Method for sintering nanometer powder modified chrysoberyl synthetic material

Abstract

The application relates to the technical field of inorganic non-metallic materials and building decoration materials, in particular to a sintering forming method of a nanometer powder modified jade crystal stone synthetic material, which comprises the following steps: S1, preparing a nanometer powder modified mother glass raw material: preparing a basic glass component by taking industrial solid waste as a main raw material, and adding nanometer powder as a functional modified component; the nanometer powder comprises nanometer TiO2 and / or nanometer ZrO2, and the average particle size of the nanometer powder is 20nm-100nm; S2, establishing a material thermophysical parameter database of the nanometer powder modified microcrystalline glass, including thermal conductivity, specific heat capacity, density, thermal expansion coefficient, crystallization activation energy and Avrami index. Through gradient crystallization control technology and the synergistic densification effect of nanometer powder-crack glass, in combination with numerical simulation optimization, the surface porosity of the microcrystalline stone product can be reduced to below 0.5%.

Description

Technical Field

[0001] This invention relates to the field of inorganic non-metallic materials and building decoration materials, and in particular to a sintering and molding method for a synthetic material of jade crystal modified with nanopowder. Background Technology

[0002] Microcrystalline glass (also known as microcrystalline stone, jade crystal stone, artificial marble) is a composite material of crystal and glass, which is made by heat-treating base glass to precipitate microcrystals on the glass matrix. It belongs to a new type of green and environmentally friendly building decoration material, and has a bright and shiny, magnificent and luxurious, elegant and noble decorative effect.

[0003] The traditional production methods for microcrystalline glass used in architectural decoration mainly include sintering and rolling, with sintering being the most widely used. The advantages of sintering technology are that the product has a granular texture on the surface, the thickness and specifications are easy to adjust, and the production process is easy to control.

[0004] However, the sintering process has the following long-standing technical defects: porosity defects, the gaps between the base glass particles are difficult to completely eliminate during the sintering process, which seriously affects product quality; monotonous texture, only granular texture can be obtained; defects, the mother glass particles are easily contaminated during the process, leading to defects; low production capacity and high energy consumption, it is an intermittent production process.

[0005] To overcome the shortcomings of traditional sintering techniques, China University of Geosciences developed a cracked glass crystallization method. Its core advantage lies in using cracked glass sheets as the mother glass. The glass fragments, apart from tiny cracks, have no large pores, making them easy to sinter into microcrystalline glass with low porosity. The cracked glass crystallization method has achieved a breakthrough in the field of microcrystalline glass preparation, effectively solving the porosity problem of traditional sintering methods. It offers numerous advantages, including easy adjustment of the mother glass composition, low porosity, and the ability to create realistic textures resembling natural stone within the microcrystalline glass.

[0006] However, with the increasing demands on the performance of microcrystalline stone products, especially the rapid development of nanoparticle-modified jade crystal materials, the existing crack glass crystallization method still has the following technical shortcomings:

[0007] (1) Lack of adaptability after the introduction of nanoparticles. The existing process parameter system for the crystallization of cracked glass is based on the formulation of ordinary microcrystalline glass and has not been systematically optimized for the nanoparticle-modified system. The introduction of nanoparticles changes the sintering kinetics and crystallization behavior of the parent glass, and the existing uniform heat treatment regime is difficult to adapt to the control requirements of grain size and distribution under the nanoparticle system.

[0008] (2) The control of the crystallization process is still too crude. Existing technologies use isothermal treatment with a uniform temperature field or simple segmented heat treatment, which does not provide precise control over the formation of crystal nuclei and the growth of grains during the crystallization process. In the presence of nanoparticles, the overlap between the nucleation and crystallization processes in the temperature range is more obvious. The traditional two-step method of "nucleation first and then crystallization" is difficult to separate precisely, resulting in abnormal grain growth and pore blockage. Summary of the Invention

[0009] The purpose of this invention is to provide a method for suppressing porosity in nano-powder microcrystalline stone based on gradient crystallization control and numerical simulation optimization, and the resulting product. This aims to solve the technical problems in existing technologies where nano-powder-modified microcrystalline stone exhibits high porosity, uneven grain size, coarse control of the crystallization process, and reliance on trial-and-error in process parameter design during sintering. By establishing a finite element numerical simulation model of nano-powder-modified microcrystalline stone, multi-scale coupled simulations are performed on the temperature field distribution, grain evolution behavior, and residual stress during gradient crystallization heat treatment. Based on the simulation results, the gradient crystallization parameters are inversely optimized, and combined with experimental verification to form a closed-loop optimization system, achieving a significant reduction in microcrystalline stone porosity and refined control of its microstructure.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a sintering and molding method for a nanopowder-modified jade crystal synthetic material, the core technical solution of which includes:

[0011] 1). Composition design and crack structure control of nanopowder-modified master glass

[0012] The design utilizes industrial tailings / slag as the main raw material and adds nanoparticles as functional components to create a master glass formulation system. After the master glass is melted and rolled, a "nanoparticle-cracked glass" composite master glass plate with a specific crack structure is prepared by controlling the cracking process parameters.

[0013] 2) Gradient crystallization heat treatment regime

[0014] To address the crystallization kinetics in the presence of nanoparticles, a four-stage gradient crystallization heat treatment regime was designed, comprising a preheating stage, a gradient nucleation stage, a gradient crystallization stage, and an annealing cooling stage. A positive temperature gradient is established in the gradient nucleation stage to preferentially form and controllably distribute crystal nuclei within the mother glass plate; a reverse temperature gradient is established in the gradient crystallization stage to control the directional growth of grains along the thickness direction, promoting the densification and filling of crack gaps.

[0015] 3). Multi-scale numerical simulation of gradient crystallization process based on finite element method

[0016] A coupled thermo-mechanical-crystallization numerical model of nanopowder-modified microcrystalline glass during gradient crystallization heat treatment was established, including three scale levels: macroscopic temperature field simulation, mesoscopic crystallinity evolution simulation, and residual stress simulation. The gradient temperature field distribution, the feedback of latent heat release on the temperature field, grain growth kinetics, and residual stress distribution were coupled and solved using the finite element method.

[0017] 4). Inverse optimization of gradient crystallization parameters based on numerical simulation results

[0018] With the goals of minimizing porosity and homogenizing grain size, a genetic algorithm or multi-objective optimization method is used to perform inverse search optimization on the key parameters of the gradient crystallization regime, using numerical simulation results as the basis for evaluating the objective function, to obtain the optimal process window.

[0019] 5). A synergistic densification mechanism between nanopowder and cracked glass based on matching thermal expansion coefficients.

[0020] By utilizing the difference in thermal expansion coefficients between nanoparticles and the parent glass matrix, micro-regional compressive stress is formed during gradient crystallization, which actively drives the closure of crack gaps and the elimination of residual pores.

[0021] 6) Real-time monitoring and closed-loop feedback control system

[0022] Multiple temperature sensors and infrared thermal imagers are deployed in the crystallization kiln to collect temperature field distribution data in real time. This data is then compared with the target gradient temperature curve obtained through numerical simulation optimization. The heating power of each heating zone is dynamically adjusted by the controller to ensure that the deviation between the actual temperature field and the target curve is controlled within the set threshold.

[0023] 1. Composition design of improved master glass using nanoparticles

[0024] 1.1 The nanopowder-modified master glass of the present invention uses industrial solid waste as the main raw material and is formulated into basic glass components according to the following weight percentage range:

[0025] SiO2: 45%~65%

[0026] CaO: 15%~25%

[0027] Al2O3: 5%~12%

[0028] MgO: 2%~8%

[0029] Na₂O / K₂O: 3%~8%

[0030] B2O3: 0~5%

[0031] Based on the above-mentioned basic components, the following nanoparticles are added as functional modification components:

[0032] Nano TiO2: 1%~5%

[0033] Nano ZrO2: 0.5%~3%

[0034] Nano CeO2: 0~2%

[0035] The average particle size of the nanoparticles is controlled within the range of 20 nm to 100 nm. Nano TiO2 and nano ZrO2 act as nucleating agents during the crystallization process, promoting the crystallization of glass-ceramics at lower temperatures and providing uniformly dispersed heterogeneous nucleation sites to promote the formation of fine and uniform grains.

[0036] 1.2 Preparation and Crack Structure Control of Cracked Glass

[0037] After the raw materials in the above proportions are mixed evenly, they are melted at 1450℃~1550℃ for 1.5h~2.5h. After clarification and homogenization, the glass melt is formed into a master glass plate with a thickness of 3mm~15mm by calendering.

[0038] The mother glass plate was then subjected to a cracking treatment. The cracking treatment employed either water quenching or cold air flow cracking methods: after preheating the mother glass plate to 500℃~650℃, it was rapidly immersed in a cooling medium (the temperature of the cooling medium was controlled between 15℃~40℃), generating a microcrack network within the mother glass plate through thermal shock. The cracking process parameters were adjusted according to the content and type of nanoparticles.

[0039] The "nanopowder-cracked glass" composite mother glass plate after cracking treatment has the structural characteristic of "cracked but not scattered"—the glass fragments are tightly connected in contact with each other at the crack surface, and there are no large pores between the fragments except for the tiny cracks. The enriched distribution of nanopowder at the crack interface provides favorable conditions for interfacial diffusion and densification in the subsequent gradient crystallization process.

[0040] 1.3 Multi-scale numerical simulation of gradient crystallization process based on finite element method

[0041] One of the core innovations of this invention lies in the establishment of a multi-scale finite element numerical simulation model for the gradient crystallization heat treatment process of nanoparticles modified microcrystalline glass. This model includes three coupled scale levels: macroscopic temperature field simulation, mesoscopic crystallinity evolution simulation, and residual stress simulation.

[0042] (1) Simulation of macroscopic temperature field

[0043] The three-dimensional unsteady temperature field inside the mother glass plate during gradient crystallization heat treatment was numerically simulated using the ANSYS finite element analysis platform. Using the geometric dimensions and thermophysical parameters (thermal conductivity, specific heat capacity, and density) of the mother glass plate as input, and considering anisotropic heat transfer boundary conditions, a three-dimensional transient heat conduction governing equation was established.

[0044] In the finite element discretization process, the mother glass plate is divided into several finite elements, with the temperature at each element node as the basic unknown. The spatiotemporal distribution of the temperature field during the entire heat treatment process is obtained by solving the heat conduction equations. During the simulation, segmented boundary conditions for the preheating section, gradient nucleation section, gradient crystallization section, and annealing cooling section are applied to the model respectively. The focus is on analyzing the temperature distribution curves along the thickness direction inside the mother glass plate under the conditions of gradient nucleation section (positive temperature gradient) and gradient crystallization section (reverse temperature gradient), as well as the magnitude and uniformity of the temperature gradient.

[0045] (2) Simulation of mesoscopic crystallinity evolution

[0046] Crystallinity evolution is a mesoscale phenomenon, with its spatial characteristic scale being much smaller than that of the macroscopic temperature field. This study draws on existing multiscale coupled simulation methods—macroscopic temperature simulation based on the finite element method, mesoscopic crystallization simulation based on the Monte Carlo method, and the Uhlmann model used to describe the crystallization dynamics of glass—to construct a crystallinity evolution simulation module suitable for nanopowder-modified glass-ceramic systems.

[0047] In the simulation, the temperature value obtained from the macroscopic temperature field simulation is used as the input condition for crystallinity calculation. Simultaneously, the latent heat of phase change released during crystallization is used as the heat source feedback term for the temperature field calculation, achieving bidirectional coupling between the temperature field and crystallinity. The relationship between the latent heat release rate and the rate of change in crystallinity is handled as follows: the JMAK (Johnson-Mehl-Avrami-Kolmogorov) equation is used to describe the isothermal crystallization kinetics. Combined with the differential form under non-isothermal conditions, the crystallinity is numerically integrated to calculate the crystallinity increment and the corresponding latent heat release within each time step, and this is fed back into the temperature field calculation.

[0048] A pixel-based method is used to handle the collision process between grain interfaces, simulating the preferred growth behavior of grains driven by a gradient temperature field. This module can predict the gradient distribution characteristics of grain size, grain morphology, and crystallinity along the thickness direction of the mother glass plate under different gradient crystallization regimes.

[0049] (3) Residual stress simulation

[0050] Due to the difference in thermal expansion coefficients and elastic moduli between the glass matrix and the precipitated crystalline phase, residual stress is generated inside the crystallized product. This study uses the positional finite element method to develop a nonlinear geometric analysis tool to simulate the distribution of residual stress generated during crystal growth within the glass matrix.

[0051] The residual stress mainly originates from two aspects: thermal stress caused by the gradient temperature field and thermal expansion mismatch stress between the crystalline phase and the glass matrix. In the simulation, the crystalline phase and the glass matrix are modeled as finite elements with different material properties. The crystalline mesh is embedded into the glass mesh through motion compatibility conditions, without introducing additional degrees of freedom. The thermal stress and crystallization shrinkage stress at each temperature node are calculated to determine the residual stress distribution inside the product.

[0052] The residual stress simulation results were correlated with the risk of porosity formation. When the local tensile stress exceeds the tensile strength of the glass matrix, microcracks are easily generated or porosity expansion occurs, thereby reducing density. By adjusting the gradient crystallization parameters, the residual stress is kept below the safe threshold, ensuring densification while avoiding porosity expansion caused by excessive stress.

[0053] (4) Multi-scale coupling mechanism

[0054] The bidirectional coupling between temperature field simulation and crystallinity simulation is the core of the numerical simulation model of this invention. During the calculation, temperature values ​​are considered as input conditions for crystallinity calculation, while the crystallization result, in the form of latent heat, serves as the input condition for temperature calculation, forming an iterative coupled solution of temperature field and crystallinity. Within each time step, the evolution of crystallinity and its latent heat release are first calculated based on the current temperature field, and then the temperature field is updated based on the latent heat. This process is iterated until convergence.

[0055] (5) Establishment of simulation parameter database

[0056] Through systematic experimental design, a database of basic material thermophysical parameters was established, encompassing different combinations of nanoparticle types, particle sizes, and addition amounts. This database includes parameters such as thermal conductivity, specific heat capacity, density, coefficient of thermal expansion, crystallization activation energy, and Avrami index. This database provides accurate constitutive parameter inputs for numerical simulation models, improving the accuracy of simulation predictions.

[0057] 1.4 Inverse Optimization of Gradient Crystallization Parameters Based on Numerical Simulation Results

[0058] (1) Definition of the objective function

[0059] A multi-objective optimization problem is defined with the overall performance of microcrystalline stone products as the objective. The objective function includes the following sub-objectives:

[0060] Minimize porosity: (porosity)

[0061] Grain size homogenization: (Grain size standard deviation)

[0062] Minimize residual stress: (Maximum residual tensile stress)

[0063] Minimize energy consumption: (Unit energy consumption)

[0064] The above sub-objectives are transformed into a single-objective optimization problem through weighted summation, and the weight coefficients can be adjusted according to the product application scenario.

[0065] (2) Optimize variables

[0066] The optimization variables include key parameters in the gradient crystallization heat treatment regime: the temperature gradient of the gradient nucleation section. (20℃~50℃), holding time of gradient nucleation section (60min~180min), temperature gradient in the gradient crystallization section (20℃~50℃), holding time of gradient crystallization section (120min~240min), heating rate v (2℃ / min~5℃ / min), cooling rate (1℃ / min~3℃ / min) as well as the amount and particle size of nanoparticles added.

[0067] (3) Optimization algorithm

[0068] A multi-objective particle swarm optimization algorithm or a genetic algorithm is used to perform a global search optimization on the above parameter space. The numerical simulation model is used as a fitness function evaluation tool, and the Pareto optimal front is gradually approximated by iteratively calculating the objective function values ​​under different parameter combinations.

[0069] The optimization process is as follows: First, an initial population is randomly generated in the parameter space, with each individual representing a set of optimization variables. For each individual, a numerical simulation model is called to calculate the corresponding performance indicators such as porosity, grain size, and residual stress. The fitness is calculated according to the objective function, and a new generation of population is generated through genetic operations such as selection, crossover, and mutation. The above iterations are repeated until the convergence condition is met (e.g., the change in population fitness is less than a set threshold or the maximum number of iterations is reached). After convergence, several sets of parameters with porosity below 0.5% and the smallest standard deviation of grain size are selected from the Pareto front as the optimal process window.

[0070] (4) Experimental verification and model correction

[0071] The optimal process window obtained from numerical simulation optimization is verified experimentally. The experimental test results (measured porosity, grain size, mechanical properties, etc.) are compared with the simulated predictions to calculate the deviation. Bayesian inference or Kalman filtering methods are used to correct the material parameters of the numerical simulation model, realizing a closed-loop optimization of "simulation prediction → experimental verification → model correction" and gradually improving the prediction accuracy of the numerical simulation model.

[0072] 3.5 Gradient crystallization heat treatment regime

[0073] Based on the combined results of numerical simulation optimization and experimental verification, the preferred gradient crystallization heat treatment regime of this invention includes the following four stages:

[0074] (1) Preheating section (uniform temperature section)

[0075] The mother glass plate is heated to 580℃~680℃ at a heating rate of 3℃ / min~8℃ / min and held at this temperature for 30min~90min. The preheating section uses a uniform temperature field to eliminate residual stress inside the mother glass plate, so that the nanoparticles can be initially uniformly dispersed in the glass matrix.

[0076] (2) Gradient kernelization segment

[0077] After preheating, the gradient nucleation stage begins. In this stage, a temperature gradient is established along the thickness of the mother glass plate: upper surface temperature... Set to 710℃~760℃, lower surface temperature Set to 680℃~730℃, with the upper surface temperature higher than the lower surface temperature, temperature gradient. The temperature is controlled within the range of 20℃ to 50℃, and the temperature gradient rate along the thickness direction is approximately 2℃ / mm to 10℃ / mm.

[0078] Under the influence of a gradient temperature field, the nano-nucleating agent in the mother glass plate preferentially undergoes heterogeneous nucleation in the higher temperature region, forming fine nanoscale crystal nuclei. Due to the presence of the temperature gradient, the formation of crystal nuclei exhibits a "top-down" distribution characteristic in the thickness direction, creating conditions for the subsequent directional growth of grains. The holding time of the gradient nucleation section is 60 min to 180 min, during which the temperature gradient remains constant.

[0079] (3) Gradient crystallization segment

[0080] After the gradient nucleation stage, the gradient crystallization stage begins. In this stage, a reverse temperature gradient is established along the thickness direction of the mother glass plate: the upper surface temperature... Set to 850℃~950℃, lower surface temperature Set to 880℃~980℃, with the lower surface temperature higher than the upper surface temperature, temperature gradient. The temperature should be controlled within the range of 20℃ to 50℃.

[0081] The design intent of the reverse temperature gradient is that, during the nucleation stage, the crystal nuclei formed are mainly concentrated in the upper surface region. Upon entering the crystallization stage, the reverse temperature gradient drives the grains to grow directionally from top to bottom. During grain growth, the grains continuously fill crack gaps and residual pores, ultimately achieving complete densification throughout the thickness of the entire mother glass plate. The holding time for the gradient crystallization section is 120 min to 240 min, and the heating rate is controlled at 2℃ / min to 5℃ / min.

[0082] (4) Annealing cooling section

[0083] After crystallization, the product enters the annealing cooling section. The temperature is reduced to room temperature at a cooling rate of 1℃ / min to 3℃ / min to eliminate residual thermal stress inside the product and prevent cracking. A uniform temperature field is used during annealing to avoid generating new thermal stress.

[0084] 1.6 Real-time monitoring and closed-loop feedback control system

[0085] To ensure the precise execution of the aforementioned gradient crystallization heat treatment process, the crystallization kiln of this invention is equipped with the following monitoring and control system:

[0086] (1) Multi-point temperature sensor array: Thermocouple arrays are deployed along the length and thickness of the mother glass plate inside the kiln to collect temperature data at each location in real time. The sensor deployment density is no less than one measuring point per 100 mm, and the temperature measurement accuracy is better than ±1℃.

[0087] (2) Infrared thermal imager: An infrared thermal imager is deployed at the observation window of the kiln to monitor the temperature distribution uniformity on the upper surface of the mother glass plate in real time, with a spatial resolution better than 5mm×5mm.

[0088] (3) PID controller or fuzzy logic controller: Compare the above monitoring data with the target gradient temperature curve obtained by numerical simulation optimization, calculate the temperature deviation, and dynamically adjust the heating power of each heating zone through PID algorithm or fuzzy logic algorithm to ensure that the deviation between the actual temperature field and the target curve is controlled within ±5℃.

[0089] (4) Data recording and traceability system: Record the complete temperature curve data of each heat treatment to form a process database for comparison and analysis with numerical simulation prediction results and continuous optimization of process parameters.

[0090] 3.7 Post-processing of finished products

[0091] After the aforementioned gradient crystallization heat treatment, the microcrystalline stone blank is removed from the mold and subjected to coarse grinding, fine grinding, and polishing processes to obtain the finished product. Because the gradient crystallization process effectively suppresses the generation and residue of pores, the surface porosity of the finished product after polishing is less than 0.5%, and the surface finish reaches mirror level.

[0092] Compared with the prior art, the beneficial effects of the present invention are:

[0093] 1. Significantly reduced porosity: Through gradient crystallization control technology and the synergistic densification effect of nanopowder-cracked glass, combined with numerical simulation optimization, the surface porosity of microcrystalline stone products can be reduced to below 0.5% (the measured value in the example is 0.16%~0.21%), which is far lower than 2%~5% of the traditional sintering method and 1%~2% of the conventional cracked glass crystallization method. The porosity suppression effect is improved by about 80%~90%.

[0094] 2. Improved mechanical properties: The densified microstructure leads to a significant improvement in mechanical properties. In the examples, the flexural strength reached 53.1 MPa~59.2 MPa, and the bulk density reached 2.69 g / cm³~2.73 g / cm³, which is superior to similar products prepared by traditional sintering methods (flexural strength 35~45 MPa).

[0095] 3. Shorten the process development cycle: Through numerical simulation optimization, the process parameter design is transformed from the traditional "trial and error" method to a scientific "prediction-optimization-verification" method. In the example, it only takes 2-4 weeks from formula determination to obtaining the optimal process window, while the traditional trial and error method usually takes 3-6 months, improving R&D efficiency by about 300%.

[0096] 4. Reduced production costs: Numerical simulation optimization reduces the number of trial and error experiments, thereby lowering the costs of raw materials, energy, and labor. Taking Example 1 as an example, the optimal process window was determined through only 3 rounds of verification experiments via simulation optimization, while traditional methods typically require 20 to 30 rounds of experiments.

[0097] 5. Controllable and uniform grain size: Gradient temperature field control enables directional growth of grains along the thickness direction, significantly improving grain size uniformity and avoiding performance degradation caused by abnormal grain growth in traditional processes.

[0098] 6. Strong process adaptability: Driven by a material database and capable of rapid adaptation to multiple formulations, the process window of this invention can cover multi-level product needs, from ordinary building decoration to high-end functional applications. When the nanopowder formulation is adjusted, only the material database parameters need to be updated and the numerical simulation model needs to be rerun to quickly obtain suitable process parameters.

[0099] 7. Digital quality control: The introduction of real-time monitoring and closed-loop feedback control system and numerical simulation model correction mechanism has enabled the crystallization process to be visualized, controllable, traceable and optimizable, which has significantly improved the consistency of products between batches. Attached Figure Description

[0100] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0101] Figure 1 This is a flowchart of the sintering and molding method for the nanopowder-modified jade crystal synthetic material of the present invention. Detailed Implementation

[0102] 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.

[0103] Please see Figure 1 The technical solution provided by this invention:

[0104] Example 1

[0105] (1) Raw material preparation

[0106] The base glass composition is prepared according to the following weight percentages: SiO2: 55%, CaO: 20%, Al2O3: 8%, MgO: 5%, Na2O: 4%, K2O: 3%, B2O: 5%. 3： 5%. Added nanoparticles: nano TiO2: 3%, nano ZrO2: 2%, average particle size of nanoparticles is 50nm. Total raw materials: 100 parts, nanoparticles are added externally.

[0107] (2) Determination of thermophysical parameters of basic materials

[0108] The glass transition temperature of the master glass material with the above formulation was determined by DSC differential thermal analysis. =620℃, nucleation temperature =700℃~740℃, crystallization temperature =880℃~930℃. Thermal conductivity λ = 1.2 W / (m·K) was determined using laser scintillation, and the coefficient of thermal expansion α = 8.5 × 10⁻⁻⁻⁶ was determined using thermomechanical analysis. 6 / K (glass matrix) and (Crystal phase). The DSC curve was fitted using the JMAK equation to obtain the Avrami exponent n = 2.8 and the crystallization activation energy. The above parameters are entered into the materials database and used as input for the numerical simulation model.

[0109] (3) Numerical simulation and parameter optimization

[0110] Based on the material parameters determined in step S2, a three-dimensional heat conduction model of an 8mm thick mother glass plate was established in the ANSYS finite element platform. Initial boundary conditions and material properties were set, and the temperature gradient of the gradient nucleation section was simulated. (Upper surface 730℃, lower surface 700℃) and temperature gradient of the gradient crystallization section Temperature field distribution under conditions of 900℃ for the upper surface and 930℃ for the lower surface. Simulation results show that a stable linear temperature gradient is formed in the thickness direction, with a temperature gradient rate of approximately 3.75℃ / mm, which satisfies the thermodynamic conditions for directional grain growth.

[0111] The results of macroscopic temperature field simulation were used as input conditions for crystallinity evolution simulation. Mesoscopic crystallization simulation showed that, driven by a reverse temperature gradient, grains preferentially grow from the upper surface to the lower surface, and the grain size exhibits a gradient distribution along the thickness direction—the grain size in the upper surface region is about 200 nm, in the middle region about 350 nm, and in the lower surface region about 500 nm. The standard deviation of grain size was controlled within 80 nm, and the uniformity was significantly better than that of traditional uniform heating methods.

[0112] Residual stress simulations show that, under the aforementioned gradient parameters, the maximum residual tensile stress inside the product is approximately 18 MPa, lower than the tensile strength of the glass matrix (approximately 35 MPa), and within the safe threshold range. The simulated porosity is predicted to be 0.20%–0.25%.

[0113] A genetic algorithm is used to perform inverse optimization of the gradient crystallization parameters. The optimization variables include... (20℃~50℃) (60min~180min) (20℃~50℃) (120 min ~ 240 min) and heating rate v (2℃ / min ~ 5℃ / min). Population size was set to 50, number of generations to 100, crossover probability 0.8, and mutation probability 0.05. The optimal process window was obtained after optimization convergence. =30℃~40℃, =120min~150min, =30℃~40℃, =180min~210min, v=3℃ / min~4℃ / min.

[0114] (4) Glass melting

[0115] After the above-prepared raw materials are mixed evenly, they are put into a tank furnace and melted at 1500℃ for 2 hours. After clarification and homogenization, glass melt is obtained.

[0116] (5) Calendering and cracking treatment

[0117] The molten glass was formed into a master glass plate with a thickness of 8 mm using a rolling molding method. After preheating the master glass plate at 600℃ for 30 minutes, it was quickly immersed in cooling water at 20℃ for water quenching and cracking, which generated a uniformly distributed microcrack network inside the master glass plate, thus obtaining a "nanopowder-cracked glass" composite master glass plate.

[0118] (6) Gradient crystallization heat treatment

[0119] Based on the numerical simulation optimization results, the gradient crystallization heat treatment parameters are set as follows:

[0120] Preheating section: Heat to 650℃ at a heating rate of 5℃ / min, hold for 60min, and uniform temperature field.

[0121] Gradient nucleation section: Establishes a positive temperature gradient, upper surface temperature 735℃, lower surface temperature =700℃, temperature gradient 35℃, hold for 135min.

[0122] Gradient crystallization section: Establishing a reverse temperature gradient, upper surface temperature =900℃, lower surface temperature =940℃, temperature gradient 40℃, hold for 195min, heating rate 3.5℃ / min.

[0123] Annealing cooling section: cooled to room temperature at a cooling rate of 2℃ / min.

[0124] (7) Real-time monitoring and control

[0125] During the gradient crystallization heat treatment process, temperature data is collected in real time by a K-type thermocouple array deployed on the inner wall of the kiln and compared with the target temperature curve. The PID controller dynamically adjusts the heating power of each heating zone according to the deviation to ensure that the deviation between the actual temperature field and the target curve is controlled within ±3℃.

[0126] (8) Post-processing and performance testing

[0127] The heat-treated microcrystalline stone blank is subjected to coarse grinding, fine grinding and polishing in sequence to obtain the finished product.

[0128] Performance test results:

[0129] Test Project Test methods result Simulated predicted values Surface porosity Image analysis 0.21% 0.20%~0.25% Bulk density Archimedes 2.69 g / cm³ 2.68~2.72 g / cm³ flexural strength GB / T 9966.2 53.1 MPa 50~55 MPa Water absorption rate GB / T 9966.3 0.05% <0.08% Surface smoothness Roughness tester Ra 0.07 μm — Grain size (average) SEM Image Analysis 320 nm 300~350 nm Grain size standard deviation SEM Image Analysis 72 nm <80 nm

[0130] Results Analysis: The measured porosity of Example 1 was 0.21%, which is highly consistent with the numerical simulation prediction and far lower than the 2%~5% of the traditional sintering method. The flexural strength reached 53.1 MPa, the grain size standard deviation was 72 nm, and the uniformity was excellent. The consistency between the numerical simulation prediction and the experimental verification results indicates that the multi-scale numerical simulation model of this invention has high prediction accuracy and can effectively guide the design of process parameters.

[0131] Example 2

[0132] Based on Example 1, the amount of nanoparticles added was adjusted to: 5% nano TiO2, 3% nano ZrO2, and 1% nano CeO2. The thermophysical parameters of the materials were re-measured for the adjusted formulation, and the material database was updated. The cracking treatment conditions were adjusted to: preheating temperature 650°C and cooling medium temperature 15°C (more intense cracking conditions).

[0133] Using the same numerical simulation optimization process as in Example 1, the optimal process window was searched again. The optimization results are as follows: =40℃~45℃ =150min~180min, =45℃~50℃, =210min~240min. The gradient crystallization temperature is adjusted accordingly: the upper surface temperature of the gradient nucleation section is 750℃ and the lower surface temperature is 710℃, and the upper surface temperature of the gradient crystallization section is 880℃ and the lower surface temperature is 935℃.

[0134] Performance test results:

[0135] Test Project Test methods result Simulated predicted values Surface porosity Image analysis 0.16% 0.15%~0.20% Bulk density Archimedes 2.73 g / cm³ 2.71~2.74 g / cm³ flexural strength GB / T 9966.2 59.2 MPa 56~60 MPa Water absorption rate GB / T 9966.3 0.03% <0.05% Surface smoothness Roughness tester Ra 0.06 μm —

[0136] Comparative Example 1 (Traditional Sintering Method)

[0137] Using the same basic glass composition and nanoparticle addition amount as in Example 1, a conventional sintering process was employed: the glass melt was water-quenched into glass particles, dried, sieved, and then molded. The particles were preheated at 650°C for 60 min in a uniform temperature field, then crystallized at 930°C for 180 min with a temperature increase of 5°C / min, followed by annealing and cooling. Cracked glass technology and gradient crystallization control were not employed, nor was numerical simulation optimization performed.

[0138] Performance test results:

[0139] Test Project Test methods result Surface porosity Image analysis 2.8% Bulk density Archimedes 2.51 g / cm³ flexural strength GB / T 9966.2 38.6 MPa Water absorption rate GB / T 9966.3 0.28% Surface smoothness Roughness tester Ra 0.35 μm

[0140] Comparative Example 2 (Cracked Glass Crystallization Method, Gradient-Free Crystallization and Numerical Simulation Optimization)

[0141] Using the same basic glass composition and nanopowder addition amount as in Example 1, the cracked glass crystallization method was adopted but without gradient crystallization control and numerical simulation optimization: a cracked glass mother glass plate was prepared and heat-treated in a uniform temperature field (heating to 650°C at 5°C / min for 60 min, crystallizing to 930°C at 5°C / min for 180 min, followed by annealing and cooling).

[0142] Performance test results:

[0143] Test Project Test methods result Surface porosity Image analysis 1.2% Bulk density Archimedes 2.58 g / cm³ flexural strength GB / T 9966.2 44.3 MPa Water absorption rate GB / T 9966.3 0.16%

[0144] Comparative analysis of results:

[0145] project Comparative Example 1 Comparative Example 2 Example 1 Example 2 Surface porosity 2.8% 1.2% 0.21% 0.16% flexural strength 38.6 MPa 44.3 MPa 53.1 MPa 59.2 MPa Bulk density 2.51 g / cm³ 2.58 g / cm³ 2.69 g / cm³ 2.73 g / cm³

[0146] Compared to Comparative Example 1 (conventional sintering method), Comparative Example 2 (cracked glass crystallization method) showed a reduction in porosity from 2.8% to 1.2%, and an increase in flexural strength of approximately 15%, demonstrating the effectiveness of the cracked glass crystallization method in suppressing porosity. Example 1, compared to Comparative Example 2, further reduced porosity by approximately 82.5% and increased flexural strength by approximately 20%, validating the significant gains from gradient crystallization control and numerical simulation optimization. Example 2, with its higher nanoparticle content, exhibited a more significant synergistic effect with the gradient crystallization regime.

[0147] Numerical simulation optimization effect verification:

[0148] In Example 1, the porosity predicted by numerical simulation was 0.20%~0.25%, while the measured value was 0.21%, with a relative error of approximately 5%. The predicted flexural strength was 50~55 MPa, while the measured value was 53.1 MPa, with a relative error of approximately 6%. The simulation predictions in Example 2 also showed a high degree of agreement with the measured values. These results demonstrate that the multi-scale finite element numerical simulation model of this invention has high prediction accuracy and can effectively guide the reverse optimization design of process parameters, shortening the process development cycle from the traditional 3~6 months to 2~4 weeks, significantly improving R&D efficiency.

[0149] 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 sintering and molding method for a nano-powder-modified jade crystal synthetic material, characterized in that, Includes the following steps: S1. Preparation of master glass raw materials modified by nanopowder: The basic glass components are prepared using industrial solid waste as the main raw material, and nanopowder is added as a functional modification component. The nanopowder contains nano TiO2 and / or nano ZrO2, and the average particle size of the nanopowder is 20nm~100nm. S2. Establish a database of material thermophysical parameters for nanopowder-modified glass-ceramics, including thermal conductivity, specific heat capacity, density, coefficient of thermal expansion, crystallization activation energy, and Avrami index; S3. A multi-scale numerical simulation model of the gradient crystallization heat treatment process is established based on the finite element method. The model includes a macroscopic temperature field simulation module, a mesoscopic crystallinity evolution simulation module, and a residual stress simulation module. The three modules achieve bidirectional coupling between temperature field and crystallinity. S4. Based on the numerical simulation model in step S3, with the goals of minimizing porosity and homogenizing grain size, the gradient crystallization heat treatment parameters are optimized by reverse search using a genetic algorithm or a multi-objective particle swarm optimization algorithm to obtain the optimal process window. S5. Glass melting and forming: After the raw materials prepared in step S1 are mixed evenly, they are melted and clarified at a temperature of 1450℃~1550℃, and the glass melt is formed into a master glass plate of a predetermined thickness by a rolling forming method. S6. Cracking treatment: The mother glass plate obtained in step S5 is subjected to cracking treatment to form a microcrack network inside the mother glass plate, thereby obtaining a "nanopowder-cracked glass" composite mother glass plate. S7. Gradient Crystallization Heat Treatment: Based on the preferred process window obtained in step S4, the composite mother glass plate obtained in step S6 is placed in a crystallization furnace for a four-stage gradient crystallization heat treatment, including a preheating stage, a gradient nucleation stage, a gradient crystallization stage, and an annealing and cooling stage. In the gradient nucleation stage, a first temperature gradient is established along the thickness direction of the mother glass plate, allowing crystal nuclei to preferentially form and controllably distribute along the thickness direction within the mother glass plate. In the gradient crystallization stage, a second temperature gradient opposite to the first temperature gradient is established along the thickness direction of the mother glass plate, controlling the directional growth of grains along the thickness direction. S8. Real-time monitoring and closed-loop control: During the gradient crystallization heat treatment process, the temperature field distribution data is collected in real time by a multi-point temperature sensor array and an infrared thermal imager deployed in the kiln. The data is compared with the target temperature curve in the preferred process window determined in step S4. The heating power of each heating zone is dynamically adjusted by the controller to ensure that the deviation between the actual temperature field and the target curve is controlled within the set threshold. S9. The microcrystalline stone blank plate after heat treatment in step S7 is post-processed to obtain the finished product.

2. The sintering and molding method for the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, In the multi-scale numerical simulation model described in step S3, the macroscopic temperature field simulation is based on solving the three-dimensional transient heat conduction equation using the finite element method, the mesoscopic crystallinity evolution simulation is based on the Monte Carlo method and uses the Uhlmann model and JMAK equation to describe the crystallization dynamics, and the residual stress simulation uses the positional finite element method to analyze the residual stress distribution caused by the thermal expansion mismatch between the crystalline phase and the glass matrix. The temperature field simulation and the crystallinity simulation are bidirectionally coupled—the temperature value is used as the input condition for the crystallinity calculation, and the latent heat of phase change released during the crystallization process is fed back to the temperature field calculation as a heat source.

3. The sintering and molding method for the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, The optimization variables for the multi-objective optimization problem described in step S4 include: the temperature gradient of the gradient kernel segment. The temperature range is 20℃~50℃, and the holding time of the gradient nucleation section is... The temperature gradient ranges from 60 min to 180 min, and the gradient crystallization section has a temperature gradient. The temperature range is 20℃~50℃, and the holding time in the gradient crystallization section is... The heating rate v ranges from 120 min to 240 min, and the cooling rate ranges from 2 °C / min to 5 °C / min. The range is 1℃ / min to 3℃ / min; the objective function is the weighted sum of porosity, grain size standard deviation, maximum residual tensile stress and unit energy consumption; the optimization algorithm adopts genetic algorithm or multi-objective particle swarm optimization algorithm.

4. The sintering and forming method of the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, The weight percentages of the basic glass components are as follows: SiO2: 45%~65%, CaO: 15%~25%, Al2O3: 5%~12%, MgO: 2%~8%, Na2O and / or K2O: 3%~8%, B2O 3: : 0~5%; the amount of nanopowder added is: nano TiO 2： 1%~5%, Nano ZrO 2： 0.5%~3%, Nano CeO 2： 0~2%.

5. The sintering and forming method of the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, The cracking treatment employs water quenching or cold air flow cracking methods: after preheating the mother glass plate to 500℃~650℃, it is rapidly immersed in a cooling medium with a temperature of 15℃~40℃; the cracking intensity is adjusted according to the total amount of nanoparticles added—a milder cracking condition is used when the total amount of nanoparticles added is 1%~3%, and a stronger cracking condition is used when the total amount of nanoparticles added is 3%~8%.

6. The sintering and forming method of the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, The specific conditions for the four-stage gradient crystallization heat treatment are as follows: Preheating section: Heat to 580℃~680℃ at a heating rate of 3℃ / min~8℃ / min, hold for 30min~90min, and use a uniform temperature field; Gradient nucleation section: Establishes a positive temperature gradient, upper surface temperature The lower surface temperature is 710℃~760℃. The temperature ranges from 680℃ to 730℃. =20℃~50℃, keep warm for 60min~180min; Gradient crystallization section: Establishing a reverse temperature gradient, upper surface temperature The lower surface temperature is 850℃~950℃. The temperature ranges from 880℃ to 980℃. =20℃~50℃, hold for 120min~240min, heating rate 2℃ / min~5℃ / min; Annealing cooling section: cooled to room temperature at a cooling rate of 1℃ / min to 3℃ / min.

7. The sintering and forming method of the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, In the real-time monitoring and closed-loop control described in step S8, the temperature sensor deployment density is no less than one measuring point per 100 mm, and the temperature measurement accuracy is better than ±1℃; the spatial resolution of the infrared thermal imager is better than 5mm×5mm; the controller is a PID controller or a fuzzy logic controller, and the deviation between the actual temperature field and the target curve is controlled within ±5℃.

8. The sintering and forming method of the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, The method further includes a model correction step: comparing the measured performance parameters of the finished product obtained in step S9 with the predicted values ​​of the numerical simulation model in step S3, calculating the deviation, and using Bayesian inference or Kalman filtering methods to correct the material parameters of the numerical simulation model, thereby achieving closed-loop optimization of simulation prediction, experimental verification, and model correction.

9. The sintering and forming method of the nanopowder-modified jade crystal synthetic material according to claim 1, characterized in that, During the cracking process, the nanoparticles are enriched and distributed at the crack interface. During the gradient crystallization process, the difference in the coefficient of thermal expansion between the nanoparticles and the mother glass matrix creates micro-region compressive stress, which drives the active closure of crack gaps and the elimination of residual pores.

10. A nanopowder-modified microcrystalline stone product prepared by the sintering and molding method of the nanopowder-modified jade crystal synthetic material according to any one of claims 1 to 9, characterized in that, The microcrystalline stone product has a surface porosity of less than 0.5%, a bulk density of not less than 2.65 g / cm³, and a flexural strength of not less than 50 MPa.

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