 1 and spatial sub-region 2. Therefore, a smaller compensation adjustment factor of 0.8 is applied. In specific operation, the original compensation value is first calculated (assuming raw material A: +2.8ml, raw material B: +1.2ml), and then multiplied by the compensation adjustment factor to obtain the final compensation value (raw material A: +2.24ml, raw material B: +0.96ml).

[0061] After completing all calculations, based on the injection volume of raw materials, diffusion compensation control parameters, and spatial location information of each spatial sub-region, time-sequential multi-point injection control commands are generated. For spatial sub-region 1, the generated injection control commands include: injection time (t=0s), injection position (x=5cm, y=5cm, z=5cm), injection volume (raw material A: 73.5ml, raw material B: 31.5ml), and injection rate (raw material A: 2.45ml / s, raw material B: 1.02ml / s). The injection commands for spatial sub-region 2 are: injection time (t=5s), injection position (x=15cm, y=5cm, z=5cm), injection volume (raw material A: 56.24ml, raw material B: 36.96ml), and injection rate (raw material A: 1.87ml / s, raw material B: 1.23ml / s). The injection control commands for the remaining spatial sub-regions are generated in a similar manner.

[0062] This method enables precise control of the proportions of raw materials in complex spaces, effectively compensates for concentration changes caused by raw material diffusion, ensures a smooth transition in proportions between adjacent areas, and significantly improves the precision of the injection process and the consistency of the final product quality.

[0063] In one optional implementation, based on the raw material injection volume of each spatial sub-region and the geometric shape information of that spatial sub-region, the raw material diffusion boundary of each spatial sub-region is calculated. Based on the raw material diffusion boundary, the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted, including: Based on the raw material injection volume of each spatial sub-region, the raw material diffusion radius is calculated, and based on the geometric shape constraint coefficient of the spatial sub-region, the raw material diffusion radius is geometrically adapted to obtain the corrected raw material diffusion radius. Using the geometric center of the spatial sub-region as the diffusion center and the modified raw material diffusion radius of the spatial sub-region as the diffusion distance, the raw material diffusion boundary of the spatial sub-region is constructed in combination with the geometric boundary contour of the spatial sub-region. Based on the raw material diffusion boundary, the spatial location of the interface between each spatial sub-region and its corresponding adjacent spatial sub-region is determined, and the interface features are extracted. The interface features include the interface location, interface area, and interface normal vector. Based on the difference in target density values ​​between each spatial sub-region and its corresponding adjacent spatial sub-region in the constrained density distribution field, the interface features are corrected by density difference correlation to obtain the corrected interface features.

[0064] In this embodiment, the raw material injection volume and geometric shape information of each spatial sub-region are obtained, including the geometric center position, geometric boundary contour and geometric shape constraint coefficient of the spatial sub-region. It is assumed that the raw material injection volume of a certain spatial sub-region A is 150 cubic centimeters, the geometric center position is three-dimensional spatial coordinates (50, 60, 70) centimeters, the region is approximately ellipsoidal, and its geometric shape constraint coefficient is 0.85.

[0065] The diffusion radius of the raw material is calculated based on the injection volume of the raw material in the spatial sub-region A. In this embodiment, the diffusion radius is proportional to the cube root of the injection volume. That is, when the injection volume of the raw material is 150 cubic centimeters, the initial diffusion radius is calculated to be 25 centimeters. The initial diffusion radius is corrected by using the geometric constraint coefficient. The initial diffusion radius is multiplied by the geometric constraint coefficient to obtain the corrected diffusion radius as 25 × 0.85 = 21.25 centimeters.

[0066] Using the geometric center (50, 60, 70) of spatial sub-region A as the diffusion center and the modified diffusion radius of 21.25 cm as the diffusion distance, the raw material diffusion boundary of the spatial sub-region is constructed by combining the ellipsoidal geometric boundary profile of the region. Since the region is ellipsoidal, the diffusion boundary will present an ellipsoidal shape intersecting with the ellipsoidal boundary. By traversing the points of diffusion from the diffusion center in each direction, the diffusion boundary points in each direction are determined, forming a complete diffusion boundary profile.

[0067] For an adjacent spatial sub-region B, assuming its raw material injection volume is 100 cubic centimeters, its geometric center is located at (80, 60, 70) centimeters, and it is approximately cylindrical with a geometric shape constraint coefficient of 0.9, the initial diffusion radius of B is calculated to be 22 centimeters, and the corrected diffusion radius is 22 × 0.9 = 19.8 centimeters.

[0068] Based on the raw material diffusion boundaries of spatial sub-regions A and B, the interface between the two regions is determined. Since the geometric center of A is (50, 60, 70) and the corrected diffusion radius is 21.25 cm, and the geometric center of B is (80, 60, 70) and the corrected diffusion radius is 19.8 cm, the distance between the two regions is 30 cm, which is less than the sum of their diffusion radii (41.05 cm). Therefore, the diffusion boundaries of the two regions intersect, forming an interface.

[0069] By solving for the intersection of the two diffusion boundaries, the spatial location of the interface is determined. In this example, the interface is located on the perpendicular plane of the line connecting the two geometric centers, with specific coordinates around (65, 60, 70). The area of ​​the interface is calculated to be 314 square centimeters, and the normal vector of the interface is (1, 0, 0), indicating that the interface is perpendicular to the X-axis.

[0070] The target density values ​​of spatial sub-regions A and B in the constrained density distribution field are obtained, which are 1.2 g / cm³ and 0.9 g / cm³, respectively. Based on the difference of 0.3 g / cm³ between the target density values ​​of these two regions, feature corrections are made to the interface features based on density difference correlation.

[0071] In the feature correction of density difference association, the interface position is offset and corrected according to the density difference of 0.3 g / cm³. Specifically, when the density difference is positive and greater than the preset threshold of 0.1 g / cm³, the interface position is shifted towards the B region with lower density. The shift distance is the density difference value multiplied by the preset coefficient of 5 cm / (g / cm³), that is, 0.3×5=1.5 cm. The corrected interface position is (66.5, 60, 70).

[0072] Meanwhile, the interface area is corrected. Based on the density difference of 0.3 g / cm³, the area correction coefficient is calculated using the area correction formula to be 1.06. That is, the original interface area of ​​314 square centimeters is multiplied by this coefficient to obtain the corrected interface area of ​​314 × 1.06 = 333 square centimeters.

[0073] In addition, the interface normal vector is fine-tuned to ensure that it still accurately reflects the direction of material flow between the two regions when there is a density difference. The corrected normal vector is kept at (1, 0, 0), but the influence of density difference on material flow will be considered in subsequent calculations.

[0074] Through the above steps, the interface features between spatial sub-region A and its adjacent spatial sub-region B were extracted and corrected, including the corrected interface position (66.5, 60, 70), the corrected interface area of ​​333 square centimeters, and the interface normal vector (1, 0, 0). Similar operations were performed on all adjacent spatial sub-region pairs to obtain a complete set of interface features, providing a foundation for subsequent material flow analysis and simulation.

[0075] The polyurethane sound insulation material performance optimization system of this invention includes: The first unit is used to obtain the target sound insulation performance of the polyurethane sound insulation material to be molded. The second unit is used to generate a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded by establishing a mapping relationship between the sound insulation performance and the material density based on the target sound insulation performance. The density distribution field includes multiple spatial sub-regions and their corresponding target density values. The third unit is used to constrain the density change rate between adjacent spatial sub-regions based on the density distribution field, and to construct a coupling function of the ratio parameters of adjacent spatial sub-regions when determining the foaming agent component ratio parameters and the curing agent component ratio parameters, and to solve the problem by introducing a deviation penalty term to obtain the raw material ratio scheme. The fourth unit is used to generate a time-sequential multi-point injection control command containing diffusion compensation control parameters by calculating the interface characteristics between the raw material diffusion boundary of each spatial sub-region and the adjacent region, based on the geometric topological relationship between the raw material ratio scheme and the spatial sub-region. The diffusion compensation control parameters are used to adjust the diffusion behavior of raw materials with different ratios at the interface to maintain the target density gradient. The fifth unit is used to drive the injection equipment to perform zoned injection molding of the polyurethane sound insulation material to be molded, based on the time-sequential multi-point injection control command.

[0076] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0077] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0078] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the performance of polyurethane sound insulation materials, characterized in that, include: To obtain the target sound insulation performance of the polyurethane sound insulation material to be molded; Based on the target sound insulation performance, by establishing a mapping relationship between sound insulation performance and material density, a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded is generated. The density distribution field includes multiple spatial sub-regions and their corresponding target density values. Based on the density distribution field, the density change rate between adjacent spatial sub-regions is constrained. When determining the foaming agent component ratio parameters and the curing agent component ratio parameters, a coupling function of the ratio parameters of adjacent spatial sub-regions is constructed, and a deviation penalty term is introduced for solution to obtain the raw material ratio scheme. Based on the raw material ratio scheme and the geometric topological relationship of the spatial sub-region, by calculating the interface characteristics of the raw material diffusion boundary of each spatial sub-region and the adjacent region, a time-sequential multi-point injection control command containing diffusion compensation control parameters is generated. The diffusion compensation control parameters are used to adjust the diffusion behavior of raw materials with different ratios at the interface to maintain the target density gradient. Based on the time-sequential multi-point injection control command, the injection equipment is driven to perform zoned injection molding of the polyurethane sound insulation material to be molded.

2. The method according to claim 1, characterized in that, Based on the target sound insulation performance, a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded is generated by establishing a mapping relationship between sound insulation performance and material density. This density distribution field includes multiple spatial sub-regions and their corresponding target density values, including: Based on the target sound insulation performance, multiple frequency bands and their corresponding sound wave energy attenuation characteristics and sound wave propagation impedance characteristics are extracted to obtain a set of multi-frequency band sound insulation performance indicators. By establishing structural correlation rules between material density and pore structure parameters, and based on the influence of the pore structure parameters on the propagation attenuation mechanism of sound waves in different frequency bands, a multi-band mapping model from material density to sound insulation performance index is constructed. The entire space of the polyurethane sound insulation material to be formed is divided into multiple spatial sub-regions, and the corresponding target sound insulation performance indicators are extracted. Frequency band matching is then performed in the multi-frequency band sound insulation performance indicator set to obtain the matching result. Based on the multi-band mapping model, the matching results are subjected to multi-band weighted comprehensive calculation to solve the target density value of each spatial sub-region. Each spatial sub-region is spatially mapped and associated with its corresponding target density value to generate a density distribution field.

3. The method according to claim 2, characterized in that, By establishing structural correlation rules between material density and pore structure parameters, and based on the influence of these pore structure parameters on the propagation attenuation mechanism of sound waves in different frequency bands, a multi-band mapping model from material density to sound insulation performance indicators is constructed, including: Pore ​​structure parameters of polyurethane sound insulation materials with different material densities are collected. The pore structure parameters include average pore size parameters and porosity parameters. Function fitting is performed on the different material densities and the pore structure parameters to obtain density-pore size mapping functions and density-porosity mapping functions. For each frequency band, the characteristic wavelength value of each frequency band is calculated, and the scale matching degree between it and the average aperture parameter is determined. Based on its influence on the acoustic resonance absorption effect, the aperture attenuation response function of the average aperture parameter to the acoustic energy attenuation characteristics is established. Furthermore, by analyzing the influence of the porosity parameter on the effective propagation medium density and acoustic impedance when the acoustic wave propagates in the material, the porosity impedance response function of the porosity parameter to the acoustic wave propagation impedance characteristics of this frequency band is established. The density-pore size mapping function, the density-porosity mapping function, the pore size attenuation response relationship, and the porosity impedance response relationship are cascaded and combined to construct a multi-band mapping model from material density to the set of sound insulation performance indicators.

4. The method according to claim 1, characterized in that, Based on the density distribution field, the density change rate between adjacent spatial sub-regions is constrained. When determining the foaming agent component ratio parameters and the curing agent component ratio parameters, a coupling function for the ratio parameters of adjacent spatial sub-regions is constructed. A deviation penalty term is introduced for solution, resulting in a raw material ratio scheme, including: Extract the target density value of each spatial sub-region in the density distribution field, calculate the density gradient vector between each spatial sub-region and its adjacent spatial sub-regions, construct a gradient field regularization term that characterizes the smoothness of density spatial variation, and use the gradient field regularization term as a constraint condition to adjust the target density value to obtain the constrained density distribution field. Based on the mapping relationship between material density and proportioning parameters, and combined with the constrained density distribution field, the initial proportioning parameters of the foaming agent component and the initial proportioning parameters of the curing agent component in each spatial sub-region are calculated. Based on the initial proportioning parameters, a proportioning parameter coupling function between adjacent spatial sub-regions is established, and the spatial distance between adjacent spatial sub-regions is used as the adjustment variable of the proportioning parameter coupling function. A ratio deviation penalty term is introduced into the spatial coupling function of the ratio parameters. The initial ratio parameters are adjusted with the goal of minimizing the deviation penalty term to obtain the raw material ratio scheme.

5. The method according to claim 4, characterized in that, Based on the initial proportioning parameters, a proportioning parameter coupling function is established between adjacent spatial sub-regions, and the spatial distance between adjacent spatial sub-regions is used as the adjustment variable of the proportioning parameter coupling function, including: Based on the initial proportioning parameters of each spatial sub-region, the gradient features of its proportioning parameters with those of adjacent spatial sub-regions are extracted, and the density gradient vector direction of its proportioning parameters with those of adjacent spatial sub-regions in the constrained density distribution field is calculated. The proportioning parameter gradient features are then oriented to obtain the oriented oriented gradient features of the foaming agent component proportioning parameters and the gradient features of the curing agent component proportioning parameters. Based on the gradient characteristics of the foaming agent component proportioning parameters after orientation alignment, and the gradient characteristics of the curing agent component proportioning parameters, a proportioning parameter coupling function is constructed for each spatial sub-region. The proportioning parameter coupling function characterizes the coupling constraint relationship between the change in the foaming agent component proportioning parameters and the change in the curing agent component proportioning parameters. The spatial distance between adjacent spatial sub-regions is calculated, and the coupling strength of the proportioning parameter coupling function is adjusted according to the spatial distance.

6. The method according to claim 1, characterized in that, Based on the raw material proportioning scheme and the geometric topological relationship of the spatial sub-regions, by calculating the interface characteristics between the raw material diffusion boundary of each spatial sub-region and adjacent regions, a time-sequential multi-point injection control command containing diffusion compensation control parameters is generated, including: The raw material ratio scheme is mapped to each spatial sub-region to obtain the raw material injection amount corresponding to each spatial sub-region; Based on the raw material injection volume of each spatial sub-region and the geometric shape information of that spatial sub-region, the raw material diffusion boundary of each spatial sub-region is calculated, and the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted according to the raw material diffusion boundary. Based on the interface characteristics, diffusion compensation control parameters for each spatial sub-region are calculated. The diffusion compensation control parameters include raw material injection volume compensation value and raw material injection rate compensation value. Based on the ratio parameter difference value, the ratio difference correlation compensation adjustment is performed. Based on the raw material injection volume, the diffusion compensation control parameters, and the spatial location information of each spatial sub-region, a time-sequential multi-point injection control command is generated. The time-sequential multi-point injection control command includes the injection time, injection position, injection volume, and injection rate of each spatial sub-region.

7. The method according to claim 6, characterized in that, Based on the raw material injection volume and geometric shape information of each spatial sub-region, the raw material diffusion boundary of each spatial sub-region is calculated. According to the raw material diffusion boundary, the interface features between each spatial sub-region and its corresponding adjacent spatial sub-region are extracted, including: Based on the raw material injection volume of each spatial sub-region, the raw material diffusion radius is calculated, and based on the geometric shape constraint coefficient of the spatial sub-region, the raw material diffusion radius is geometrically adapted to obtain the corrected raw material diffusion radius. Using the geometric center of the spatial sub-region as the diffusion center and the modified raw material diffusion radius of the spatial sub-region as the diffusion distance, the raw material diffusion boundary of the spatial sub-region is constructed in combination with the geometric boundary contour of the spatial sub-region. Based on the raw material diffusion boundary, the spatial location of the interface between each spatial sub-region and its corresponding adjacent spatial sub-region is determined, and the interface features are extracted. The interface features include the interface location, interface area, and interface normal vector. Based on the difference in target density values ​​between each spatial sub-region and its corresponding adjacent spatial sub-region in the constrained density distribution field, the interface features are corrected by density difference correlation to obtain the corrected interface features.

8. A polyurethane sound insulation material performance optimization system for implementing the method as described in any one of claims 1-7, characterized in that, include: The first unit is used to obtain the target sound insulation performance of the polyurethane sound insulation material to be molded. The second unit is used to generate a density distribution field covering the entire domain of the polyurethane sound insulation material to be molded by establishing a mapping relationship between the sound insulation performance and the material density based on the target sound insulation performance. The density distribution field includes multiple spatial sub-regions and their corresponding target density values. The third unit is used to constrain the density change rate between adjacent spatial sub-regions based on the density distribution field, and to construct a coupling function of the ratio parameters of adjacent spatial sub-regions when determining the foaming agent component ratio parameters and the curing agent component ratio parameters, and to solve the problem by introducing a deviation penalty term to obtain the raw material ratio scheme. The fourth unit is used to generate a time-sequential multi-point injection control command containing diffusion compensation control parameters by calculating the interface characteristics between the raw material diffusion boundary of each spatial sub-region and the adjacent region, based on the geometric topological relationship between the raw material ratio scheme and the spatial sub-region. The diffusion compensation control parameters are used to adjust the diffusion behavior of raw materials with different ratios at the interface to maintain the target density gradient. The fifth unit is used to drive the injection equipment to perform zoned injection molding of the polyurethane sound insulation material to be molded, based on the time-sequential multi-point injection control command.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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