Method for predicting and controlling performance of plate based on quality detection of basalt fiber mat material

Abstract

The present application belongs to the technical field of composite material quality control, and relates to a kind of plate performance prediction and control method based on basalt fiber mat quality detection, the mat is detected with surface density, surface density uniformity, thermoplastic fiber content and dispersion degree and other indexes;Combined with the influence of the residual rate of the impregnating agent on the odor and VOC emission of the plate;Establish a performance prediction model based on multiple linear regression, which is used to predict the thickness control accuracy, volume shrinkage, mechanical properties and environmental protection indexes of the plate;Based on the model results, the process conditions such as layering method and hot pressing parameters are adjusted in real time to realize closed-loop control of performance. This method realizes the accurate mapping and feedback control of raw material quality on the terminal performance, can control the plate thickness accuracy within ±0.3mm, the volume shrinkage within 0.5%-2.0%, the mechanical property fluctuation is small, the odor grade is <3, and the VOC emission is low. This method significantly improves the performance stability of the plate and reduces the scrap rate.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of composite material quality control, and particularly relates to a method for realizing performance prediction and control of plate materials based on basalt fiber mat detection standards, which is suitable for the manufacturing process of hot-pressed composite plates in the fields of rail transit and automobiles. BACKGROUND

[0002] At present, basalt fibers have been widely used in the processing and manufacturing of high-performance composite materials due to their excellent mechanical properties, corrosion resistance and environmental friendliness. Basalt fiber needle felt made of basalt chopped fibers and thermoplastic fibers such as polypropylene (PP) and polyethylene (PE) is an important intermediate material for preparing lightweight and high-strength composite plates such as rail vehicle structure plates and vehicle interior panels. However, in the actual production process, the composition and structure of basalt fiber mats fluctuate greatly, resulting in inaccurate thickness control and unstable impact resistance of downstream plates. At the same time, the plates may produce high volatile organic compound (VOC) emissions during hot pressing, affecting environmental friendliness and use safety.

[0003] The prior art lacks a systematic detection index system and real-time feedback mechanism, and cannot effectively map the basic physical properties of basalt fiber mats to the final performance of the plates, resulting in problems such as production dependence on experience, poor consistency of finished products and low yield. SUMMARY

[0004] The purpose of the present application is to provide a method for realizing performance control of plate materials based on basalt fiber mat detection standards, which establishes a performance prediction model through multiple detection indexes to solve the problem of realizing real-time feedback and regulation of process parameters, thereby constructing a closed-loop control system for the plate manufacturing process. The method can significantly improve the performance stability of the plates, reduce the scrap rate, and has high universality and practical value.

[0005] The present application is realized by the following technical solutions:

[0006] A basalt fiber mat quality detection-based plate performance prediction and control method, comprising the following steps:

[0007] A. Multiple quality detections are performed on basalt fiber mats used for plate manufacturing, and the quality detection indexes include surface density, surface density uniformity, thermoplastic fiber content and fiber dispersion;

[0008] B. The type and residual rate of the basalt fiber surface wetting agent are detected to evaluate its influence on the odor and volatile organic compound emissions of the plates;

[0009] C. The determination threshold values corresponding to each quality detection index are set, and a standardized evaluation system is established according to the performance requirements of the products;

[0010] D. Input the detection data into the performance prediction model to predict the thickness control accuracy, volume shrinkage, mechanical properties, odor level, and volatile organic compound emission level of the plate;

[0011] E. According to the prediction results, real-time feedback and adjust the process parameters in the plate production process, including the laying method, heating temperature, holding time and pressure, to realize closed-loop control of the process;

[0012] F. Compare the actual performance of the formed plate with the model prediction output, and dynamically optimize the detection standard and process parameters based on the error results to realize continuous and stable control of the plate quality.

[0013] Further, in step A, the basalt fibers of the basalt fiber mat are in chopped form and are combined with thermoplastic fibers to form a non-woven structure of the fiber mat through a needle punching process.

[0014] Further, the thermoplastic fibers are at least one of polypropylene fibers and polyethylene fibers.

[0015] Further, in step B, the residual rate of the impregnating agent is determined by solvent extraction combined with gravimetric method, thermogravimetric analysis infrared spectrometry, or gas chromatography-mass spectrometry to predict the odor level and volatile organic compound emission during the hot pressing process of the plate.

[0016] Further, in step C, the areal density of the basalt fiber mat ranges from 600 to 1400 g / m², and the areal density deviation is controlled within ±5%.

[0017] Further, the areal density deviation is controlled to be ±3%.

[0018] Further, in step C, the dispersity of the basalt fibers is evaluated by grayscale and binary processing, the image is divided into grids, the fiber proportion of each partition is calculated, and the ratio of the standard deviation to the average value is taken as the normalized dispersity index, with a value range of 0-1, and the proportion of fiber bundles with a diameter less than or equal to 50 microns is not less than 90%.

[0019] Further, the dispersity is greater than 0.9 and the proportion of fiber bundles with a diameter less than or equal to 50 microns is not less than 90%.

[0020] Further, in step C, the content of the thermoplastic fibers is determined by calcination and weighing to predict the volume shrinkage of the plate.

[0021] Further, step D, the performance prediction model is constructed based on a multiple linear regression algorithm, for establishing the quantitative correlation between the basalt fiber mat detection index and the key performance of the plate, the input variables of the performance prediction model include the mat area density, the area density uniformity, the proportion of thermoplastic fiber PP, the fiber dispersion degree and the residual rate of impregnant, and the output variables include the thickness standard deviation of the formed plate, the volume shrinkage rate, the tensile strength, the impact strength relative change, the volatile organic compound emission and the odor grade.

[0022] Compared with the prior art, the beneficial effects of the present application are:

[0023] The present method realizes the accurate mapping and feedback control of the raw material quality on the terminal performance, can control the plate thickness precision within ±0.3mm, the volume shrinkage rate within the range of 0.5%-2.0%, the mechanical properties, such as the tensile strength and the elongation at break, fluctuate less than 5%, the odor grade is less than 3, the VOC emission is reduced by more than 20%, and has good applicability and promotion prospect. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0025] Fig. 1 Structure diagram of basalt fiber mat;

[0026] Fig. 2 Flow chart of feedback regulation based on basalt fiber mat detection data. DETAILED DESCRIPTION

[0027] The present application will be further described below in conjunction with the embodiments:

[0028] The present application will be further described below in conjunction with the embodiments:

[0029] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present application, the terms "first", "second" and the like are only used for distinguishing description, and cannot be understood as indicating or implying relative importance.

[0030] As Figs. 1-2 shown, the present application is based on the performance prediction and control method of basalt fiber mat quality detection for board, including the following steps:

[0031] 1. Perform multiple quality tests on basalt fiber mat used for board manufacturing, including areal density, areal density uniformity, thermoplastic fiber content, and fiber dispersion;

[0032] 2. Detect the type and residual rate of basalt fiber surface wetting agent to evaluate its impact on board odor and volatile organic compound (VOC) emissions;

[0033] 3. Set the judgment threshold corresponding to each detection index, and establish a standardized evaluation system according to product performance requirements;

[0034] 4. Input the detection data into the performance prediction model to predict the thickness control accuracy, volume shrinkage, mechanical properties, odor level, and VOC emission level of the board;

[0035] 5. According to the prediction results, real-time feedback and adjust the process parameters in the board production process, including lamination method, heating temperature, holding time and pressure, etc., to realize closed-loop control of the process;

[0036] 6. Compare the actual performance of the formed board with the model prediction output, and dynamically optimize the detection standard and process parameters based on the error results to realize continuous and stable control of the board quality.

[0037] Specifically, the basalt fiber is in chopped form and forms a non-woven structure of fiber mat together with thermoplastic fiber through needle punching process.

[0038] The thermoplastic fiber is at least one of polypropylene (PP) fiber and polyethylene (PE) fiber.

[0039] The areal density of the mat ranges from 600 to 1400 g / m², and according to specific product requirements, the areal density deviation is controlled within ±5%, preferably ±3%.

[0040] The dispersion of the basalt fiber is evaluated by image recognition technology, such as grayscale and binary processing method. After the image is divided into grids, the fiber proportion of each partition is calculated, and the ratio of standard deviation to average value is taken as the normalized dispersion index, with a value range of 0-1; and the proportion of fiber bundles with a diameter less than or equal to 50 microns is not less than 90%. According to specific product requirements, the dispersion is greater than 0.9 and the proportion of fiber bundles with a diameter less than or equal to 50 microns is not less than 90%.

[0041] The content of thermoplastic fiber is determined by calcination weighing method to predict the volume shrinkage of the board.

[0042] The performance prediction model is based on a multiple linear regression algorithm and is used to establish a quantitative correlation between the mat detection indicators and the key properties of the plate. Specifically, the input variables of the performance prediction model include the mat area density, the area density uniformity, the proportion of thermoplastic fibers PP, the fiber dispersion, and the sizing agent residual rate, and the output variables include the thickness standard deviation of the formed plate, the volume shrinkage, the tensile strength, the impact strength relative change, the volatile organic compound emission, and the odor grade.

[0043] The sizing agent residual rate is determined by solvent extraction combined with gravimetric method, thermogravimetric analysis-infrared spectroscopy (TG-IR), or gas chromatography-mass spectrometry (GC-MS) technology to predict the odor grade and VOC emission of the plate during hot pressing.

[0044] The method is suitable for the manufacturing process of rail transit composite material plates, automotive hot-pressed structural plates, or other high-performance lightweight structural plates.

[0045] Example 1:

[0046] In the production process of rail vehicle floor, basalt fiber mat with area density A = 600 g / m², area density uniformity U = 3%, dispersion D = 0.93, sizing agent residual rate S = 9%, and thermoplastic fiber content R PP = 0.5 is selected. According to the model prediction of plate performance, the thickness standard deviation is 0.41 mm, the volume shrinkage is 2.6%, the tensile strength is 90.2 MPa, the impact strength change is 8%, the VOC emission is 201 μg / m³, and the odor grade is 2.7. The process is adjusted to the layering mode [0° / 90°]s, the hot pressing temperature is 172 ℃, the pressure is 25 MPa, and the time is 4 min. The actual measurement results of the plate performance are: thickness standard deviation 0.28 mm, volume shrinkage 1.8%, tensile strength 95.1 MPa, impact strength change about 3%, VOC emission 95 μg / m³, odor grade 2.4, which meets the mechanical and environmental protection requirements of rail vehicle floor.

[0047] Example 2:

[0048] In the production process of automotive door plate, basalt fiber mat with area density A = 800 g / m², area density uniformity U = 3%, dispersion D = 0.95, sizing agent residual rate S = 9%, and thermoplastic fiber content R PP=0.3 basalt fiber mat. According to the model prediction of the board performance results: thickness standard deviation 0.36 mm, volume shrinkage 2.4%, tensile strength 91.1 MPa, impact strength variation 7%, VOC emissions 203 μg / m³, odor level 2.6. Adjust the process for the layering method [0° / ±45° / 90°]s, hot pressing temperature 174 ℃, pressure 26 MPa, time 4.5 min. The measured results of the board performance: thickness standard deviation 0.22 mm, volume shrinkage 1.3%, tensile strength 96.3 MPa, impact strength fluctuation about 2%, VOC emissions 96 μg / m³, odor level 2.2, meet the requirements of lightweight and environmental protection of the automobile door panel.

[0049] Example 3:

[0050] In the production process of rail vehicle protective cover, the basalt fiber mat with the area density A=800 g / m², the area density uniformity U=3%, the dispersion D=0.95, the residual rate of the impregnating agent S=9%, and the content of the thermoplastic fiber R PP =0.4 is selected. According to the model prediction of the board performance results: thickness standard deviation 0.36 mm, volume shrinkage 2.3%, tensile strength 91.9 MPa, impact strength variation 7%, VOC emissions 202 μg / m³, odor level 2.6. Adjust the process for the layering method [0° / 90°] bidirectional, hot pressing temperature 170 ℃, pressure 24 MPa, time 4 min. The measured results of the board performance: thickness standard deviation 0.24 mm, volume shrinkage 1.4%, tensile strength 96.2 MPa, impact strength fluctuation about 0%, VOC emissions 92 μg / m³, odor level 2.1.

[0051] Example 4:

[0052] In the production process of automobile underguard, the basalt fiber mat with the area density A=800 g / m², the area density uniformity U=3%, the dispersion D=0.95, the residual rate of the impregnating agent S=9%, and the content of the thermoplastic fiber R PP =0.5 is selected. According to the model prediction of the board performance results: thickness standard deviation 0.36 mm, volume shrinkage 2.2%, tensile strength 92.7 MPa, impact strength variation 6%, VOC emissions 201 μg / m³, odor level 2.6. Adjust the process for the layering method four-layer staggered [0° / 45° / 90° / -45°], hot pressing temperature 175 ℃, pressure 27 MPa, time 5 min. The measured results of the board performance: thickness standard deviation 0.23 mm, volume shrinkage 1.1%, tensile strength 97.9 MPa, impact strength fluctuation about 2%, VOC emissions 92 μg / m³, odor level 2.0.

[0053] Example 5:

[0054] In the production process of automotive underbody protection plates, the following parameters are selected: areal density A=800g / m², areal density uniformity U=3%, dispersion D=0.95, sizing agent residue rate S=9%, and thermoplastic fiber content R. PP Basalt fiber felt with a thickness of 0.6 was used. Predicted board performance results based on the model were: thickness standard deviation 0.36 mm, volume shrinkage 2.3%, tensile strength 93.5 MPa, impact strength variation 6%, VOC emission 202 μg / m³, and odor grade 2.6. The process was adjusted to a three-way interleaved layup, hot-pressing temperature 176℃, pressure 28 MPa, and time 5 min. Actual measured board performance results were: thickness standard deviation 0.25 mm, volume shrinkage 1.3%, tensile strength 98.0 MPa, impact strength fluctuation approximately 1%, VOC emission 95 μg / m³, and odor grade 2.2.

[0055] Example 6:

[0056] In the production process of automotive interior panels, the following parameters are selected: areal density A = 800 g / m², areal density uniformity U = 3%, dispersion D = 0.95, sizing agent residue S = 9%, and thermoplastic fiber content R. PP Basalt fiber felt with a thickness of 0.7. Predicted board performance results based on the model: thickness standard deviation 0.36 mm, volume shrinkage 2.4%, tensile strength 94.3 MPa, impact strength variation 6%, VOC emission 203 μg / m³, odor grade 2.6. The process was adjusted to a layup method of [0° / 90°]s, hot pressing temperature 177℃, pressure 29 MPa, and time 5.5 min. Actual measured board performance results: thickness standard deviation 0.25 mm, volume shrinkage 1.4%, tensile strength 99.2 MPa, impact strength variation approximately 3%, VOC emission 97 μg / m³, odor grade 2.2.

[0057] Example 7:

[0058] In the production process of automotive interior panels, the following parameters are selected: areal density A = 800 g / m², areal density uniformity U = 5%, dispersion D = 0.95, sizing agent residue S = 9%, and thermoplastic fiber content R. PP Basalt fiber felt with a thickness of 0.5 was used. Predicted board performance results based on the model were: thickness standard deviation 0.37 mm, volume shrinkage 2.3%, tensile strength 90.7 MPa, impact strength variation 5%, VOC emission 211 μg / m³, and odor rating 3.0. The process was adjusted to a multi-directional interlaced layup, hot-pressing temperature 175℃, pressure 27 MPa, and time 5 min. Actual measured board performance results were: thickness standard deviation 0.18 mm, volume shrinkage 1.6%, tensile strength 95.5 MPa, impact strength fluctuation approximately 0%, VOC emission 93 μg / m³, and odor rating 2.8.

[0059] Example 8:

[0060] In the production process of automotive interior panels, the following parameters are selected: areal density A = 800 g / m², areal density uniformity U = 3%, dispersion D = 0.90, sizing agent residue S = 9%, and thermoplastic fiber content R. PP Basalt fiber felt with a thickness of 0.5 was used. Predicted board performance results based on the model were: thickness standard deviation 0.39 mm, volume shrinkage 2.3%, tensile strength 91.4 MPa, impact strength variation 7%, VOC emission 202 μg / m³, and odor grade 2.9. The process was adjusted to a double-layer staggered layup, hot-pressing temperature 174℃, pressure 26 MPa, and time 4.5 min. Actual measured board performance results were: thickness standard deviation 0.28 mm, volume shrinkage 1.3%, tensile strength 96.2 MPa, impact strength variation approximately 3%, VOC emission 94 μg / m³, and odor grade 2.6.

[0061] Example 9:

[0062] In the production process of rail vehicle shell panels, the following parameters are selected: areal density A = 1000 g / m², areal density uniformity U = 3%, dispersion D = 0.91, sizing agent residue rate S = 9%, and thermoplastic fiber content R. PP Basalt fiber felt with a thickness of 0.5 was used. Predicted board performance results based on the model were: thickness standard deviation 0.34 mm, volume shrinkage 1.8%, tensile strength 93.7 MPa, impact strength variation 6%, VOC emission 202 μg / m³, and odor grade 2.9. The process was adjusted to a three-layer staggered layup, hot-pressing temperature 176℃, pressure 28 MPa, and time 5 min. Actual measured board performance results were: thickness standard deviation 0.25 mm, volume shrinkage 1.1%, tensile strength 97.6 MPa, impact strength fluctuation approximately 1%, VOC emission 98 μg / m³, and odor grade 2.5.

[0063] Example 10:

[0064] In the production process of rail vehicle shell panels, the following parameters were selected: areal density A = 1200 g / m², areal density uniformity U = 3%, dispersion D = 0.94, sizing agent residue rate S = 9%, and thermoplastic fiber content R. PP= 0.5. According to the model prediction of the board performance results: thickness standard deviation 0.37mm, volume shrinkage 2.2%, tensile strength 92.4MPa, impact strength variation 7%, VOC emission 201pg / m3, odor level 2.7. Adjust the process for the layering mode [0° / 90°] bidirectional, hot pressing temperature 178℃, pressure 28MPa, time 6min. The measured results of the board performance: thickness standard deviation 0.25mm, volume shrinkage 1.1%, tensile strength 96.0MPa, impact strength fluctuation about 2%, VOC emission 90pg / m3, odor level 2.2.

[0065] Example 11:

[0066] In the production process of rail transit protective plate, basalt fiber mat with areal density A = 1400g / m2, areal density uniformity U = 3%, dispersity D = 0.93, residual rate of impregnating agent S = 9%, and thermoplastic fiber content R PP = 0.3 is selected. According to the model prediction of the board performance results: thickness standard deviation 0.41mm, volume shrinkage 2.8%, tensile strength 88.6MPa, impact strength variation 8%, VOC emission 203pg / m3, odor level 2.7. Adjust the process for the layering mode symmetric staggered, hot pressing temperature 177℃, pressure 29MPa, time 5.5min. The measured results of the board performance: thickness standard deviation 0.24mm, volume shrinkage 1.6%, tensile strength 93.0MPa, impact strength fluctuation about 4%, VOC emission 92pg / m3, odor level 2.4.

[0067] Example 12:

[0068] In the production process of automobile lightweight composite board, basalt fiber mat with areal density A = 1400g / m2, areal density uniformity U = 3%, dispersity D = 0.96, residual rate of impregnating agent S = 9%, and thermoplastic fiber content R PP = 0.5 is selected. According to the model prediction of the board performance results: thickness standard deviation 0.40mm, volume shrinkage 2.6%, tensile strength 90.9MPa, impact strength variation 7%, VOC emission 201pg / m3, odor level 2.6. Adjust the process for the layering mode three-layer staggered, hot pressing temperature 178℃, pressure 30MPa, time 6min. The measured results of the board performance: thickness standard deviation 0.22mm, volume shrinkage 1.4%, tensile strength 94.5MPa, impact strength fluctuation about 3%, VOC emission 98pg / m3, odor level 2.2.

[0069] Example 13:

[0070] In the production process of lightweight composite panels for automobiles, the following parameters were selected: areal density A = 1400 g / m², areal density uniformity U = 5%, dispersion D = 0.92, sizing agent residue S = 9%, and thermoplastic fiber content R. PP Basalt fiber felt with a thickness of 0.7 was used. Predicted board performance results based on the model were: thickness standard deviation 0.43 mm, volume shrinkage 2.9%, tensile strength 89.5 MPa, impact strength variation 6%, VOC emission 213 μg / m³, and odor grade 3.2. The process was adjusted to a layup pattern of [0° / ±45° / 90°]s, hot pressing temperature 177℃, pressure 29 MPa, and time 5.5 min. Actual measured board performance results were: thickness standard deviation 0.30 mm, volume shrinkage 1.5%, tensile strength 93.3 MPa, impact strength fluctuation approximately 1%, VOC emission 95 μg / m³, and odor grade 2.9.

[0071] Comparative Example 1:

[0072] In the production process of rail transit interior panels, the following parameters were selected: surface density A = 800 g / m², surface density uniformity U = 3%, dispersion D = 0.95, sizing agent residue rate S = 9%, and thermoplastic fiber content R. PP Basalt fiber felt material with a thickness of 0.5. Based on model predictions, the board performance results are: thickness standard deviation 0.36 mm, volume shrinkage rate 2.2%, tensile strength 92.7 MPa, impact strength variation 6%, VOC emission 201 μg / m³, and odor rating 2.6. No process control was implemented; conventional hot pressing (temperature 165℃, pressure 20 MPa, time 3 min) was directly applied. Actual measured board performance results show: large thickness deviation, insufficient mechanical properties, and high VOC emissions and odor rating, failing to meet the long-term service requirements for rail transit interior panels.

[0073] Comparative Example 2:

[0074] During the trial production of the rail vehicle floor, the surface density A = 1600 g / m² (exceeding the model's set range of 600–1400 g / m²), surface density uniformity U = 3%, dispersion D = 0.95, and thermoplastic fiber content R were selected. PPBasalt fiber mat with area density A = 800 g / m2, area density uniformity U = 6% (out of the model setting range ≤ 5%), dispersity D = 0.95, RPP = 0.5, S = 9%. According to the model prediction of the board performance results: thickness standard deviation 0.44 mm, volume shrinkage rate 3.0%, tensile strength 88.7 MPa, impact strength change 8%, VOC emission 201 μg / m3, odor level 2.6. The measured results of the board performance: thickness standard deviation 0.32 mm, volume shrinkage rate 3.4%, tensile strength 91.5 MPa, impact strength change 9%, VOC emission 178 μg / m3, odor level 2.4. Compared with the model setting accuracy: volume shrinkage rate: predicted 3.0% vs. measured 3.4%, deviation 0.4 > ± 0.3; the rest of the indicators are within the accuracy range. Therefore, the area density out of the range leads to inaccurate prediction of the volume shrinkage rate, which has the greatest impact on the dimensional stability of the board.

[0075] Comparative Example 3:

[0076] In the production process of the automobile door panel, basalt fiber mat with area density A = 800 g / m2, area density uniformity U = 6% (out of the model setting range ≤ 5%), dispersity D = 0.95, RPP = 0.5, S = 9% was selected. According to the model prediction of the board performance results: thickness standard deviation 0.38 mm, volume shrinkage rate 2.4%, tensile strength 89.7 MPa, impact strength change 5%, VOC emission 216 μg / m3, odor level 3.2. The measured results of the board performance: thickness standard deviation 0.48 mm, volume shrinkage rate 3.3%, tensile strength 91.0 MPa, impact strength change 8%, VOC emission 200 μg / m3, odor level 3.0. Compared with the model setting accuracy: thickness standard deviation: deviation 0.10 mm = critical; volume shrinkage rate: deviation 0.9 > ± 0.3. The area density uniformity out of the range mainly leads to inaccurate prediction of the thickness stability and volume shrinkage rate, which has a greater impact on the forming size of the board.

[0077] Comparative Example 4:

[0078] In the production process of interior panel of rail vehicle, basalt fiber felt material with areal density A = 800 g / m2, uniformity U = 3%, dispersity D = 0.86 (lower than the set range ≥ 0.90), RPP = 0.5, and S = 9% is selected. According to the model prediction panel performance results: thickness standard deviation 0.41 mm, volume shrinkage rate 2.3%, tensile strength 90.4 MPa, impact strength change 7%, VOC emission 203 μg / m3, and odor level 3.2. The panel performance measured results are: thickness standard deviation 0.22 mm, volume shrinkage rate 1.2%, tensile strength 95.0 MPa, impact strength change 4%, VOC emission 170 μg / m3, and odor level 2.8. Compared with the model setting accuracy: thickness standard deviation: deviation 0.19 > ± 0.1; volume shrinkage rate: deviation 1.1 > ± 0.3; tensile strength: deviation 4.6 > ± 3; impact strength change: deviation 3 = critical; VOC emission: deviation 33 > ± 30; odor level: deviation 0.4 > ± 0.3. Therefore, the too low dispersity causes the performance deviation, especially the most significant deviation of mechanical properties and VOC odor level.

[0079] Comparative Example 5

[0080] In the production process of automobile underbody panel, basalt fiber felt material with areal density A = 800 g / m2, uniformity U = 3%, dispersity D = 0.95, RPP = 0.2 (lower than the recommended range 0.3-0.7), and S = 9% is selected. According to the model prediction panel performance results: thickness standard deviation 0.36 mm, volume shrinkage rate 2.5%, tensile strength 90.3 MPa, impact strength change 7%, VOC emission 203 μg / m3, and odor level 2.6. The panel performance measured results are: thickness standard deviation 0.35 mm, volume shrinkage rate 2.1%, tensile strength 87 MPa, impact strength change 4%, VOC emission 197 μg / m3, and odor level 2.3. Compared with the model setting accuracy: tensile strength: deviation 3.3 > ± 3; the rest of the indicators are within the accuracy range. Therefore, the insufficient thermoplastic fiber content directly leads to the too large prediction deviation of tensile strength, the decrease of mechanical properties, and the failure to meet the use requirements.

[0081] Comparative Example 6

[0082] In the production process of the rail vehicle shell plate, the basalt fiber felt material with the area density A = 800 g / m2, the uniformity U = 3%, the dispersion D = 0.95, the RPP = 0.5, but the residual rate S = 16% (exceeding the recommended value 9%) of the infiltrating agent is selected. According to the model prediction plate performance results: thickness standard deviation 0.50 mm, volume shrinkage rate 2.7%, tensile strength 89.9 MPa, impact strength change 8%, VOC emission 306 μg / m3, odor level 3.2. The measured results of the plate performance: thickness standard deviation 0.44 mm, volume shrinkage rate 2.5%, tensile strength 92 MPa, impact strength change 4.9%, VOC emission 310 μg / m3, odor level 3.3. Compared with the model setting accuracy: impact strength change: deviation 3.1 > ± 3; VOC emission: deviation 4 < ± 30, which meets the accuracy; odor level: deviation 0.1 < ± 0.3. Therefore, the too high residual rate of the infiltrating agent mainly leads to the failure of the prediction accuracy of the impact strength, and the high VOC emission level has the greatest impact on the environmental protection performance.

[0083] Table 1 Typical plate performance corresponding relationship

[0084]

[0085] It can be seen from the test results of the above examples and comparative examples that each detection index and process control has a significant influence on the final performance of the plate. If the process is not controlled through the performance prediction model (such as Comparative Example 1), the thickness standard deviation, volume shrinkage rate and mechanical property fluctuation of the plate and the deterioration of the odor level, etc. are easily caused. The area density A in the range of 600-1400 g / m2 can obtain stable structural performance, and too high (such as Comparative Example 2) can easily cause the volume shrinkage rate to rise and the mechanical properties to decrease. When the area density uniformity U is controlled within ± 3%, the thickness standard deviation and the mechanical property fluctuation are the smallest, and the deviation exceeding ± 5% (such as Comparative Example 3) will cause the thickness to be uneven and the shrinkage to increase. The fiber dispersion D has a particularly significant influence on the performance, when D ≥ 0.90, the plate thickness is stable, the shrinkage rate is low, and the mechanical properties are good; and the decrease of the dispersion (such as Comparative Example 4) will cause the strength to decrease and the VOC emission to rise. The content of the thermoplastic fiber R PP It is good in the range of 0.3-0.7, wherein R PPThe sample (Example 4) with S = 0.5 has the smallest thickness fluctuation and the best tensile and impact performance; deviating from this point (Comparative Example 5) leads to a significant decrease in mechanical properties. The environmental indicators are significantly affected by the S value of the residual amount of the impregnating agent; when the residual amount is about 9%, the VOC emission and odor level are at a relatively optimal level, while too high (Comparative Example 6), S = 16%, the VOC emission is significantly increased to 310 μg / m³, and the odor level is significantly deteriorated. The comprehensive results show that the detection and prediction model established in the present application can accurately reflect the correlation between the raw material parameters and the properties of the board, and the performance and environmental indicators can be synergistically controlled by optimizing the parameters A, U, D, R PP , S, etc., thereby effectively improving the consistency and stability of the product.

[0086] Performance prediction model:

[0087] 1. Input variables (independent variables):

[0088] A: areal density of the mat material, unit g / m² (range 600-1400)

[0089] U: areal density uniformity (expressed in %, i.e., local deviation percentage, for example, ±3% indicates U = 3)

[0090] R PP : proportion of thermoplastic fibers PP (expressed as a fraction, 0.3-0.7)

[0091] D: fiber dispersion (0-1, for example: 92% → D = 0.92)

[0092] S: residual amount of the impregnating agent (%)

[0093] 2. Output variables (predicted targets):

[0094] Tsd: standard deviation of the thickness of the formed board (mm)

[0095] Shr: volume shrinkage rate (%)

[0096] Tens: tensile strength (MPa)

[0097] Imp: relative change in impact strength (%), positive for improvement, negative for decrease

[0098] VOC: volatile organic compound emission (μg / m³)

[0099] Od: odor level (1-5, the larger the value, the heavier the odor, and the upper limit of the model is 5)

[0100] 3. Regression model (example linear form):

[0101] To facilitate the clear calculable results given in the invention / embodiments, the following linear combinations are used:

[0102] (1) Thickness standard deviation (mm):

[0103] Tsd = 0.10 + 0.0002 |A - 1000| + 0.005U + 0.5(1 - D) + 0.02S

[0104] (2) Volume shrinkage rate (%)

[0105] Shr = 0.80 + 0.002 |A - 1000| + 0.06U + 1.5(1 - D) + 0.08S + 1.0 |RPP - 0.5|

[0106] (3) Tensile strength (MPa)

[0107] Tens = 100 - 0.01 |A - 1000| - 1.0U + 25(D - 0.9) + 8(RPP - 0.5) - 0.4S

[0108] (4) Impact strength change (%)

[0109] Imp = 6 + 0.005 |A - 1000| - 0.8U + 10(0.95 - D) + 3(0.5 - RPP) + 0.2S

[0110] (5) VOC (μg / m³)

[0111] VOC = 50 + 15S + 20(1 - D) + 8 |RPP - 0.5| + 5U

[0112] (6) Odor rating (1-5)

[0113] Od = min(5, 1 + 0.08S + 6(1 - D) + 0.2U) (upper limit 5)

[0114] 4. Model input verification and explanation:

[0115] (1) The areal density (A) is recommended to be in the range of 600-1400 g / m²; deviation from 1000 g / m² will bring about effects on thickness and shrinkage.

[0116] (2) The areal density uniformity (U) is recommended to be controlled at ±5%, preferably ±3%.

[0117] (3) The higher the fiber dispersion (D) (tending to 1), the smaller the thickness, shrinkage and mechanical fluctuation. It is required that the proportion of fiber bundles with a diameter ≤20 μm be ≥90% as an index to achieve D ≥ 0.90.

[0118] (4) The content of thermoplastic fibers (RPP ) Generally between 0.3 - 0.7. In this model 0.5 is the symmetrically preferred point (equilibrium fiber composition).

[0119] (5) The saturant residue rate (S) is expressed in %, in the example common values are 6 - 12%, the higher the residue the greater the VOC and odor. In this model the basalt fiber residue rate used is around 9%, the greater the deviation from 9% the greater the predicted VOC emission fluctuation.

[0120] 5. Model predicted data vs. measured values deviation range:

[0121] (1) Thickness standard deviation (Tsd): predicted vs. measured deviation < ± 0.1 mm;

[0122] (2) Volume shrinkage (Shr): predicted vs. measured deviation < ± 0.3;

[0123] (3) Tensile strength (Tens): predicted vs. measured deviation < ± 3 MPa;

[0124] (4) Impact strength variation (Imp): predicted vs. measured deviation < ± 3%

[0125] (5) VOC emissions: predicted vs. measured deviation < ± 30 g / m3;

[0126] (6) Odor rating: predicted vs. measured deviation < 0.3 rating.

[0127] This method can control the panel thickness accuracy within ± 0.3 mm, the volume shrinkage within the range of 0.5% - 2.0%, the mechanical properties, such as tensile strength and elongation at break, fluctuate less than 5%, the odor rating < 3, and the VOC emissions are reduced by more than 20%.

[0128] Note that the design of the above digital prototype model, the parameter setting of each material, and the selection of the measurement plane are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for predicting and controlling the performance of a board based on the quality of a basalt fiber mat, characterized in that, The method comprises the following steps: A. Perform multiple quality tests on basalt fiber mat for panel manufacturing, including areal density, areal density uniformity, thermoplastic fiber content, and fiber dispersion; B. Test the type and residual rate of the basalt fiber surface sizing agent to evaluate its impact on panel odor and volatile organic compound emissions; C. Set the determination threshold for each quality test index; D. Input the test data into the performance prediction model to predict the thickness control accuracy, volume shrinkage, mechanical properties, odor level, and volatile organic compound emission level of the panel; E. Based on the prediction results, real-time feedback and adjustment of process parameters during panel production, including lamination method, heating temperature, holding time and pressure, to achieve closed-loop process control; F. Compare the actual performance of the formed panel with the model prediction output, and dynamically optimize the detection standard and process parameters based on the error results to achieve continuous and stable control of panel quality.

2. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 1, characterized in that: Step A, the basalt fibers of the basalt fiber mat are in chopped form and are combined with thermoplastic fibers to form a non-woven structure of the fiber mat through a needle punching process.

3. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 2, characterized in that: The thermoplastic fibers are at least one of polypropylene fibers and polyethylene fibers.

4. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 1, characterized in that: Step B, the sizing agent residual rate is determined by solvent extraction combined with gravimetric method, thermogravimetric analysis infrared spectrometry, or gas chromatography-mass spectrometry to predict the odor level and volatile organic compound emissions of the panel during hot pressing.

5. The basalt fiber mat quality detection-based plate performance prediction and control method according to claim 1, characterized in that: Step C, the areal density of the basalt fiber mat ranges from 600 to 1400 g / m², and the areal density deviation is controlled within ±5%.

6. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 5, characterized in that: The areal density deviation is controlled within ±3%.

7. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 1, characterized in that: Step C, the dispersion of the basalt fibers is evaluated by grayscale and binary processing, the image is divided into grids, the fiber proportion of each partition is calculated, and the normalized dispersion index is the ratio of the standard deviation to the average value, with a value range of 0-1, and the proportion of fiber bundles with a diameter less than or equal to 20 microns is not less than 90%.

8. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 7, characterized in that: The dispersion is greater than 0.9, and the proportion of fiber bundles with a diameter less than or equal to 20 microns is not less than 90%.

9. The basalt fiber mat quality detection-based board performance prediction and control method according to claim 1, characterized in that: Step C, the content of thermoplastic fibers is determined by calcination and weighing to predict the volume shrinkage of the panel.

10. The basalt fiber mat quality detection-based plate performance prediction and control method according to claim 1, characterized in that: Step D, the performance prediction model is based on a multiple linear regression algorithm, which is used to establish a quantitative correlation between the basalt fiber mat test indexes and the key performance of the panel. The input variables of the performance prediction model include mat areal density, areal density uniformity, thermoplastic fiber PP proportion, fiber dispersion, and sizing agent residual rate. The output variables include panel thickness standard deviation, volume shrinkage, tensile strength, impact strength relative change, volatile organic compound emissions, and odor level.

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