Method and System for Detecting Thermal Defects of Workpieces Based on Big Data Processing

Abstract

The present invention discloses a workpiece thermal defect detection method and system based on big data processing, which relates to the technical field of mechanical workpiece detection. The method includes obtaining a workpiece surface condition feature data set and comparing to obtain a first deviation value for workpiece thermal defect detection; obtaining a workpiece material feature data set and comparing to obtain a second deviation value for workpiece thermal defect detection; obtaining a workpiece outer surface thermal defect detection data set and comprehensively analyzing to obtain a workpiece outer surface thermal defect detection feature value; obtaining a workpiece internal thermal defect detection data set and comprehensively analyzing to obtain a workpiece internal thermal defect detection feature value. The present invention solves the problems of errors and inconsistencies in detection results, complex and time-consuming data processing and interpretation, and poor comparability and consistency of detection results, and can perform comprehensive quality assessment, ensure accurate judgment of workpiece quality, take timely measures to prevent problems from occurring, flexibly adjust detection strategies and standards, reduce resource waste, and optimize the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical workpiece detection, and specifically to a workpiece thermal defect detection method and system based on big data processing. Background Art

[0002] Thermal defects refer to a class of defects or problems that occur in mechanical parts or workpieces during the processes of machining, use, or service due to the action or influence of heat. These defects usually involve changes in the structure, properties, or shape of the material, and typically include problems such as cracks, fatigue, and deformation. These thermal defects can have a serious impact on the performance, lifespan, and safety of mechanical parts. Therefore, during the manufacturing and use processes, it is necessary to timely detect and repair these thermal defects through appropriate detection techniques and methods to ensure the quality and reliability of the products. The thermal defect detection of mechanical workpieces refers to the process of detecting and evaluating the possible thermal defects that may occur in mechanical components during the machining or use process through different methods and techniques. It is an important step to ensure product quality, safety, and reliability, reduce manufacturing and maintenance costs, and extend the service life of parts.

[0003] Currently, there are still some deficiencies in the research on the thermal defect detection of mechanical workpieces. Specifically, traditional mechanical workpiece thermal defect detection methods can usually only detect surface defects of workpieces, have weak recognition ability for internal defects, and the accuracy and stability of detection equipment are affected by various factors, such as environmental changes, temperature, and workpiece materials, resulting in possible errors and inconsistencies in detection results. Dependence on manual analysis makes data processing and interpretation complex and time-consuming, increasing the operation difficulty and error risk. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a workpiece thermal defect detection method and system based on big data processing, which solves the problems that traditional mechanical workpiece thermal defect detection methods have weak recognition ability for internal defects, possible errors and inconsistencies in detection results, complex and time-consuming data processing and interpretation, increased operation difficulty and error risk, and poor comparability and consistency of detection results.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A workpiece thermal defect detection method based on big data processing, comprising the following steps: obtaining a workpiece surface condition feature dataset, and based on the obtained workpiece surface condition feature dataset, comparing to obtain a first deviation value for workpiece thermal defect detection; obtaining a workpiece material feature dataset, and based on the obtained workpiece material feature dataset, comparing to obtain a second deviation value for workpiece thermal defect detection; performing an external surface thermal defect detection on the workpiece, obtaining an external surface thermal defect detection dataset of the workpiece, and comprehensively analyzing the external surface thermal defect detection dataset of the workpiece to obtain an external surface thermal defect detection feature value of the workpiece; performing an internal thermal defect detection on the workpiece, obtaining an internal thermal defect detection dataset of the workpiece, and comprehensively analyzing the internal thermal defect detection dataset of the workpiece to obtain an internal thermal defect detection feature value of the workpiece; comprehensively analyzing the external surface thermal defect detection feature value, the internal thermal defect detection feature value, the first deviation value for workpiece thermal defect detection, and the second deviation value for workpiece thermal defect detection to obtain a workpiece thermal defect detection data evaluation value; judging whether the thermal defect of the workpiece is qualified based on the workpiece thermal defect detection data evaluation value, and marking the qualified workpiece.

[0006] Further, the workpiece surface condition feature dataset specifically includes workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, and workpiece surface contamination area.

[0007] Further, for the step of comparing to obtain the first deviation value for workpiece thermal defect detection based on the obtained workpiece surface condition feature dataset, the specific analysis process is as follows: based on the obtained workpiece surface condition feature dataset, comprehensively analyzing to obtain a workpiece surface condition feature value, and using the workpiece surface condition feature value as the analysis basis for comparing to obtain the first deviation value for workpiece thermal defect detection; comparing the workpiece surface condition feature value with the first deviation value for workpiece thermal defect detection corresponding to each workpiece surface condition feature value stored in the database to obtain the first deviation value for workpiece thermal defect detection corresponding to this workpiece surface condition feature value.

[0008] Further, the workpiece material feature dataset specifically includes workpiece density, workpiece melting point, workpiece thermal conductivity, and workpiece thermal expansion coefficient.

[0009] Further, for the step of comparing to obtain the second deviation value for workpiece thermal defect detection based on the obtained workpiece material feature dataset, the specific analysis process is as follows: based on the obtained workpiece material feature dataset, comprehensively analyzing to obtain a workpiece material feature value, and using the workpiece material feature value as the analysis basis for comparing to obtain the second deviation value for workpiece thermal defect detection; comparing the workpiece material feature value with the second deviation value for workpiece thermal defect detection corresponding to each workpiece material feature value stored in the database to obtain the second deviation value for workpiece thermal defect detection corresponding to this workpiece material feature value.

[0010] Further, the dataset for detecting thermal defects on the outer surface of the workpiece specifically includes the average temperature of the workpiece surface, the number of cracks on the workpiece surface, and the thickness of the oxide layer on the workpiece surface; the characteristic value for detecting thermal defects on the outer surface of the workpiece is calculated by the following formula:

[0011] ;

[0012] In the formula: is the average temperature of the workpiece surface, is the reference value of the average temperature of the workpiece surface stored in the database, is the number of cracks on the workpiece surface, is the thickness of the oxide layer on the workpiece surface, is the weight factor of the average temperature of the workpiece surface set in the database, is the weight factor of the number of cracks on the workpiece surface set in the database, is the weight factor of the thickness of the oxide layer on the workpiece surface set in the database, is the characteristic value for detecting thermal defects on the outer surface of the workpiece.

[0013] Further, the dataset for detecting thermal defects inside the workpiece specifically includes the pore density inside the workpiece, the number of defects inside the workpiece, and the residual stress inside the workpiece.

[0014] Further, the evaluation value of the workpiece thermal defect detection data is calculated by the following formula:

[0015] ;

[0016] In the formula: is the characteristic value for detecting thermal defects on the outer surface of the workpiece, is the characteristic value for detecting thermal defects inside the workpiece, is the weight factor of the characteristic value of the workpiece surface condition set in the database, is the weight factor of the characteristic value of the workpiece material set in the database, is the evaluation value of the workpiece thermal defect detection data, is the first deviation value of the workpiece thermal defect detection, is the second deviation value of the workpiece thermal defect detection, is the natural constant.

[0017] Further, based on the evaluation value of the workpiece thermal defect detection data, it is determined whether the thermal defect of the workpiece is qualified, and the qualified workpieces are marked. The specific analysis process is as follows: The evaluation value of the workpiece thermal defect detection data is compared with the qualified definition value of the workpiece thermal defect detection stored in the database; if the evaluation value of the workpiece thermal defect detection data is higher than the qualified definition value of the workpiece thermal defect detection, the thermal defect detection of the workpiece is unqualified; if the evaluation value of the workpiece thermal defect detection data is lower than or equal to the qualified definition value of the workpiece thermal defect detection, the thermal defect detection of the workpiece is qualified, and the workpieces with qualified thermal defect detection are marked with qualified thermal defects.

[0018] A workpiece thermal defect detection system based on big data processing includes a workpiece surface condition feature data acquisition module, a workpiece material feature data acquisition module, a workpiece outer surface thermal defect data acquisition module, a workpiece internal thermal defect data acquisition module, a workpiece thermal defect detection data evaluation value acquisition module, and a workpiece thermal defect judgment module, where: The workpiece surface condition feature data acquisition module is used to acquire the workpiece surface condition feature data set, and based on the acquired workpiece surface condition feature data set, the first deviation value of the workpiece thermal defect detection is obtained by comparison; The workpiece material feature data acquisition module is used to acquire the workpiece material feature data set, and based on the acquired workpiece material feature data set, the second deviation value of the workpiece thermal defect detection is obtained by comparison; The workpiece outer surface thermal defect data acquisition module is used to perform outer surface thermal defect detection on the workpiece, acquire the workpiece outer surface thermal defect detection data set, and perform comprehensive analysis on the workpiece outer surface thermal defect detection data set to obtain the workpiece outer surface thermal defect detection feature value; The workpiece internal thermal defect data acquisition module is used to perform internal thermal defect detection on the workpiece, acquire the workpiece internal thermal defect detection data set, and perform comprehensive analysis on the workpiece internal thermal defect detection data set to obtain the workpiece internal thermal defect detection feature value; The workpiece thermal defect detection data evaluation value acquisition module is used to perform comprehensive analysis on the workpiece outer surface thermal defect detection feature value, the workpiece internal thermal defect detection feature value, the first deviation value of the workpiece thermal defect detection, and the second deviation value of the workpiece thermal defect detection to obtain the workpiece thermal defect detection data evaluation value; The workpiece thermal defect judgment module is used to judge whether the thermal defect of the workpiece is qualified based on the workpiece thermal defect detection data evaluation value, and mark the qualified workpieces.

[0019] The present invention has the following beneficial effects:

[0020] (1) The workpiece thermal defect detection method and system based on big data processing integrate the data of external surface and internal thermal defect detection characteristic values, and the first and second deviation values to conduct a comprehensive quality assessment, ensuring accurate judgment of the workpiece quality. It can identify potential quality risks at an early stage, especially before the product enters the downstream process or the market, and take timely measures to prevent problems. It can flexibly adjust the detection strategies and standards according to different types of workpieces or specific requirements of different customers, reduce resource waste caused by producing unqualified products, optimize the production process, promote environmental protection and sustainable development, improve the safety of products, and avoid safety accidents caused by product defects.

[0021] (2) The workpiece thermal defect detection method and system based on big data processing integrate multiple key factors (temperature, crack, oxide layer) affecting workpiece thermal defects into one characteristic value, providing a comprehensive evaluation index. This can avoid the deviation that may be caused by a single parameter, making the evaluation of workpiece thermal defects more comprehensive and accurate. This characteristic value can effectively reflect the condition of workpiece surface thermal defects, including the influence of cracks and oxide layers, as well as potential problems brought by temperature changes, helping to detect and diagnose thermal defects in a timely manner and prevent the expansion of defects or the occurrence of more serious problems.

[0022] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flowchart of the workpiece thermal defect detection method based on big data processing of the present invention;

[0024] Figure 2 is a schematic diagram of the module connection of the workpiece thermal defect detection system based on big data processing of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] The workpiece thermal defect detection method and system based on big data processing in the embodiments of the present application solve the problems that the traditional mechanical workpiece thermal defect detection method has weak recognition ability for internal defects, the detection results may have errors and inconsistencies, the data processing and interpretation are complex and time-consuming, increasing the operation difficulty and error risk, and the comparability and consistency of the detection results are poor.

[0026] Please refer to Figure 1 , the embodiments of the present invention provide a technical solution: a workpiece thermal defect detection method based on big data processing, including: obtaining a workpiece surface condition feature data set, and based on the obtained workpiece surface condition feature data set, comparing to obtain a first deviation value for workpiece thermal defect detection.

[0027] Specifically, the workpiece surface condition feature dataset specifically includes workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, and workpiece surface contamination area.

[0028] In this implementation scheme, the workpiece surface roughness is the microscopic unevenness of the workpiece surface. Usually, through an optical interference microscope, using the principle of light interference, the fine undulations of the workpiece surface are measured non - contact. It is a key factor affecting friction, wear, lubrication, and contact performance. By controlling the roughness of the workpiece surface, the service life of the workpiece can be extended and the maintenance cost can be reduced. The workpiece surface reflectivity represents the ability of the workpiece surface to reflect light and is an important indicator for evaluating the surface finish and cleanliness of the workpiece, which affects the performance of optical components. Usually, the reflectivity of light with different wavelengths is measured by a spectral reflectometer, which is often used to evaluate the optical properties of materials. By controlling and optimizing the optical properties of optical devices, high - quality imaging or optical performance can be ensured. The workpiece surface coating thickness determines the protective performance of the coating, such as anti - corrosion, anti - wear, insulation, etc., and affects the mechanical properties and durability of the coating. Usually, it is detected by an X - ray fluorescence thickness gauge. By detecting X - ray fluorescence, the coating thickness can be accurately measured, which is applicable to a variety of coating materials, ensuring that the coating has sufficient thickness to provide protection while avoiding material waste. By controlling the coating thickness, the performance and service life of the product can be improved. The workpiece surface contamination area affects the cleanliness of the workpiece and the effect of surface treatment. Contaminants may affect the quality of subsequent processing such as painting and electroplating. Usually, it is inspected by a microscope, and an optical microscope or an electron microscope is used to observe and measure the contamination area to ensure that the surface cleanliness meets the processing or use requirements and to avoid the influence of contaminants on the effects of processes such as coating and bonding.

[0029] In this implementation, during the thermal defect detection of the workpiece, a high-roughness surface can cause the scattering of infrared radiation, resulting in uneven signals received by the thermal imager or infrared thermal imager, increasing the background noise, and may also cause signal instability of the detection equipment. High roughness will affect the coupling and propagation of ultrasonic waves, leading to inaccurate data. The uneven surface may cause poor contact of the temperature sensor, resulting in errors. In high-precision temperature measurement, it may lead to misjudgment of local overheating or uneven cooling. Understanding the surface roughness of the workpiece helps to correct the infrared thermal imaging or ultrasonic signal scattering caused by the rough surface, reducing measurement errors. Based on the surface roughness data, the parameters of the detection equipment (such as probe contact pressure, scanning speed, etc.) can be adjusted to improve signal stability and detection accuracy, clarify the roughness characteristics, and avoid misinterpreting temperature changes caused by the rough surface as internal defects. A high-reflectivity surface may reflect a large amount of infrared radiation, causing the infrared thermal imager to be unable to accurately measure the surface temperature, which may mask potential thermal defects, especially when detecting subtle temperature changes. Areas with different surface reflectivities may lead to inconsistent temperature measurements, thus affecting the positioning and identification of thermal defects. In laser detection, high reflectivity may cause optical interference, making it difficult for the data acquisition system to capture clear signals. Understanding the reflectivity helps to correct the measurement results of the thermal imager, ensure accurate temperature readings, reduce the influence of reflected light on temperature measurement, and the reflectivity data helps to select appropriate optical detection equipment and wavelength range, reducing optical interference and improving signal acquisition quality. The thickness of the workpiece surface coating affects heat conduction. A thick coating may act as a heat insulator, making the temperature measurement of the workpiece surface not reflect the true internal temperature distribution, which may mask internal thermal defects or cause misjudgment. In the case of multi-layer coatings, the difference in thermal conductivity of different layers may lead to complex temperature gradients, making thermal imaging analysis more complex, and different coating materials have different infrared emissivities, affecting the accuracy of infrared thermal imaging. Understanding the coating thickness helps to perform data correction during thermal defect detection, ensuring the consistency of detection results. By controlling the coating thickness, ensure that the coating has sufficient anti-corrosion, anti-wear and other protection properties. Understanding the coating thickness helps to select appropriate detection methods to avoid the coating shielding the signals of internal defects. The contaminated area of the workpiece surface directly reflects the cleanliness of the workpiece surface and has an important impact on the quality of subsequent processes such as painting and electroplating. Contaminants such as grease and dust may absorb or scatter infrared radiation, distorting the thermal imaging signal and making it unable to accurately reflect the actual location and size of thermal defects. The presence of contaminants may cause local temperature anomalies, resulting in poor contact of the temperature sensor or temperature measurement errors, especially in high-temperature or low-temperature applications. The thermal characteristics of contaminants may significantly affect the measurement results. Large-area contamination may reduce the sensitivity of the detection equipment, making it difficult to detect small thermal defects. By removing surface contaminants, the absorption and scattering of contaminants on infrared thermal imaging or ultrasonic signals can be reduced, enhancing the clarity and accuracy of detection signals, and clarifying the contaminated area data.It helps to distinguish between true material defects and artifacts caused by contaminants, reducing false alarms. Contamination area data helps to assess surface cleanliness, improve the quality and uniformity of surface treatments (such as coating and electroplating), and ensure consistency of surface properties.

[0030] Specifically, based on the acquired workpiece surface condition characteristic data set, a first deviation value for workpiece thermal defect detection is obtained by comparison. The specific analysis process is: based on the acquired workpiece surface condition characteristic data set, a comprehensive analysis is performed to obtain a workpiece surface condition characteristic value, and the workpiece surface condition characteristic value is used as an analysis basis for obtaining the first deviation value for workpiece thermal defect detection by comparison; the workpiece surface condition characteristic value is compared with the first deviation value for workpiece thermal defect detection corresponding to each workpiece surface condition characteristic value stored in the database to obtain the first deviation value for workpiece thermal defect detection corresponding to the workpiece surface condition characteristic value.

[0031] In this embodiment, by obtaining parameters such as the workpiece surface roughness, surface reflectivity, surface coating thickness and surface contamination area, these data are comprehensively analyzed to calculate a workpiece surface condition characteristic value, and the calculated workpiece surface condition characteristic value is compared with the historical data stored in the database to obtain the current workpiece surface condition characteristic value minus the absolute value of the historical workpiece surface condition characteristic value stored in the database. The first deviation value of the workpiece thermal defect detection corresponding to the historical workpiece surface condition characteristic value stored in the database corresponding to the value with the smallest absolute value is the first deviation value of the workpiece thermal defect detection corresponding to the current workpiece surface condition characteristic value. The first deviation value reflects the impact of the workpiece surface characteristics on the detection process. By introducing this deviation value, the measurement error caused by these surface characteristics can be corrected to ensure the accuracy of the detection results. In the weighted and accumulated process, the first deviation value helps to compensate for the errors caused by changes in surface characteristics and avoid the accumulation of errors, thereby improving the accuracy of the subsequent overall evaluation value.

[0032] What needs to be explained is that the characteristic value of the workpiece surface condition is calculated as follows:

[0033] ;

[0034] Where: is the surface roughness of the workpiece, is the reflectivity of the workpiece surface, is the coating thickness on the workpiece surface, is the contaminated area of the workpiece surface, is the weight factor of the workpiece surface roughness set in the database, is the weight factor of the workpiece surface reflectivity set in the database, is the weight factor of the workpiece surface coating thickness set in the database, is the weight factor of the workpiece surface contamination area set in the database, It is the characteristic value of the workpiece surface condition.

[0035] It should be noted that: The formula integrates multiple surface feature parameters into one characteristic value, providing a comprehensive evaluation index, avoiding biases that may be caused by a single parameter, and reflecting the workpiece surface quality more comprehensively. Through the characteristic value, a unified evaluation standard can be provided for different workpiece surface characteristics, facilitating quality control and comparison. Different workpieces or application scenarios may have higher requirements for certain surface features. By adjusting the weight factors, the focus can be placed on the features that have the greatest impact on the workpiece quality, optimizing the quality control measures. Mathematical operations such as logarithms and square roots are used in the formula. These processes can effectively handle the non-linear relationships between different surface feature parameters, capturing the complex interactions between workpiece surface features more precisely. Through the use of logarithms and square roots, the data range can be compressed, reducing the influence of extreme values on the characteristic value, thereby improving the stability and reliability of the evaluation. Monitoring and recording these surface parameters helps in quality control during the production process, ensuring that the surface condition of each workpiece is within an acceptable range, thus stabilizing the thermal defect detection results. Understanding these parameters can help identify factors that may affect the surface quality during the production process, such as coating application and surface cleanliness, thereby improving the process flow and reducing the generation of surface defects. The set weight factors for the workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, and workpiece surface contamination area are obtained from the database. Through the historically measured workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, workpiece surface contamination area, and the workpiece surface condition characteristic value, a mapping set of the workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, workpiece surface contamination area, and their corresponding weight factors is established. Input the current workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, and workpiece surface contamination area into this mapping set to obtain the weight factors corresponding to the current workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, and workpiece surface contamination area.

[0036] It should be noted that: Under normal circumstances, there is a certain correlation among these parameters. Between the surface roughness of the workpiece and the surface reflectivity of the workpiece, as well as between the surface roughness of the workpiece and the surface coating thickness of the workpiece, the rougher the workpiece surface, the more severe the scattering of the reflected light, the lower the reflectivity. An increase in roughness will lead to a decrease in reflectivity, making the surface more "matte". The rough surface will scatter light, making the reflected light no longer concentrated. This scattering effect not only reduces the reflectivity but may also cause difficulties in optical detection. Moreover, excessive roughness may lead to uneven coating, resulting in uneven thickness. The surface roughness will affect the flow and spreading performance of the coating, leading to local changes in the coating thickness. The rough surface may cause the coating to accumulate in the depressions and form a thinner coating at the protrusions, affecting the subsequent detection results. Between the surface reflectivity of the workpiece and the surface coating thickness of the workpiece, the change in the coating thickness will affect the stability and consistency of the surface reflectivity. The coating with uneven thickness may cause local changes in the surface reflectivity, which is particularly significant in optical devices. Between the surface roughness of the workpiece and the surface contamination area of the workpiece, the rough surface may increase the adhesion of contaminants. Higher surface roughness will provide more surface area and micro-pits, and these areas are prone to accumulating contaminants such as grease and dust, which are difficult to remove. Between the surface coating thickness of the workpiece and the surface contamination area of the workpiece, the surface contaminants before coating application may affect the uniformity and adhesion of the coating, resulting in uneven coating thickness or coating defects such as bubbles and peeling. The coating can provide a protective barrier to reduce the impact of contaminants on the substrate.

[0037] Obtain a workpiece material feature data set, and based on the obtained workpiece material feature data set, compare to obtain the second deviation value for workpiece thermal defect detection.

[0038] Specifically, the workpiece material feature data set specifically includes the workpiece density, workpiece melting point, workpiece thermal conductivity, and workpiece thermal expansion coefficient.

[0039] In this embodiment, the density of the workpiece is the mass of the material per unit volume of the workpiece, which reflects the mass distribution characteristics of the material and affects the mechanical properties, mass and energy absorption capacity of the material. It is usually obtained through an X-ray densitometer, which uses the attenuation of X-rays when passing through the material to calculate the density of the material. The density can ensure that the material meets the quality standards required by the design and help estimate the material cost and transportation cost. The melting point of the workpiece is the temperature at which the workpiece material changes from solid to liquid, which affects the high-temperature performance of the workpiece material and the selection of processing technology. It is usually measured by a thermocouple. The thermocouple is inserted into the material and gradually heated to the temperature at which the material melts. By understanding the melting point of the workpiece material, it can help to select the appropriate welding, forging or casting process to ensure the material The stability and durability of the material in a high temperature environment. The thermal conductivity of the workpiece is the ability of the workpiece material to conduct heat, which affects the heat dissipation performance and temperature distribution of the workpiece material. It is usually obtained through laser flash analysis method. The workpiece material is heated by laser and the temperature response is measured to calculate the thermal conductivity. High thermal conductivity materials are suitable for radiators and high-power electronic components, which help to predict and control the temperature change of the material during the heating process. The thermal expansion coefficient of the workpiece is the ratio of the dimensional change of the workpiece material when the temperature changes, which affects the dimensional stability of the material in a temperature change environment. It is usually obtained by isomorphic thermomechanical analysis method. It is calculated by heating the workpiece material and measuring its length change. Choosing the right material can avoid dimensional deformation caused by temperature change.

[0040] It needs to be explained that: during the thermal defect detection process of the workpiece, the density of the workpiece affects the thermal conductivity and heat absorption properties of the material. Materials with higher density may affect the penetration depth of ultrasonic and X-ray detection, and materials with higher density usually have higher heat capacity and can absorb and conduct more heat, resulting in a decrease in temperature gradient and a decrease in the sensitivity of thermal defect detection. Density affects the propagation speed and attenuation coefficient of ultrasonic and X-rays. Higher density will increase the signal attenuation of these detection methods, making the detection of internal defects more difficult. Understanding density can help distinguish material types and locate defects in specific material areas in multi-material structures. Accurate density data can optimize ultrasonic detection Frequency and power settings can improve the sensitivity of defect detection, and can also help predict the thermal response characteristics of the workpiece and correct the temperature error caused by density in thermal imaging detection. The melting point of the workpiece determines the stability and shape retention ability of the material in a high-temperature environment. In thermal defect detection, high-melting-point materials may require a higher detection temperature, which increases the difficulty of detection. Materials with high melting points are not prone to melting defects during heat treatment, but may form thermal cracks or other thermal defects under extremely high temperatures. Understanding the melting point of the material helps to select the appropriate thermal detection temperature to avoid material melting or deformation caused by excessively high temperatures, thereby accurately identifying real thermal defects. Melting point data can help analyze heat treatment and welding processes The types of defects that may occur during the process, such as melting or thermal stress cracks caused by overheating, high thermal conductivity materials can distribute heat more evenly and faster, reduce local overheating, and help detection equipment identify thermal defects more accurately. Thermal conductivity affects the cooling and heating speed of the workpiece during processing or use, which is especially important for thermal imaging detection. High thermal conductivity helps to distribute heat quickly and evenly, so that internal thermal defects can be more clearly displayed in thermal imaging detection. Understanding the thermal conductivity of the material can help control the cooling and heating rate of the workpiece, avoiding thermal stress and cracks caused by too fast or too slow thermal changes. Accurate thermal conductivity data helps to correct the heat conduction effect in thermal imaging detection and improve the detection of small or deep thermal defects. The thermal expansion coefficient determines the dimensional change of the material when the temperature changes. Materials with high thermal expansion coefficients may undergo large deformations in an environment with large temperature fluctuations, resulting in thermal stress concentration and potential thermal cracks. In composite materials or multi-material systems, differences in thermal expansion coefficients of different materials may lead to stress concentration at the interface, increasing the risk of thermal defects. Understanding the thermal expansion coefficient of the material helps to predict and analyze the areas of thermal stress concentration caused by temperature changes. These areas are potential points of occurrence of cracks or other defects. In multi-material workpieces, differences in thermal expansion coefficients may lead to stress concentration and delamination at the interface. Obtaining thermal expansion coefficient data can help identify and analyze these interface defects.

[0041] Specifically, based on the obtained workpiece material feature dataset, the second deviation value for workpiece thermal defect detection is obtained through comparison. The specific analysis process is as follows: Based on the obtained workpiece material feature dataset, the workpiece material feature value is comprehensively analyzed. The workpiece material feature value serves as the analysis basis for obtaining the second deviation value for workpiece thermal defect detection through comparison. The workpiece material feature value is compared with the second deviation values for workpiece thermal defect detection corresponding to each workpiece material feature value stored in the database to obtain the second deviation value for workpiece thermal defect detection corresponding to this workpiece material feature value.

[0042] In this implementation plan, by obtaining the material feature data of the workpiece (such as density, melting point, thermal conductivity, coefficient of thermal expansion) and calculating the comprehensive material feature value, and comparing it with the historical data stored in the database, the second deviation value for workpiece thermal defect detection is obtained. The second deviation value provides key information about the workpiece material properties, which helps to identify potential problems in the production process (such as uneven heat treatment, improper material ratio, etc.), guides process improvement, improves product consistency and quality. By recording and analyzing the second deviation value data, historical data analysis and trend prediction can be carried out, providing data support for long-term quality monitoring and improvement. There is a close correlation among the four parameters of the workpiece's density, melting point, thermal conductivity, and coefficient of thermal expansion. Density is often related to the structure and composition of the material, affecting its thermal conductivity and coefficient of thermal expansion. High-density materials usually have higher thermal conductivity and can conduct heat more effectively. High-melting-point materials usually exhibit lower coefficients of thermal expansion in high-temperature environments, reducing the concentration of thermal stress. Thermal conductivity and coefficient of thermal expansion jointly affect the behavior of the material under temperature changes. Materials with high thermal conductivity can quickly and evenly distribute heat, reducing local uneven thermal expansion and thus reducing thermal stress. These parameters jointly determine the thermal response characteristics of the material, which is crucial for understanding and controlling the formation of workpiece thermal defects.

[0043] It should be explained that the workpiece material feature value, and its calculation formula is:

[0044] ;

[0045] In the formula: is the workpiece density, is the workpiece melting point, is the workpiece thermal conductivity, is the workpiece coefficient of thermal expansion, is the weight factor of the workpiece density set in the database, is the weight factor of the workpiece melting point set in the database, is the weight factor of the workpiece thermal conductivity set in the database, is the weight factor of the workpiece coefficient of thermal expansion set in the database, is the workpiece material feature value.

[0046] It should be noted that by synthesizing the density, melting point, thermal conductivity, and coefficient of thermal expansion of the workpiece and weighting them with weight factors, a workpiece material characteristic value is obtained. This comprehensive characteristic value can comprehensively reflect the overall thermophysical properties of the workpiece material, providing an intuitive and unified standard for quality assessment and comparison. Through the composition analysis of the material characteristic value, the material properties that have a major impact on the detection of thermal defects in the workpiece can be identified, which is of great significance for key control and optimization in the production and detection processes. This characteristic value can also help optimize the parameter settings for thermal defect detection, ensuring the accuracy and reliability of the detection. By comparing with historical data, trend analysis and prediction can be achieved, promoting data-driven quality control and continuous improvement, thereby improving the consistency and performance of the workpiece. The weight factors for the set workpiece density, workpiece melting point, workpiece thermal conductivity, and workpiece coefficient of thermal expansion are obtained from the database. Based on the historically measured workpiece density, workpiece melting point, workpiece thermal conductivity, workpiece coefficient of thermal expansion, and workpiece material characteristic value, a mapping set of the workpiece density, workpiece melting point, workpiece thermal conductivity, workpiece coefficient of thermal expansion, and their corresponding weight factors is established. Inputting the current workpiece density, workpiece melting point, workpiece thermal conductivity, and workpiece coefficient of thermal expansion into this mapping set, the weight factors corresponding to the current workpiece density, workpiece melting point, workpiece thermal conductivity, and workpiece coefficient of thermal expansion are obtained.

[0047] Perform external surface thermal defect detection on the workpiece to obtain a dataset for external surface thermal defect detection of the workpiece, and conduct a comprehensive analysis of the dataset for external surface thermal defect detection of the workpiece to obtain a characteristic value for external surface thermal defect detection of the workpiece.

[0048] Specifically, the dataset for external surface thermal defect detection of the workpiece specifically includes the average surface temperature of the workpiece, the number of surface cracks of the workpiece, and the thickness of the surface oxide layer of the workpiece; the characteristic value for external surface thermal defect detection of the workpiece, and its calculation formula is:

[0049] ;

[0050] In the formula: is the average surface temperature of the workpiece, is the reference value of the average surface temperature of the workpiece stored in the database, is the number of surface cracks of the workpiece, is the thickness of the surface oxide layer of the workpiece, is the weight factor of the average surface temperature of the workpiece set in the database, is the weight factor of the number of surface cracks of the workpiece set in the database, is the weight factor of the thickness of the surface oxide layer of the workpiece set in the database, is the characteristic value for external surface thermal defect detection of the workpiece.

[0051] In this embodiment, the average surface temperature of the workpiece reflects the thermal state of the workpiece during heat treatment or use, and is an important indicator for evaluating thermal stress, thermal fatigue and potential thermal defects. The temperature distribution on the workpiece surface is usually measured by an infrared thermal imager, and the average temperature is calculated. The number of cracks on the workpiece surface is an important parameter for evaluating the structural integrity of the workpiece, which directly affects the mechanical properties and durability of the workpiece. A large number of cracks will lead to fatigue crack expansion, material failure or increased corrosion. It is usually detected by ultrasonic testing, eddy current testing or X-ray testing to accurately locate and count cracks. The thickness of the oxide layer on the workpiece surface reflects the degree of surface oxidation of the workpiece during heat treatment or use, and is a key factor affecting the corrosion resistance and mechanical properties of the material. An excessively thick or uneven oxide layer may affect the electrical conductivity, thermal conductivity and optical properties of the workpiece. The coating thickness gauge measures the thickness of the oxide layer on the metal surface. There is a positive correlation between the average surface temperature of the workpiece and the number of cracks on the workpiece surface, the average surface temperature of the workpiece and the thickness of the oxide layer on the workpiece surface, and the number of cracks on the workpiece surface and the thickness of the oxide layer on the workpiece surface. Changes in the average surface temperature will cause thermal expansion and contraction of the material, especially when the average surface temperature is uneven, thermal stress will be generated. This thermal stress will trigger or aggravate the formation and expansion of surface cracks. An increase in the average surface temperature will usually accelerate the oxidation reaction, resulting in an increase in the thickness of the oxide layer. Workpieces with higher average surface temperatures are more likely to form thicker oxide layers, especially when exposed to air or other oxidizing environments. Surface cracks can become channels for oxidation reactions, making it easier for oxidizing gases or liquids to penetrate into the material, accelerating the oxidation process around the cracks, and increasing the thickness of the local oxide layer.

[0052] It needs to be explained that: during the thermal defect detection process of the workpiece, uneven surface temperature distribution may lead to thermal gradients inside the workpiece, generate thermal stress, and induce cracks or other thermal defects. Excessively high or low surface temperature may mask actual defects or cause misjudgment, affecting the accuracy of the detection results. By monitoring the average surface temperature, the heat treatment process can be optimized, overheating or overcooling can be avoided, and thermal defects can be reduced. Accurate temperature measurement helps to correct measurement errors caused by temperature and enhance the accuracy and reliability of thermal imaging detection. Cracks may affect the propagation of ultrasonic, X-ray or thermal imaging signals, resulting in misjudgment or missed detection results, as well as affect thermal radiation and reflection characteristics, interfering with the temperature distribution analysis of thermal imaging detection. Detecting cracks helps to repair them in time, prevent crack expansion, and ensure the safety and reliability of the workpiece. Crack counting can help evaluate the fatigue life of the workpiece and guide preventive maintenance and service life prediction. The oxide layer will affect the thermal conductivity, resulting in signal attenuation or distortion of thermal imaging detection. In electromagnetic detection, it will also affect the electrical conductivity of the material, resulting in measurement errors. Monitoring the thickness of the oxide layer can prevent corrosion, extend the service life of the workpiece, and ensure that the oxide layer is within a reasonable range, which helps to maintain the mechanical properties and surface characteristics of the material.

[0053] It should be explained that integrating multiple key factors affecting the thermal defects of the workpiece (temperature, cracks, oxide layer) into one characteristic value provides a comprehensive evaluation index, which can avoid the deviation caused by a single parameter and make the evaluation of the thermal defects of the workpiece more comprehensive and accurate. This characteristic value can effectively reflect the condition of the thermal defects on the workpiece surface, including the influence of cracks and oxide layer, as well as potential problems brought about by temperature changes, which helps to detect and diagnose thermal defects in a timely manner, prevent the expansion of defects or cause more serious problems. The weight factors of the average temperature on the workpiece surface, the number of cracks on the workpiece surface, and the thickness of the oxide layer on the workpiece surface are obtained from the database. By using the average temperature on the workpiece surface, the number of cracks on the workpiece surface, the thickness of the oxide layer on the workpiece surface measured historically, and the detection characteristic value of the thermal defects on the outer surface of the workpiece, a mapping set of the average temperature on the workpiece surface, the number of cracks on the workpiece surface, the thickness of the oxide layer on the workpiece surface, and their corresponding weight factors is established. Input the current average temperature on the workpiece surface, the number of cracks on the workpiece surface, and the thickness of the oxide layer on the workpiece surface into this mapping set to obtain the weight factors corresponding to the current average temperature on the workpiece surface, the number of cracks on the workpiece surface, and the thickness of the oxide layer on the workpiece surface.

[0054] Perform internal thermal defect detection on the workpiece to obtain an internal thermal defect detection data set of the workpiece, and conduct a comprehensive analysis of the internal thermal defect detection data set of the workpiece to obtain an internal thermal defect detection characteristic value of the workpiece.

[0055] Specifically, the internal thermal defect detection data set of the workpiece specifically includes the internal pore density of the workpiece, the number of internal defects of the workpiece, and the internal residual stress of the workpiece.

[0056] In this implementation scheme, the internal pore density of the workpiece reflects the porosity inside the material and is an important index for evaluating the internal structural integrity of the material. A high pore density usually means a reduction in the strength and toughness of the material, which may affect the mechanical properties and durability of the workpiece. It is usually detected by ultrasonic waves. By using the ultrasonic reflection and transmission characteristics, the presence and density of internal pores are detected. The number of internal defects of the workpiece includes cracks, inclusions, shrinkage cavities, etc., which are key factors directly affecting the structural integrity and reliability of the workpiece. The existence of defects will weaken the load-bearing capacity and fatigue life of the material. It is usually detected by techniques such as ultrasonic testing, magnetic particle inspection, and X-ray inspection, which are used to accurately locate and count internal defects. The internal residual stress of the workpiece is the stress that is not fully released during the manufacturing or processing of the material and is an important factor affecting the dimensional stability and structural integrity of the workpiece. It will cause the workpiece to deform or crack during service. It is usually measured by X-ray diffraction technology to measure the lattice strain of the material and calculate the residual stress.

[0057] It should be noted that during the thermal defect detection of workpieces, the presence of pores will change the thermal conduction characteristics of the material, leading to local thermal stress concentration, increasing the risk of thermal defects, and also affecting the signal intensity and resolution of thermal imaging or ultrasonic detection, thus affecting the sensitivity and accuracy of detection. Accurately measuring the pore density inside the workpiece can help evaluate the strength and toughness of the material, prevent material failure caused by internal defects, contribute to optimizing processes such as casting and welding, and reduce the generation of pore defects. Internal defects in the workpiece will affect the propagation of ultrasonic, X-ray or thermal imaging signals, resulting in misjudgment or missed detection of the detection results. The defect area may become a thermal stress concentration point, increasing the risk of crack propagation or other thermal defects. Early detection of internal defects helps to repair them in time, prevent the expansion of defects, and ensure the safety and reliability of the workpiece. The defect number data can be used to predict the fatigue life of the workpiece and guide preventive maintenance. The superposition of internal residual stress and external thermal stress in the workpiece will lead to stress concentration and thermal defects such as crack propagation. Residual stress may cause dimensional changes in the workpiece, affecting the assembly and function of precision components. Understanding the distribution of residual stress helps to take stress relief measures such as heat treatment, avoid adverse deformation or cracking of the workpiece during service, and can also help optimize the processing technology, reduce the introduction of stress during processing, and improve product quality and precision.

[0058] It should be noted that: Usually, there is a positive correlation between the internal pore density of the workpiece, the number of internal defects of the workpiece, and the internal residual stress of the workpiece. Internal pores and internal defects in the workpiece may be formed simultaneously during the manufacturing or processing of the material. High pore density usually means that there are more unstable factors during the manufacturing process, such as uneven cooling rate or poor material fluidity. The material strength in the pore area is usually low and is prone to become the starting point of crack initiation. Therefore, areas with high pore density are often accompanied by more defects such as cracks. The material discontinuity increases in areas with high pore density, and these areas are prone to cause stress concentration and form residual stress during cooling or other processing. Internal defects (such as cracks and inclusions) are stress concentration points. Under the action of external loads or thermal stress, these stress concentration areas are prone to further generate residual stress or exacerbate the existing stress state.

[0059] It should be noted that: The characteristic value for the thermal defect detection inside the workpiece has the following calculation formula:

[0060] ;

[0061] In the formula: is the internal pore density of the workpiece, is the number of internal defects of the workpiece, is the internal residual stress of the workpiece, is the weight factor of the internal pore density of the workpiece set in the database, is the weight factor of the number of internal defects of the workpiece set in the database, is the weight factor of the internal residual stress of the workpiece set in the database, is the detection eigenvalue of the internal thermal defect of the workpiece, is the natural constant.

[0062] It should be noted that: by comprehensively analyzing the internal pore density, the number of defects and the residual stress, the eigenvalue can predict potential thermal defects at an early stage, help implement preventive maintenance, reduce downtime or rework caused by defects, reduce product scrapping and rework caused by defects by improving the accuracy and timeliness of detection, reduce production costs. At the same time, preventive maintenance and early repair can extend the service life of equipment and workpieces, improve the overall economic benefits. Precise thermal defect detection can significantly improve the quality and safety of products, especially in key application fields such as aerospace, automotive and medical equipment. The weight factors of the set internal pore density, the number of internal defects and the internal residual stress of the workpiece are obtained from the database. Through the historically measured internal pore density, the number of internal defects, the internal residual stress of the workpiece and the detection eigenvalue of the internal thermal defect of the workpiece, a mapping set of the internal pore density, the number of internal defects, the internal residual stress of the workpiece and their corresponding weight factors is established. Input the current internal pore density, the number of internal defects, the internal residual stress of the workpiece into this mapping set to obtain the weight factors corresponding to the current internal pore density, the number of internal defects, the internal residual stress of the workpiece.

[0063] Comprehensively analyze the detection eigenvalue of the external surface thermal defect of the workpiece, the detection eigenvalue of the internal thermal defect of the workpiece, the first deviation value of the workpiece thermal defect detection and the second deviation value of the workpiece thermal defect detection to obtain the evaluation value of the workpiece thermal defect detection data.

[0064] Specifically, for the evaluation value of the workpiece thermal defect detection data, its calculation formula is:

[0065] ;

[0066] In the formula: is the detection eigenvalue of the external surface thermal defect of the workpiece, is the detection eigenvalue of the internal thermal defect of the workpiece, is the weight factor of the surface condition eigenvalue of the workpiece set in the database, is the weight factor of the material eigenvalue of the workpiece set in the database, is the evaluation value of the workpiece thermal defect detection data, is the first deviation value of the workpiece thermal defect detection, is the second deviation value of the workpiece thermal defect detection, is the natural constant.

[0067] In this implementation, the evaluation value of the workpiece thermal defect detection data integrates the thermal defect detection data on the outer surface and inside of the workpiece into a comprehensive index, covering both surface and internal characteristics, providing a comprehensive evaluation of the workpiece thermal defects, helping to more accurately reflect the overall quality status of the workpiece. Incorporating the first deviation value and the second deviation value of the workpiece thermal defect detection into the calculation of the evaluation value can effectively calibrate and correct the systematic errors in the detection data, ensuring the accuracy and reliability of the final evaluation result. By integrating the characteristic values on the outer surface and inside and combining deviation correction, the evaluation value of the workpiece thermal defect detection data reduces the deviation and misjudgment that may be brought by single detection data, improves the overall accuracy of thermal defect detection, can identify the parameters that have the greatest impact on the final quality, helps to focus on controlling key process parameters, optimize the production process, is convenient for training new operators, making it easier for them to understand and master the key parameters and evaluation methods of thermal defect detection, and improves the overall operation level. The weight factors of the characteristic values of the workpiece outer surface thermal defect detection and the workpiece internal thermal defect detection are obtained from the database. Through the historical measured characteristic values of the workpiece outer surface thermal defect detection, the workpiece internal thermal defect detection, and the evaluation value of the workpiece thermal defect detection data, a mapping set of the characteristic values of the workpiece outer surface thermal defect detection, the workpiece internal thermal defect detection, and their corresponding weight factors is established. Inputting the current characteristic values of the workpiece outer surface thermal defect detection and the workpiece internal thermal defect detection into this mapping set, the weight factors corresponding to the current characteristic values of the workpiece outer surface thermal defect detection and the workpiece internal thermal defect detection are obtained.

[0068] Judge whether the thermal defect of the workpiece is qualified based on the evaluation value of the workpiece thermal defect detection data, and mark the qualified workpieces.

[0069] Specifically, judge whether the thermal defect of the workpiece is qualified based on the evaluation value of the workpiece thermal defect detection data, and mark the qualified workpieces. The specific analysis process is as follows: Compare the evaluation value of the workpiece thermal defect detection data with the qualified boundary value of the workpiece thermal defect detection stored in the database; if the evaluation value of the workpiece thermal defect detection data is higher than the qualified boundary value of the workpiece thermal defect detection, the thermal defect detection of the workpiece is unqualified; if the evaluation value of the workpiece thermal defect detection data is lower than or equal to the qualified boundary value of the workpiece thermal defect detection, the thermal defect detection of the workpiece is qualified, and a thermal defect qualified mark is made for the workpieces with qualified thermal defect detection.

[0070] In this implementation scheme, by setting a clear qualified boundary value for workpiece thermal defect detection, a standardized evaluation criterion is provided, making the quality judgment process objective and consistent, avoiding the deviation of subjective judgment, ensuring that the quality inspection of all workpieces is based on the same standard. By comparing the evaluation value with the qualified boundary value, workpieces can be quickly classified as qualified or unqualified, improving the efficiency of the detection process. The automated judgment and marking system can process a large number of workpieces, reducing the time and human resources consumption of manual inspection. This process can accurately identify thermal defects in workpieces, ensure that unqualified workpieces are excluded from the production line at an early stage, prevent them from entering the market or affecting subsequent production processes. Strict quality control helps improve the overall reliability and quality of products. By quickly identifying and processing unqualified workpieces, more resources can be concentrated on the subsequent processing of qualified workpieces and the priority processing of qualified workpieces, optimizing the allocation of production resources and improving production efficiency.

[0071] The workpiece thermal defect detection system based on big data processing, as Figure 2 shown, includes a workpiece surface condition feature data acquisition module, a workpiece material feature data acquisition module, a workpiece outer surface thermal defect data acquisition module, a workpiece internal thermal defect data acquisition module, a workpiece thermal defect detection data evaluation value acquisition module, and a workpiece thermal defect judgment module. Among them: The workpiece surface condition feature data acquisition module is used to acquire the workpiece surface condition feature data set, and based on the acquired workpiece surface condition feature data set, the first deviation value of workpiece thermal defect detection is obtained by comparison; The workpiece material feature data acquisition module is used to acquire the workpiece material feature data set, and based on the acquired workpiece material feature data set, the second deviation value of workpiece thermal defect detection is obtained by comparison; The workpiece outer surface thermal defect data acquisition module is used to perform outer surface thermal defect detection on the workpiece, acquire the workpiece outer surface thermal defect detection data set, and comprehensively analyze the workpiece outer surface thermal defect detection data set to obtain the workpiece outer surface thermal defect detection characteristic value; The workpiece internal thermal defect data acquisition module is used to perform internal thermal defect detection on the workpiece, acquire the workpiece internal thermal defect detection data set, and comprehensively analyze the workpiece internal thermal defect detection data set to obtain the workpiece internal thermal defect detection characteristic value; The workpiece thermal defect detection data evaluation value acquisition module is used to comprehensively analyze the workpiece outer surface thermal defect detection characteristic value, the workpiece internal thermal defect detection characteristic value, the first deviation value of workpiece thermal defect detection, and the second deviation value of workpiece thermal defect detection to obtain the workpiece thermal defect detection data evaluation value; The workpiece thermal defect judgment module is used to judge whether the thermal defect of the workpiece is qualified based on the workpiece thermal defect detection data evaluation value and mark the qualified workpieces.

[0072] In summary, the present application has at least the following effects: The workpiece thermal defect detection method and system based on big data processing integrate the data of the external surface and internal thermal defect detection feature values, the first and second deviation values, perform comprehensive quality assessment, ensure accurate judgment of the workpiece quality, can identify potential quality risks at an early stage, especially before the product enters the downstream process or the market, take timely measures to prevent problems from occurring, flexibly adjust the detection strategies and standards according to different types of workpieces or specific requirements of different customers, reduce resource waste caused by producing unqualified products, optimize the production process, promote environmental protection and sustainable development, improve the safety of the product, and avoid safety accidents caused by product defects.

[0073] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented 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.

[0074] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0075] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide means for implementing the specified functions in the flowFigure 1 one process or multiple processes and / or boxes Figure 1 steps of functions specified in one box or multiple boxes

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A workpiece thermal defect detection method based on big data processing, characterized in that It includes the following steps: Obtain the workpiece surface condition feature dataset. Based on the obtained workpiece surface condition feature dataset, compare to obtain the first deviation value for workpiece thermal defect detection. The workpiece surface condition feature dataset specifically includes workpiece surface roughness, workpiece surface reflectivity, workpiece surface coating thickness, and workpiece surface contamination area; Obtain the workpiece material feature dataset. Based on the obtained workpiece material feature dataset, compare to obtain the second deviation value for workpiece thermal defect detection. The workpiece material feature dataset specifically includes workpiece density, workpiece melting point, workpiece thermal conductivity, and workpiece coefficient of thermal expansion; Conduct external surface thermal defect detection on the workpiece to obtain the workpiece external surface thermal defect detection dataset, which specifically includes workpiece surface average temperature, number of workpiece surface cracks, and workpiece surface oxide layer thickness. Conduct comprehensive analysis on the workpiece external surface thermal defect detection dataset to obtain the workpiece external surface thermal defect detection characteristic value, and its calculation formula is: ; Wherein: is the average temperature of the workpiece surface, is the reference value of the average temperature of the workpiece surface stored in the database, is the number of cracks on the workpiece surface, is the thickness of the oxide layer on the workpiece surface, is the weight factor of the average temperature of the workpiece surface set in the database, is the weight factor of the number of cracks on the workpiece surface set in the database, is the weight factor of the thickness of the oxide layer on the workpiece surface set in the database, is the detection eigenvalue of the thermal defect on the outer surface of the workpiece; Conduct internal thermal defect detection on the workpiece to obtain the workpiece internal thermal defect detection dataset, which specifically includes workpiece internal pore density, number of workpiece internal defects, and workpiece internal residual stress. Conduct comprehensive analysis on the workpiece internal thermal defect detection dataset to obtain the workpiece internal thermal defect detection characteristic value; Conduct comprehensive analysis on the workpiece external surface thermal defect detection characteristic value, workpiece internal thermal defect detection characteristic value, first deviation value for workpiece thermal defect detection, and second deviation value for workpiece thermal defect detection to obtain the workpiece thermal defect detection data evaluation value, and its calculation formula is: ; Where: is the detection eigenvalue of the thermal defect on the outer surface of the workpiece, is the detection eigenvalue of the thermal defect inside the workpiece, is the weight factor of the workpiece surface condition eigenvalue set in the database, is the weight factor of the workpiece material eigenvalue set in the database, is the evaluation value of the workpiece thermal defect detection data, is the first deviation value of the workpiece thermal defect detection, is the second deviation value of the workpiece thermal defect detection, is the natural constant; Based on the workpiece thermal defect detection data evaluation value, determine whether the thermal defect of the workpiece is qualified, and mark the qualified workpieces.

2. The workpiece thermal defect detection method based on big data processing according to claim 1, wherein: The specific analysis process for comparing to obtain the first deviation value for workpiece thermal defect detection based on the obtained workpiece surface condition feature dataset is as follows: Based on the obtained workpiece surface condition feature dataset, conduct comprehensive analysis to obtain the workpiece surface condition characteristic value. The workpiece surface condition characteristic value serves as the analysis basis for comparing to obtain the first deviation value for workpiece thermal defect detection; Compare the workpiece surface condition characteristic value with the first deviation value for workpiece thermal defect detection corresponding to each workpiece surface condition characteristic value stored in the database to obtain the first deviation value for workpiece thermal defect detection corresponding to this workpiece surface condition characteristic value.

3. The workpiece thermal defect detection method based on big data processing according to claim 1, wherein: The specific analysis process for comparing to obtain the second deviation value for workpiece thermal defect detection based on the obtained workpiece material feature dataset is as follows: Based on the obtained workpiece material feature dataset, conduct comprehensive analysis to obtain the workpiece material characteristic value. The workpiece material characteristic value serves as the analysis basis for comparing to obtain the second deviation value for workpiece thermal defect detection; Compare the workpiece material characteristic value with the second deviation value for workpiece thermal defect detection corresponding to each workpiece material characteristic value stored in the database to obtain the second deviation value for workpiece thermal defect detection corresponding to this workpiece material characteristic value.

4. The workpiece thermal defect detection method based on big data processing according to claim 1, characterized in that: The specific analysis process for determining whether the thermal defect of the workpiece is qualified based on the workpiece thermal defect detection data evaluation value and marking the qualified workpieces is as follows: Compare the workpiece thermal defect detection data evaluation value with the qualified definition value for workpiece thermal defect detection stored in the database; If the workpiece thermal defect detection data evaluation value is higher than the qualified definition value for workpiece thermal defect detection, then the thermal defect detection of the workpiece is unqualified; If the evaluation value of the workpiece thermal defect detection data is lower than or equal to the qualified definition value of the workpiece thermal defect detection, the thermal defect detection of the workpiece is qualified, and a thermal defect qualified mark is made for the workpiece with qualified thermal defect detection.

5. A workpiece thermal defect detection system based on big data processing, applying the workpiece thermal defect detection method based on big data processing according to any one of claims 1-4, characterized in that, It includes a workpiece surface condition feature data acquisition module, a workpiece material feature data acquisition module, a workpiece outer surface thermal defect data acquisition module, a workpiece internal thermal defect data acquisition module, a workpiece thermal defect detection data evaluation value acquisition module, and a workpiece thermal defect judgment module, where: The workpiece surface condition feature data acquisition module is used to acquire the workpiece surface condition feature data set, and based on the acquired workpiece surface condition feature data set, the first deviation value of the workpiece thermal defect detection is obtained by comparison. The workpiece material feature data acquisition module is used to acquire the workpiece material feature data set, and based on the acquired workpiece material feature data set, the second deviation value of the workpiece thermal defect detection is obtained by comparison. The workpiece outer surface thermal defect data acquisition module is used to perform outer surface thermal defect detection on the workpiece, acquire the workpiece outer surface thermal defect detection data set, and comprehensively analyze the workpiece outer surface thermal defect detection data set to obtain the workpiece outer surface thermal defect detection characteristic value. The workpiece internal thermal defect data acquisition module is used to perform internal thermal defect detection on the workpiece, acquire the workpiece internal thermal defect detection data set, and comprehensively analyze the workpiece internal thermal defect detection data set to obtain the workpiece internal thermal defect detection characteristic value. The workpiece thermal defect detection data evaluation value acquisition module is used to comprehensively analyze the workpiece outer surface thermal defect detection characteristic value, the workpiece internal thermal defect detection characteristic value, the first deviation value of the workpiece thermal defect detection, and the second deviation value of the workpiece thermal defect detection to obtain the workpiece thermal defect detection data evaluation value. The workpiece thermal defect judgment module is used to judge whether the thermal defect of the workpiece is qualified based on the workpiece thermal defect detection data evaluation value, and mark the qualified workpiece.

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