Defective product detection method and system for high-temperature insulating brick production

Abstract

The invention relates to the technical field of defect detection, and discloses a defective product detection method and system for high-temperature insulating brick production, and the detection system comprises a detection module, a heating determination module, a heating adjustment module and an evaluation module. The detection module adopts a laser heat conduction instrument and an infrared camera to perform periodic thermal wave heating on the to-be-detected brick body, and records temperature data to calculate thermal diffusivity so as to judge whether a defect area exists in the brick body or not; the heating determination module obtains the thickness of the brick body and determines the heating pulse width, the period duration and the heating power according to the thickness of the brick body; the heating adjusting module is used for compensating and adjusting the heating power according to the real-time environment temperature; the evaluation module is used for calculating an evaluation score. Through automatic and intelligent means, accurate detection of the internal defects of the high-temperature insulating bricks is realized, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of defect detection, and more particularly, to a method and system for detecting defective products in the production of high-temperature heat-insulating bricks. Background Art

[0002] High-temperature heat-insulating bricks are commonly used in the lining of high-temperature kilns to reduce heat loss from the kiln, thereby improving the thermal energy utilization efficiency, reducing energy consumption, and extending the service life of equipment. The thermal-related properties of high-temperature heat-insulating bricks directly affect the overall operating state of the kiln. If the heat-insulating effect is not good, it will not only cause uneven temperature distribution in the furnace body but also lead to structural stress problems, affecting the stable operation of the kiln.

[0003] In the prior art, the detection of defective products of high-temperature heat-insulating bricks mainly focuses on the detection of appearance characteristics such as size, flatness, and surface defects. For example, whether there are cracks, corners missing, pores, or size deviations in the brick body. Although these detection methods can identify some unqualified products, they cannot evaluate the key performance indicators of high-temperature heat-insulating bricks. Since the main function of high-temperature heat-insulating bricks is to provide good heat insulation effect, relying solely on appearance detection cannot guarantee their true performance in use, which may lead to some products with insufficient heat-insulating ability flowing into the market.

[0004] In addition, the existing detection methods usually adopt manual sampling inspection or traditional laboratory testing methods, which cannot ensure that each brick meets the quality standards and may miss defective products with performance defects. And even if the thermal-related performance obtained through laboratory testing is accurate, it usually requires sending samples to specialized detection equipment, and the detection cycle is long, making it difficult to meet the rapid quality detection requirements of large-scale production lines.

[0005] Therefore, how to achieve the detection of thermal-related properties of high-temperature heat-insulating bricks, especially to evaluate their heat-insulating performance and thermal stability without damaging the brick body, has become an urgent problem to be solved in the current technology. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a method and system for detecting defective products in the production of high-temperature heat-insulating bricks.

[0007] On the one hand, a defective product detection system for the production of high-temperature heat-insulating bricks proposed by the present invention includes: A detection module, including a laser thermal conductivity meter and an infrared camera. The detection module is configured to perform periodic thermal wave heating on the brick to be tested through the laser thermal conductivity meter, and then record the temperature data of the brick to be tested through the infrared camera and calculate the thermal diffusivity; judge whether there is a defective area inside the brick to be tested according to the thermal diffusivity; A heating determination module, connected to the detection module, configured to obtain the brick thickness of the brick to be tested, and determine the pulse width, cycle duration, and heating power of periodic thermal wave heating according to the brick thickness; A heating adjustment module, connected to the detection module and the heating determination module respectively, configured to adjust the heating power during the periodic thermal wave heating according to the ambient temperature; An evaluation module, configured to calculate the evaluation score of the brick to be tested according to the ratio of the number of processing points to the number of suspected defect points.

[0008] Further, when the detection module is configured to perform periodic thermal wave heating on the brick to be tested through a laser thermal conductivity meter, it includes: Performing periodic heating on the brick to be tested in the form of square wave pulse heating, selecting several points on the surface of the brick to be tested as processing points, where the processing points are points with known position coordinates, obtaining the temperature data of the processing points and the corresponding data acquisition times, and obtaining the thermal diffusivity through the following relationship: ; where, is the thermal diffusivity, is the distance from the processing point to the heat source, >0, is the heating signal frequency, represents the phase lag.

[0009] Further, when judging whether there is a defect area inside the brick to be tested according to the thermal diffusivity, it includes: Taking the data acquisition time as the abscissa and the thermal diffusivity corresponding to the same processing point at the data acquisition time as the ordinate, establishing a processing point diffusivity change curve, and calculating the slope of the diffusivity change curve; Selecting the slopes of different processing points at the same data acquisition time to establish a slope set, and calculating the average value and standard deviation of the slope set; Defining a slope threshold according to the average value and the standard deviation, and the slope threshold satisfies the following relationship: ; where, is the slope threshold, is the average value, is the standard deviation, is the slope sensitivity coefficient, 1≤ ≤3; Obtaining the position coordinates corresponding to all the slopes not within the slope threshold, marking the position coordinates as suspected defect points, and determining the defect area according to the suspected defect points.

[0010] Further, determining the defect area according to the suspected defect points includes: Analyze each of the suspected defect points one by one. Take each of the suspected defect points as the target point, and obtain the position coordinates of the remaining suspected defect points within the circular range with a radius of R. If the number of adjacent or consecutive suspected defect points exceeds the quantity threshold, calculate the average value of the abscissas and the average value of the ordinates of the position coordinates of the remaining suspected defect points outside the target point. Calculate the distance value between this point and the center of the circle using the average value of the abscissas and the average value of the ordinates as the abscissa and ordinate respectively. Select the minimum distance value when analyzing each of the suspected defect points one by one, and determine the circular range corresponding to the minimum distance value as the defect area.

[0011] Further, the quantity threshold satisfies the following relationship: ; where is the quantity threshold, is the average number of suspected defect points of the standard brick body within the circular range at the same data acquisition time, is the quantity sensitivity coefficient, 2 ≤ ≤ 3, is the standard deviation of the suspected defect points of the standard brick body within the circular range at the same data acquisition time.

[0012] Further, determining the pulse width, cycle duration, and heating power of the periodic thermal wave heating according to the brick body thickness includes: The pulse width, cycle duration, and heating power are calculated and obtained through the following relationships respectively: ; ; ; where is the pulse width, is the brick body thickness, is the cycle duration, is the design coefficient, 2 < < 3, is the heating power, is the brick body density, is the preset target temperature rise, is the heated area, is the specific heat capacity of the brick body to be measured.

[0013] Further, when adjusting the heating power during the periodic thermal wave heating according to the ambient temperature, it includes: Obtain the ambient temperature and the target temperature of the brick body. The adjusted heating power satisfies the following relationship: ; where is the adjusted heating power, is the ambient temperature, is the target temperature of the brick body.

[0014] Further, calculate the evaluation score of the brick body to be measured according to the ratio of the number of processing points to the number of suspected defect points, including: Judge whether there is a defect area. If there is no defect area, obtain the calculation evaluation score through the following method: ; wherein, is the evaluation score, is the number of processing points, is the number of suspected defect points.

[0015] Further, if there is a defect area, judge that the brick body to be measured is a defective product.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: Traditional high-temperature heat-insulating brick detection methods mainly rely on appearance detection and cannot effectively evaluate the thermal performance and internal defects of the brick body. This solution uses a laser thermal conductivity meter combined with an infrared camera to obtain the thermal diffusivity of the brick body through periodic thermal wave heating, which can directly reflect the internal thermal conductivity of the brick body. By calculating the thermal diffusivity change curves at different positions, potential defect areas inside the brick body can be identified, avoiding misdetection or missed detection caused by relying solely on appearance judgment.

[0017] Existing detection methods mostly adopt manual sampling inspection, with low detection efficiency and subjectivity. Moreover, laboratory tests usually require long-time sampling, preparation, and analysis, making it difficult to adapt to large-scale production. This solution automatically calculates heating parameters through a heating determination module to ensure that different brick bodies can obtain appropriate detection conditions; uses a heating adjustment module to perform real-time compensation on the heating power to eliminate the influence of environmental factors (such as room temperature changes) on the detection accuracy; scores the quality of the brick body through an evaluation module to reduce manual intervention and make the detection process more intelligent.

[0018] Traditional detection methods usually need to be adjusted according to different brick body specifications. However, this solution obtains the brick body thickness through a heating determination module and dynamically calculates the pulse width, cycle duration, and heating power of periodic thermal wave heating, which can adapt to brick bodies of different thicknesses and sizes; adopts an automatic compensation algorithm to adjust the heating power according to environmental temperature changes to ensure detection consistency and make it applicable to various production environments.

[0019] Traditional thermal detection methods usually rely on the intuitive changes in the temperature field and cannot accurately quantify defects. This solution establishes a data-driven defect identification model by calculating the slope set of the thermal diffusivity, defines the slope threshold through statistical methods (mean, standard deviation), and realizes high-precision defect location; adopts a spatial clustering algorithm, combines the thermal diffusion data of the bricks, analyzes the distribution of suspected defect points, and accurately demarcates the defect area to avoid misjudgment.

[0020] This system can not only identify defective products, but also calculate the quality score of the bricks through an evaluation module: if there are no defects, the score is calculated according to the uniformity of the thermal diffusivity of the bricks to ensure the refined grading of product quality; if a defect is detected, the system can quickly determine the defective product, prevent unqualified products from entering the market, and improve the reliability of quality control.

[0021] Traditional laboratory testing methods usually require a long detection cycle, while this solution adopts a non-contact and real-time detection method, which can be directly detected online to improve production efficiency; by accurately identifying defective bricks, it reduces the heat energy loss during the subsequent use of the kiln and reduces the energy waste caused by the use of low-quality insulating bricks.

[0022] On the other hand, the present invention also proposes a method for detecting defective products in the production of high-temperature insulating bricks, including: Performing periodic thermal wave heating on the brick to be tested by a laser thermal conductivity meter, then recording the temperature data of the brick to be tested by an infrared camera, and calculating the thermal diffusivity; judging whether there is a defective area inside the brick to be tested according to the thermal diffusivity; Obtaining the thickness of the brick to be tested, and determining the pulse width, cycle duration and heating power of the periodic thermal wave heating according to the brick thickness; Adjusting the heating power during the periodic thermal wave heating according to the ambient temperature; After judging that there is a defective area, evaluating the score of the brick to be tested according to the defective area, and when the score is less than or equal to the lowest score, judging the brick to be tested as a defective product.

[0023] It can be understood that the above-provided method for detecting defective products in the production of high-temperature insulating bricks and its system have the same beneficial effects, which will not be elaborated here. Brief Description of the Drawings

[0024] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 It is the functional framework diagram of the defective product detection system for the production of high-temperature insulating bricks provided by the embodiment of the present invention.

[0025] Figure 2 This is a flowchart of the defective product detection method for the production of high-temperature heat-insulating bricks provided by the embodiments of the present invention. Detailed implementation manners

[0026] Hereinafter, the exemplary embodiments disclosed in the present application will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in combination with the embodiments.

[0027] Refer to Figure 1 As shown, this embodiment provides a defective product detection system for the production of high-temperature heat-insulating bricks, including: A detection module, including a laser thermal conductivity meter and an infrared camera. The detection module is configured to perform periodic thermal wave heating on the brick body to be measured through the laser thermal conductivity meter, and then record the temperature data of the brick body to be measured through the infrared camera to calculate the thermal diffusivity; determine whether there is a defective area inside the brick body to be measured according to the thermal diffusivity; A heating determination module, connected to the detection module. The heating determination module is configured to obtain the brick thickness of the brick body to be measured and determine the pulse width, cycle duration, and heating power of the periodic thermal wave heating according to the brick thickness; A heating adjustment module, respectively connected to the detection module and the heating determination module. The heating adjustment module is configured to adjust the heating power during the periodic thermal wave heating according to the ambient temperature; An evaluation module, configured to calculate the evaluation score of the brick body to be measured according to the ratio of the number of processing points to the number of suspected defect points.

[0028] It should be noted that by using a laser thermal conductivity meter and an infrared camera to obtain the thermal diffusivity of the brick through periodic thermal wave heating, internal defects of the brick can be detected non-destructively, avoiding misjudgment and missed detection caused by relying solely on appearance inspection, and improving the accuracy of detection. The heating determination module automatically adjusts the pulse width, cycle duration, and heating power according to the thickness of the brick to ensure that bricks of different thicknesses can obtain appropriate heating and detection conditions, improving the adaptability and general applicability of the system. Traditional methods are greatly affected by environmental temperature fluctuations, while in this embodiment, through the heating adjustment module, the heating power during the periodic thermal wave heating process is dynamically adjusted to ensure the stability of the detection conditions and improve the reliability and repeatability of detection. The evaluation module can not only identify defective products, but also grade the bricks according to the defect area, achieving more detailed quality control and avoiding waste of resources caused by misjudgment. By using non-contact thermal wave heating + infrared detection, automated on-line detection can be realized, avoiding the inefficiency of traditional manual sampling inspection, being applicable to large-scale production, and improving the production line efficiency. By reducing manual intervention through the intelligent detection system, the possibility of human misjudgment is reduced, while defective products are accurately removed, the scrap rate is reduced, the material utilization rate is improved, which conforms to the concept of green manufacturing.

[0029] In some embodiments of the present application, when the detection module is configured to perform periodic thermal wave heating on the brick to be tested through a laser thermal conductivity meter, it includes: Performing periodic heating on the brick to be tested in the form of square wave pulse heating, selecting several points on the surface of the brick to be tested as processing points, the processing points being points with known position coordinates, obtaining the temperature data of the processing points and the corresponding data acquisition times, and obtaining the thermal diffusivity through the following relationship: ; Wherein, is the thermal diffusivity, is the distance from the processing point to the heat source, > 0, is the heating signal frequency, represents the phase lag.

[0030] It should be noted that when selecting several points on the surface of the brick to be tested as processing points, specifically, as many points as possible within a circular area are selected as processing points, and the number of processing points can be determined according to the processing capacity of the device or system.

[0031] Phase Lag, also known as phase delay, is the time delay of the output signal relative to the input signal in a periodic fluctuation system. Phase lag refers to the delay between the phase of the temperature change on the brick surface and the change of the heat source (such as a pulsed heat source) during the propagation of the thermal wave.

[0032] The method for obtaining phase lag data is as follows: Use an infrared camera to record the temperature change T(t) on the surface of the brick over time. Obtain the input signal Q(t) of periodic heating through data acquisition. Perform Fourier transform on the input heating signal Q(t) to obtain the phase of the main frequency component f, denoted as Q. Perform Fourier transform on the temperature response signal T(t) to obtain the phase of the main frequency component, denoted as T.

[0033] Calculate the phase lag according to the following relationship: .

[0034] Here, Q is the phase of the heating signal, T is the phase of the temperature response, and the unit is usually radians (rad) or degrees (°).

[0035] It should be noted that in thermal wave analysis, the thermal wave propagates in the form of a sine wave, so the linear frequency f or angular frequency ω can be used to describe the change of the thermal wave. When calculating using the angular frequency ω, the corresponding thermal diffusivity formula will substitute ω for 2πf, resulting in the following form: .

[0036] The technical solution provided in this embodiment uses a laser thermal conductivity meter to perform periodic thermal wave heating on the brick to be measured, and uses the square wave pulse heating method, which can uniformly apply periodic thermal excitation to the brick in a short time. Compared with the traditional continuous heating method, this method can generate a temperature gradient faster, making the temperature response of internal defects more obvious and improving the sensitivity of defect detection.

[0037] During the data acquisition process, this embodiment selects multiple processing points on the surface of the brick. These processing points are all known position coordinates, so that the temperature change characteristics can be accurately analyzed. Especially, as many points as possible are selected within a circular area as processing points. This method can maximize the data acquisition volume under the same heating conditions, improve the spatial resolution of detection, and make the detection results of defects more accurate. Since the number of processing points can be flexibly adjusted according to the system processing ability, an optimal balance can be achieved between detection accuracy and calculation efficiency.

[0038] This embodiment determines whether there are defects inside the brick by calculating the thermal diffusivity. The thermal diffusivity is an important indicator of the heat conduction ability of materials. If there are defects (such as pores, cracks or impurities) inside the brick, its thermal diffusivity usually shows abnormal changes. Compared with the traditional surface temperature detection method, calculating the thermal diffusivity can more directly characterize the integrity of the internal structure of the brick, thus achieving more accurate defect detection.

[0039] During the detection process, this solution introduces the calculation of phase lag. Phase lag is a key parameter in the propagation of periodic thermal waves and can effectively reflect the thermal response characteristics of the brick. Due to the time-dependence of the heat diffusion process, when a heating source acts periodically on the surface of the brick, the temperature response will have a time delay with respect to the input heating signal, and this delay is the phase lag. This solution extracts the phase information of the main frequency component through Fourier transform, thereby calculating the phase difference Δ between the heating signal and the temperature response signal. This calculation method can remove the influence of environmental noise and non-periodic disturbances, improve the robustness of the detection, and make the calculation of the thermal diffusivity more stable and reliable.

[0040] In this embodiment, the Fourier transform is used to analyze the temperature signal T(t) and the input heating signal Q(t). Compared with traditional time-domain analysis methods, the Fourier transform can decompose the signal into components of different frequencies, making the detection more accurate and efficient. By extracting the phase information of the main frequency component, the interference of external temperature fluctuations can be eliminated, ensuring the stability and repeatability of the detection. At the same time, since the Fourier transform can highlight the characteristics of periodic signals, high-precision defect detection can still be achieved at a lower heating power, reducing energy consumption and improving system efficiency.

[0041] In addition, the solution of this embodiment has a high degree of automation in data processing. Through steps such as real-time data acquisition, Fourier transform analysis, and phase lag calculation, automatic identification and classification of defects can be achieved, reducing manual intervention and improving detection efficiency. Traditional manual detection or laboratory detection methods usually require a long time for data analysis, while the detection system of this embodiment can quickly complete the detection on the production line, greatly improving production efficiency.

[0042] In some embodiments of this application, when judging whether there is a defect area inside the brick to be tested according to the thermal diffusivity, it includes: Taking the data acquisition time as the abscissa and the thermal diffusivity corresponding to the data acquisition time at the same processing point as the ordinate, establishing a diffusion rate change curve for the processing point, and calculating the slope of the diffusion rate change curve; Selecting the slopes at different processing points at the same data acquisition time to establish a slope set, and calculating the average value and standard deviation of the slope set; Defining a slope threshold according to the average value and standard deviation, and the slope threshold satisfies the following relationship: ; where, is the slope threshold, is the average value, is the standard deviation, is the slope sensitivity coefficient, 1 ≤ ≤ 3; Obtain the position coordinates corresponding to all slopes that are not within the slope threshold, mark the position coordinates as suspected defect points, and determine the defect area based on the suspected defect points.

[0043] It should be noted that in this embodiment, by analyzing the change trend of the thermal diffusivity over time, a diffusivity change curve is established and its slope is calculated, so as to accurately characterize the heat conduction characteristics inside the brick. Compared with directly using the absolute value of the thermal diffusivity for judgment, using the slope change trend can more sensitively identify local anomalies and improve the accuracy of defect detection.

[0044] This embodiment adopts a method of multi-point data comparison and analysis. Under the same data acquisition time, the slope values of different processing points are extracted, a slope set is established, and its average value and standard deviation are calculated. This method can effectively reduce the influence brought by single-point measurement errors and improve the stability and reliability of detection.

[0045] By defining the slope threshold, this embodiment can adapt to the characteristic changes of different brick materials. The calculation method of the slope threshold combines the average value and the standard deviation, and introduces a slope sensitivity coefficient (1 ≤ ≤ 3), enabling the system to be flexibly adjusted according to different detection requirements, which can not only enhance the sensitivity of defect detection but also avoid misjudgment.

[0046] This embodiment screens out the points exceeding the slope threshold, marks the corresponding position coordinates as suspected defect points, and further analyzes the defect area. Compared with the traditional single-point anomaly judgment method, this method can provide more complete defect morphology information, make the boundary recognition of the defect area more accurate, and thus improve the accuracy of defect location.

[0047] In some embodiments of the present application, determining the defect area based on the suspected defect points includes: Analyze each suspected defect point one by one, take each suspected defect point as the target point, obtain the position coordinates of the remaining suspected defect points within the circular range with a radius of R. If the number of adjacent or consecutive suspected defect points exceeds the number threshold, calculate the average value of the abscissas and the average value of the ordinates of the position coordinates of the remaining suspected defect points outside the target point, calculate the distance value between this point and the center of the circle with the average value of the abscissas and the average value of the ordinates as the abscissa and ordinate respectively, select the minimum distance value when analyzing each suspected defect point one by one, and determine the circular range corresponding to the minimum distance value as the defect area.

[0048] It should be noted that according to historical data, the length of cracks in the brick is statistically counted, and the radius R is the average value of the crack lengths in the historical data / 2.

[0049] In the case of "if the number of adjacent or consecutive suspected defect points exceeds the quantity threshold", the adjacent situation means that taking the suspected defect point (target point) being analyzed as the center, the number of suspected defect points around it exceeds the quantity threshold; the consecutive situation means that the number of defect points on a straight line or curve including the target point exceeds the quantity threshold. "Exceeds" indicates a greater than (not including equal to) relationship.

[0050] In some embodiments of the present application, the quantity threshold satisfies the following relationship: ; where is the quantity threshold, is the average number of suspected defect points of the standard brick body within the circular range at the same data acquisition time, is the quantity sensitivity coefficient, 2 ≤ ≤ 3, is the standard deviation of the suspected defect points of the standard brick body within the circular range at the same data acquisition time.

[0051] It can be understood that in this embodiment, by performing local clustering analysis on the suspected defect points, the determination of the defect area is ensured to be more accurate and scientific. Traditional methods usually judge defects only based on a single abnormal point, while in this embodiment, by statistically analyzing the spatial distribution of multiple suspected defect points, it can effectively avoid misjudgment caused by measurement errors or local anomalies and improve the reliability of defect recognition.

[0052] This embodiment adopts a circular area clustering method, that is, taking each suspected defect point as the center and examining other suspected defect points within its radius R. The selection of R is based on the average value of the crack length of the brick body in historical data. This design ensures that the judgment of the defect area conforms to the actual physical characteristics of the brick body, rather than arbitrarily setting a threshold, enhancing the scientificity and adaptability of defect recognition.

[0053] When determining the defect area, this embodiment not only pays attention to the number of adjacent suspected defect points but also considers their distribution characteristics. By analyzing whether there are defect points on a continuous straight line or curve (i.e., the possible extension direction of the crack), it can more accurately distinguish local scattered point anomalies and the true defect area and improve the accuracy of crack detection.

[0054] This embodiment introduces a calculation method for the quantity threshold. Using the statistical data of the standard brick body under the same heating detection conditions, combined with the average value, standard deviation, and quantity sensitivity coefficient (2 ≤ m ≤ 3), it ensures that the setting of the quantity threshold can adapt to the production situations of different brick bodies. Compared with the fixed threshold method, this method has stronger self - adaptability and can be dynamically adjusted according to the material characteristics and heat conduction characteristics of different brick bodies, improving the generalization ability of the system.

[0055] In some embodiments of the present application, determining the pulse width, cycle duration, and heating power of periodic thermal wave heating based on the brick thickness includes: the pulse width, cycle duration, and heating power are respectively calculated and obtained through the following relationships: ; ; ; Wherein, is the pulse width, is the brick thickness, is the cycle duration, is the design coefficient, 2 < < 3, is the heating power, is the brick density, is the preset target temperature rise, is the heated area, is the specific heat capacity of the brick to be measured.

[0056] It should be noted that in this embodiment, by adjusting the dynamic heating parameters based on the brick thickness, the periodic thermal wave heating process is optimized to ensure that bricks of different thicknesses can obtain appropriate heat input, thereby improving the accuracy and stability of the detection.

[0057] During the pulse thermal wave heating process, the pulse width ( ) is positively correlated with the brick thickness (d), ensuring that heat can be effectively transferred into the brick interior, without insufficient energy caused by too short a pulse or affecting the detection accuracy due to overheating of the surface caused by too long a pulse. Adjustment using the design coefficient enables the pulse width to adapt to the characteristics of different brick materials, improving the applicability of the method.

[0058] This embodiment also optimizes the heating cycle duration (T). The setting of the cycle duration also depends on the brick thickness (d) and a design coefficient. This can ensure that sufficient heating and cooling cycles are completed within a reasonable time range, preventing the influence of heat accumulation on subsequent data acquisition, and enabling the change of the temperature curve to better reflect the internal defect situation of the brick.

[0059] For the calculation of the heating power (P), this embodiment fully considers the brick density (ρ), target temperature rise (ΔT), and heated area (A) to ensure that the input energy matches the thermal physical properties of the brick, avoiding underheating or overheating. Compared with the method of fixed heating power, this embodiment can dynamically adjust the power according to the physical characteristics of the brick, making the calculation of the thermal diffusivity more stable and improving the reliability of the detection results.

[0060] Overall, in this embodiment, by adaptively adjusting the heating parameters based on the brick thickness, the pulse width, cycle duration, and heating power are optimized, enabling the detection system to adapt to high-temperature insulation bricks of different specifications and materials, improving the accuracy and applicable range of defect detection, and enhancing the intelligent level of the system.

[0061] The design coefficient γ is obtained through the following experiments: The first step in determining the design coefficient is to obtain the thermophysical properties of the brick. Under laboratory conditions, the basic parameters of the brick are measured, including the density, specific heat capacity, and thermal conductivity of the brick. These parameters determine the heat diffusion ability of the brick and affect the time and intensity required for heating. Especially when the thickness of the brick is relatively large, the heat diffusion speed will be relatively slow, so the heating time needs to be appropriately extended, and the design coefficient also needs to be adjusted accordingly.

[0062] The second step is to conduct experimental tests. In the initial stage, different design coefficients can be selected, and the temperature changes on the surface of the brick can be observed during the actual detection process. The dynamic temperature distribution is recorded by an infrared camera to analyze whether the heating process is uniform and ensure that the temperature does not become too high or too low. The test methods can include: gradually adjusting the design coefficient while keeping the pulse heating time fixed and observing the temperature distribution; then, continuing to adjust the design coefficient while keeping the cycle duration fixed to ensure that the heat diffusion achieves the expected effect.

[0063] The third step is data analysis and optimization. The experimental data under different design coefficients are collected and statistically analyzed to compare the temperature distribution, heating uniformity, and defect recognition accuracy of the brick under different design coefficients. Data fitting methods can be used, such as comparing the temperature change situations under different conditions to determine which design coefficient can make the heating process the most stable and effectively identify the defect area. In addition, error analysis can be introduced, such as calculating the deviation value between the target temperature and the actual measured temperature, or calculating the accuracy rate of defect detection, to select the optimal design coefficient.

[0064] The fourth step is to establish an empirical formula. Based on the experimental data, the relationship between the design coefficient, brick thickness, and heat diffusion ability can be summarized. For example, when the brick thickness increases, the design coefficient usually needs to be appropriately increased to compensate for the longer heat diffusion time; if the thermal conductivity of the brick is strong, the design coefficient can be appropriately decreased to prevent the temperature from changing too quickly and affecting the measurement accuracy. Through comparative analysis of a large amount of experimental data, the value range of the design coefficient can be finally determined, and a calculation rule can be established to enable it to automatically adapt to the detection requirements of different bricks.

[0065] Finally, determine the final design coefficient and verify it in actual applications. Multiple tests can be carried out using standard bricks to ensure that the selected design coefficient can maintain stability under different detection conditions. If it is found that the heating effect is not good in some special cases during the application process, the value range of the design coefficient can be further adjusted to make it more accurately adapt to different brick materials and thicknesses.

[0066] In some embodiments of the present application, when adjusting the heating power during the periodic thermal wave heating process according to the ambient temperature, it includes: obtaining the ambient temperature and the target temperature of the brick, and the adjusted heating power satisfies the following relationship: ; Wherein, is the adjusted heating power, is the ambient temperature, is the target temperature of the brick.

[0067] In some embodiments of the present application, calculating the evaluation score of the brick to be tested according to the ratio of the number of treatment points to the number of suspected defect points includes: Determine whether there is a defect area. If there is no defect area, the following method is used to obtain the calculated evaluation score: ; Wherein, is the evaluation score, is the number of treatment points, is the number of suspected defect points.

[0068] It should be noted that in this embodiment, by compensating the heating power according to the ambient temperature, the heating process is made more stable and accurate, and the reliability of defect detection is improved. The ambient temperature will affect the initial temperature of the brick, and thus affect the calculation accuracy of the thermal diffusivity. Therefore, in this embodiment, by correcting the heating power P', it is ensured that under different environmental conditions, the brick can reach a reasonable target temperature, avoiding misjudgment caused by ambient temperature deviation. The adjustment formula adopts a linear correction method, which can not only effectively correct the temperature difference effect, but also keep the calculation simple and efficient, thereby enhancing the adaptability of the system and enabling it to operate stably under different climatic conditions.

[0069] In terms of defect evaluation, this embodiment proposes a scoring mechanism based on the slope threshold, and evaluates the brick quality by counting the number of valid points in the slope circle threshold. The formula of Score fully considers the uniformity of the thermal diffusivity on the brick surface, and the scoring range is 0 - 100%, so that the detection result can not only distinguish qualified and unqualified products, but also provide a more refined quality grade classification. This method can effectively reflect the severity of brick defects, help the production process to carry out hierarchical screening, and improve the accuracy of quality control.

[0070] This scoring method has strong robustness because it does not rely on single-point data alone, but is based on the overall statistical information of multiple processing points. Even if there are individual error points, it will not affect the overall evaluation result. In addition, the setting of the scoring criteria can adapt to different types of brick detection, enhancing the versatility of the detection system.

[0071] Generally speaking, these two improvement points in this embodiment ensure the stability of detection through environmental temperature compensation on the one hand, and provide a more intuitive quality evaluation standard through the scoring mechanism on the other hand, overall improving the intelligent level and practical application value of the system.

[0072] In some embodiments of this application, if there is a defective area, it is determined that the brick to be tested is a defective product.

[0073] Refer to Figure 2 As shown, this embodiment provides a method for detecting defective products in the production of high-temperature insulation bricks, including: S1: Periodically heat the brick to be tested with a laser thermal conductivity meter, then record the temperature data of the brick to be tested with an infrared camera, and calculate the thermal diffusivity; judge whether there is a defective area inside the brick to be tested according to the thermal diffusivity; S2: Obtain the brick thickness of the brick to be tested, and determine the pulse width, cycle duration, and heating power of the periodic thermal wave heating according to the brick thickness; S3: Adjust the heating power during the periodic thermal wave heating according to the environmental temperature; S4: Calculate the evaluation score of the brick to be tested according to the ratio of the number of processing points to the number of suspected defective points.

[0074] It should be noted that first, this method realizes non-contact detection of internal defects of bricks by combining a laser thermal conductivity meter and an infrared camera. Compared with traditional destructive detection methods, this method can accurately obtain its thermal diffusion characteristics without affecting the integrity of the brick, so as to judge whether there are internal defects, improving the detection efficiency and avoiding material waste at the same time.

[0075] Secondly, this method adaptively adjusts the pulse width, cycle duration, and heating power of the periodic thermal wave heating according to the brick thickness, ensuring that bricks of different thicknesses can be reasonably heated. This adjustment mechanism can effectively improve the adaptability of detection, making it applicable to the production of insulation bricks of different specifications and helping to improve the versatility of the system.

[0076] In addition, the method further dynamically adjusts the heating power in combination with the ambient temperature, which can effectively reduce the influence of ambient temperature changes on the detection accuracy. In the actual production environment, temperature fluctuations may cause detection errors. By adjusting the heating power, the stability of brick heating can be ensured, thereby improving the accuracy and consistency of detection.

[0077] Finally, the method ensures the objectivity of defect determination by quantifying the evaluation score. When there are defects inside the brick, the system can calculate the specific score and determine defective products according to the set minimum score threshold. This scoring mechanism can effectively reduce the errors of manual judgment, improve the consistency and reliability of detection, and also provide more accurate data support for quality control.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still modifications or equivalent substitutions can be made to the specific embodiments of the present invention, and any modifications or equivalent substitutions without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A defective product detection system for high temperature insulation brick production, characterized in that: include: The detection module includes a laser thermal conductivity meter and an infrared camera. The detection module is configured to perform periodic thermal wave heating on the brick body to be tested by the laser thermal conductivity meter, and then record the temperature data of the brick body to be tested by the infrared camera to calculate the thermal diffusivity; and determine whether there is a defective area inside the brick body to be tested according to the thermal diffusivity; A heating determination module connected to the detection module, the heating determination module is configured to obtain the thickness of the brick body to be tested, and determine the pulse width, cycle duration and heating power of the periodic heat wave heating according to the brick body thickness; a heating adjustment module, connected to the detection module and the heating determination module respectively, and configured to adjust the heating power in the periodic heat wave heating process according to the ambient temperature; The evaluation module is configured to calculate the evaluation score of the brick body to be tested according to the ratio of the number of processed points to the number of suspected defect points.

2. The defective product detection system for high temperature insulation brick production according to claim 1 is characterized in that: The detection module is configured to perform periodic heat wave heating on the brick body to be tested by a laser thermal conductivity meter, comprising: The brick body to be tested is periodically heated in a square wave pulse heating manner, and several points on the surface of the brick body to be tested are selected as processing points. The processing points are points with known position coordinates. The temperature data of the processing points and the corresponding data acquisition time are obtained. The thermal diffusivity is obtained by the following relationship: ； in, is the thermal diffusivity, is the distance from the processing point to the heat source, >0, is the heating signal frequency, Indicates phase lag.

3. The defective product detection system for high temperature insulation brick production according to claim 2 is characterized in that: When judging whether there is a defective area inside the brick to be tested according to the thermal diffusivity, it includes: Taking the data collection time as the abscissa and the thermal diffusivity at the same processing point corresponding to the data collection time as the ordinate, a diffusion rate change curve of the processing point is established, and the slope of the diffusion rate change curve is calculated; Select slopes of different processing points at the same data collection time to establish a slope set, and calculate the mean value and standard deviation of the slope set; The slope threshold is defined according to the mean value and the standard deviation, and the slope threshold satisfies the following relationship: ； in, is the slope threshold, is the average value, is the standard deviation, is the slope sensitivity coefficient, 1≤ ≤3; The position coordinates corresponding to all slopes that are not within the slope threshold are obtained, the position coordinates are marked as suspected defect points, and the defect area is determined according to the suspected defect points.

4. The defective product detection system for high temperature insulation brick production according to claim 3 is characterized in that: Determining defect areas based on suspected defect points includes: The suspected defect points are analyzed one by one, and the suspected defect points are taken as target points one by one, and the position coordinates of the remaining suspected defect points within the circular range of radius R are obtained. If the number of adjacent or continuous suspected defect points exceeds the quantity threshold, the horizontal coordinate mean and the vertical coordinate mean of the position coordinates of the remaining suspected defect points other than the target point are calculated, and the distance value between the point and the center of the circle is calculated using the horizontal and vertical coordinate mean as the horizontal and vertical coordinates respectively, the minimum distance value when analyzing the suspected defect points one by one is selected, and the circular range corresponding to the minimum distance value is determined as the defect area.

5. The defective product detection system for high temperature insulation brick production according to claim 4 is characterized in that: The quantity threshold satisfies the following relationship: ； in, is the quantity threshold, is the average number of suspected defect points of the standard brick within the circular range at the same data collection time, is the quantity sensitivity coefficient, 2≤ ≤3, It is the standard deviation of suspected defect points of the standard brick within the circular range at the same data collection time.

6. The defective product detection system for high temperature insulation brick production according to claim 5 is characterized in that: Determining the pulse width, cycle duration and heating power of periodic heat wave heating according to the brick thickness includes: the pulse width, cycle duration and heating power are respectively calculated and obtained through the following relationships: ； ； ； in, is the pulse width, is the brick thickness, is the cycle duration, is the design coefficient, 2< <3, is the heating power, is the brick density, is the preset target temperature rise, is the area of ​​the heated region, is the specific heat capacity of the brick to be tested.

7. The defective product detection system for high temperature insulation brick production according to claim 6 is characterized in that: When the heating power in the periodic heat wave heating process is adjusted according to the ambient temperature, it includes: obtaining the ambient temperature and the target temperature of the brick body, and the adjusted heating power satisfies the following relationship: ； in, is the adjusted heating power, is the ambient temperature, is the target temperature of the brick body.

8. The defective product detection system for high temperature insulation brick production according to claim 7 is characterized in that: The evaluation score of the brick to be tested is calculated based on the ratio of the number of treated points to the number of suspected defect points, including: Determine whether there is a defective area. If there is no defective area, obtain the calculated evaluation score through the following method: ； in, To evaluate the score, is the number of processing points, is the number of suspected defect points.

9. The defective product detection system for high temperature insulation brick production according to claim 8 is characterized in that: If there is a defective area, the brick to be tested is judged to be a defective product.

10. A defective product detection method for high temperature insulation brick production, applied to the defective product detection system for high temperature insulation brick production according to any one of claims 1 to 9, characterized in that: include: The laser thermal conductivity meter is used to perform periodic heat wave heating on the brick body to be tested, and then the temperature data of the brick body to be tested is recorded by an infrared camera to calculate the thermal diffusivity; Judging whether there is a defective area inside the brick to be tested according to the thermal diffusivity; Obtaining the thickness of the brick body to be tested, and determining the pulse width, cycle duration and heating power of the periodic heat wave heating according to the brick body thickness; adjusting the heating power during the periodic heat wave heating process according to the ambient temperature; The evaluation score of the brick to be tested is calculated based on the ratio of the number of processed points to the number of suspected defect points.

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