Plasma spray based air conditioner rack surface treatment system

Abstract

The application relates to the technical field of plasma spraying, and discloses an air conditioner rack surface treatment system based on plasma spraying, which comprises a spraying parameter acquisition module, a plasma characteristic analysis module, a molten state monitoring module, a multi-source fusion judgment module, a regulation parameter generation module, a stress coupling analysis and dynamic feedback execution module, and the like. The system determines the coating bonding failure risk by acquiring parameters such as surface roughness and environmental temperature and humidity, evaluates the thermal stability by extracting jet parameters, predicts the molten qualified rate by monitoring the particle spreading form, fuses multiple parameters to generate a quality index and an adjustment signal, matches strategies to generate regulation parameters, monitors the stress matching degree and corrects the index, and dynamically adjusts the process through closed-loop control. The system realizes real-time monitoring and self-adaptive regulation of multiple parameters, improves the coating quality and process stability, and is suitable for air conditioner rack surface treatment.

Description

Technical Field

[0001] This invention relates to the field of plasma spraying technology, specifically to a surface treatment system for air conditioner brackets based on plasma spraying. Background Technology

[0002] In the manufacturing of air conditioning equipment, air conditioner brackets, as key structural components supporting and fixing air conditioning units, directly affect the reliability, corrosion resistance, and service life of the equipment due to their surface properties. Plasma spraying technology, capable of forming high-performance coatings on metal substrates, has become an important means of improving the surface quality of air conditioner brackets. However, existing plasma spraying processes still face many technical bottlenecks in the surface treatment of air conditioner brackets, restricting the stability of coating quality and the improvement of production efficiency.

[0003] From the perspective of coating adhesion strength, traditional plasma spraying processes lack real-time dynamic monitoring and collaborative analysis of multi-dimensional parameters during the spraying process. For example, fluctuations in the surface roughness of the air conditioner bracket substrate, changes in ambient temperature and humidity, and instability in the velocity of sprayed particles can all lead to defects such as porosity and cracks at the coating-substrate interface, thereby increasing the risk of coating adhesion failure. Current technologies often rely solely on operator experience to adjust single parameters, failing to establish a failure risk assessment model under the coupled effects of multiple parameters, resulting in insufficient accuracy in predicting coating adhesion quality.

[0004] In plasma characteristic analysis, traditional processes rely on limited methods for extracting temperature gradient and energy density parameters from plasma jets. These methods typically involve coarse measurements using thermocouples or simple optical instruments, making it difficult to accurately quantify the jet's thermal stability. For example, drastic changes in the axial temperature gradient and inhomogeneous radial energy distribution can lead to inconsistent melting states of the sprayed particles, affecting the coating's density and mechanical properties. Current technologies lack a systematic evaluation method for plasma uniformity, thus failing to provide a scientific basis for optimizing process parameters.

[0005] Traditional methods for monitoring the melting state of sprayed particles mainly rely on offline detection techniques, such as observing the cross-sectional morphology of the coating using a scanning electron microscope. However, these methods cannot achieve real-time dynamic tracking of the instantaneous spread of particles upon impact with the substrate. This lag makes it difficult to detect problems such as insufficient particle melting or excessive splattering in a timely manner, hindering real-time control of the spraying process and thus affecting the stability of coating quality.

[0006] In the process parameter adjustment stage, existing plasma spraying systems typically employ a fixed parameter matching mode, lacking the ability to fuse and analyze multi-source data on coating adhesion quality and to respond dynamically. For example, when there are sudden changes in ambient temperature and humidity or changes in the substrate surface condition, they cannot quickly generate targeted process adjustment signals and parameter optimization strategies, resulting in poor process adaptability and problems such as uneven coating thickness and fluctuating adhesion strength.

[0007] Furthermore, existing technologies do not adequately address the matching degree between coating deposition stress and the substrate's coefficient of thermal expansion, and lack real-time monitoring and evaluation of residual stress gradients and thermal mismatch coefficients. During the spraying process, the internal stress generated by the difference in the coefficients of thermal expansion between the coating and the substrate may lead to coating cracking or peeling, severely affecting the long-term performance of the air conditioner bracket. Simultaneously, traditional systems lack a closed-loop feedback mechanism, making it impossible to dynamically correct the entire processing flow based on the execution results after parameter adjustments, thus hindering the formation of an efficient process control loop. Summary of the Invention

[0008] The purpose of this invention is to provide a surface treatment system for air conditioner brackets based on plasma spraying, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a surface treatment system for air conditioner brackets based on plasma spraying, the system comprising:

[0010] The spraying parameter acquisition module is used to dynamically acquire the surface roughness of the target air conditioner bracket substrate, the ambient temperature and humidity parameters, and the spraying particle velocity to obtain a surface treatment parameter set. Based on the surface treatment parameter set, the risk of coating bonding failure is judged and analyzed, and a spraying abnormal signal is generated. The generated spraying abnormal signal triggers a collaborative processing instruction, and the plasma characteristic analysis module and the melt state monitoring module are executed according to the triggered collaborative processing instruction.

[0011] The plasma characteristic analysis module is used to extract the temperature distribution gradient and energy density parameters of the plasma jet, quantitatively evaluate the thermal stability of the jet, and obtain the plasma homogeneity index.

[0012] The melt state monitoring module is used to extract the spreading morphology parameters of the sprayed particles at the moment of impact with the substrate, predict the trend of the integrity of the particle melt state, and obtain the melt state qualification rate.

[0013] The multi-source fusion judgment module is used to receive the plasma uniformity index and the melt state qualification rate, perform collaborative analysis on the coating bonding quality, and generate process adjustment signals and parameter optimization signals.

[0014] The parameter generation module receives process adjustment signals and parameter optimization signals, performs coating optimization strategy matching, and generates plasma power adjustment parameters and powder feeding rate correction parameters.

[0015] Preferably, the analysis of coating adhesion failure risk includes:

[0016] By collecting the surface roughness parameters of the target air conditioner bracket substrate in real time, calculating the percentage deviation from the optimal roughness range, and marking it as the substrate fit anomaly;

[0017] Extract the relative humidity value and temperature fluctuation from the environmental temperature and humidity parameters, calculate the normalized weighted product of the two, and label it as the environmental disturbance coefficient;

[0018] The average velocity and velocity dispersion of the sprayed particles are collected, and their geometric harmonic mean is calculated and marked as the particle kinetic energy characteristic value.

[0019] The substrate adaptation anomaly, environmental interference coefficient, and particle kinetic energy characteristic value are compared with preset thresholds. When any parameter exceeds the corresponding threshold, a spraying anomaly signal is generated.

[0020] Preferably, the quantitative evaluation of the thermal stability of the jet includes:

[0021] The light intensity distribution data of the plasma jet is collected by a spectrometer to generate an axial temperature gradient map and a radial energy distribution map.

[0022] The difference between the highest and lowest temperature points is extracted from the axial temperature gradient diagram. The ratio of this difference to the average temperature is calculated and the reciprocal is taken to obtain the thermal stability coefficient.

[0023] The energy proportion of the core area and the edge diffusion angle are extracted from the radial energy distribution map, and the arithmetic mean of the two is calculated and marked as the energy concentration index.

[0024] The plasma homogeneity index is obtained by weighting and fusing the thermal stability coefficient and the energy concentration index.

[0025] Preferably, the trend prediction of the integrity of the particle melting state includes:

[0026] Real-time acquisition of high-speed image data of particles impacting the matrix, and extraction of characteristic parameters such as particle spreading diameter and number of splashed particles;

[0027] A melting state prediction model is constructed. The particle spreading diameter is input into the model and processed by Gaussian filtering. The unmelted probability of the current batch of particles is output.

[0028] Extract the number of particles per unit area and particle size distribution dispersion from the characteristic parameters of the number of splashed particles, calculate the ratio of the two and take the logarithm to obtain the splash suppression factor;

[0029] The unmelted probability is linearly combined with the splash suppression factor to obtain the molten state qualification rate.

[0030] Preferably, the synergistic analysis of coating adhesion quality includes:

[0031] Retrieve matrix adaptation anomaly data, set its correction weights, and obtain the matrix impact compensation value through weighted processing;

[0032] The values ​​of plasma uniformity index, melt state qualification rate and substrate influence compensation value are normalized and calculated to generate coating quality fusion index.

[0033] Set a coating quality judgment threshold. If the fusion index is lower than the threshold, a process adjustment signal is generated; if it is higher than the threshold, a parameter optimization signal is generated.

[0034] Preferably, the process of matching spraying optimization strategies includes:

[0035] If a process adjustment signal is detected, a power correction command is triggered. Based on the command, the current parameters and gas flow rate ratio of the plasma generator are dynamically adjusted to generate plasma power adjustment parameters.

[0036] If a parameter optimization signal is detected, a powder feeding control command is triggered. Based on the command, the vibration frequency and carrier gas pressure of the powder feeder are optimized and configured to generate powder feeding rate correction parameters.

[0037] Preferably, the system further includes:

[0038] The stress coupling analysis module is used to monitor the matching degree between coating deposition stress and substrate thermal expansion coefficient, extract residual stress gradient value and thermal mismatch coefficient, and generate stress coupling evaluation value.

[0039] The multi-source fusion determination module further incorporates stress coupling evaluation values ​​to perform a secondary correction on the coating quality fusion index.

[0040] Preferably, monitoring the matching degree between the coating deposition stress and the substrate thermal expansion coefficient includes:

[0041] Data on the residual stress distribution of the coating were collected using an X-ray diffractometer. The difference between the maximum stress point and the average stress was calculated and marked as the residual stress gradient value.

[0042] Extract the difference in the linear expansion coefficient of the interface region between the substrate and the coating, and calculate the thermal expansion difference in combination with the temperature change rate, which is marked as the thermal mismatch coefficient;

[0043] The residual stress gradient value and the thermal mismatch coefficient are weighted and summed to generate the stress coupling evaluation value.

[0044] Preferably, the system further includes:

[0045] The dynamic feedback execution module is used to adjust the trajectory of the motion mechanism of the processing system in real time according to the generated plasma power adjustment parameters and powder feeding rate correction parameters, and feed the execution results back to the spraying parameter acquisition module to form a closed-loop control link.

[0046] Preferably, the specific execution process of the dynamic feedback execution module includes:

[0047] If the plasma power adjustment parameters involve current intensity adjustment, the density of jet temperature monitoring points should be increased simultaneously.

[0048] If the powder feed rate correction parameter involves changes in carrier gas pressure, then the sampling frequency of the particle velocity sensor should be increased simultaneously.

[0049] Input the adjusted parameters into the spraying parameter acquisition module and restart the processing flow.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] The system dynamically collects parameters such as the surface roughness of the air conditioner bracket substrate, ambient temperature and humidity, and spray particle velocity in real time through a spraying parameter acquisition module. This enables the construction of a multi-dimensional failure risk assessment model covering substrate compatibility, environmental interference, and particle kinetic energy. By calculating the substrate compatibility anomaly, environmental interference coefficient, and particle kinetic energy characteristic values ​​and comparing them with preset thresholds, the system can promptly and accurately identify coating bonding failure risks. This avoids the limitations of traditional processes that rely on single-parameter judgments, improving the accuracy and timeliness of risk prediction.

[0052] The plasma characteristic analysis module collects light intensity distribution data of the plasma jet using a spectrometer, generates axial temperature gradient maps and radial energy distribution maps, and then calculates the thermal stability coefficient and energy concentration index, achieving a quantitative assessment of the thermal stability and energy distribution uniformity of the plasma jet. This process provides data support for a deeper understanding of the intrinsic relationship between plasma characteristics and coating quality, enabling process engineers to adjust plasma power parameters specifically based on the plasma uniformity index, optimize the temperature and energy distribution of the jet, ensure that the sprayed particles obtain stable and sufficient melting energy, and improve the density and bonding strength of the coating.

[0053] The melt state monitoring module, through high-speed image data acquisition and the construction of a melt state prediction model, extracts characteristic parameters such as particle spreading diameter and the number of splashed particles in real time, enabling dynamic tracking and integrity trend prediction of the melt state at the moment of particle impact on the substrate. By calculating the probability of incomplete melting and the splash suppression factor and performing a linear combination to obtain the melt state qualification rate, it can promptly detect problems such as insufficient particle melting or excessive splashing, providing a key basis for generating powder feeding rate correction parameters and effectively reducing coating defects caused by abnormal particle melt state.

[0054] The multi-source fusion judgment module normalizes and weights plasma uniformity index, melt state qualification rate, substrate compatibility anomaly data, and stress coupling evaluation values ​​to generate a coating quality fusion index, and based on this, generates process adjustment signals or parameter optimization signals. This multi-source data collaborative analysis mode breaks through the limitations of traditional single-parameter evaluation, comprehensively considering the influence of multiple factors such as physical field, particle state, substrate characteristics, and stress matching during the spraying process, achieving a comprehensive and accurate evaluation of coating bonding quality, and providing a scientific and comprehensive decision-making basis for optimizing process parameters.

[0055] The parameter generation module dynamically matches the spraying optimization strategy based on the signal output by the multi-source fusion judgment module. When a process adjustment signal is detected, the plasma power is optimized by adjusting the current parameters and gas flow ratio of the plasma generator; when a parameter optimization signal is detected, the powder feeding rate is optimized by adjusting the vibration frequency and carrier gas pressure of the powder feeder. This differentiated parameter adjustment mechanism enables dynamic adaptive control of the spraying process, improves the adaptability of the process system to complex working conditions, and ensures that high-quality coatings can be obtained under different environmental conditions and substrate conditions.

[0056] The stress coupling analysis module collects residual stress distribution data of the coating using an X-ray diffractometer. Combining this with the difference in thermal expansion coefficients at the interface between the substrate and the coating, it calculates the residual stress gradient and thermal mismatch coefficient, generates a stress coupling assessment value, and performs a secondary correction on the coating quality fusion index. This design fully considers the stress matching problem during the coating deposition process, effectively predicts and controls the risk of coating cracking caused by thermal stress, and further improves the long-term reliability of the coating.

[0057] The dynamic feedback execution module adjusts the trajectory of the processing system's motion mechanism in real time based on the generated plasma power adjustment parameters and powder feed rate correction parameters. It also feeds back the execution results to the spraying parameter acquisition module by increasing the density of jet temperature monitoring points or raising the sampling frequency of the particle velocity sensor, forming a closed-loop control chain. This closed-loop control mechanism ensures the effectiveness and timeliness of process parameter adjustments. Through continuous monitoring-adjustment-feedback cycles, the spraying process is continuously optimized, improving the stability of coating quality and production efficiency. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the working principle of the air conditioner bracket surface treatment system based on plasma spraying as described in this invention.

[0059] Figure 2 Design diagram for plasma characteristic analysis;

[0060] Figure 3 Design drawings for monitoring the molten state;

[0061] Figure 4 Design diagram for multi-source fusion judgment;

[0062] Figure 5 Design diagram for dynamic feedback execution. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Please see Figures 1-5 The present invention relates to a surface treatment system for air conditioner racks based on plasma spraying. The system includes a spraying parameter acquisition module, a plasma characteristic analysis module, a melt state monitoring module, a multi-source fusion determination module, and a control parameter generation module. These modules work collaboratively to achieve closed-loop control of the air conditioner rack surface treatment. The specific steps are as follows:

[0065] The spraying parameter acquisition module operates by dynamically acquiring data on the surface roughness of the target air conditioner bracket substrate (e.g., using a stylus profilometer), ambient temperature and humidity (e.g., using a temperature and humidity transmitter), and spraying particle velocity (e.g., using a laser Doppler velocimeter) via a sensor array, forming a surface treatment parameter set containing multi-dimensional parameters. Based on this parameter set, a preset algorithm is used to analyze and determine the risk of coating bonding failure. If an anomaly is detected, a spraying anomaly signal is generated, triggering a collaborative processing command to activate the plasma characteristic analysis module and the melt state monitoring module.

[0066] Plasma characteristic analysis module operation: It uses a spectrometer (such as a fiber optic spectrometer) to collect light intensity distribution data of the plasma jet, generates axial temperature gradient map and radial energy distribution map, extracts temperature distribution gradient and energy density parameters, quantitatively evaluates the thermal stability of the jet, and outputs plasma homogeneity index.

[0067] The melt state monitoring module operates by using a high-speed camera to collect real-time image data of the moment the sprayed particles hit the substrate, extracting morphological parameters such as particle spreading diameter and the number of splashed particles, and combining them with a preset prediction model to perform trend analysis on the integrity of the particle melt state and calculate the melt state pass rate.

[0068] The multi-source fusion judgment module operates by receiving data such as plasma uniformity index and melt state qualification rate, and using a multi-parameter fusion algorithm to perform collaborative analysis on coating bonding quality, generating process adjustment signals or parameter optimization signals.

[0069] The parameter generation module operates by matching the corresponding spraying optimization strategy based on the received signal type. If the signal is a process adjustment signal, it generates plasma power adjustment parameters; if the signal is a parameter optimization signal, it generates powder feed rate correction parameters, thereby achieving dynamic control of the spraying process.

[0070] The present invention will be further described below with reference to Examples 1 to 5:

[0071] Example 1: In the surface treatment system for air conditioner racks based on plasma spraying, the spraying parameter acquisition module analyzes the risk of coating adhesion failure by acquiring and processing multi-dimensional parameters in real time. The specific implementation method is as follows:

[0072] For the acquisition and analysis of surface roughness of the target air conditioner bracket substrate, the system scans the substrate surface using a stylus-type profilometer. The probe of the stylus-type profilometer moves along the substrate surface with constant pressure, capturing the height changes and spacing characteristics of microscopic surface undulations. This physical displacement is converted into an electrical signal and transmitted to the data processing unit. The data processing unit calculates in real time the deviation of the measured roughness parameters (such as the arithmetic mean deviation Ra value) from the preset optimal roughness range. The preset optimal roughness range is determined based on the material properties of the air conditioner bracket substrate, the type of coating material, and the requirements of the spraying process. For example, when the substrate is aluminum alloy and the coating is a zinc-aluminum composite coating, the optimal roughness range can be set to Ra 2.5-6.3 μm. The system compares the measured value with the upper or lower limit of this range and calculates the deviation percentage: if the measured value is greater than the upper limit, the deviation percentage is (measured value - upper limit) / upper limit × 100%; if the measured value is less than the lower limit, the deviation percentage is (lower limit - measured value) / lower limit × 100%. This deviation percentage is labeled as the substrate fit anomaly, which reflects the compatibility between the substrate surface condition and the spraying process. The higher the anomaly, the greater the risk of coating adhesion failure.

[0073] The acquisition and processing of environmental temperature and humidity parameters are accomplished through a temperature and humidity transmitter. This transmitter incorporates humidity and temperature sensors to monitor the relative humidity and temperature fluctuations in the spraying environment in real time. Relative humidity directly reflects the water vapor content in the environment; excessive humidity may cause water vapor to adsorb onto the substrate surface, affecting the adhesion between the coating and the substrate. Temperature fluctuation is measured by the magnitude of temperature change per unit time (e.g., temperature change every 10 minutes); drastic temperature fluctuations can lead to unstable thermal stress during spraying, thus affecting coating quality. The system normalizes the relative humidity and temperature fluctuation values, mapping them to the 0-1 range for subsequent calculations. After normalization, the system assigns different weights to relative humidity and temperature fluctuation based on process experience; for example, the relative humidity weight is set to 0.6, and the temperature fluctuation weight is set to 0.4. The product of these two values ​​is then calculated using weighted averages to obtain the environmental interference coefficient. This coefficient comprehensively reflects the degree of interference from environmental factors on the spraying process; a higher coefficient value indicates a higher potential risk to coating adhesion from environmental conditions.

[0074] The acquisition of particle velocity parameters in spraying relies on a laser Doppler velocimeter. This device measures particle velocity by emitting a laser beam towards the sprayed particles and utilizing the Doppler frequency shift effect generated by particle motion. It can simultaneously obtain the average velocity and velocity dispersion of the particles. The average velocity reflects the overall kinetic energy of the particles, while the velocity dispersion characterizes the uniformity of particle velocity. The greater the dispersion, the more uneven the particle velocity distribution, which may lead to some particles failing to fully melt or spread due to insufficient kinetic energy. The system calculates the geometrically harmonic mean of the average velocity and velocity dispersion. Specifically, the product of the two is calculated, then multiplied by 2, and finally divided by the sum of the two to obtain the particle kinetic energy characteristic value. This characteristic value comprehensively reflects the kinetic energy state of the particles. The higher the characteristic value, the more sufficient and uniform the overall kinetic energy of the particles, which is conducive to the formation of a high-quality coating.

[0075] After calculating the above three types of parameters, the system compares the substrate compatibility anomaly, environmental interference coefficient, and particle kinetic energy characteristic value with their respective preset thresholds. The preset thresholds are determined based on the characteristics of the spraying material, process standards, and long-term production experience. For example, the substrate compatibility anomaly threshold can be set to ±20%, meaning that an anomaly is considered when the deviation percentage exceeds 20%. The environmental interference coefficient threshold is set to 0.5; when the coefficient value is greater than 0.5, the environmental conditions are considered to significantly interfere with the spraying process. The particle kinetic energy characteristic value threshold is set according to the melting characteristics of the particle material and the requirements of the spraying process. For example, for aluminum-based alloy particles, the threshold can be set to a specific kinetic energy benchmark value. When any parameter exceeds its corresponding threshold, the system determines that there is a risk of coating bonding failure and generates a spraying anomaly signal. This signal is transmitted to the system control unit via the data bus, triggering a collaborative processing command to activate the plasma characteristic analysis module and the melt state monitoring module for in-depth analysis and control of the spraying process.

[0076] It should be noted that the acquisition frequency of the above parameters is matched with the rhythm of the spraying process. For example, during the batch spraying of air conditioner racks, the system initiates a parameter acquisition and risk assessment process after each workpiece is sprayed, ensuring real-time monitoring of spraying quality. Simultaneously, the system has a parameter calibration function, which can periodically perform zero-point calibration and accuracy verification on equipment such as stylus profilometers, temperature and humidity transmitters, and laser Doppler velocimeters, avoiding inaccurate judgment results due to equipment errors. Furthermore, the calculation processes for substrate fit anomaly, environmental interference coefficient, and particle kinetic energy characteristic values ​​are all automatically executed by the system's built-in algorithm module, requiring no manual intervention, ensuring the objectivity and consistency of the judgment process. Through this entire process, the system can promptly identify potential risks during the spraying process, providing accurate trigger signals for the collaborative work of subsequent modules, thereby achieving dynamic control over the surface treatment quality of air conditioner racks.

[0077] Example 2: In the surface treatment system for air conditioner racks based on plasma spraying, the plasma characteristic analysis module achieves quantitative evaluation of the jet thermal stability through spectral acquisition, data processing, and multi-parameter fusion. The specific implementation method is as follows:

[0078] The system acquires the light intensity distribution of the plasma jet in real time using a spectrometer. The spectrometer employs a fiber-optic coupling design, with its probe positioned to the side of the spray gun nozzle at a specific angle (e.g., 45°) to the jet axis to ensure the capture of light signals from different regions of the jet. The fiber optic probe transmits the acquired light signals to the spectrometer's main unit. The main unit's built-in dispersive elements (such as gratings) decompose the composite light into monochromatic light of different wavelengths. The intensity of each wavelength is then detected by a charge-coupled device (CCD) array, generating light intensity distribution data. This data is transmitted in real time, in time-series format, to the system's data processing unit to generate axial temperature gradient maps and radial energy distribution maps.

[0079] The axial temperature gradient map is generated based on the light intensity distribution data along the jet axis. The system divides the jet axis into multiple monitoring intervals, for example, starting from the nozzle exit, with 5mm intervals up to 50mm, setting a total of 10 monitoring points. The light intensity data of each monitoring point corresponds to the radiation intensity of a specific wavelength. According to Planck's radiation law, light intensity and temperature have a non-linear relationship. Therefore, the system uses a built-in temperature inversion algorithm to convert the light intensity values ​​of each monitoring point into temperature values. During the inversion process, factors such as plasma emissivity and background radiation interference need to be considered, and corrections are made using table lookup or polynomial fitting. After the temperature conversion is completed, the system plots the axial temperature gradient map with the monitoring point location as the x-axis and the temperature value as the y-axis, visually displaying the temperature distribution characteristics of the jet along the axis, such as the location of the highest temperature point and the temperature decay trend.

[0080] The radial energy distribution map is generated based on the light intensity distribution data along the cross-section of the jet. The system sets multiple radial monitoring points on a plane perpendicular to the jet axis, for example, starting from the center (radius 0 mm), with 2 mm intervals up to 20 mm, for a total of 10 monitoring points. Light intensity data from each radial monitoring point is simultaneously acquired using the two-dimensional scanning function of a spectrometer or a multi-channel fiber optic array. After normalization, the light intensity data is converted into energy density values ​​(energy per unit area), and a radial energy distribution map is plotted with radius as the x-axis and energy density as the y-axis. This map reflects the distribution of jet energy along the cross-section, such as the extent of the core region (high energy density area) and the degree of energy diffusion at the edges.

[0081] After generating the axial temperature gradient map and radial energy distribution map, the system extracts key parameters from the maps to evaluate the jet's thermal stability. For the axial temperature gradient map, the positions and corresponding temperature values ​​of the highest and lowest temperature points are first identified, and the temperature difference between them is calculated. The temperature difference reflects the temperature uniformity of the jet along the axial direction; the smaller the temperature difference, the more uniform the temperature distribution and the higher the thermal stability. Subsequently, the system calculates the ratio of this temperature difference to the average temperature of each monitoring point along the axial direction, and takes the reciprocal of this ratio to obtain a dimensionless parameter characterizing thermal stability. The physical meaning of this parameter is: when the temperature difference is small relative to the average temperature, the reciprocal is large, corresponding to higher thermal stability; conversely, when the temperature difference is large, the reciprocal is small, corresponding to lower thermal stability.

[0082] For the radial energy distribution map, the system first defines the core region as the area where the energy density is higher than the peak energy by a certain percentage (e.g., 50%), and calculates the proportion of energy in the core region to the total energy (core region energy percentage). A higher core region energy percentage indicates that the energy is more concentrated in the central region of the jet, which is beneficial for the complete melting of particles. Simultaneously, the system extracts the radial distance at which the energy density drops to a certain percentage (e.g., 20%) of the peak energy, and calculates the corresponding diffusion angle (edge ​​diffusion angle). A smaller diffusion angle indicates a lower degree of energy diffusion towards the edge, and a more concentrated energy distribution. The system performs an arithmetic mean calculation on the core region energy percentage and the edge diffusion angle to obtain an energy concentration index, which comprehensively reflects the concentration of jet energy in the cross-sectional direction.

[0083] Finally, the system performs a weighted fusion of the thermal stability parameter and the energy concentration index. During the weighted fusion process, different weights are assigned to the two parameters based on the different requirements of the plasma spraying process for temperature stability and energy concentration. For example, when the sprayed material is temperature-sensitive, the thermal stability parameter can be given a higher weight (e.g., 0.7), while the weight of the energy concentration index is correspondingly reduced (e.g., 0.3); conversely, if the material melting depends more on the degree of energy concentration, the weight allocation is adjusted. The plasma homogeneity index is obtained through weighted calculation. This index serves as a comprehensive indicator for quantitatively evaluating the thermal stability of the jet, typically ranging from 0 to 1. A higher value indicates a more uniform temperature distribution and higher energy concentration in the plasma jet, which is more conducive to forming a coating with high bonding strength and uniform quality.

[0084] It is important to note that the spectrometer's acquisition frequency must match the timescale of the spraying process. For example, in high-frequency pulsed plasma spraying scenarios, the acquisition frequency needs to reach the kHz level to capture transient changes in the jet. Furthermore, the system periodically performs wavelength calibration and sensitivity correction on the spectrometer to ensure the accuracy of the light intensity data. During data processing, a sliding window filtering algorithm is used to denoise the raw light intensity data, avoiding interference from random noise in temperature inversion and energy calculation. Through the complete parameter acquisition, graph generation, and quantitative evaluation process described above, the plasma characteristic analysis module can provide crucial jet stability data for the multi-source fusion determination of coating bonding quality, supporting the system's dynamic optimization and control of the spraying process.

[0085] Example 3: In the surface treatment system for air conditioner racks based on plasma spraying, the trend prediction of particle melt state integrity by the melt state monitoring module is achieved through high-speed image acquisition, parameter extraction, and model analysis. The specific implementation method is as follows:

[0086] The system uses a high-speed camera to capture real-time images of the instantaneous impact of sprayed particles on the substrate. The high-speed camera's frame rate is set to ≥10000fps to ensure the capture of microsecond-level dynamic changes in particle impact on the substrate. The camera lens is positioned perpendicular to the substrate surface, covering the center of the sprayed area. A near-infrared supplementary lighting device enhances the contrast of the particle images, preventing image blurring due to insufficient light. The acquired image data is transmitted to the image processing unit as a raw video stream for real-time analysis and parameter extraction.

[0087] The image processing unit first preprocesses the high-speed image, including noise reduction, contrast enhancement, and edge detection. Noise reduction employs a median filtering algorithm to remove random noise points; contrast enhancement is achieved through histogram equalization to improve the grayscale difference between particles and the substrate background; edge detection uses the Canny operator to accurately identify the contour boundaries after particle impact. After preprocessing, the system separates the particle spreading area from the background using a threshold segmentation algorithm and calculates the particle spreading diameter. The particle spreading diameter is defined as the maximum chord length of the flattened particle region after impact. This parameter directly reflects the fluidity of the melted particles—a larger spreading diameter indicates more complete particle melting, better fluidity during impact, and greater potential for forming a dense coating.

[0088] To extract the characteristic parameters of the number of splashed particles, the system first identifies splashed particles in the image using a connected component analysis algorithm. Connected component analysis, based on the continuity of pixel grayscale values, divides adjacent high-grayscale pixels into the same particle region and calculates the geometric features of each particle, such as area and perimeter. The number of particles per unit area is the ratio of the total number of splashed particles to the area of ​​the monitored region, reflecting the intensity of the splashing phenomenon; the particle size distribution dispersion is obtained by calculating the ratio of the standard deviation to the mean of the splashed particle size, characterizing the uniformity of the splashed particle size. These two parameters are generated in real time through image statistics, providing a quantitative basis for assessing the molten state.

[0089] After parameter extraction, the system analyzes the probability of unmelted particles using a melt state prediction model. This model, built on a Gaussian process regression algorithm, is pre-trained with a large amount of labeled particle spreading diameter data to establish a nonlinear mapping relationship between spreading diameter and unmelted probability. The model input is the real-time collected particle spreading diameter, and the output is the unmelted probability value of the current batch of particles. The unmelted probability reflects the likelihood of particles not completely melting; a higher probability value indicates a larger proportion of insufficiently melted particles, potentially leading to more unmelted particles at the coating interface and affecting bonding strength.

[0090] For the analysis of splashed particles, the system generates a splash suppression factor through the following steps: First, the ratio of the number of particles per unit area to the particle size distribution dispersion is calculated. This ratio reflects the combined effect of the density of splashed particles and the non-uniformity of particle size per unit area. Then, the natural logarithm of this ratio is taken to obtain the splash suppression factor. The mathematical expression is:

[0091]

[0092] in, As a splash inhibitor, The number of particles per unit area (unit: particles / square millimeter). This represents the particle size distribution dispersion (dimensionless). The physical meaning of the splash suppression factor is: when the number of particles per unit area is greater and the particle size distribution is more uneven, the ratio... The larger the value, the more likely it is to be logarithmic. The larger the value, the more significant the negative impact of splashing on the integrity of the molten state.

[0093] The system linearly combines the probability of non-melting with the splash suppression factor to obtain the melt state qualification rate. During the linear combination process, weighting coefficients are assigned to the two parameters based on process experience. Assume the weight of the probability of non-melting is... The weight of the splash suppression factor is And satisfy The formula for calculating the melt state qualification rate is:

[0094]

[0095] in, The molten state pass rate (value range 0-1). This represents the probability of not melting (range 0-1). This is the splash suppression factor (range determined based on actual data). This formula, through a comprehensive quantification of the risk of unmelted material and the impact of splashing, transforms the complex assessment of the molten state into a single pass rate indicator, facilitating the system's rapid determination of the integrity of the particle's molten state.

[0096] In practical applications, the resolution of the high-speed camera needs to be adjusted according to the size of the sprayed particles. For example, for micron-sized particles, the resolution should be no less than 1024×1024 pixels to ensure accurate identification of particle outlines. The image processing unit uses Field-Programmable Gate Array (FPGA) hardware acceleration technology to achieve real-time processing of image data, avoiding parameter extraction lag caused by computational delays. In addition, the system periodically calibrates the focal length and exposure parameters of the high-speed camera to ensure consistency of image acquisition conditions during different batches of spraying. The melt state prediction model has online learning capabilities and can automatically update model parameters based on newly accumulated spraying data to improve prediction accuracy.

[0097] Example 4: In the surface treatment system for air conditioner racks based on plasma spraying, the multi-source fusion judgment module achieves collaborative analysis and control parameter generation of coating bonding quality through data retrieval, weight setting, normalization processing, and strategy matching. The specific implementation method is as follows:

[0098] Taking the application of a zinc-aluminum composite coating to a certain type of aluminum alloy air conditioner bracket as an example, the system first retrieves the substrate fit anomaly data generated by the spraying parameter acquisition module. The substrate fit anomaly reflects the degree of deviation of the surface roughness of the air conditioner bracket substrate from the optimal range. For example, when the optimal roughness range is Ra2.5-6.3μm, if the measured value is Ra7.2μm, the deviation percentage is (7.2-6.3) / 6.3×100%≈14.3%, which is marked as substrate fit anomaly. Based on the material characteristics of the aluminum alloy substrate (soft texture, high surface activity), the system sets a correction weight (e.g., 0.3). Through weighted processing, the substrate influence compensation value is obtained, which is the substrate fit anomaly multiplied by the correction weight. This compensation value is used to adjust the influence of the substrate surface condition on the coating bonding quality.

[0099] Subsequently, the system acquires the plasma homogeneity index output by the plasma characteristic analysis module and the melt state qualification rate output by the melt state monitoring module. The plasma homogeneity index quantitatively characterizes the temperature distribution uniformity and energy concentration of the plasma jet. For example, when the axial temperature difference of the jet is small and the radial energy core region accounts for a high proportion, the homogeneity index is high (e.g., 0.8). The melt state qualification rate reflects the sufficiency of melting and the spreading quality when particles impact the matrix. If the particle spreading diameter is large and there are few splashed particles, the qualification rate may reach 0.9. The system normalizes these three parameters (plasma homogeneity index, melt state qualification rate, and matrix influence compensation value), mapping the values ​​of each parameter to the 0-1 range to eliminate the influence of dimensional differences on the fusion analysis. The normalization method uses a linear transformation; for example, the actual value of a certain parameter is... The maximum value is The minimum value is The normalized value is .

[0100] After normalization, the system generates a coating quality fusion index through a linear combination of multiple parameters. The weights of the linear combination are set according to the degree of influence of each parameter on the coating bonding quality. For example, the plasma uniformity index has a weight of 0.5, the melt state qualification rate has a weight of 0.4, and the substrate influence compensation value has a weight of 0.1, with the sum of the three weights being 1. The fusion index comprehensively reflects the synergistic effect of jet stability, particle melting state, and substrate compatibility during the spraying process. The system presets a coating quality judgment threshold (e.g., 0.7). If the fusion index is lower than the threshold, it indicates that there is a risk to the coating bonding quality, and a process adjustment signal is generated; if it is higher than the threshold, it indicates that the process parameters can be further optimized, and a parameter optimization signal is generated.

[0101] The parameter generation module executes the corresponding spraying optimization strategy based on the received signal type. If a process adjustment signal is detected, the system triggers a power correction command to dynamically adjust the current parameters and gas flow ratio of the plasma generator. Taking current parameter adjustment as an example, when the fusion index is low and analysis indicates uneven jet temperature distribution, the system can increase the current parameter by 10% (e.g., from 300A to 330A) while simultaneously adjusting the argon to hydrogen flow ratio proportionally (e.g., from 7:3 to 8:2) to improve jet temperature stability. After adjustment, plasma power adjustment parameters are generated, which include specific data such as current and gas flow rates, to guide the real-time control of the plasma generator.

[0102] If a parameter optimization signal is detected, the system triggers a powder feeding control command to optimize the vibration frequency and carrier gas pressure of the powder feeder. For example, when the fusion index is high but there is still room for improvement, the system can increase the vibration frequency of the powder feeder from 50Hz to 70Hz to enhance the uniformity of powder delivery; simultaneously, the carrier gas pressure can be adjusted from 0.2MPa to 0.25MPa to increase the particle delivery speed and kinetic energy. By optimizing the vibration frequency and carrier gas pressure, the melting state and spreading quality of the particles can be further improved, generating powder feeding rate correction parameters.

[0103] The entire collaborative analysis and control process is closely aligned with the actual spraying conditions of the air conditioner bracket. For example, when the ambient temperature and humidity change (e.g., relative humidity increases to 70%), causing moisture adsorption on the substrate surface, the substrate compatibility anomaly may increase, thus affecting the coating quality fusion index. In this case, if the system detects that the fusion index is below the threshold, it will preferentially trigger a process adjustment signal, increasing the plasma power to raise the jet temperature and offset the adverse effects of ambient humidity on coating adhesion. Furthermore, when changing the coating material (e.g., from a zinc-aluminum composite coating to a pure aluminum coating), the weight of the plasma uniformity index may be adjusted to 0.6 (because pure aluminum coatings are more sensitive to jet temperature), the weight of the melt state qualification rate may be adjusted to 0.3, and the weight of the substrate influence compensation value may remain at 0.1 to adapt to the process requirements of the new material.

[0104] The system's multi-source fusion judgment module possesses dynamic learning capabilities, automatically optimizing the weight allocation of various parameters based on historical spraying data. For example, by analyzing the correlation between coating bonding strength and various parameters in numerous spraying cases, the system can adaptively adjust the weights of plasma uniformity index and melt state qualification rate, improving the accuracy of the fusion index in predicting actual coating quality. The control parameter generation module incorporates multiple optimization strategy libraries, including parameter adjustment schemes for different material combinations and environmental conditions. It can quickly match the optimal strategy based on real-time signals, avoiding control delays caused by manual intervention.

[0105] During execution, data is transmitted in real time between modules via industrial Ethernet, ensuring signal response time is controlled within milliseconds. For example, after the multi-source fusion judgment module generates a process adjustment signal, it can transmit the instruction to the control parameter generation module within 0.1 seconds. The latter completes the optimization strategy matching and outputs the adjustment parameters within 0.5 seconds, ensuring real-time adjustment of the spraying process. In addition, the system has a parameter backtracking function, which can record the parameter values ​​and fusion index changes before and after each adjustment, facilitating process engineers to analyze the control effect and accumulate process optimization experience.

[0106] Example 5:

[0107] This embodiment involves the integrated operation of the stress coupling analysis module and the dynamic feedback execution module. The implementation method is described in detail below with reference to a specific application scenario:

[0108] Taking the ceramic coating applied to a steel air conditioner bracket as an example, the stress coupling analysis module monitors the residual stress of the coating using an X-ray diffractometer. The X-ray source of the X-ray diffractometer emits a beam of specific wavelength. After irradiating the coating surface, according to Bragg's law, different stress states in the crystal structure lead to changes in the position and intensity of diffraction peaks. The system collects diffraction data from multiple measuring points on the coating surface, calculates the residual stress distribution using the Rietveld refinement algorithm, and extracts the difference between the maximum stress point and the average stress, which is recorded as the residual stress gradient value. For example, if the average stress is 150 MPa and the maximum stress point is 280 MPa, the residual stress gradient value is 130 MPa. This value reflects the non-uniformity of stress distribution within the coating; the larger the gradient value, the higher the risk of coating cracking.

[0109] Simultaneously, the stress coupling analysis module extracts the difference in the linear expansion coefficients at the interface between the substrate and the coating. The linear expansion coefficient of the steel substrate is approximately 11 × 10⁻⁻⁻⁶. 6 At / ℃, the coefficient of linear expansion of the ceramic coating is approximately 3×10⁻ 6 / ℃, the difference between the two is 8×10⁻ 6 / ℃. The thermal expansion difference is calculated based on the substrate's temperature change rate during spraying (e.g., a temperature increase of 50℃ per minute), and denoted as the thermal mismatch coefficient. This coefficient reflects the accumulation of thermal stress caused by the difference in thermal expansion characteristics between the substrate and the coating. The greater the difference, the greater the thermal stress at the interface after cooling, which may lead to coating peeling. The system weights and sums the residual stress gradient value and the thermal mismatch coefficient according to preset weights (e.g., 0.6 and 0.4) to generate a stress coupling evaluation value, used to quantitatively evaluate the matching degree between the coating deposition stress and the substrate's thermal expansion coefficient.

[0110] After receiving the stress coupling assessment value, the multi-source fusion judgment module performs a secondary correction on the original coating quality fusion index. For example, if the original fusion index is 0.68, and the stress coupling assessment value shows a high residual stress gradient and a large thermal mismatch coefficient, the system reduces the fusion index to 0.62 through a correction algorithm, indicating that the coating quality risk has further increased due to stress factors. At this time, the system generates corresponding process adjustment signals or parameter optimization signals based on the corrected fusion index to ensure that the quality assessment results more comprehensively reflect the actual working conditions.

[0111] The dynamic feedback execution module adjusts the plasma power adjustment parameters and powder feeding rate correction parameters output by the control parameter generation module in real time, and adjusts the trajectory of the motion mechanism of the processing system. For example, when the plasma power adjustment parameters require an increase in current intensity, the system synchronously controls the moving speed of the spray gun to decrease by 10% to prolong the residence time of particles in the high-temperature jet and ensure sufficient melting. At the same time, in order to monitor the jet temperature change after the current intensity adjustment in real time, the system automatically increases the density of jet temperature monitoring points. A thermocouple sensor is added every 3 mm along the path from the nozzle outlet to the substrate surface (originally spaced at 5 mm), and the jet temperature distribution is fed back in real time through multi-point temperature measurement to avoid local overheating or insufficient temperature.

[0112] If the powder feeding rate correction parameter involves changes in carrier gas pressure (e.g., from 0.15 MPa to 0.2 MPa), the dynamic feedback execution module simultaneously increases the sampling frequency of the particle velocity sensor from 100 times per second to 200 times per second to monitor particle velocity fluctuations more intensively. Increased carrier gas pressure may lead to faster particle delivery; high-frequency sampling can promptly detect velocity anomalies, preventing excessively high or low particle kinetic energy from affecting the molten state. The adjusted carrier gas pressure parameter and sampling frequency are transmitted to the powder feeder and sensor module via the control bus, enabling precise control of the powder feeding process.

[0113] After adjustment, the dynamic feedback execution module feeds back the new process parameters (such as adjusted current value, gas flow rate, powder feeding rate, etc.) to the spraying parameter acquisition module, triggering a new round of data acquisition. For example, after completing the plasma power adjustment, the spraying parameter acquisition module restarts the acquisition of parameters such as surface roughness, ambient temperature and humidity, and particle velocity, forming a closed-loop control link. If a high substrate fit abnormality is detected again (such as local oxidation of the substrate surface due to increased current, causing the roughness to deviate from the optimal range), the system can further adjust the distance between the spray gun and the substrate or introduce inert gas purging to dynamically compensate for the chain effect caused by the process adjustment.

[0114] In practical applications, the monitoring frequency of the stress coupling analysis module is matched with the spraying cycle. For mass-produced air conditioner brackets, stress detection is automatically initiated after every 5 pieces are sprayed to prevent stress accumulation caused by continuous spraying from going undetected. The motion mechanism of the dynamic feedback execution module (such as the spray gun robotic arm) is driven by a servo motor, achieving a positioning accuracy of ±0.1mm, ensuring accurate trajectory adjustment. Furthermore, the system has a fault-tolerant mechanism. If a sensor malfunctions during parameter adjustment (such as thermocouple signal interruption), it automatically switches to a redundant sensor to continue operating and issues an equipment maintenance prompt, preventing the entire control chain from being interrupted due to a single point of failure.

[0115] Taking another application scenario as an example, when the base material of the air conditioner bracket is changed to aluminum alloy and the coating is metallic aluminum, the weight settings of the stress coupling analysis module are automatically adjusted to a residual stress gradient value weight of 0.4 and a thermal mismatch coefficient weight of 0.6 (because the difference in thermal expansion coefficients between aluminum alloy and aluminum coating is small, the impact of thermal mismatch is reduced). Based on the characteristics of the new material, the dynamic feedback execution module limits the current adjustment range of the plasma power adjustment parameter to within ±5% (to avoid overheating and deformation of the aluminum alloy base material), and refines the carrier gas pressure adjustment step in the powder feeding rate correction parameter to 0.01 MPa to accommodate the tendency of aluminum powder to agglomerate.

[0116] Through the synergistic effect of the stress coupling analysis module and the dynamic feedback execution module, the system achieves real-time monitoring of stress factors and dynamic compensation of process parameters during the spraying process. The introduction of stress coupling evaluation values ​​enhances the dimensionality of coating quality assessment, enabling the system to identify potential failure risks caused by stress mismatch in advance. Meanwhile, the closed-loop control mechanism of the dynamic feedback execution module ensures the timeliness and accuracy of process adjustments, forming a complete control chain from risk identification to parameter optimization, effectively improving the reliability and consistency of plasma spraying surface treatment for air conditioner racks.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A plasma spray based air conditioner rack surface treatment system characterized by, The method comprises the following steps: A spraying parameter acquisition module is used to dynamically collect the surface roughness of the target air conditioner rack base, the environmental temperature and humidity parameters, and the spraying particle velocity, obtain a surface treatment parameter set, determine and analyze the coating bonding failure risk based on the surface treatment parameter set, generate a spraying abnormal signal, trigger a collaborative processing instruction according to the generated spraying abnormal signal, and execute the plasma characteristic analysis module and the melting state monitoring module according to the triggered collaborative processing instruction; The plasma characteristic analysis module is used to extract the temperature distribution gradient and the energy density parameters of the plasma jet, quantitatively evaluate the thermal stability of the jet, and obtain a plasma uniformity index; The melting state monitoring module is used to extract the spreading shape parameters of the spraying particles at the moment of impacting the base, predict the integrity of the particle melting state, and obtain a melting state qualification rate; A multi-source fusion determination module is used to receive the plasma uniformity index and the melting state qualification rate, collaboratively analyze the coating bonding quality, and generate a process adjustment signal and a parameter optimization signal; A control parameter generation module is used to receive the process adjustment signal and the parameter optimization signal, match the spraying optimization strategy, generate plasma power adjustment parameters and powder feeding rate correction parameters, and output the plasma power adjustment parameters and the powder feeding rate correction parameters. The method for predicting the integrity of the particle melting state comprises the following steps: Real-time high-speed image data of the particles impacting the base are collected, and particle spreading diameter and spatter particle quantity characteristic parameters are extracted; A melting state prediction model is constructed, the particle spreading diameter is input into the model for Gaussian filtering processing, and the unmelting probability of the current batch of particles is output; The unit area particle number and the particle size distribution dispersion in the spatter particle quantity characteristic parameters are extracted, the ratio of the two is calculated, and the logarithm of the ratio is taken to obtain a spatter suppression factor; The unmelting probability and the spatter suppression factor are linearly combined to obtain the melting state qualification rate.

2. The plasma spray based air conditioner rack surface treatment system of claim 1, wherein, The method for determining and analyzing the coating bonding failure risk comprises the following steps: The surface roughness parameters of the target air conditioner rack base are collected in real time, the deviation percentage of the surface roughness parameters from the optimal roughness range is calculated, and the deviation percentage is marked as a base adaptation abnormality degree; The relative humidity value and the temperature fluctuation quantity in the environmental temperature and humidity parameters are extracted, the normalized weighted product of the two is calculated, and the normalized weighted product is marked as an environmental interference coefficient; The average speed and the speed dispersion in the spraying particle velocity parameters are collected, the geometric harmonic mean of the two is calculated, and the geometric harmonic mean is marked as a particle kinetic energy characteristic value; The base adaptation abnormality degree, the environmental interference coefficient, and the particle kinetic energy characteristic value are compared with preset threshold values respectively, and a spraying abnormal signal is generated when any parameter exceeds the corresponding threshold value.

3. The plasma spray based air conditioner rack surface treatment system of claim 1, wherein, The method for quantitatively evaluating the thermal stability of the jet comprises the following steps: The light intensity distribution data of the plasma jet are collected by a spectrometer, an axial temperature gradient graph and a radial energy distribution graph are generated, The difference between the highest temperature point and the lowest temperature point in the axial temperature gradient graph is extracted, the ratio of the difference to the average temperature is calculated, and the inverse of the ratio is taken to obtain a thermal stability coefficient; The core area energy proportion and the edge diffusion angle in the radial energy distribution graph are extracted, the arithmetic mean of the two is calculated, and the arithmetic mean is marked as an energy concentration degree index; The thermal stability coefficient and the energy concentration degree index are weighted and fused to obtain the plasma uniformity index.

4. The plasma spray based air conditioner rack surface treatment system of claim 1, wherein, The synergistic analysis of the coating bonding quality comprises: The matrix adaptation abnormality data is called, and a correction weight is set, and a matrix influence compensation value is obtained through weighted processing; The plasma uniformity index, the melting state qualified rate and the numerical value of the matrix influence compensation value are normalized to generate a coating quality fusion index; A coating quality judgment threshold is set, and if the fusion index is lower than the threshold, a process adjustment signal is generated, and if it is higher than the threshold, a parameter optimization signal is generated.

5. The plasma spray based air conditioner rack surface treatment system of claim 1, wherein, The spraying optimization strategy matching comprises: If the process adjustment signal is captured, a power correction instruction is triggered, and the current parameters and gas flow ratios of the plasma generator are dynamically adjusted according to the instruction to generate plasma power adjustment parameters; If the parameter optimization signal is captured, a powder control instruction is triggered, and the vibration frequency and carrier gas pressure value of the powder feeder are optimized according to the instruction to generate powder feeding rate correction parameters.

6. The plasma spray based air conditioner rack surface treatment system of claim 1, wherein, Further comprising: A stress coupling analysis module is used to monitor the matching degree of the coating deposition stress and the thermal expansion coefficient of the matrix, extract the residual stress gradient value and the thermal mismatch coefficient, and generate a stress coupling evaluation value; The multi-source fusion judgment module further combines the stress coupling evaluation value to make a secondary correction to the coating quality fusion index.

7. The plasma spray based air conditioner rack surface treatment system of claim 6, wherein, The monitoring of the matching degree of the coating deposition stress and the thermal expansion coefficient of the matrix comprises: The X-ray diffractometer is used to collect coating residual stress distribution data, calculate the difference between the maximum stress point and the average stress, and mark it as the residual stress gradient value; The linear expansion coefficient difference of the matrix and the coating junction area is extracted, and the thermal expansion difference is calculated by combining the temperature change rate, and marked as the thermal mismatch coefficient; The residual stress gradient value and the thermal mismatch coefficient are weighted and summed to generate the stress coupling evaluation value.

8. The plasma spray based air conditioner rack surface treatment system of claim 1, wherein, Further comprising: A dynamic feedback execution module is used to adjust the motion mechanism trajectory of the processing system in real time according to the generated plasma power adjustment parameters and powder feeding rate correction parameters, and feedback the execution results to the spraying parameter acquisition module to form a closed-loop control link.

9. The plasma spray based air conditioner rack surface treatment system of claim 8, wherein, The specific execution process of the dynamic feedback execution module comprises: If the plasma power adjustment parameter involves current intensity adjustment, the density of the jet temperature monitoring point is increased synchronously; If the powder feeding rate correction parameter involves carrier gas pressure change, the sampling frequency of the particle velocity sensor is increased synchronously; The adjusted parameters are input into the spraying parameter acquisition module, and the processing flow is restarted.

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