Industrial particle 3D printer cavity temperature heat recovery system and cavity temperature control method

Abstract

The invention discloses an industrial particle 3D printer cavity temperature heat recovery system and a cavity temperature control method, and relates to the technical field of 3D printing, and the method comprises the following steps: establishing a three-dimensional geometric analysis model of a printing component, based on the structural morphology, thickness distribution and spatial topology characteristics of the component, extracting a spatial temperature control demand corresponding to each region, and calculating a three-dimensional geometric analysis model of the printing component; and determining an adjusting target of the hot air circulation system. According to the method, a space temperature control demand model is established based on geometrical characteristics of a component, oriented distribution of heat energy is realized by dynamically adjusting a hot air circulation system, a cooling characteristic curve is constructed in combination with real-time temperature and stress data, and a warping resonance sensitive area is identified and regulated. And a self-adaptive temperature control system is formed through fixed-point thermal compensation and parameter closed-loop correction. Compared with a traditional strategy, the method has the advantages that the temperature control precision and the response efficiency are remarkably improved, thermal stress resonance and structural deformation in the cooling process are effectively restrained, and the method is suitable for stable forming of high-precision complex components and has wide application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D printing, and in particular to a cavity temperature heat recovery system and a cavity temperature control method for an industrial particle 3D printer. Background Art

[0002] Industrial particle 3D printer chamber temperature control refers to the process of dynamically adjusting and precisely controlling the temperature inside the printing chamber during the additive manufacturing process of particle materials. The purpose is to maintain a balanced and stable temperature in each layer of the cavity to ensure the quality of material deposition and the accuracy of structural formation. Specifically, this control method monitors the temperature changes of each area from top to bottom of the printing chamber in real time by setting up multiple thermal sensors. It combines the blower and heating ring system in the external heat source, and the top hot air recovery pipe to form a hot air circulation path. The upper heat energy is recovered and heated and then re-injected into the bottom to form a closed-loop flow of heat energy. The control system uses a PID algorithm to finely adjust the fan speed and heating power to achieve rapid response and stable maintenance of the temperature field, and ultimately solve the problem of upper and lower temperature differences caused by uneven heat convection in the traditional printing chamber, thereby improving the molding consistency of particle prints and equipment operation efficiency.

[0003] The existing technology has the following deficiencies: The molding process of complex geometric components, characterized by multi-layered, irregularly shaped features, non-uniform thickness distribution, and sudden changes in spatial morphology, places higher demands on the uniformity of the temperature field within the print chamber and the responsiveness and stability of the hot air circulation control. However, in actual operation, if the hot air circulation system suffers from uneven flow rate distribution, delayed heating response, or reduced recovery efficiency, it can easily lead to the formation of nonlinear temperature gradients within the print chamber. This is characterized by significant differences in the rate and absolute value of temperature change in different spatial regions, exhibiting discontinuous heating or cooling. Particularly during the late molding or cooling stages of the component, such nonlinear temperature gradients can further trigger asymmetric accumulation of thermal stress distribution within the structure, inducing periodic stress release and structural rebound in localized regions of the component. For geometries with large spans, thin walls, or closed features, this stress change, once coupled with the structural natural frequency, can form a deformation resonance effect during the cooling process, causing the component to experience multiple directional reversal warping and jitter. This can ultimately lead to overall warping of the printed structure, severe deviations in molding accuracy, and even serious consequences such as platform interference or component fracture.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide an industrial particle 3D printer cavity temperature heat recovery system and cavity temperature control method. Based on the geometric characteristics of the components, a spatial temperature control demand model is established. The directional distribution of heat energy is achieved by dynamically adjusting the hot air circulation system. In combination with real-time temperature and stress data, a cooling characteristic curve is constructed to identify and regulate warpage resonance sensitive areas. An adaptive temperature control system is formed through fixed-point thermal compensation and parameter closed-loop correction. Compared with traditional strategies, this method significantly improves temperature control accuracy and response efficiency, effectively suppresses thermal stress resonance and structural deformation during the cooling process, is suitable for the stable molding of high-precision complex components, and has broad application prospects to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: an industrial particle 3D printer cavity temperature control method, comprising the following steps: S1: Establish a 3D geometric analysis model of the printed component. Based on the structural morphology, thickness distribution, and spatial topological characteristics of the component, extract the spatial temperature control requirements corresponding to each area and determine the adjustment target of the hot air circulation system. S2, dynamically adjusts the air volume and hot air temperature distribution of the hot air circulation system according to the space temperature control requirements to optimize the heat energy distribution state in the printing chamber; S3, during the hot air circulation system adjustment process, continuously collect temperature change data and structural stress change data of each area of the printing cavity during the cooling process, and generate a cooling dynamic characteristic curve; S4, based on the cooling dynamic characteristic curve, analyzes the natural frequency changes of various structural parts of the component and identifies sensitive structural areas with thermal stress coupling risks and warping resonance risks during the cooling process; S5: Implement fixed-point thermal compensation adjustment on sensitive structural areas to adjust the local cooling rate to keep it consistent with the overall cooling rate of the printed component, thereby suppressing the asymmetric accumulation and periodic release of thermal stress; S6 comprehensively analyzes the feedback results of fixed-point thermal compensation adjustment and the adjustment status of the hot air circulation system, cyclically corrects the air volume and temperature distribution parameters, continuously optimizes the temperature distribution of the printing cavity, achieves an overall temperature balance during the component cooling process, and eliminates the risk of warping resonance.

[0007] Preferably, step S1 includes: Obtain 3D modeling data of the target printed component and extract structural morphology, thickness distribution and spatial topological features; Based on the structural morphology and thickness characteristics, a volume unit division model is constructed to extract the heat capacity parameters of each area and generate the spatial temperature control requirements; Using multi-physics simulation methods, the effects of air volume and hot air input from different directions on the temperature field of each area of the component are analyzed; According to the simulation analysis results, the wind speed control target, hot air temperature adjustment range and heat energy injection timing parameters of the hot air circulation system are determined.

[0008] Preferably, step S2 includes: Construct a spatial correlation matrix to map component sub-areas with cavity spatial positions, target temperatures, allowable airflow, and thermal response parameters; The wind speed boundary value and heat source output parameters of the hot air circulation system are set according to the spatial correlation matrix, and the air volume and temperature are dynamically adjusted through the adaptive proportional integral differential algorithm; The influence of wind speed on hot air attenuation is predicted based on cavity flow characteristics, and the hot air diversion direction and injection port distribution are dynamically adjusted; Continuously collect temperature feedback data, build a dynamic correction data set, and cyclically optimize air volume and temperature setting parameters.

[0009] Preferably, step S3 includes: Set up multiple data collection points with corresponding spatial coordinates in the key structural areas of the components to collect temperature and stress data synchronously; During the acquisition process, the sampling frequency is set according to the thermal diffusion characteristics of the material, and the data is processed using a noise reduction algorithm and time synchronization is performed; Fit the temperature and stress data of each collection point to generate a cooling dynamic characteristic curve; All cooling dynamic characteristic curves are processed collectively to construct a spatial cooling characteristic distribution map and a cooling process dynamic database.

[0010] Preferably, step S4 includes: Fit and differentiate the temperature and stress data during the cooling process to extract the time series characteristic values of the temperature gradient change rate and stress response rate; Establish a three-dimensional finite element model of the local area, apply time series thermal boundary conditions and perform thermal-mechanical coupled modal analysis to extract the natural frequency change curve; Calculate the stress release frequency and perform overlap criterion and dynamic response analysis with the natural frequency to identify sensitive structural areas where frequency coupling occurs; A mathematical relationship model is established between the natural frequency change trend and thermal response characteristics, which is used to predict the resonance risk and optimize the control parameters during the printing process.

[0011] Preferably, step S5 includes: Based on the cooling dynamic characteristic curve and natural frequency analysis results, locate the specific position of the sensitive structural area in three-dimensional space; Compare the cooling dynamic curve of the sensitive structural area with the cooling curve of the entire component, set the target cooling rate adjustment curve and calculate the required local thermal compensation; Implement local thermal intervention operations, build closed-loop control logic based on real-time collected temperature feedback data, and dynamically adjust the heat source output intensity and duration; Continuously monitor the temperature change trend and stress evolution state of the compensation area to ensure that its cooling trajectory is consistent with the overall component, thereby effectively suppressing the risk of structural deformation.

[0012] Preferably, step S6 includes: Real-time monitoring of the temperature response and stress release status of sensitive structural areas during thermal compensation to obtain local temperature control feedback data; Synchronously collect the overall air volume distribution and temperature control parameters of the hot air circulation system to form global thermal regulation status information; Couple local feedback data with global thermal regulation parameters to build a temperature response deviation model, identify key heat flow factors affecting thermal control imbalance, and modify the hot air input strategy. The hot air circulation control target is dynamically updated based on the correction results, and the iterative optimization logic is embedded to continuously adjust the air volume and temperature distribution parameters to achieve an overall temperature equilibrium state during the component cooling process.

[0013] The heat recovery system for the chamber temperature of an industrial particle 3D printer includes a geometric perception modeling module, a hot air dynamic adjustment module, a cooling process data acquisition module, a thermal response analysis module, a local thermal compensation control module, and a closed-loop temperature control optimization module. The geometric perception modeling module establishes a 3D geometric analysis model of the printed component. Based on the structural morphology, thickness distribution, and spatial topological characteristics of the component, it extracts the spatial temperature control requirements corresponding to each area and determines the adjustment target of the hot air circulation system. The hot air dynamic adjustment module dynamically adjusts the air volume and hot air temperature distribution of the hot air circulation system according to the space temperature control requirements, optimizing the heat energy distribution state in the printing chamber; The cooling process data acquisition module continuously collects temperature change data and structural stress change data of each area of the printing cavity during the cooling process during the hot air circulation system adjustment process, and generates a cooling dynamic characteristic curve; The thermal response analysis module analyzes the natural frequency changes of various structural parts of the component based on the cooling dynamic characteristic curve, and identifies sensitive structural areas with risks of thermal stress coupling and warping resonance during the cooling process; The local thermal compensation control module implements fixed-point thermal compensation adjustment in sensitive structural areas, adjusts the local cooling rate to keep it consistent with the overall cooling rate of the printed component, and suppresses the asymmetric accumulation and periodic release of thermal stress; The closed-loop temperature control optimization module comprehensively analyzes the feedback results of fixed-point thermal compensation adjustment and the adjustment status of the hot air circulation system, cyclically corrects the air volume and temperature distribution parameters, and continuously optimizes the temperature distribution of the printing cavity to achieve overall temperature balance during the component cooling process, eliminating the risk of warping resonance.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention accurately models the components based on their geometric features, establishes a corresponding relationship between spatial temperature control requirements and hot air regulation targets, realizes directional supply and distribution optimization of thermal energy by dynamically adjusting the hot air circulation system, and combines real-time temperature and stress data acquisition to construct a dynamic characteristic curve of the cooling process, thereby identifying sensitive areas of potential warping resonance. On this basis, fixed-point thermal compensation adjustment is implemented to effectively coordinate local and global cooling rhythms, and finally, through feedback fusion and parameter cycle correction, a temperature control closed-loop system with adaptive capabilities is constructed. Compared with traditional fixed thermal control strategies, this method not only improves the spatial resolution and response speed of temperature control, but also significantly reduces the risk of resonance caused by the coupling of thermal stress release frequency and structural natural frequency, eliminates structural warping, rebound or fracture problems in the cooling stage, and improves the molding quality, dimensional stability and system operation reliability of printed components. It is particularly suitable for engineering application scenarios of large-scale, high-precision and complex structural printing tasks, and has broad technical promotion value and industrial application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 This is a flow chart of the method for controlling the chamber temperature of an industrial particle 3D printer according to the present invention.

[0017] Figure 2 This is a module schematic diagram of the chamber temperature heat recovery system of the industrial particle 3D printer of the present invention. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0019] The present invention provides Figure 1The chamber temperature control method of the industrial particle 3D printer shown includes the following steps: S1: Establish a 3D geometric analysis model of the printed component. Based on the structural morphology, thickness distribution, and spatial topological characteristics of the component, extract the spatial temperature control requirements corresponding to each area and determine the adjustment target of the hot air circulation system. To address the temperature control requirements for complex geometric components during industrial particle 3D printing, the first step is to obtain complete 3D modeling data for the target printed component. This data can come from CAD drawings, STL files, or scanned digital geometric models. After importing the 3D data, geometric recognition operations are performed based on the component's overall configurational features to extract key geometric features, such as multi-layered, irregular structures, spatially abrupt boundaries, large-span areas, thin-walled sections, enclosed cavities, and sandwich structures. These structural morphology parameters are analyzed through algorithms, and a volume unit partitioning model corresponding to the component's spatial distribution is established, providing a geometric partitioning foundation for subsequent temperature control analysis.

[0020] Based on the three-dimensional component geometric units divided as above, the thickness distribution and heat capacity characteristics of each sub-region are extracted. The specific operation is: calculate the minimum wall thickness, maximum wall thickness and average thermal mass distribution of each voxel unit, and combine the thermal physical parameters such as thermal conductivity and specific heat capacity of the granular printing material to derive the thermal response time constant of each sub-region during the heating and cooling process. This is used to judge the sensitivity and stability of each region to thermal energy input. The core of this process is to convert geometric properties into a thermal regulation demand mapping table to obtain the temperature response demand and cooling delay threshold of each sub-region, thereby forming a regionalized temperature control strategy indicator.

[0021] Based on the overall topological structure of the component, the influence of spatial morphology on the hot air flow path and convection efficiency is further considered. Specifically, this includes identifying possible backflow dead corners, hot air turbulence points, heat-blocking nodes, and asymmetric convection paths that may be formed by the airflow, and predicting the influence of air volume and hot air input in different directions on the temperature field changes in each sub-region through simulation analysis. At this stage, by establishing a three-dimensional mapping relationship between the component and the printing cavity space, a three-dimensional coupling matrix of cavity space-component area-temperature response is formed, realizing the mapping modeling between the spatial position of each region of the component and its thermal response characteristics, providing a quantitative basis for the hot air input strategy.

[0022] To simulate and analyze the impact of air volume and hot air input from different directions on the temperature field changes in each sub-region of the component, a variety of multi-physics field coupling simulation technologies and data-driven methods can be used, including the following methods: Computational fluid dynamics (CFD) simulation methods can be used, using professional simulation software such as ANSYS Fluent, COMSOL Multiphysics, or OpenFOAM, to build a three-dimensional flow field and temperature field coupling model of the printing cavity and components. In this model, the air outlet distribution in different directions, wind speed input boundary conditions, and hot air temperature parameters are set. The hot air flow path, local flow velocity distribution, and convection heat transfer coefficient are solved using the finite volume method to achieve a visual simulation of the air volume and heat spatial transport behavior. Combining the three-dimensional structural model of the component and the cavity layout, a multi-point transient temperature detection point array can be introduced. By arranging virtual sensing points, the temperature response curves of various areas under different wind directions can be simulated to assist in identifying areas with delayed temperature rise and hotspot enrichment. Based on the convection heat transfer model and radiation heat flux equation in heat transfer, a simplified heat flow network model can be used to perform parametric analysis on specific areas to quickly evaluate the heat input contribution of specific wind directions to areas with uneven wall thickness. In order to improve simulation efficiency and adapt to complex geometric structures, artificial intelligence optimization algorithms or data-driven modeling methods can also be combined, such as using genetic algorithms, BP neural networks, etc. to train and predict the response relationship between wind direction, wind speed and temperature field, thereby establishing a fast-response digital twin model for real-time control assistance; finally, the above method can also be integrated with experimental calibration data for verification, that is, the simulation boundary conditions and model parameters can be inferred through actual test data to improve prediction accuracy and engineering adaptability.

[0023] Through the combined application of the above methods, not only can the impact of different hot air input strategies on the cavity temperature distribution be quantitatively evaluated, but the optimal hot air circulation path and air volume configuration scheme can also be formulated for complex components to ensure the synchronization and stability of the temperature control response of each sub-area of the component.

[0024] Based on the comprehensive analysis of the impact of the component's structural morphology, thickness distribution, and spatial topography on hot air flow, the corresponding spatial temperature control requirements are determined and converted into the wind speed control target, hot air temperature adjustment range, and heat injection timing parameters for the hot air circulation system. Combined with the adjustable capabilities of the heat supply device, a spatial target temperature distribution map is configured as a reference boundary condition for subsequent dynamic adjustments. The output of this stage not only provides a foundation for precise temperature control during the printing process but also establishes the control target for the entire closed-loop hot air regulation process, ensuring that the cavity temperature response is controllable, predictable, and compensable.

[0025] The core role of this step is to establish a highly targeted and responsive control basis for the subsequent temperature control of the printing cavity, thereby realizing the precise and differentiated regulation of the hot air circulation system. In the process of industrial particle 3D printing, since the target components often have complex geometric structures, such as multi-layer special-shaped contours, non-uniform thickness areas, closed cavities and spatial mutation characteristics, the heat required by each region during the heating and cooling process, the heat dissipation capacity and temperature stability are significantly different. If a unified hot air regulation strategy is adopted, it will not be able to meet the refined requirements of temperature control in each region, and then cause problems such as local overheating, cooling lag, and thermal stress accumulation, which ultimately affect the molding accuracy and structural integrity of the printed parts. Therefore, by establishing a three-dimensional geometric analysis model, extracting and quantifying the structural morphology, thickness distribution and spatial topological characteristics of the components, the structural characteristic parameters can be converted into spatial temperature control demand indicators, realizing the mapping conversion from "geometric logic" to "thermal control target". This process not only sets clear target parameters for the hot air circulation system, such as air volume distribution, temperature gradient, and heat source placement, but also provides a spatial coordinate-based control basis for the entire temperature control process, ensuring precise alignment of thermal energy distribution with component structure, significantly improving the local accuracy, response speed, and overall stability of temperature control. In short, this step plays a key role in "pre-modeling and zoning identification of thermal control logic," a fundamental prerequisite for achieving precise temperature control and high-quality molding.

[0026] S2, dynamically adjusts the hot air circulation system according to the space temperature control requirements. The adjustment content includes adjusting the air volume and hot air temperature distribution of the hot air circulation system to optimize the heat energy distribution state in the entire printing chamber; To meet the spatial temperature control requirements of complex geometric components during the printing process and ensure the balance and dynamic stability of the heat energy distribution inside the printing chamber, the hot air circulation system is dynamically adjusted throughout the entire process. The adjustment content specifically includes air volume adjustment and hot air temperature distribution adjustment. The execution process is as follows: Based on the spatial temperature control requirements extracted from the previously constructed 3D geometric analysis model, the print chamber is divided into regions, and each component region is precisely mapped to its spatial position within the chamber. Based on this mapping relationship, the target temperature range and air volume distribution ratio required to be maintained for each component sub-region at different printing stages are clearly defined. In this stage, a spatial correlation matrix is used to parameterize the target thermal control requirements, allowing subsequent adjustments to be differentiated based on the actual component distribution. Instead of using the traditional single temperature control curve or unified air speed strategy, a personalized hot air input strategy is developed based on the thermal response characteristics of the component.

[0027] The spatial correlation matrix refers to a two-dimensional or three-dimensional data structure used to describe the correspondence between the various structural areas of the printed component and the spatial position of the printing cavity. Its essence is to establish a mapping relationship between the "component structure distribution" and the "cavity thermal control position" at the spatial level, thereby providing a parameterized basis for precise control of hot air input and air volume distribution. Its function is to uniformly encode factors such as the spatial position, required temperature value, and thermal response characteristics of complex three-dimensional structural components in the printing cavity, so that each component sub-area can be associated with the corresponding spatial coordinate point and thermal control demand index, thereby supporting the execution of differentiated and localized control strategies in the hot air circulation adjustment process. The process of obtaining the spatial correlation matrix includes the following steps: Based on the 3D modeling data, the component is spatially segmented, for example, into multiple voxel units or grid blocks; According to the actual spatial structure of the printing cavity, the cavity space is also divided into corresponding grid areas; Through the spatial overlap relationship between the component voxel unit and the cavity grid, the cavity position index of each component unit is established, thereby forming a spatial correlation matrix; each row or column in the matrix can correspond to a component sub-area, and each cell records the target temperature, allowable air volume, thermal response time constant and other parameters required for the area.

[0028] Through this matrix, unstructured thermal control requirements are converted into an operational set of adjustment parameters, enabling automatic allocation of hot air input and temperature set points under specific spatial coordinates. This gives the entire hot air adjustment process spatial resolution, significantly improving the accuracy and response sensitivity of temperature control.

[0029] Before implementing hot air regulation, the wind speed boundary values and heat source output capacity ranges of different air outlets are preset, and the fan speed and heater power output are controlled by a real-time control device. During the regulation process, a closed-loop feedback mechanism is adopted. The real-time temperature data in the cavity collected by the thermal sensor is compared with the target temperature of the corresponding area, the temperature deviation is calculated, and the air volume and hot air output temperature are adjusted through an adaptive proportional integral differential algorithm. For areas near the top of the printing chamber, which are easily affected by the external environment and lose heat faster, it is prioritized to increase the input temperature of the hot air recovered from the top; while for the bottom of the cavity or areas where there is a risk of heat accumulation, the air volume is prioritized to ensure that heat does not accumulate excessively while maintaining a stable temperature.

[0030] The adaptive proportional-integral-differential (PID) algorithm is an advanced control algorithm that introduces self-regulation capabilities based on traditional proportional-integral-differential (PID) control. Its core function is to dynamically adjust control parameters (proportional, integral, and differential coefficients) based on real-time changes in operating conditions, thereby improving the control system's responsiveness and stability in complex, nonlinear, or dynamic environments. In the hot air circulation control process of industrial particle 3D printers, this algorithm enables precise regulation of air volume and hot air output temperature. Specifically, when the real-time cavity temperature deviates from the target temperature, the algorithm first calculates the temperature error and its trend. It then uses historical data to determine the current response characteristics of the thermal control system and automatically adjusts the proportional, integral, and differential parameters to make the regulation process more adaptive. The proportional component rapidly responds to temperature differences, the integral component eliminates steady-state errors, and the differential component suppresses oscillations. This allows precise control of fan speed to adjust air volume and heater power to adjust hot air temperature, achieving real-time matching of heat input to various regions of the component. This algorithm is particularly suitable for scenarios where the heat load changes frequently during the cooling process of complex geometric components. It can significantly improve the dynamic response speed, control accuracy and operational stability of the thermal control system, and is an important control means to achieve closed-loop fine temperature control.

[0031] Throughout the entire implementation process, the hot air flow path and temperature attenuation within the cavity space are continuously and predictively controlled. Specifically, the cavity flow characteristic parameters obtained during simulation modeling are used to dynamically calculate the spatial attenuation effect of wind speed changes on the hot air distribution. Based on this information, the angle of the wind deflectors and the location of the hot air inlets are dynamically adjusted. Spatial guidance intervention enables multi-directional injection and balanced dispersion of hot air, avoiding local hot air short-circuits or heat energy accumulation. This regulation method not only ensures a more synchronized temperature response across all regions, but also effectively improves thermal energy utilization efficiency, reduces energy consumption, and increases the response speed of the control system.

[0032] After the hot air circulation adjustment strategy is executed, the temperature change trend of the cavity continues to be collected, and the actual temperature distribution is compared with the expected thermal control target in multiple dimensions to identify areas of adjustment deviation or temperature control hysteresis. A dynamic correction data set is formed in multiple consecutive comparisons, and the air volume configuration ratio and heater temperature setting value of the next cycle are re-optimized based on the data set. This optimization not only covers the instantaneous response correction, but also takes into account the cumulative effect of the lag in the conduction of heat energy inside the component, so that the hot air adjustment during the entire printing cycle is more forward-looking and stable. Through this dynamic adjustment and feedback closed-loop control process, the uniformity of the temperature distribution inside the cavity can be continuously maintained at all stages of component forming, greatly reducing the risk of warping and deformation caused by temperature differences, improving the structural forming quality and dimensional consistency of granular material prints, and achieving the comprehensive technical effect of high-precision and high-stability printing.

[0033] The purpose of this step is to dynamically adjust the air volume and spatial distribution of hot air temperature in the hot air circulation system according to the specific temperature control targets required by the printed components in different spatial areas, so as to establish a thermal energy environment with precise response, uniform distribution and stable changes inside the entire printing cavity. Since industrial particle 3D printed components usually have complex structural features, such as large-span connections, thin-walled layers, partially closed cavities and geometric mutation areas, different structures have obvious differences in their demand for thermal energy during the material deposition and cooling stages. If the traditional unified air supply and fixed temperature control method is still used, it is easy to cause overheating or insufficient temperature in certain areas, thereby causing molding defects such as warping, cracking, and rebound. Therefore, The purpose of implementing this step is to break the limitations of traditional "integrated thermal control". Through multi-point adjustment of the hot air circulation duct, combined with PID or adaptive algorithms, the fan speed and heating unit power are controlled in real time. According to the target temperature control requirements extracted from each area of the component, heat energy is finely distributed on demand, and the spatial distribution requirements of heat energy at each stage of the component are dynamically responded to. This dynamic adjustment process not only takes into account the instantaneous temperature difference changes, but also incorporates factors such as heat conduction hysteresis, material thermal inertia and structural sensitivity, realizing multi-dimensional closed-loop control. The final effect is to keep each area in a constant state close to the target temperature without affecting the overall energy consumption and efficiency, thereby significantly improving the material deposition quality, structural forming consistency and overall equipment operation stability during the printing process. It is the core link in the entire temperature control system to achieve "control by demand and respond to shape with heat".

[0034] S3, during the hot air circulation system adjustment process, continuously collecting temperature change data and structural stress change data of each area of the printing cavity during the cooling process, and generating a corresponding cooling dynamic characteristic curve based on the collected results; To accurately perceive the thermal behavior of each region of the print cavity during cooling and provide a data basis for the subsequent prediction and control of structural warpage resonance, temperature and structural stress change data are continuously collected from each region of the print cavity during the hot air circulation system adjustment process. Based on the obtained multi-dimensional data, a cooling dynamic characteristic curve is generated. The specific operation process is as follows: After the printed component is completed and enters the cooling phase, multiple temperature and stress data collection points are pre-set within the cavity based on the spatial division of the print cavity and the component's three-dimensional geometric characteristics. These collection points should cover key structural areas of the component, including but not limited to thin-walled areas, transition areas, long-span connections, enclosed chambers, and boundaries with sudden changes in heat capacity. The selection of collection points is based on a previously constructed spatial correlation matrix, establishing a correspondence between each collection point and a specific component region and its spatial coordinates. This ensures that the collection results are spatially resolved and structurally representative, providing a clear regional boundary foundation for subsequent data analysis.

[0035] During the natural or controlled cooling of the printed component, a temperature sensor collects real-time temperature change data at each sampling point. Combined with a stress sensor, the stress change trend at each sampling point during the cooling process is simultaneously recorded. The acquisition frequency must be set based on the thermal diffusion rate and cooling rate of the component material, typically sampling no less than 5 to 10 times per second to ensure accurate capture of transient response characteristics in rapidly changing areas. To prevent data noise from interfering with judgment, a Kalman filter algorithm can be used to perform real-time noise reduction on the collected data. A time synchronization mechanism is used to pair and tag the temperature and stress data to ensure consistency of different physical quantities along the time axis.

[0036] After collecting data from the entire cooling process, the temperature and stress changes at each acquisition point are fitted in chronological order to generate corresponding temperature-time and stress-time curves, respectively. Furthermore, the two are fused through coordinate synchronization to construct a data surface model with "temperature-stress-time" as the three-dimensional axes, forming a cooling dynamic characteristic curve for each acquisition point. This characteristic curve not only reflects the changing trend of the cooling rate in a local area but also reveals the real-time response of stress release or accumulation in that area under the influence of temperature changes. High-order inflection points, oscillation segments, or abnormal fluctuation segments that appear in the curve can be further marked as potential risk periods, providing prior data for resonance sensitivity analysis.

[0037] To enhance the systematic nature and visualization of the analysis, the cooling dynamic characteristic curves of all collected points are aggregated to construct a spatial cooling characteristic distribution map for the entire component. This distribution map is presented using color gradients, stress intensity fields, or time-synchronized sequences, intuitively demonstrating the differences in the response of each region during the cooling process. On this basis, a dynamic database of the cooling process is constructed, allowing this cooling curve data to be used not only for stress prediction and control decisions during a single printing process, but also to form empirical models through cross-task data analysis, providing a reference for the formulation of temperature control strategies for different components and structural conditions.

[0038] The purpose of this step is to achieve accurate perception of the temperature and structural stress state of each area of the printing cavity during the cooling process, and to build a dynamic cooling behavior model in a data-driven manner, providing a reliable basis for subsequent resonance risk identification and fixed-point thermal control. In the industrial particle 3D printing process, the component enters the cooling stage after the component is formed. At this time, if the cooling rates of each area are inconsistent, it is very easy to cause abnormal accumulation of thermal stress in the local area, thereby causing serious problems such as structural warping, deformation, and even fracture. Traditional processes usually rely on empirical parameters or global average temperature for control, which cannot capture the transient temperature gradient and stress mutation phenomena in complex structural areas. Therefore, this step continuously collects temperature change data and structural stress change data of each area during the adjustment process of the hot air circulation system. It can not only track the heat conduction rate and heat release dynamics of different positions of the component in real time during the cooling process, but also reveal the coupling relationship between temperature change and stress evolution, thereby accurately identifying which areas have nonlinear thermal-stress responses, or risk behaviors such as stress jumps and delayed release. After processing, the collected data generates a cooling dynamic characteristic curve for each region. This curve establishes a three-dimensional mapping relationship between temperature, stress, and time, serving as an important input for subsequent analysis of structural natural frequency changes, identification of resonance-sensitive areas, and implementation of differentiated thermal compensation. In other words, this step is the core link from "data perception" to "behavioral modeling," ensuring the evolution of the entire temperature control process from static presets to dynamic response. This technological advancement holds the key value of enhancing system intelligence, ensuring component molding quality, and ensuring structural stability.

[0039] S4, based on the cooling dynamic characteristic curve, the natural frequency analysis of each structural part of the component is carried out to identify sensitive structural areas with risks of thermal stress coupling and warping resonance during the cooling process; To identify potential risk areas for structural resonance and warping due to uneven thermal stress during cooling, we conducted a natural frequency analysis of each component's structural location based on the previously generated cooling dynamic characteristic curves. This allowed us to identify sensitive structural areas with a risk of resonance coupling during temperature change and stress release. The specific steps are as follows: Based on the collected and generated cooling dynamic characteristic curves for different structural regions of the component, the temperature change trend and stress evolution process of each region are simultaneously analyzed. Based on this, the three-dimensional temperature-stress-time data are fitted and differentiated to extract the time series characteristic values of the temperature gradient change rate and the stress response rate. This allows the identification of key periods during the cooling process where rapid temperature changes, stress jumps, or periodic oscillations occur. By identifying the characteristics of these periods, structural regions in the component with dynamically unstable cooling behavior are preliminarily screened, providing directional input for subsequent frequency analysis.

[0040] Fitting and differentiating the three-dimensional temperature-stress-time data extracts the time-series characteristic values of the temperature gradient change rate and stress response rate during the cooling process. First, the temperature and stress data collected during the printing process must be organized chronologically to form a continuous, unified time series. To ensure that the data accurately reflects the changing trend, the raw data must be smoothed. Typically, methods such as sliding average, locally weighted regression, or polynomial fitting are used to remove high-frequency noise and preserve the overall trend. Second, the smoothed temperature and stress data are analyzed for rate of change. This involves observing the magnitude of increases and decreases between consecutive time points to calculate the temperature and stress change rates at each moment. Third, by analyzing the time curves of these rate changes, time periods with dramatic temperature or stress fluctuations are identified, particularly when temperature changes suddenly accelerate or decelerate, or when stress values rapidly increase or decrease within a short period of time. Furthermore, the rate changes must be observed to determine whether they exhibit periodic fluctuations to determine whether repetitive dynamic stress release behavior exists. Finally, these time points or time periods are marked as "critical periods" in the cooling process, serving as important indicators for subsequent resonance risk identification, fixed-point thermal compensation, and control strategy adjustments. Through this processing flow, the most engineering-significant dynamic features can be effectively extracted from a large amount of continuous temperature and stress data, realizing the transformation of the cooling process from perception data to behavior recognition, and significantly improving the accuracy and responsiveness of thermal anomaly prediction during the cooling stage.

[0041] In the unstable areas initially screened out above, a corresponding three-dimensional geometric structure simulation model is established, and time-varying modal analysis is carried out in combination with the actual temperature boundary conditions and material thermoelastic parameters of the area in the cooling characteristic data. This analysis uses the finite element calculation method to simulate the real-time impact of temperature field changes on structural stiffness and stress distribution during the cooling process, and then extracts the natural frequency change curve of the area at different cooling time nodes. Compared with the traditional static modal analysis method, this step adopts the thermomechanical coupling modal analysis method driven by cooling, which can more realistically reflect the actual vibration characteristics of the component under the background of thermal stress, and provide highly matched dynamic frequency characteristic data for identifying resonance risks.

[0042] Thermomechanical coupling modal analysis driven by cooling refers to a multi-physics field calculation method that dynamically analyzes the vibration characteristics of a component by comprehensively considering the influence of thermal stress on the stiffness and boundary conditions of the component material under the action of the continuously changing temperature field during the cooling process. Unlike traditional modal analysis that is only based on static structures at room temperature, this method introduces thermal load changes, material performance degradation, and internal stress redistribution during the cooling process into the vibration characteristic analysis, more realistically reflecting the changes in the natural frequency and mode shape of the component under the background of thermal stress. Its function is to reveal whether the structural deformation and stress accumulation caused by heat in a non-constant temperature environment cause its natural frequency to decrease, drift, or couple with the stress release frequency, providing a dynamic frequency evolution basis for identifying potential warping resonances. The specific steps are as follows: First, a three-dimensional finite element model of the component is established and divided into integrated mesh elements suitable for thermal-structural analysis. Second, time-series temperature data collected during the cooling process are input and applied to the model surface as time-varying thermal boundary conditions. Temperature-dependent material parameter changes are also introduced, including the functional relationship between the elastic modulus, thermal expansion coefficient, and damping coefficient as the temperature decreases. Third, a thermal-structural coupling calculation is performed, calculating the temperature-driven structural deformation and internal stress distribution time-step by time, and updating the structural stiffness and mass matrix in real time at each cooling time node. Finally, a modal analysis is performed at each time node to extract the component's natural frequency and corresponding mode shape at that time, ultimately generating a time-evolving natural frequency curve. This analysis method not only reflects the impact of stress and material property degradation during the cooling process on the structural dynamic performance, but also predicts when and where structural vibration resonance is most likely to occur, providing a reliable basis for taking compensatory and control measures in advance.

[0043] The natural frequency change curves of the aforementioned regions are compared and analyzed with the stress release frequencies of the corresponding periods in the cooling dynamic characteristic curves. Using the frequency overlap criterion and dynamic response analysis method, situations in which the structural stress release frequency and the regional natural frequency are close, coincident, or harmonically coupled during the cooling process are identified. Such frequency proximity will cause the structural region to be excited to produce structural resonance when stress is released, thereby exhibiting macroscopic deformation phenomena such as periodic warping, direction reversal and jitter. Through this analysis, sensitive structural regions with the risk of thermal stress resonance excitation are further identified and located in the spatial model, providing input basis for subsequent control strategies.

[0044] The frequency overlap criterion and dynamic response analysis method is a judgment and calculation method used to evaluate whether there is a risk of vibration resonance in the cooling process of the structure. The core idea is to compare the dynamic excitation frequency generated by the internal stress release of the structure during the cooling process with the natural frequency of the structure itself in the current state, and identify whether the two are numerically close, overlap, or form harmonic relationships such as frequency doublings and sub-frequency, so as to determine whether there is a potential risk of the structure being excited to resonate. In this step, the function of this method is to identify whether the local structural vibration caused by the periodic release of thermal stress during the cooling stage is frequency-coupled with the natural vibration characteristics of the component, thereby causing warping, periodic jitter or severe structural deformation. The specific steps are as follows: Based on the dynamic characteristic curve of the early cooling process, the typical pulses or oscillation segments in the stress release process are extracted, and the corresponding stress release frequencies are calculated by Fourier transform or time domain frequency analysis methods; Combined with the results of thermal-mechanical coupling modal analysis, the natural frequency data of the component at each cooling time node is obtained; Compare the stress release frequency with the natural frequency of the corresponding structural area within the time period, and set a frequency overlap criterion threshold. That is, when the difference between the two values is less than the set critical range, it is judged as "close frequencies", if the frequencies are equal, it is judged as "complete overlap", and if the frequencies are integer multiples, it is considered "harmonic coupling"; Dynamic response analysis further verifies whether this frequency matching results in a response amplification effect in the structure during this period, such as sudden displacement increases or periodic rebound. Once frequency overlap or coupling occurs, accompanied by a significant increase in the structural response, the area is identified as a resonance-sensitive region, requiring subsequent targeted thermal compensation or cooling strategy adjustments to mitigate the effects.

[0045] This analysis method can be used to predictively identify the dynamic behavior of structures, avoid deformation and failure of printed components caused by uncontrollable resonance, and ensure overall molding quality and stability.

[0046] Based on the identified sensitive structural areas, a spatial risk distribution map is constructed and correlated with the spatial temperature control strategy of the print chamber. A temperature buffer zone is set up around the risk area to reserve space for local adjustment, providing a structural foundation for subsequent implementation of fixed-point thermal compensation, delayed cooling, or wind direction adjustment. Furthermore, a mathematical relationship model is established between the natural frequency variation trend and the thermal response characteristics of the corresponding area, which serves as an important parameter input for subsequent cooling process risk prediction and iterative optimization of the control system.

[0047] Through the above analysis process, a logical closed loop from cooling behavior data perception to structural dynamic characteristics identification is achieved. The risk area of the component no longer relies on empirical judgment, but is based on the dynamic identification of the actual structural response driven by thermal stress. This significantly improves the accuracy of structural deformation risk prediction and the foresight of thermal control adjustment, ensuring stable cooling and high-quality molding of complex components after printing.

[0048] The purpose of establishing a mathematical relationship model between the natural frequency change trend and the thermal response characteristics of the corresponding area is to construct a correlation model that can quantitatively describe the evolution of the structural dynamic performance with temperature changes, thereby realizing the resonance risk prediction during the cooling process and the intelligent iterative optimization of the thermal control strategy. Specifically, the modeling process includes the following steps: Based on the results of thermal-mechanical coupled modal analysis, the natural frequency change data of each region of the component at different cooling time points are extracted, and thermal response parameters such as temperature, temperature change rate, thermal gradient, and local stress level at the corresponding time points are collected. Through multivariate fitting methods such as polynomial regression, support vector regression, or neural network modeling, a functional relationship is established between the changing trend of the natural frequency and the above-mentioned thermal response parameters, forming a predictable mathematical mapping model. That is, by inputting the thermal response characteristics of the local area, the current or future natural frequency value can be predicted; This model is embedded in the temperature control logic of the printing equipment as the input basis for dynamic adjustment, enabling early identification of risk periods and real-time correction of hot air strategies. By continuously collecting temperature and frequency data from the actual cooling process and updating the model parameters, adaptive optimization of the control system is achieved. This mathematical relationship model not only improves the ability to explain the relationship between frequency changes and thermal behavior, but also transforms complex thermal dynamic processes into controllable algorithm inputs, providing accurate, efficient, and scalable technical support for risk prediction, response adjustment, and deformation control of components during the printing cooling phase.

[0049] This step aims to quantitatively analyze the dynamic vibration characteristics of each structural part during the cooling process, identifying sensitive areas that may produce structural resonance or warping deformation due to thermal stress changes, thereby providing a preemptive decision-making basis for subsequent fixed-point thermal compensation adjustment and temperature control strategy optimization. In the 3D printing process of complex geometric components, the cooling stage is often accompanied by material volume shrinkage, thermal stress release, and changes in structural stiffness. The superposition of these factors can lead to unstable vibration in local areas. Especially when the stress release frequency is close to, coincides with, or harmonically couples with the natural frequency of the structure, it is more likely to cause uncontrollable deformation behaviors such as periodic structural jitter, direction reversal warping, etc., ultimately affecting the molding accuracy and even causing component failure. Therefore, this step, by performing natural frequency analysis based on the cooling dynamic characteristic curve, can dynamically track the frequency evolution process of each structural part under different temperature environments and stress states, accurately identifying the time period and which area has the potential risk of resonance induced by thermal-mechanical coupling. This analysis not only considers the combined effects of component geometry, material properties, and thermal field evolution, but also closely integrates vibration behavior with cooling time sequence, realizing the transformation from "structural response prediction" to "risk area location." By identifying these sensitive areas, resonant excitation conditions can be proactively avoided during the cooling control process, enabling early intervention for thermal compensation or cooling rate reduction, effectively suppressing structural deformation caused by thermal-mechanical-vibration coupling. This ensures the stability of the component cooling process and the consistency and reliability of the final molding. This step plays a crucial role in connecting the entire temperature control system, serving as a critical link between data perception, behavioral modeling, and control decisions.

[0050] S5, performing fixed-point thermal compensation adjustment on the identified sensitive structural areas, adjusting the local cooling rate through local heat source intervention to keep it consistent with the overall cooling rate of the printed component, thereby suppressing the asymmetric accumulation and periodic release of thermal stress in the sensitive areas; In order to effectively suppress the structural resonance and warping caused by the asymmetric accumulation of thermal stress during the cooling process, fixed-point thermal compensation adjustment is implemented for sensitive structural areas identified in the early stage. By introducing thermal energy intervention in the local area, the cooling rate is adjusted to keep the thermal change trend consistent with the overall cooling behavior of the printed component. The specific steps are as follows: Based on the sensitive structural areas identified based on the cooling dynamic characteristic curve and natural frequency analysis results, their specific locations in three-dimensional space are accurately located, including their geometric shape, size boundaries, component hierarchy, and their relative relationship to the airflow path within the cavity. Combined with the spatial risk distribution map, areas with obvious thermal stress concentration, high frequency coupling intensity, or prominent deformation response are preferentially selected as the core control objects for implementing fixed-point thermal compensation. The focus of this step is to coordinately match the structural risk location results with the thermal control device control strategy to establish precise local thermal energy intervention target points.

[0051] Based on the comparison results of the cooling dynamic curve of each sensitive area with the average cooling curve of the entire component, the degree of deviation of the actual cooling rate is analyzed, and the target cooling rate adjustment curve is set according to the trend of cooling lag or advance. By constructing the target temperature change trajectory of the area, the required local thermal compensation amount is calculated, that is, the amount of heat energy to be injected per unit time. Then, according to the compensation amount and the heat transfer characteristics of the component surface, an appropriate thermal compensation method is selected, including but not limited to far-infrared heating, micro hot air injection or heat reflection shielding. Global heating is not used here, but spatially directional temperature control is achieved at a local location to achieve differentiated intervention with high efficiency and low energy consumption.

[0052] When implementing fixed-point thermal compensation, temperature feedback data from the surface of sensitive areas is collected in real time and compared with the preset target temperature curve to establish closed-loop control logic. During the compensation process, the heat source output intensity can be automatically adjusted based on the feedback error to ensure a stable compensation effect and neither over-compensation nor under-compensation. If there are areas with large local temperature fluctuations during the cooling process, a time-segmented compensation strategy can be introduced, that is, multiple interventions in stages and small amplitudes to avoid stress rebound or sudden drop in material properties caused by instantaneous temperature changes. At the same time, the wind direction distribution is reasonably adjusted in combination with the cavity airflow path to prevent excessive dilution of the local heating area by cold air.

[0053] Before the end of the entire cooling cycle, the temperature change trend and stress evolution state of the sensitive area are continuously monitored to determine whether the thermal compensation has achieved the synchronous cooling target. If it is found that the compensation in individual areas is insufficient, the duration of thermal intervention can be appropriately extended or the local thermal energy density can be increased until the temperature change curve is highly consistent with the overall cooling trajectory of the component. Ultimately, through this fixed-point thermal compensation process, the cooling rate of the sensitive structural area is balanced, avoiding the formation of thermal gradient stress differences due to excessive cooling, fundamentally suppressing the formation of structural vibration excitation sources, and significantly reducing the risk of abnormal deformation such as direction reversal warping, resonance jitter, etc. at the end of cooling, ensuring structural forming accuracy and dimensional stability.

[0054] This step aims to address the asymmetric accumulation and periodic release of thermal stresses in localized structural regions caused by uneven cooling rates during the cooling process of printed components. Specifically, within identified warpage resonance-sensitive regions, fixed-point thermal compensation is implemented to align the temperature trend in these regions with the overall component cooling behavior, significantly reducing the risk of structural deformation caused by temperature differences. In industrial particle 3D printing, complex geometric components often feature irregular features such as thin walls, bridges, and enclosed cavities. During cooling, these regions cool much faster than other regions due to differences in surface area, heat capacity, and varying convection conditions. This results in a rapid temperature drop in these regions and the generation of significant thermal stresses. If these regions also possess high structural natural frequencies, dynamic resonance may form once their stress release frequency matches the frequency response, leading to repeated warping, deformation, and even fracture. Implementing fixed-point thermal compensation in these high-risk regions effectively slows the cooling rate, disrupting the mechanism of rapid thermal stress accumulation and sudden release, and achieving "thermal response synchronization," aligning the cooling process of sensitive regions with the cooling rhythm of the entire component, thereby avoiding stress concentration and periodic oscillations. In addition, this step also enhances the spatial resolution capability of the temperature control strategy. It no longer relies on a single global temperature control, but instead achieves on-demand, differentiated fine temperature control, which helps to reduce energy consumption, improve the stability of the cooling process and the consistency of molding. It is a key step in the component cooling process from "passive waiting" to "active intervention", and it is also the technical core to ensure that the components can achieve high-precision and high-reliability molding in the later stage of printing.

[0055] S6: Comprehensively analyze the feedback results of the fixed-point thermal compensation adjustment and the overall hot air circulation adjustment status, cyclically correct the air volume and temperature distribution parameters of the hot air circulation system, continuously optimize the temperature distribution of the printing cavity, and ultimately achieve an overall temperature balance during the component cooling process, eliminating the risk of warping resonance; To achieve dynamic and balanced control of the cavity temperature field during the cooling process of printed components, ensure that the risk of structural deformation is controllable, and completely eliminate warping resonance caused by localized uneven cooling, the feedback results of fixed-point thermal compensation adjustments are comprehensively analyzed with the overall adjustment status of the hot air circulation system to achieve cyclic correction and continuous optimization of hot air system parameters. This process not only integrates the interactive relationship between local temperature control effects and global heat flow behavior, but also dynamically optimizes air volume and heat energy distribution through a closed-loop adaptive strategy. The specific steps are as follows: While performing fixed-point thermal compensation adjustments, the temperature response of each sensitive area is monitored in real time, recording its actual cooling rate, temperature recovery, and stress release trend during the compensation process. This feedback data is compared with the original target cooling curve for that area to quantify the intensity and lag of the local control effect and analyze its impact on the thermal field of the surrounding structure. Simultaneously, the current overall operating parameters of the hot air circulation system are collected, including fan speed, wind direction guidance status, output power of each heat source, and the temperature distribution characteristics formed within the print chamber space, forming a complete global thermal regulation status information library.

[0056] By coupling local feedback data with overall hot air parameters for analysis, a temperature response deviation model is constructed to identify key heat flow factors that can influence local temperature control imbalance or overcompensation, such as short circuits in the hot air circulation path, excessively concentrated air volume distribution, or insufficient heat recovery efficiency in the upper cavity. Based on this analysis, the control effects of each sensitive area are comprehensively considered, and differentiated adjustments are made to the air volume distribution and heat source output strategies. Wind resources and heat injection directions are reallocated to ensure a coordinated and stable heat flow relationship between the thermal compensation area and the overall temperature control behavior, preventing new thermal imbalances caused by local interventions.

[0057] Based on the revised air volume and hot air temperature distribution parameters, the hot air circulation control targets are reset and embedded into the iterative logic of the thermal control program to dynamically adjust the air speed and heating response time, while optimizing the hot air flow path and redistribution efficiency within the cavity space. For example, buffer air ducts are established around the compensation area to prevent hot air from overcooling or heat concentration; or a temperature threshold segmented control strategy is introduced into the heat recovery path to make heat redistribution more flexible and responsive. Through parameter optimization and process reconstruction at this stage, the hot air circulation process forms a closed-loop regulation mechanism with self-learning and adaptive capabilities.

[0058] Before the end of each printed component's cooling cycle, the system continuously monitors the temperature equilibrium state and stress release curves of each structural region, conducting an overall assessment of the thermal field changes. If deviations persist, iterative corrections to the hot air system parameters are continued until the overall cooling behavior of the component stabilizes, the temperature gradients in each region are controlled within the set tolerance range, and no new stress fluctuations or warping resonance responses occur. This integrated feedback and optimization loop mechanism not only achieves dynamic equilibrium of the temperature field within the print cavity, but also effectively improves thermal energy utilization, reduces energy consumption fluctuations during the cooling process, and ultimately ensures the geometric stability and dimensional accuracy of the component after molding.

[0059] This step aims to establish a closed-loop mechanism for hot air regulation optimization based on real-time feedback. By integrating the effects of local thermal compensation with the overall operating status of the hot air circulation system, the thermal energy distribution strategy is dynamically modified, thereby achieving overall balanced control of the temperature field within the printing cavity during the cooling process, ultimately effectively preventing component warping resonance caused by thermal stress coupling. Specifically, during the cooling phase of industrial particle 3D printing, although fixed-point thermal compensation in sensitive areas has been implemented to address the problems of localized excessive cooling and abnormal thermal stress concentration, the thermal compensation behavior itself can create new interference with the overall heat flow path, air volume distribution, and energy balance within the cavity. If not properly controlled, this may cause new temperature imbalances or affect the cooling effect in other areas. Therefore, this step comprehensively analyzes the real-time feedback results of local compensation (such as temperature correction amplitude, stress release curve changes, cooling delay response, etc.) with the current operating parameters of the hot air circulation system (such as fan speed, wind guide status, heating power, etc.) to identify problems or deviations in the regulation and subsequently redistribute and correct the air volume and hot air temperature. By continuously iterating this process, we can gradually approach the optimal thermal control conditions required by each region of the component during the cooling process, ultimately establishing a dynamic temperature control mechanism that responds to changes in the structural thermal state at any time. This mechanism not only improves the efficiency of hot air utilization and reduces energy consumption fluctuations, but more importantly, it prevents the coupling of thermal stress and structural vibration characteristics at the source through balanced regulation of the thermal field, effectively eliminating the risk of warping, deformation, or structural resonance caused by thermal excitation. It is a key control link in ensuring component forming accuracy and cooling process stability.

[0060] The above-mentioned industrial particle 3D printer cavity temperature control method can achieve refined, responsive, and closed-loop optimization control of the thermal energy distribution inside the printing cavity, improving the temperature balance and thermal stress stability of complex geometric components during the cooling process. This method accurately models the component's geometric characteristics, establishes a correspondence between spatial temperature control requirements and hot air adjustment targets, and achieves directional supply and distribution optimization of thermal energy by dynamically adjusting the hot air circulation system. In combination with real-time temperature and stress data collection, it constructs a dynamic characteristic curve of the cooling process, thereby identifying sensitive areas of potential warpage resonance. On this basis, fixed-point thermal compensation adjustment is implemented to effectively coordinate local and global cooling rhythms. Finally, through feedback fusion and parameter loop correction, a temperature control closed-loop system with adaptive capabilities is constructed. Compared with traditional fixed thermal control strategies, this method not only improves the spatial resolution and response speed of temperature control, but also significantly reduces the resonance risk caused by the coupling of thermal stress release frequency and structural natural frequency, eliminates structural warping, rebound or fracture problems during the cooling stage, and improves the molding quality, dimensional stability and system operation reliability of printed components. It is particularly suitable for engineering application scenarios of large-scale, high-precision and complex structural printing tasks, and has broad technology promotion value and industrial application prospects.

[0061] The present invention provides Figure 2 The heat recovery system for the chamber temperature of an industrial particle 3D printer shown in the figure includes a geometric perception modeling module, a hot air dynamic adjustment module, a cooling process data acquisition module, a thermal response analysis module, a local thermal compensation control module, and a closed-loop temperature control optimization module: The geometric perception modeling module establishes a 3D geometric analysis model of the printed component. Based on the structural morphology, thickness distribution, and spatial topological characteristics of the component, it extracts the spatial temperature control requirements corresponding to each area and determines the adjustment target of the hot air circulation system. The hot air dynamic adjustment module dynamically adjusts the air volume and hot air temperature distribution of the hot air circulation system according to the space temperature control requirements, optimizing the heat energy distribution state in the printing chamber; The cooling process data acquisition module continuously collects temperature change data and structural stress change data of each area of the printing cavity during the cooling process during the hot air circulation system adjustment process, and generates a cooling dynamic characteristic curve; The thermal response analysis module analyzes the natural frequency changes of various structural parts of the component based on the cooling dynamic characteristic curve, and identifies sensitive structural areas with risks of thermal stress coupling and warping resonance during the cooling process; The local thermal compensation control module implements fixed-point thermal compensation adjustment in sensitive structural areas, adjusts the local cooling rate to keep it consistent with the overall cooling rate of the printed component, and suppresses the asymmetric accumulation and periodic release of thermal stress; The closed-loop temperature control optimization module comprehensively analyzes the feedback results of fixed-point thermal compensation adjustment and the adjustment status of the hot air circulation system, cyclically corrects the air volume and temperature distribution parameters, and continuously optimizes the temperature distribution of the printing cavity to achieve overall temperature balance during the component cooling process, eliminating the risk of warping resonance.

[0062] The industrial particle 3D printer cavity temperature control method provided in an embodiment of the present invention is implemented by the above-mentioned industrial particle 3D printer cavity temperature heat recovery system. The specific method and process of the industrial particle 3D printer cavity temperature heat recovery system are detailed in the embodiment of the industrial particle 3D printer cavity temperature control method, which will not be repeated here.

[0063] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0064] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0065] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0066] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0067] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0069] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0070] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0071] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0072] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. Industrial particle 3D printer cavity temperature control method, characterized in that, The following steps are involved: S1: Establish a 3D geometric analysis model of the printed component. Based on the structural morphology, thickness distribution, and spatial topological characteristics of the component, extract the spatial temperature control requirements corresponding to each area and determine the adjustment target of the hot air circulation system. S2, dynamically adjusts the air volume and hot air temperature distribution of the hot air circulation system according to the space temperature control requirements to optimize the heat energy distribution state in the printing chamber; S3, during the hot air circulation system adjustment process, continuously collect temperature change data and structural stress change data of each area of the printing cavity during the cooling process, and generate a cooling dynamic characteristic curve; S4, based on the cooling dynamic characteristic curve, analyzes the natural frequency changes of various structural parts of the component and identifies sensitive structural areas with thermal stress coupling risks and warping resonance risks during the cooling process; S5: Implement fixed-point thermal compensation adjustment on sensitive structural areas to adjust the local cooling rate to keep it consistent with the overall cooling rate of the printed component, thereby suppressing the asymmetric accumulation and periodic release of thermal stress; S6, comprehensively analyzes the feedback results of the fixed-point thermal compensation adjustment and the adjustment status of the hot air circulation system, cyclically corrects the air volume and temperature distribution parameters, and continuously optimizes the temperature distribution of the printing chamber.

2. The method for controlling the chamber temperature of an industrial particle 3D printer according to claim 1, wherein: Step S1 includes: Obtain 3D modeling data of the target printed component and extract structural morphology, thickness distribution and spatial topological features; Based on the structural morphology and thickness characteristics, a volume unit division model is constructed to extract the heat capacity parameters of each area and generate the spatial temperature control requirements; Using multi-physics simulation methods, the effects of air volume and hot air input from different directions on the temperature field of each area of the component are analyzed; According to the simulation analysis results, the wind speed control target, hot air temperature adjustment range and heat energy injection timing parameters of the hot air circulation system are determined.

3. The method for controlling the chamber temperature of an industrial particle 3D printer according to claim 1, wherein: Step S2 includes: Construct a spatial correlation matrix to map component sub-areas with cavity spatial positions, target temperatures, allowable airflow, and thermal response parameters; The wind speed boundary value and heat source output parameters of the hot air circulation system are set according to the spatial correlation matrix, and the air volume and temperature are dynamically adjusted through the adaptive proportional integral differential algorithm; The influence of wind speed on hot air attenuation is predicted based on cavity flow characteristics, and the hot air diversion direction and injection port distribution are dynamically adjusted; Continuously collect temperature feedback data, build a dynamic correction data set, and cyclically optimize air volume and temperature setting parameters.

4. The method for controlling the chamber temperature of an industrial particle 3D printer according to claim 1, wherein: Step S3 includes: Set up multiple data collection points with corresponding spatial coordinates in the key structural areas of the components to collect temperature and stress data synchronously; During the acquisition process, the sampling frequency is set according to the thermal diffusion characteristics of the material, and the data is processed using a noise reduction algorithm and time synchronization is performed; Fit the temperature and stress data of each collection point to generate a cooling dynamic characteristic curve; All cooling dynamic characteristic curves are processed collectively to construct a spatial cooling characteristic distribution map and a cooling process dynamic database.

5. The method for controlling chamber temperature of an industrial particle 3D printer according to claim 1, wherein: Step S4 includes: Fit and differentiate the temperature and stress data during the cooling process to extract the time series characteristic values of the temperature gradient change rate and stress response rate; Establish a three-dimensional finite element model of the local area, apply time series thermal boundary conditions and perform thermal-mechanical coupled modal analysis to extract the natural frequency change curve; Calculate the stress release frequency and perform overlap criterion and dynamic response analysis with the natural frequency to identify sensitive structural areas where frequency coupling occurs; A mathematical relationship model is established between the natural frequency change trend and thermal response characteristics, which is used to predict the resonance risk and optimize the control parameters during the printing process.

6. The method for controlling chamber temperature of an industrial particle 3D printer according to claim 1, wherein: Step S5 includes: Based on the cooling dynamic characteristic curve and natural frequency analysis results, locate the specific position of the sensitive structural area in three-dimensional space; Compare the cooling dynamic curve of the sensitive structural area with the cooling curve of the entire component, set the target cooling rate adjustment curve and calculate the required local thermal compensation; Implement local thermal intervention operations, build closed-loop control logic based on real-time collected temperature feedback data, and dynamically adjust the heat source output intensity and duration; Continuously monitor the temperature change trend and stress evolution state of the compensation area to ensure that its cooling trajectory is consistent with the overall component, thereby effectively suppressing the risk of structural deformation.

7. The method for controlling chamber temperature of an industrial particle 3D printer according to claim 1, wherein: Step S6 includes: Real-time monitoring of the temperature response and stress release status of sensitive structural areas during thermal compensation to obtain local temperature control feedback data; Synchronously collect the overall air volume distribution and temperature control parameters of the hot air circulation system to form global thermal regulation status information; Couple local feedback data with global thermal regulation parameters to build a temperature response deviation model, identify key heat flow factors affecting thermal control imbalance, and modify the hot air input strategy. The hot air circulation control target is dynamically updated based on the correction results, and the iterative optimization logic is embedded to continuously adjust the air volume and temperature distribution parameters to achieve an overall temperature equilibrium state during the component cooling process.

8. An industrial particle 3D printer cavity temperature heat recovery system, used to implement the industrial particle 3D printer cavity temperature control method according to any one of claims 1 to 7, characterized in that: It includes geometric perception modeling module, hot air dynamic adjustment module, cooling process data acquisition module, thermal response analysis module, local thermal compensation control module and closed-loop temperature control optimization module: The geometric perception modeling module establishes a 3D geometric analysis model of the printed component. Based on the structural morphology, thickness distribution, and spatial topological characteristics of the component, it extracts the spatial temperature control requirements corresponding to each area and determines the adjustment target of the hot air circulation system. The hot air dynamic adjustment module dynamically adjusts the air volume and hot air temperature distribution of the hot air circulation system according to the space temperature control requirements, optimizing the heat energy distribution state in the printing chamber; The cooling process data acquisition module continuously collects temperature change data and structural stress change data of each area of the printing cavity during the cooling process during the hot air circulation system adjustment process, and generates a cooling dynamic characteristic curve; The thermal response analysis module analyzes the natural frequency changes of various structural parts of the component based on the cooling dynamic characteristic curve, and identifies sensitive structural areas with risks of thermal stress coupling and warping resonance during the cooling process; The local thermal compensation control module implements fixed-point thermal compensation adjustment in sensitive structural areas, adjusts the local cooling rate to keep it consistent with the overall cooling rate of the printed component, and suppresses the asymmetric accumulation and periodic release of thermal stress; The closed-loop temperature control optimization module comprehensively analyzes the feedback results of fixed-point thermal compensation adjustment and the adjustment status of the hot air circulation system, cyclically corrects the air volume and temperature distribution parameters, and continuously optimizes the temperature distribution of the printing chamber.

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