Method and system for evaluating mechanical properties of laser cladding coating based on parameter analysis

Abstract

This invention relates to the field of laser cladding performance evaluation technology, and particularly to a method and system for evaluating the mechanical properties of laser cladding coatings based on parameter analysis. The invention analyzes the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate based on laser process parameter data and molten pool monitoring data; it analyzes the degree of thermophysical property matching between the substrate and the cladding coating based on material parameter data and laser process parameter data; it evaluates the mechanical properties of the laser cladding coating based on the results of the analysis of the solidification strengthening state of the inter-fusion zone and the analysis of the degree of thermophysical property matching; and it screens products based on the evaluation results of the mechanical properties of the laser cladding coating. This significantly improves the efficiency and coverage of performance evaluation, ensuring the overall service reliability of the coated products.

Description

Technical Field

[0001] This invention relates to the field of laser cladding performance evaluation technology, and in particular to a method and system for evaluating the mechanical properties of laser cladding coatings based on parameter analysis. Background Technology

[0002] Laser cladding technology, as a core means of enhancing the performance of key components in high-end equipment and remanufacturing damaged parts, directly determines the service safety, service life, and reliability of the repaired components through the mechanical properties of its coatings. Coatings must operate under harsh loads, wear, and corrosion environments for extended periods. Any vulnerabilities in their bonding strength, crack resistance, and load-bearing capacity could lead to sudden component failure during operation, causing equipment downtime, production interruptions, and even serious safety accidents. Traditional coating mechanical property assessment relies entirely on post-processing destructive testing, such as tensile, bending, and metallographic observation. This is not only inefficient and costly but also provides only delayed results, failing to achieve real-time feedback and comprehensive quality control during the manufacturing process. In recent years, with the development of online monitoring and data analysis technologies, research has attempted to indirectly assess coating quality by acquiring laser process parameters or molten pool images. This provides new possibilities for online prediction of coating performance and, to some extent, eliminates the reliance on single destructive testing methods.

[0003] However, when performing performance prediction and evaluation of laser cladding coatings, existing technologies often view process parameters or molten pool characteristics in isolation, failing to establish a quantitative mapping relationship between them and the microstructure of the intermetallic zone during rapid solidification, which determines the interfacial strength. This results in the prediction of coating bonding strength and toughness relying solely on the quality analysis of the finished product or the substrate itself, while ignoring the strengthening caused by the substrate and cladding powder during the rapid solidification of the intermetallic layer. Summary of the Invention

[0004] To overcome the defects and shortcomings of existing technologies, this invention provides a method and system for evaluating the mechanical properties of laser cladding coatings based on parameter analysis. By constructing a solidification strengthening analysis model for the inter-melting zone and a thermal mismatch analysis model based on physical mechanisms, the metallurgical quality of the coating interface and the thermal stress risk of the material combination are quantified respectively. The two are then weighted and integrated into a mechanical property effect, realizing online and quantitative performance evaluation of the coating and intelligent product screening.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis, comprising the following steps:

[0007] S1. Collect laser process parameter data and material parameter data during the laser cladding process, and simultaneously obtain molten pool monitoring data during the cladding process;

[0008] S2. Based on laser process parameter data and molten pool monitoring data, the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate is analyzed.

[0009] S3. Based on material parameter data and laser process parameter data, analyze the degree of matching between the thermophysical properties of the substrate and the cladding coating;

[0010] S4. Based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties, evaluate the mechanical properties of the laser cladding coating.

[0011] S5. Based on the evaluation results of the mechanical properties of the laser cladding coating, conduct product screening.

[0012] According to the above scheme, step S2 analyzes the solidification strengthening state of the intermelting zone between the cladding coating and the substrate based on laser process parameter data and molten pool monitoring data, including the following specific steps:

[0013] S21. Obtain laser process parameter data from multiple historical laser cladding processes, including average laser energy density and scanning speed, and obtain molten pool monitoring data from the corresponding historical laser cladding processes, including average molten pool cooling rate and maximum molten pool temperature gradient.

[0014] S22. Classify the multiple historical laser cladding processes according to the different combinations of substrate type and coating material type, and set up material combination analysis units; according to the different material combination analysis units, classify the laser process parameter data and molten pool monitoring data of the multiple historical laser cladding processes to obtain the laser process parameter data and molten pool monitoring data of each material combination analysis unit.

[0015] S23. Based on the laser process parameter data and molten pool monitoring data of each material combination analysis unit, construct a solidification strengthening analysis model for the intermelting zone and quantify the solidification strengthening state of the intermelting zone in each material combination analysis unit.

[0016] According to the above scheme, step S23 involves constructing a solidification strengthening analysis model for the intermelting zone and quantifying the solidification strengthening state of the intermelting zone within each material combination analysis unit. This includes the following specific steps:

[0017] S231. Obtain the microhardness of the intermetallic fusion zone corresponding to all historical laser cladding products in each material combination analysis unit through metallographic and microhardness testing; extract the maximum microhardness of the intermetallic fusion zone from the microhardness of the intermetallic fusion zone corresponding to all historical laser cladding products in each material combination analysis unit, and use it as the maximum microhardness of the intermetallic fusion zone corresponding to each material combination analysis unit; use the laser process parameter data, molten pool monitoring data, and the corresponding intermetallic fusion zone microhardness and maximum microhardness of the intermetallic fusion zone of each material combination analysis unit as a regression analysis dataset, and divide the regression analysis dataset into a regression analysis training set and a regression analysis validation set;

[0018] S232. Construct a random forest regression model. Use the average laser energy density, scanning speed, average cooling rate of the molten pool, and maximum temperature gradient of the molten pool from multiple historical laser cladding processes in each material combination analysis unit in the regression analysis training set as input features of the random forest regression model. Use the ratio of the microhardness of the intermelting zone of the corresponding multiple historical laser cladding processes in the regression analysis training set to the maximum microhardness of the intermelting zone corresponding to each material combination analysis unit as the output target of the random forest regression model. Train the random forest regression model to obtain the initial regression analysis model.

[0019] S233. The initial regression analysis model is validated using the regression analysis validation set, and the output initial regression analysis model with an accuracy greater than or equal to the preset first model is used as the solidification strengthening analysis model of the intermelting zone for each material combination analysis unit.

[0020] According to the above scheme, step S23, which involves constructing a solidification strengthening analysis model for the intermelting zone and quantifying the solidification strengthening state of the intermelting zone within each material combination analysis unit, also includes the following specific steps:

[0021] S234. The solidification strengthening analysis model of the intermelting zone of the material combination analysis unit corresponding to the combination of substrate type and coating material type in the current laser cladding process is used as the solidification strengthening analysis model of the intermelting zone in the current laser cladding process. The laser process parameter data and molten pool monitoring data in the current laser cladding process are obtained. The laser process parameter data and molten pool monitoring data in the current laser cladding process are input into the solidification strengthening analysis model of the intermelting zone in the current laser cladding process. The ratio of the microhardness of the intermelting zone in the current laser cladding process to the maximum microhardness of the intermelting zone of the corresponding material combination analysis unit is output.

[0022] S235. Using the SHAP value analysis method, the contribution of each input feature in the solidification strengthening analysis model of the intermelting zone in the current laser cladding process is quantified. The input feature with the largest contribution is regarded as the key solidification control factor. The contribution of the key solidification control factor is multiplied by the ratio of the average microhardness of the intermelting zone in the current laser cladding process to the maximum microhardness of the intermelting zone of the corresponding material combination analysis unit to obtain the solidification strengthening state of the intermelting zone in the current laser cladding process.

[0023] According to the above scheme, step S3 analyzes the degree of thermophysical property matching between the substrate and the cladding coating based on material parameter data and laser process parameter data, specifically including:

[0024] S31. Obtain material parameter data and laser process parameter data during the current laser cladding process. The material parameter data includes the thermal expansion coefficient of the substrate and the cladding coating, the elastic modulus, Poisson's ratio, and yield strength of the cladding coating. The laser process parameter data includes the average laser cladding temperature and the ambient temperature.

[0025] S32. The absolute value of the difference between the thermal expansion coefficients of the substrate and the cladding coating is taken as the equivalent thermal expansion coefficient mismatch.

[0026] S33. Calculate the equivalent thermal mismatch stress at the interface of the two materials based on the equivalent thermal expansion coefficient mismatch, the elastic modulus and Poisson's ratio of the cladding coating, the average temperature of laser cladding, and the ambient temperature.

[0027] The formula for calculating the equivalent dual-material interface thermal mismatch stress is as follows:

[0028] ;

[0029] In the formula, The value represents the equivalent thermal mismatch stress at the interface between the two materials, where Ec is the elastic modulus of the cladding coating and vc is the Poisson's ratio of the cladding coating. This is the mismatch in the equivalent thermal expansion coefficient. This represents the difference between the average laser cladding temperature and the ambient temperature.

[0030] S34. Based on the equivalent dual-material interface thermal mismatch stress and the yield strength of the cladding coating, calculate the degree of thermophysical property matching in the current laser cladding process;

[0031] The formula for calculating the degree of thermophysical performance matching is as follows:

[0032] ;

[0033] In the formula, Cmp represents the degree of thermophysical property matching in the current laser cladding process. This represents the yield strength of the cladding coating.

[0034] According to the above scheme, in step S4, the mechanical properties of the laser cladding coating are evaluated based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties. This includes the following specific steps:

[0035] S41. Obtain the solidification strengthening state analysis results and thermophysical property matching degree analysis results of the intermelting zone in the current laser cladding process;

[0036] S42. The results of the solidification strengthening state analysis and the thermophysical property matching degree analysis of the intermelting zone in the current laser cladding process are weighted and summed to obtain the mechanical property effect of the laser cladding coating in the current laser cladding process.

[0037] According to the above scheme, step S5 involves product screening based on the evaluation results of the mechanical properties of the laser cladding coating, including the following specific steps:

[0038] S51. Obtain the calculated mechanical properties of the laser cladding coating for all laser cladding processes in the current production batch;

[0039] S52. A preset mechanical performance effect threshold is set. When the mechanical performance effect of the laser cladding coating in the laser cladding process is greater than the mechanical performance effect threshold, the performance of the laser cladding coating in the corresponding laser cladding process in the current production batch is determined to be qualified. When the mechanical performance effect of the laser cladding coating in the laser cladding process is less than or equal to the mechanical performance effect threshold, the performance of the laser cladding coating in the corresponding laser cladding process in the current production batch is determined to be unqualified.

[0040] Secondly, embodiments of the present invention also provide a system for evaluating the mechanical properties of laser cladding coatings based on parameter analysis, including:

[0041] The data acquisition module is used to collect laser process parameter data and material parameter data during the laser cladding process, and at the same time acquire molten pool monitoring data during the cladding process;

[0042] The solidification detection module is used to analyze the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate based on laser process parameter data and molten pool monitoring data.

[0043] The performance matching module is used to analyze the degree of thermophysical property matching between the substrate and the cladding coating based on material parameter data and laser process parameter data;

[0044] The effect evaluation module is used to evaluate the mechanical properties of the laser cladding coating based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties.

[0045] The product screening module is used to screen products based on the evaluation results of the mechanical properties of the laser cladding coating.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] 1. This invention establishes an intelligent mapping between process parameters, molten pool dynamics and final interface microstructure by quantitatively analyzing the solidification strengthening state of the inter-melting zone during the cladding process. This enables online, non-destructive prediction of key properties such as coating bonding strength, replacing traditional, lagging, and expensive destructive testing, and significantly improving the efficiency and coverage of quality assessment.

[0048] 2. This invention achieves a quantitative and forward-looking assessment of the inherent thermal stress risk of material combinations by calculating the degree of matching between the thermophysical properties of the substrate and the coating material using a physical model, thereby avoiding the risk of failure such as coating cracking and peeling caused by improper material selection from the source.

[0049] 3. This invention achieves a comprehensive and accurate evaluation of the overall mechanical properties of the coating by scientifically weighting and integrating the analysis results of two dimensions: solidification strengthening state and thermal matching degree. This overcomes the one-sidedness of evaluation by a single index and ensures the overall service reliability of the coating product. Attached Figure Description

[0050] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0051] Figure 1 This is a schematic diagram of the overall process of the parameter analysis-based laser cladding coating mechanical property evaluation method of the present invention;

[0052] Figure 2 This is a flowchart of step S2 in the parameter analysis-based method for evaluating the mechanical properties of laser cladding coatings of the present invention.

[0053] Figure 3 This is a schematic diagram of the mechanical property evaluation system for laser cladding coatings based on parameter analysis according to the present invention.

[0054] Figure 4 This is a flowchart of step S3 in the parameter analysis-based method for evaluating the mechanical properties of laser cladding coatings of the present invention.

[0055] Figure 5 This is a schematic diagram of the laser cladding process in the parameter analysis-based laser cladding coating mechanical property evaluation method of the present invention;

[0056] Figure 6 This is a schematic diagram of the training process of the random forest regression model in the parameter analysis-based method for evaluating the mechanical properties of laser cladding coatings of the present invention. Detailed Implementation

[0057] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment provides a method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis, specifically including the following steps:

[0060] S1. Collect laser process parameter data and material parameter data during the laser cladding process, and simultaneously obtain molten pool monitoring data during the cladding process;

[0061] S2. Based on laser process parameter data and molten pool monitoring data, the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate is analyzed.

[0062] S3. Based on material parameter data and laser process parameter data, analyze the degree of matching between the thermophysical properties of the substrate and the cladding coating;

[0063] S4. Based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties, evaluate the mechanical properties of the laser cladding coating.

[0064] S5. Based on the evaluation results of the mechanical properties of the laser cladding coating, conduct product screening.

[0065] In this embodiment, as Figure 2 As shown, step S2 analyzes the solidification strengthening state of the intermelting zone between the cladding coating and the substrate based on laser process parameter data and molten pool monitoring data, including the following specific steps:

[0066] S21. Acquire laser process parameter data from multiple historical laser cladding processes, including average laser energy density and scanning speed, and acquire corresponding molten pool monitoring data from the historical laser cladding processes, including average molten pool cooling rate and maximum molten pool temperature gradient; it should be noted that the laser cladding process in this embodiment is as follows: Figure 5As shown, the laser beam is focused onto the substrate surface, while the powder feeding nozzle delivers cladding powder through a carrier gas, and a protective gas is simultaneously ejected to isolate the air. The laser heat melts the substrate surface and the powder, forming a molten pool. The laser beam moves along a preset path, and the molten pool cools and solidifies, forming a metallurgically bonded cladding layer (i.e., containing the inter-fusion zone between the coating and the substrate) on the substrate surface, thus completing the laser cladding process of this embodiment. In this embodiment, the acquisition of laser process parameter data relies on the CNC system and sensor network integrated into the laser cladding equipment. The laser power, scanning speed, and spot diameter of each historical cladding process are recorded in real time and stored in the process log database. The average laser energy density is not a directly measured value, but is calculated by dividing the laser power by the product of the scanning speed and the spot diameter. It can comprehensively reflect the energy input acting on the cladding material per unit area and is a key macroscopic process parameter affecting the formation of the molten pool, thermal cycling, and the final microstructure. Among them, the scanning speed is directly taken from the command value of the CNC system or the encoder feedback value, which determines the interaction time between the heat source and the material and has a decisive influence on the solidification and cooling conditions. The acquisition of molten pool monitoring data utilizes an online monitoring system. This embodiment employs a high-speed CMOS camera with a narrow-band filter to capture the molten pool morphology in real time, while simultaneously using an infrared thermal imager to measure the temperature field of the molten pool region. Specifically, the process for obtaining the average cooling rate of the molten pool is as follows: From the molten pool temperature-time curve, the temperature drop data within the interval from the peak molten pool temperature to the solidus temperature is extracted. The average slope of the temperature drop data within this interval is calculated through linear fitting. The maximum temperature gradient of the molten pool is obtained by calculating the maximum temperature change per unit distance along the front edge of the solid-liquid interface perpendicular to the interface direction, based on the synchronously acquired two-dimensional temperature distribution map of the molten pool, using image processing and numerical difference algorithms. In this way, this embodiment transforms the abstract solidification process in laser cladding into a quantifiable average cooling rate and maximum temperature gradient. Based on the above data acquisition process, this embodiment abandons the traditional experience-based judgment that relies solely on limited process parameters. By introducing real-time monitoring data of the molten pool, the dynamic and transient thermophysical process is quantified, providing input features that reflect the essence of the process for the subsequent establishment of a data-driven model. This enables the evaluation of the solidification state of the intermelting zone to move from black-box speculation to white-box analysis, significantly improving the objectivity and precision of the evaluation.

[0067] S22. Classify the multiple historical laser cladding processes according to the different combinations of substrate type and coating material type, and set up material combination analysis units. Based on the different material combination analysis units, classify the laser process parameter data and molten pool monitoring data from the multiple historical laser cladding processes to obtain the laser process parameter data and molten pool monitoring data for each material combination analysis unit. In this embodiment, due to the significant differences in interaction mechanisms, metallurgical compatibility, and thermophysical properties between different substrates (e.g., 45 steel, 316L stainless steel, titanium alloy TC4) and different coating materials (e.g., nickel-based alloy Inconel 718, cobalt-based alloy Stellite 6, iron-based alloy 316L), if all data are mixed together for modeling, the model will be forced to learn many contradictory or weakly correlated relationships, leading to decreased prediction accuracy and poor generalization ability. Therefore, it is necessary to first classify all historical cladding process data according to the substrate material type and the corresponding coating material type. For example, all cladding test data for "45 steel substrate - Inconel 718 coating" are grouped into one material combination analysis unit, and all data for "316L stainless steel substrate - Stellite 6 coating" are grouped into another unit. This embodiment, by setting up the material combination analysis units, acknowledges and utilizes the fundamental constraints of intrinsic material properties on the relationship with process performance, making the patterns reflected in the data within each unit more consistent and interpretable. Data classification is automatically completed in the database or data processing script. Each unit aggregates characteristic data (average laser energy density, scanning speed, average cooling rate of the molten pool, maximum temperature gradient of the molten pool) and corresponding tag data (microhardness of the intermetallic zone) for all historical processes under its name. Through this classification and grouping, this embodiment essentially establishes an independent data archive for each engineering-significant material combination, offering the following advantages:

[0068] First, this embodiment significantly reduces the complexity of modeling. The model only needs to learn the mapping relationship between specific materials and process parameters, melt pool behavior and final performance, thus avoiding the influence of interference noise between different material systems.

[0069] Second, this embodiment makes the analysis results more targeted and instructive. The process window for optimizing the "A substrate-B coating" combination may not be applicable to "C substrate-D coating". This embodiment naturally achieves this distinction through unit modeling, providing a framework for personalized process optimization.

[0070] Third, when a new material combination is introduced, its own model can be gradually built simply by creating a new analysis unit and accumulating data, thus exhibiting good scalability.

[0071] S23. Based on the laser process parameter data and molten pool monitoring data of each material combination analysis unit, a solidification strengthening analysis model for the inter-fusion zone is constructed to quantify the solidification strengthening state of the inter-fusion zone in each material combination analysis unit. In this embodiment, the inter-fusion zone serves as the metallurgical bonding link between the coating and the substrate. Its solidification structure (such as dendrite morphology, phase composition, and defect distribution) directly determines key mechanical properties such as bonding strength and crack resistance. The solidification structure is controlled by the thermal history of the molten pool (cooling rate and temperature gradient). However, direct online observation or non-destructive testing of the microstructure of the intermelting zone is extremely difficult and costly. Therefore, the core idea of ​​constructing the solidification strengthening analysis model for the intermelting zone in this embodiment is to utilize readily available online process parameters and molten pool monitoring data, and through machine learning methods, establish a high-dimensional nonlinear mapping relationship between process parameters, molten pool monitoring data, and the solidification strengthening state characterization quantity of the intermelting zone that can only be obtained through offline, destructive testing. The goal of the model construction in this embodiment is that, for any new set of process parameters and measured molten pool data under a given material combination unit, the model can output a quantified solidification strengthening state, thereby reliably characterizing the potential ability of the intermelting zone formed by this cladding in resisting deformation and bearing stress.

[0072] In this embodiment, step S23 involves constructing a solidification strengthening analysis model for the intermelting zone to quantify the solidification strengthening state of the intermelting zone within each material combination analysis unit. This includes the following specific steps:

[0073] S231. Obtain the microhardness of the intermetallic zone corresponding to all historical laser cladding products in each material combination analysis unit through metallographic and microhardness testing; extract the maximum microhardness of the intermetallic zone from the microhardness of the intermetallic zone corresponding to all historical laser cladding products in each material combination analysis unit, and use it as the maximum microhardness of the intermetallic zone corresponding to each material combination analysis unit; use the laser process parameter data, molten pool monitoring data, and the corresponding intermetallic zone microhardness and maximum intermetallic zone microhardness of each material combination analysis unit as a regression analysis dataset, and divide the regression analysis dataset into a regression analysis training set and a regression analysis validation set; the process for obtaining the microhardness of the intermetallic zone in this embodiment is as follows: for each completed historical cladding product, a metallographic sample is prepared in the cross section of the cladding layer perpendicular to the scanning direction using the wire cutting method; after the sample is inlaid, ground, and polished, it is etched with an etchant to clearly show the outline of the intermetallic zone; observe and confirm the location of the intermetallic zone under a scanning electron microscope, wherein the intermetallic zone is a narrow area between the bottom of the coating and the upper part of the substrate, with unique microstructural characteristics different from both;

[0074] Hardness testing is performed using a microhardness tester (e.g., a Vickers hardness tester). In this embodiment, the hardness test of the interfusion zone follows the following specifications: Within the interfusion zone, a series of indentations are made at fixed intervals (20 micrometers in this embodiment) along a direction perpendicular to the bonding interface, ensuring that the indentations are completely located within the interfusion zone and avoiding any potential defects; the hardness values ​​of all indentations are recorded, and their arithmetic mean is calculated as the microhardness of the interfusion zone of the finished product from this cladding process; thus reflecting the overall hardening level of the interfusion zone.

[0075] For each material combination analysis unit, all historical cladding products under it are traversed to obtain a set of inter-fusion zone microhardness; the maximum value is found from the set of inter-fusion zone microhardness and defined as the maximum inter-fusion zone microhardness of the material combination unit, which can represent the best inter-fusion zone hardening level that can be achieved under the material combination through historical process exploration.

[0076] Construct a regression analysis dataset: Each row of data corresponds to a historical cladding process, containing 4 input features (average laser energy density, scanning speed, average cooling rate of the molten pool, and maximum temperature gradient of the molten pool in this process), and 1 output target after processing: the ratio of the microhardness of the intermelting zone in this cladding process to the maximum microhardness of the intermelting zone of the material combination unit.

[0077] Based on the above, this embodiment eliminates the influence of the difference in hardness base values ​​of different material combinations on model training, allowing the model to focus more on learning the relative influence of the process on the reinforcement effect.

[0078] S232. Construct a random forest regression model. Use the average laser energy density, scanning speed, average cooling rate of the molten pool, and maximum temperature gradient of the molten pool from multiple historical laser cladding processes in each material combination analysis unit in the regression analysis training set as input features of the random forest regression model. Use the ratio of the microhardness of the intermetallic fusion zone from multiple historical laser cladding processes in the regression analysis training set to the maximum microhardness of the intermetallic fusion zone corresponding to each material combination analysis unit as the output target of the random forest regression model. Train the random forest regression model to obtain the initial regression analysis model. It should be noted that random forest is an ensemble learning algorithm that obtains the final result by constructing a large number of decision trees and performing voting or averaging. It can handle high-dimensional data without complex feature scaling, has a natural ability to capture nonlinear relationships and interactions in the input features, and can perform effective internal validation during training through out-of-bag error estimation. It is also relatively less prone to overfitting and has good model robustness. In this embodiment, as... Figure 6As shown, the model training process is as follows: For a specific material combination analysis unit, its training set data is taken; the algorithm initializes a certain number of decision trees; for the construction of each tree, a subset of training sets is generated by random sampling with replacement from the training set, so that the data used by each tree is slightly different, increasing the diversity of the model; when splitting at each node of the tree, the algorithm does not select the best splitting feature from all four input features, but first randomly selects a subset of features, and then searches for the optimal splitting point only in this subset; this process continues until the number of samples in the node is less than the preset minimum or the tree reaches the maximum depth; each tree independently learns the mapping rule from input features to output target. For a new input sample, each tree will give a prediction value, and the final prediction result of the random forest model is the average of the prediction values ​​of all trees. The model trained in this way can comprehensively consider the complex synergistic effects of energy input, thermal action time, and solidification kinetics, providing a prediction of the relative strengthening state of the intermetallic zone. Furthermore, the model's output is a continuous value between 0 and 1, intuitively reflecting the proportion of the intermetallic zone hardness obtained under the current process relative to the historical best level for this material combination. For example, a predicted value of 0.85 means that the solidification strengthening effect has reached 85% of the historical best level. This provides an extremely clear scale for process evaluation and comparison.

[0079] S233. The initial regression analysis model is validated using the regression analysis validation set, and the output initial regression analysis model with an accuracy greater than or equal to the preset first model is used as the solidification strengthening analysis model of the intermelting zone for each material combination analysis unit.

[0080] In this embodiment, step S23, which involves constructing a solidification strengthening analysis model for the intermelting zone and quantifying the solidification strengthening state of the intermelting zone within each material combination analysis unit, also includes the following specific steps:

[0081] S234. The solidification strengthening analysis model of the intermelting zone of the material combination analysis unit corresponding to the combination of substrate type and coating material type in the current laser cladding process is used as the solidification strengthening analysis model of the intermelting zone in the current laser cladding process. The laser process parameter data and molten pool monitoring data in the current laser cladding process are obtained. The laser process parameter data and molten pool monitoring data in the current laser cladding process are input into the solidification strengthening analysis model of the intermelting zone in the current laser cladding process. The ratio of the microhardness of the intermelting zone in the current laser cladding process to the maximum microhardness of the intermelting zone of the corresponding material combination analysis unit is output.

[0082] S235. Using the SHAP value analysis method, the contribution of each input feature in the solidification strengthening analysis model of the intermetallic zone in the current laser cladding process is quantified. The input feature with the largest contribution is regarded as the key solidification control factor. The contribution of the key solidification control factor is multiplied by the ratio of the average microhardness of the intermetallic zone in the current laser cladding process to the maximum microhardness of the intermetallic zone of the corresponding material combination analysis unit to obtain the solidification strengthening state of the intermetallic zone in the current laser cladding process. In this embodiment, the SHAP value is an interpretation method based on cooperative game theory, which can fairly allocate the contribution of each feature to a single predicted sample. The specific application process is as follows:

[0083] For the input features and model prediction output of the current cladding process, the SHAP interpreter is used for calculation. The SHAP algorithm calculates the role of each feature in pushing the model's baseline output to the final predicted value in the current specific prediction by simulating the "absence" and "presence" of features multiple times. This role is the SHAP value of the feature. The input feature with the largest SHAP value is considered the most significant factor affecting the current prediction result and is defined as the key solidification control factor for this cladding process. For example, if the SHAP value of the average cooling rate of the molten pool is the largest, it indicates that the average cooling rate of the molten pool is the most critical factor determining the hardening effect of the intermetallic zone. In this embodiment, the solidification strengthening state of the intermetallic zone is the contribution of the key solidification control factor multiplied by the ratio of the average microhardness of the intermetallic zone in the current laser cladding process to the maximum microhardness of the intermetallic zone in the corresponding material combination analysis unit. Through the above calculation method, this embodiment has the following advantages:

[0084] This embodiment ensures that the final solidification strengthening state of the intermetallic cladding zone not only includes the absolute level of the strengthening effect but also incorporates the influence of the key factors that led to that level. If the ratio of the average microhardness of the intermetallic cladding zone to the maximum microhardness of the intermetallic cladding zone in the corresponding material combination analysis unit is high and dominated by a strongly related physical factor (such as a high cooling rate), then the solidification strengthening state of the intermetallic cladding zone will be higher and better. If the ratio of the average microhardness of the intermetallic cladding zone to the maximum microhardness of the intermetallic cladding zone in the corresponding material combination analysis unit is high but mainly due to secondary factors, the solidification strengthening state of the intermetallic cladding zone will be reduced, which is more in line with engineering intuition.

[0085] Second, this embodiment provides interpretable process guidance. By identifying key solidification control factors, those skilled in the art can immediately determine which process or molten pool parameter should be focused on and adjusted to improve or maintain the current state. For example, if the cooling rate is consistently the key factor, it indicates that, for this material combination, actively controlling the cooling rate through process adjustments is the most effective way to optimize the performance of the intermelting zone.

[0086] In this embodiment, as Figure 4 As shown, step S3 analyzes the degree of thermophysical property matching between the substrate and the cladding coating based on material parameter data and laser process parameter data, specifically including:

[0087] S31. Obtain material parameter data and laser process parameter data during the current laser cladding process. The material parameter data includes the thermal expansion coefficient of the substrate and the cladding coating, the elastic modulus of the cladding coating, Poisson's ratio, and yield strength. The laser process parameter data includes the average laser cladding temperature and the ambient temperature. In this embodiment, the acquisition of material parameter data needs to be based on standard testing methods or recognized material databases. The thermal expansion coefficients of the substrate and the cladding coating are obtained by testing with a thermal expansion meter. The elastic modulus and Poisson's ratio of the cladding coating are important parameters characterizing its stiffness. In this embodiment, the elastic modulus and Poisson's ratio are obtained by preparing a bulk material with the same composition as the coating and testing it with a dynamic mechanical analyzer. The yield strength of the cladding coating is obtained by performing a micro-column compression test on the cross-section of the cladding coating. In this embodiment, the average laser cladding temperature is not a single measurement point temperature, but a characteristic temperature used to represent the driving temperature difference that generates thermal stress during the cooling process from the molten pool to room temperature. It is obtained by finite element simulation calculation of the temperature at which the coating / substrate bonding area cools to the point where macroscopic stress begins to accumulate significantly after the cladding process is completed (exemplarily, in this embodiment, it is taken as the solidus temperature of the coating). The ambient temperature is the initial temperature of the substrate before the start of cladding, which is read by a workshop ambient temperature sensor.

[0088] S32. In this embodiment, the coefficient of thermal expansion describes the trend of material dimensional change when temperature changes. In laser cladding, the coating and the substrate are metallurgically bonded together at high temperature, and then cooled from the high cladding process temperature to room temperature. If the coefficients of thermal expansion of the two components are different, they tend to shrink by different amounts. However, due to the strong bond, this free shrinkage is mutually constrained, thereby generating internal stress near the bonding interface. Therefore, in this embodiment, the absolute value of the difference in the coefficients of thermal expansion between the substrate and the cladding coating is used as the equivalent coefficient of thermal expansion mismatch. The larger the equivalent coefficient of thermal expansion mismatch, the greater the difference in shrinkage between the coating and the substrate during cooling, which may lead to greater internal stress and poorer matching. The smaller the equivalent coefficient of thermal expansion mismatch, the more coordinated the thermal expansion behavior of the two components, and the better the matching foundation. Although the difference in the coefficients of thermal expansion between the substrate and the cladding coating can lead to different types of stress, such as tensile stress or compressive stress, in the matching degree assessment of this embodiment, the magnitude of the difference is the primary risk indicator. Therefore, this embodiment uses the absolute value of the difference as the equivalent coefficient of thermal expansion mismatch, which allows the analysis to grasp the main contradiction.

[0089] S33. Calculate the equivalent thermal mismatch stress at the interface of the two materials based on the equivalent thermal expansion coefficient mismatch, the elastic modulus and Poisson's ratio of the cladding coating, the average temperature of laser cladding, and the ambient temperature.

[0090] The formula for calculating the equivalent dual-material interface thermal mismatch stress is as follows:

[0091] ;

[0092] In the formula, The value represents the equivalent thermal mismatch stress at the interface between the two materials, where Ec is the elastic modulus of the cladding coating and vc is the Poisson's ratio of the cladding coating. This is the mismatch in the equivalent thermal expansion coefficient. This represents the difference between the average laser cladding temperature and the ambient temperature.

[0093] It should be noted that this embodiment aims to use a simplified mechanical model to calculate the internal stress level caused by thermal expansion mismatch. The calculation formula for the equivalent dual-material interface thermal mismatch stress is used to calculate the stress of an infinitely large double-layer plate under uniform temperature changes. Below, the average in-plane stress generated in one of the layers due to the mismatch in the equivalent thermal expansion coefficient. Its derivation is based on the fundamental principles of elasticity, assuming the material is isotropic and linearly elastic, and that the interface bonding is perfect and slip-free; in this embodiment... The thermodynamic potential driving thermal stress is the coating's elastic modulus, which reflects the material's ability to resist elastic deformation; the larger the value, the greater the stress under the same strain. Poisson's ratio reflects lateral deformation constraint and appears in the denominator to correct for plane stress state. By directly substituting these values ​​into the formula for calculating the equivalent bimaterial interface thermal mismatch stress and performing arithmetic operations, the equivalent bimaterial interface thermal mismatch stress can be obtained. .

[0094] S34. In this embodiment, the thermophysical performance matching degree of the current laser cladding process is calculated based on the equivalent dual-material interface thermal mismatch stress and the yield strength of the cladding coating. The calculated theoretical stress is then compared with the material's own failure resistance, and a thermophysical performance matching degree Cmp is output, defined between 0 and 1, with a larger value indicating a better matching degree.

[0095] The formula for calculating the degree of thermophysical performance matching is as follows:

[0096] ;

[0097] In the formula, Cmp represents the degree of thermophysical property matching in the current laser cladding process. This represents the yield strength of the cladding coating.

[0098] In this embodiment, step S4 evaluates the mechanical properties of the laser cladding coating based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties. This includes the following specific steps:

[0099] S41. Obtain the solidification strengthening state analysis results and thermophysical property matching degree analysis results of the intermelting zone in the current laser cladding process;

[0100] S42. The results of the solidification strengthening state analysis and the thermophysical property matching degree analysis of the intermelting zone in the current laser cladding process are weighted and summed to obtain the mechanical property effect of the laser cladding coating in the current laser cladding process.

[0101] In this embodiment, step S5 involves product screening based on the evaluation results of the mechanical properties of the laser cladding coating, including the following specific steps:

[0102] S51. Obtain the calculated mechanical properties of the laser cladding coating for all laser cladding processes in the current production batch;

[0103] S52. A preset mechanical performance effect threshold is set. When the mechanical performance effect of the laser cladding coating in the laser cladding process is greater than the mechanical performance effect threshold, the performance of the laser cladding coating in the corresponding laser cladding process in the current production batch is determined to be qualified. When the mechanical performance effect of the laser cladding coating in the laser cladding process is less than or equal to the mechanical performance effect threshold, the performance of the laser cladding coating in the corresponding laser cladding process in the current production batch is determined to be unqualified. It should be noted that the weight and threshold values ​​in this embodiment are determined as follows: 3000 sets of laser process parameter data, material parameter data, and molten pool monitoring data are obtained from the historical laser cladding database to calculate the mechanical performance effect of the laser cladding coating. At the same time, the judgment results of whether the mechanical performance test of the laser cladding coating in the 3000 sets of laser cladding processes is qualified according to the national standard are obtained. The mechanical performance effects of the laser cladding coating in the 3000 sets of laser cladding processes and the corresponding judgment results of whether the mechanical performance test of the laser cladding coating is qualified according to the national standard are imported into the fitting software for fitting, and the corresponding weight and threshold values ​​that meet the highest coefficient of determination are output.

[0104] Example 2

[0105] like Figure 3 As shown, this embodiment provides a system for evaluating the mechanical properties of laser cladding coatings based on parameter analysis, including:

[0106] The data acquisition module is used to collect laser process parameter data and material parameter data during the laser cladding process, and at the same time acquire molten pool monitoring data during the cladding process;

[0107] The solidification detection module is used to analyze the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate based on laser process parameter data and molten pool monitoring data.

[0108] The performance matching module is used to analyze the degree of thermophysical property matching between the substrate and the cladding coating based on material parameter data and laser process parameter data;

[0109] The effect evaluation module is used to evaluate the mechanical properties of the laser cladding coating based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties.

[0110] The product screening module is used to screen products based on the evaluation results of the mechanical properties of the laser cladding coating.

[0111] The steps for each parameter and each unit module to achieve the corresponding function in the parameter-based laser cladding coating mechanical property evaluation system of the present invention can be referred to the parameters and steps in the embodiments of the parameter-based laser cladding coating mechanical property evaluation method above, and will not be repeated here.

[0112] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for IoT devices and media are relatively simple in description because they are fundamentally similar to the method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0113] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0118] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0119] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0120] It should also be noted that 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. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0121] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis, characterized in that, Includes the following steps: S1. Collect laser process parameter data and material parameter data during the laser cladding process, and simultaneously obtain molten pool monitoring data during the cladding process; S2. Based on laser process parameter data and molten pool monitoring data, the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate is analyzed. S3. Based on material parameter data and laser process parameter data, analyze the degree of matching between the thermophysical properties of the substrate and the cladding coating; S4. Based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties, evaluate the mechanical properties of the laser cladding coating. S5. Based on the evaluation results of the mechanical properties of the laser cladding coating, conduct product screening; In step S2, based on laser process parameter data and molten pool monitoring data, the solidification strengthening state of the intermelting zone between the cladding coating and the substrate is analyzed, including the following specific steps: S21. Obtain laser process parameter data from multiple historical laser cladding processes, including average laser energy density and scanning speed, and obtain molten pool monitoring data from the corresponding historical laser cladding processes, including average molten pool cooling rate and maximum molten pool temperature gradient. S22. Classify the multiple historical laser cladding processes according to the different combinations of substrate type and coating material type, and set up material combination analysis units; according to the different material combination analysis units, classify the laser process parameter data and molten pool monitoring data of the multiple historical laser cladding processes to obtain the laser process parameter data and molten pool monitoring data of each material combination analysis unit. S23. Based on the laser process parameter data and molten pool monitoring data of each material combination analysis unit, construct a solidification strengthening analysis model for the intermelting zone and quantify the solidification strengthening state of the intermelting zone in each material combination analysis unit. Step S3, based on material parameter data and laser process parameter data, analyzes the degree of thermophysical property matching between the substrate and the cladding coating, specifically including: S31. Obtain material parameter data and laser process parameter data during the current laser cladding process. The material parameter data includes the thermal expansion coefficient of the substrate and the cladding coating, the elastic modulus, Poisson's ratio, and yield strength of the cladding coating. The laser process parameter data includes the average laser cladding temperature and the ambient temperature. S32. The absolute value of the difference between the thermal expansion coefficients of the substrate and the cladding coating is taken as the equivalent thermal expansion coefficient mismatch. S33. Calculate the equivalent thermal mismatch stress at the interface of the two materials based on the equivalent thermal expansion coefficient mismatch, the elastic modulus and Poisson's ratio of the cladding coating, the average temperature of laser cladding, and the ambient temperature. S34. Based on the equivalent dual-material interface thermal mismatch stress and the yield strength of the cladding coating, calculate the degree of thermophysical performance matching of the current laser cladding process.

2. The method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis according to claim 1, characterized in that, Step S23 involves constructing a solidification strengthening analysis model for the intermelting zone and quantifying the solidification strengthening state of the intermelting zone within each material combination analysis unit. This includes the following specific steps: S231. Obtain the microhardness of the intermetallic fusion zone corresponding to all historical laser cladding products in each material combination analysis unit through metallographic and microhardness testing; extract the maximum microhardness of the intermetallic fusion zone from the microhardness of the intermetallic fusion zone corresponding to all historical laser cladding products in each material combination analysis unit, and use it as the maximum microhardness of the intermetallic fusion zone corresponding to each material combination analysis unit; use the laser process parameter data, molten pool monitoring data, and the corresponding intermetallic fusion zone microhardness and maximum microhardness of the intermetallic fusion zone of each material combination analysis unit as a regression analysis dataset, and divide the regression analysis dataset into a regression analysis training set and a regression analysis validation set; S232. Construct a random forest regression model, and use the average laser energy density, scanning speed, average cooling rate of the molten pool, and maximum temperature gradient of the molten pool from multiple historical laser cladding processes in each material combination analysis unit in the regression analysis training set as input features of the random forest regression model. The ratio of the microhardness of the intermelting zone of multiple historical laser cladding processes in the regression analysis training set to the maximum microhardness of the intermelting zone of each material combination analysis unit is used as the output target of the random forest regression model. The random forest regression model is then trained to obtain the initial regression analysis model. S233. The initial regression analysis model is validated using the regression analysis validation set, and the output initial regression analysis model with an accuracy greater than or equal to the preset first model is used as the solidification strengthening analysis model of the intermelting zone for each material combination analysis unit.

3. The method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis according to claim 2, characterized in that, Step S23, which involves constructing a solidification strengthening analysis model for the intermelting zone and quantifying the solidification strengthening state of the intermelting zone within each material combination analysis unit, also includes the following specific steps: S234. The solidification strengthening analysis model of the intermelting zone of the material combination analysis unit corresponding to the combination of substrate type and coating material type in the current laser cladding process is used as the solidification strengthening analysis model of the intermelting zone in the current laser cladding process. The laser process parameter data and molten pool monitoring data in the current laser cladding process are obtained. The laser process parameter data and molten pool monitoring data in the current laser cladding process are input into the solidification strengthening analysis model of the intermelting zone in the current laser cladding process. The ratio of the microhardness of the intermelting zone in the current laser cladding process to the maximum microhardness of the intermelting zone of the corresponding material combination analysis unit is output. S235. Using the SHAP value analysis method, the contribution of each input feature in the solidification strengthening analysis model of the intermelting zone in the current laser cladding process is quantified. The input feature with the largest contribution is regarded as the key solidification control factor. The contribution of the key solidification control factor is multiplied by the ratio of the average microhardness of the intermelting zone in the current laser cladding process to the maximum microhardness of the intermelting zone of the corresponding material combination analysis unit to obtain the solidification strengthening state of the intermelting zone in the current laser cladding process.

4. The method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis according to claim 3, characterized in that, In step S4, the mechanical properties of the laser cladding coating are evaluated based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties. This includes the following specific steps: S41. Obtain the solidification strengthening state analysis results and thermophysical property matching degree analysis results of the intermelting zone in the current laser cladding process; S42. The results of the solidification strengthening state analysis and the thermophysical property matching degree analysis of the intermelting zone in the current laser cladding process are weighted and summed to obtain the mechanical property effect of the laser cladding coating in the current laser cladding process.

5. The method for evaluating the mechanical properties of laser cladding coatings based on parameter analysis according to claim 4, characterized in that, Step S5 involves product screening based on the evaluation results of the mechanical properties of the laser cladding coating, including the following specific steps: S51. Obtain the calculated mechanical properties of the laser cladding coating for all laser cladding processes in the current production batch; S52. A preset mechanical performance effect threshold is set. When the mechanical performance effect of the laser cladding coating in the laser cladding process is greater than the mechanical performance effect threshold, the performance of the laser cladding coating in the corresponding laser cladding process in the current production batch is determined to be qualified. When the mechanical performance effect of the laser cladding coating in the laser cladding process is less than or equal to the mechanical performance effect threshold, the performance of the laser cladding coating in the corresponding laser cladding process in the current production batch is determined to be unqualified.

6. A system for evaluating the mechanical properties of laser cladding coatings based on parameter analysis, implemented according to any one of claims 1-5, characterized in that, The system includes: The data acquisition module is used to collect laser process parameter data and material parameter data during the laser cladding process, and at the same time acquire molten pool monitoring data during the cladding process; The solidification detection module is used to analyze the solidification strengthening state of the inter-fusion zone between the cladding coating and the substrate based on laser process parameter data and molten pool monitoring data. The performance matching module is used to analyze the degree of thermophysical property matching between the substrate and the cladding coating based on material parameter data and laser process parameter data; The effect evaluation module is used to evaluate the mechanical properties of the laser cladding coating based on the analysis results of the solidification strengthening state of the intermelting zone and the analysis results of the degree of matching of thermophysical properties. The product screening module is used to screen products based on the evaluation results of the mechanical properties of the laser cladding coating.

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