Intelligent detection method and device for internal defects of mold
By using activated ray flaw detection module and material information correction technology in the detection of internal defects of molds, the problem of low accuracy and reliability of detection results in the existing detection methods is solved, and higher detection accuracy and reliability are achieved.
Patent Information
- Application Number
- CN202510139631.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-08
- Publication Date
- 2025-05-30
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The accuracy and reliability of the detection results of existing internal defect detection methods of molds are not high. It is mainly because the mold consists of a variety of materials, and the absorption and scattering of rays and ultrasonic characteristics of the material are different, resulting in the detection results being greatly affected by the material characteristics.
The activated ray flaw detection module is used to detect internal defects on the target mold, obtain preliminary target ray detection results, collect material information of the target mold, read a predetermined loss correction plan, and correct the detection results in combination with material information to reduce the impact of material characteristics on the detection results.
Through this method, the accuracy and reliability of the internal defect detection results of the mold are improved, and the impact of material characteristics on the detection results is reduced.
Smart Images
Figure CN120064341A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of non-destructive testing, and in particular to an intelligent detection method and device for internal defects of a mold. Background Art
[0002] During the manufacturing and use of molds, due to various reasons (such as uneven materials, improper processing techniques, etc.), various defects will appear inside the mold, such as air holes, slag inclusions, cracks, etc. These defects will not only affect the performance and service life of the mold, but also have a serious impact on the quality of the products produced. Therefore, internal defect detection of the mold is an important link to ensure the quality of the mold and production safety. Existing methods generally use methods such as radiographic testing and ultrasonic testing to detect internal defects of the mold. Although these methods can detect internal defects of the mold to a certain extent, since the mold is usually composed of multiple materials, and each material has different characteristics such as absorption and scattering of rays and ultrasonic waves, the detection results are easily affected by the material characteristics, resulting in low accuracy and reliability of the detection results.
[0003] In the current related technologies, there is a technical problem that the accuracy and reliability of the detection results of internal defects of the mold are not high. Summary of the Invention
[0004] This application provides an intelligent detection method and device for internal defects of a mold. By using an activated radiographic testing module to detect internal defects of the target mold to obtain a preliminary target radiographic testing result, collecting the material information of the target mold, reading a predetermined loss correction scheme, and correcting the preliminary target radiographic testing result in combination with the collected target material information and other technical means, the influence of material characteristics on the detection result is reduced, and the technical effect of improving the accuracy and reliability of the detection result is achieved.
[0005] This application provides an intelligent detection method for internal defects of a mold, including: Activating a radiographic testing module to detect internal defects of the target mold to obtain a target radiographic testing result; Collecting the target material information of the target mold, where the target material information includes multiple material types with proportion identifiers; Reading a predetermined loss correction scheme, and correcting the target radiographic testing result in combination with the multiple material types with proportion identifiers to obtain a target detection result.
[0006] In a possible implementation manner, after obtaining the target radiographic testing result, the following processing is performed: The predetermined radiographic testing information of the target mold is stored in the memory of the radiographic testing module; Obtaining the target radiographic testing information of the target mold detected by the radiographic testing module; Compare the target ray detection information with the predetermined ray detection information to obtain a comparison deviation; Obtain the target ray detection result according to the comparison deviation.
[0007] In a possible implementation, to obtain the comparison deviation, perform the following processing: Extract the first die block from the set of target die blocks obtained by partitioning the target die, where the first die block is mapped to the first detection point; Traverse the predetermined ray detection information to obtain the first ray attenuation curve at the first detection point; Traverse the target ray detection information to obtain the second ray attenuation curve at the first detection point; Based on the predetermined attenuation curve features, perform feature extraction on the first ray attenuation curve and the second ray attenuation curve in sequence to obtain the first curve feature parameter and the second curve feature parameter respectively; Add the first curve feature deviation index obtained by comparing the first curve feature parameter and the second curve feature parameter to the comparison deviation.
[0008] In a possible implementation, perform the following processing: The predetermined attenuation curve features include curve width, curve height, signal height difference between two endpoints, signal height peak value, signal height valley value, signal peak corresponding time, and signal valley corresponding time.
[0009] In a possible implementation, to obtain the target detection result, perform the following processing: Judge whether the target die meets the first predetermined constraint based on the target material information; If it meets the requirement, retrieve the first predetermined plan in the predetermined loss correction plan; Activate the ultrasonic flaw detection module according to the first predetermined plan to perform internal defect detection on the target die to obtain the target ultrasonic detection result; If it does not meet the requirement, retrieve the second predetermined plan in the predetermined loss correction plan; Activate the penetrant flaw detection module according to the second predetermined plan to perform internal defect detection on the target die to obtain the target penetrant detection result; Correct the target ray detection result according to the target ultrasonic detection result or the target penetrant detection result to obtain the target detection result.
[0010] In a possible implementation, after obtaining the target ultrasonic detection result, perform the following processing: Traverse and analyze the first material type by calling the database of acoustic properties of materials to obtain the first acoustic property information, where the first material type is any one of the multiple material types with proportion identifiers; Obtain the target acoustic feedback coefficient of the target mold based on the first acoustic feedback coefficient obtained by adjusting the first initial acoustic feedback coefficient analyzed from the first acoustic property information according to the first proportion; Feedback and adjust the target ultrasonic detection result by weighting the target acoustic feedback coefficient.
[0011] In a possible implementation, to obtain the first acoustic feedback coefficient, perform the following processing: Extract and analyze the first acoustic property information based on a predetermined acoustic property index to obtain the first initial acoustic feedback coefficient; Perform a weighted calculation on the first initial acoustic feedback coefficient using the first proportion as a weight coefficient to obtain the first acoustic feedback coefficient; Wherein, the first proportion refers to the proportion of the first material type matched based on the multiple material types with proportion identifiers in the target mold; The first acoustic property information includes the first absorption rate, first scattering rate, and first propagation speed of the first material type for ultrasonic waves.
[0012] This application also provides an intelligent detection device for internal defects of a mold, including: A target ray detection result acquisition module, which is used to activate a ray flaw detection module to detect internal defects of a target mold and obtain a target ray detection result; A target material information acquisition module, which is used to acquire the target material information of the target mold, and the target material information includes multiple material types with proportion identifiers; A target detection result acquisition module, which is used to read a predetermined loss correction scheme and correct the target ray detection result in combination with the multiple material types with proportion identifiers to obtain a target detection result.
[0013] It is intended to propose an intelligent detection method and device for internal defects of a mold through this application. By activating a ray flaw detection module to detect internal defects of a target mold to obtain a target ray detection result, then collecting the target material information of the target mold, where the target material information includes multiple material types with proportion identifiers, and then reading a predetermined loss correction scheme and correcting the target ray detection result in combination with the multiple material types with proportion identifiers to obtain a target detection result, the technical effect of improving the accuracy and reliability of the detection result is achieved. Description of the Drawings
[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the devices according to the embodiments of the present application. It should be understood that the operations above or below do not necessarily need to be performed precisely in sequence. On the contrary, as needed, various steps can be performed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.
[0015] Figure 1 It is a schematic flowchart of an intelligent detection method for internal defects of a mold provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of an intelligent detection device for internal defects of a mold provided by an embodiment of the present application.
[0016] Explanation of reference numerals: Target ray detection result acquisition module 10, target material information acquisition module 20, target detection result acquisition module 30. Detailed implementation manners
[0017] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.
[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.
[0019] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first" and "second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.
[0020] An embodiment of the present application provides an intelligent detection method for internal defects of a mold, as Figure 1 shown, the method includes: Step S100, activate the ray flaw detection module to perform internal defect detection on the target mold to obtain a target ray detection result. Specifically, ray flaw detection is a non-destructive detection method based on the absorption and scattering characteristics of different substances to rays. When rays pass through a substance, they will interact with the atoms in the substance, resulting in the attenuation of the ray energy. Different substances have different attenuation degrees for rays. Therefore, it is possible to judge whether there are defects inside the object by detecting the attenuation of the rays. Start the ray flaw detection module. The ray flaw detection module includes a ray emitter and a receiver. The ray emitter emits rays (such as X-rays or γ-rays) to penetrate the target mold, and the receiver is used to receive the rays after penetrating the target mold. When the rays penetrate the target mold, if there are defects (such as cracks, pores, etc.) inside the target mold, the rays will be attenuated to different degrees, and this attenuation degree is related to the size, shape and position of the defects. The receiver receives the ray signal after penetrating the target mold and converts it into an electrical signal. By processing and analyzing the electrical signal, information about the internal defects of the target mold is extracted to generate a target ray detection result.
[0021] In a possible implementation, to obtain the target ray detection result, step S100 further includes step S110, where the predetermined ray detection information of the target mold is stored in the ray flaw detection module. Specifically, the predetermined ray detection information includes the attenuation characteristics of the target mold after ray penetration under standard conditions, providing a standard reference for a defect-free state, which is used as a benchmark for comparative analysis. Before the ray flaw detection module is started, the ray detection information of the target mold in a preset, known defect-free or standard state is stored in the memory of the target module as the predetermined ray detection information. In this way, any deviation from the standard state can be identified, thus indicating potential defects. Step S120, obtain the target ray detection information of the target mold detected by the ray flaw detection module. Specifically, when the ray flaw detection module performs ray detection on the target mold, the ray flaw detection module receives and processes the ray signals after penetrating the target mold. These ray signals are converted and calculated to generate the target ray detection information, which is real-time data during the actual detection process and records the attenuation of the rays during the penetration of the target mold. Step S130, compare the target ray detection information with the predetermined ray detection information to obtain a comparison deviation. Specifically, compare the target ray detection information with the predetermined ray detection information stored in step S110 point by point or region by region. By comparing the differences between the two, identify the deviation of the target ray detection information relative to the predetermined ray detection information to obtain the comparison deviation. The comparison deviation is manifested as differences in the degree of ray attenuation, changes in the shape of the attenuation curve, etc. Calculate the comparison deviation to discover the differences between the target mold and the standard state, which are caused by defects inside the target mold, material inhomogeneity, or other factors. Through the comparison deviation, it can be preliminarily judged whether there are potential problems with the target mold. Step S140, obtain the target ray detection result according to the comparison deviation. Specifically, evaluate the internal state of the target mold according to factors such as the magnitude, distribution, and pattern of the comparison deviation, combined with preset algorithms and thresholds. Through analysis and calculation, generate the target ray detection result, which includes information such as the location, size, and type of the defect. The target ray detection result is the output of the ray detection process, obtained based on the analysis and calculation of the comparison deviation, and shows the situation of potential defects inside the target mold. This implementation method uses a comparative analysis method to obtain the target ray detection result. By comparing with the predetermined ray detection information, it eliminates some interferences caused by factors such as detection conditions and equipment errors, achieving the technical effect of improving the accuracy and reliability of ray detection.
[0022] In a possible implementation, to obtain the comparison deviation, step S130 further includes step S131 of extracting a first die block from the set of target die blocks obtained by partitioning the target die. Herein, the first die block is mapped to a first detection point. Specifically, according to a predetermined partitioning method, for example, based on the structure, functional area, or other relevant factors of the die, the target die is partitioned into multiple small blocks, namely the set of target die blocks. By partitioning the target die into multiple die blocks, it is used to reduce the complexity of data and specifically analyze and process the ray detection information. In the set of target die blocks, a specific die block is selected or designated as the first die block. The first die block has a clear identifier, such as a position coordinate or an identifier, and the first die block is associated with the first detection point through this identifier. Step S132 is to obtain the first ray attenuation curve of the first detection point by traversing the predetermined ray detection information. Specifically, according to the position or identifier of the first die block, the corresponding first detection point is located in the predetermined ray detection information, and the ray attenuation data at this detection point is extracted. The ray attenuation data is a series of numerical values or records regarding the degree of ray attenuation. Based on these ray attenuation data, the first ray attenuation curve is plotted or generated. The first ray attenuation curve describes the attenuation of the ray at the first detection point under a predetermined state. Step S133 is to obtain the second ray attenuation curve of the first detection point by traversing the target ray detection information. Similarly, according to the position or identifier of the first die block, the corresponding first detection point is located in the target ray detection information, and the real-time ray attenuation data at this detection point is extracted. Based on these real-time ray attenuation data, the second ray attenuation curve is plotted or generated. The second ray attenuation curve describes the attenuation of the ray at the first detection point during the actual detection process. Step S134 is to perform feature extraction on the first ray attenuation curve and the second ray attenuation curve in sequence based on the predetermined attenuation curve characteristics, respectively obtaining a first curve feature parameter and a second curve feature parameter. Specifically, according to the predetermined attenuation curve feature extraction method, the first ray attenuation curve is analyzed to extract representative feature parameters, such as peak value, valley value, slope, area, etc., to form a set of first curve feature parameters. Similarly, the second ray attenuation curve is subjected to feature extraction to obtain a set of second curve feature parameters. By extracting the curve feature parameters, it is used to quantify the difference in ray attenuation, thereby determining the state of the target die. Step S135 is to add the first curve feature deviation index obtained by comparing the first curve feature parameter and the second curve feature parameter to the comparison deviation.Specifically, the first curve characteristic parameters are compared one by one with the second curve characteristic parameters, and the differences or deviation values between the two are calculated. Based on these deviation values, methods such as coefficient of variation weighted calculation are used to calculate the first curve characteristic deviation index. The first curve characteristic deviation index is a quantitative representation of the deviation of the first die block in terms of ray attenuation characteristics. The first curve characteristic deviation index is added to the set of comparison deviations for subsequent comprehensive analysis and evaluation. This implementation method divides the target die into multiple die blocks, performs fine detection and analysis on each die block, reduces the complexity and redundancy of data, improves the detection efficiency, and achieves the technical effects of improving the accuracy and reliability of ray detection results by focusing on detecting and comparing specific areas.
[0023] In a possible implementation, step S134 further includes step S1341. The predetermined attenuation curve characteristics include curve width, curve height, signal height difference between two endpoints, signal height peak value, signal height valley value, time corresponding to signal peak value, and time corresponding to signal valley value. Specifically, the curve width reflects the duration or range of the ray attenuation process and is used to analyze the absorption or scattering ability of the material to the ray; the curve height represents the overall degree of ray attenuation and is used to compare the ray attenuation effects under different conditions or for different materials; the signal height difference between two endpoints indicates the intensity change of the ray before and after penetrating the material and is used to judge the thickness of the material or its blocking ability to the ray; the signal height peak value and signal height valley value respectively represent the maximum and minimum intensities during the ray attenuation process and are used to analyze the internal structure or composition changes of the material; the time corresponding to signal peak value and the time corresponding to signal valley value provide information on the ray attenuation speed and are used to analyze the response speed or attenuation mechanism of the material to the ray. This implementation method comprehensively and meticulously describes the shape and characteristics of the ray attenuation curve by extracting these characteristic parameters, thereby enabling more accurate analysis of the properties and states of materials and achieving the technical effects of making the ray detection results more reliable and precise.
[0024] Step S200: Collect the target material information of the target die. The target material information includes multiple material types with proportion identifiers. Specifically, the type and characteristics of the material directly affect the propagation and attenuation of rays in the target die. Therefore, it is necessary to collect the target material information of the target die. Specifically, determine the target material information to be collected, including the type, proportion, physical properties, etc. of the material. According to the characteristics of the target die and the type of information required, determine the collection method, including consulting the manufacturing documents of the target die, conducting chemical analysis on the target die, or using specialized detection equipment, etc. Record the collected target material information and organize it in a certain format.
[0025] Step S300: Read a predetermined loss correction scheme and correct the target ray detection result in combination with the multiple material types with ratio identifiers to obtain a target detection result. Specifically, read the predetermined loss correction scheme, which is formulated based on a large amount of experimental data and experience, takes into account the influence of different materials on ray detection, and is used to guide how to correct the detection result according to material information. According to the collected target material information, in combination with multiple material types with ratio identifiers, correct the target ray detection result, including adjusting the value of the target ray detection result, or re-evaluating the position and type of defects, etc. After correction, output the corrected detection result, that is, the target detection result. In the embodiment of the present application, an activated ray flaw detection module is used to detect internal defects of a target mold to obtain a preliminary target ray detection result, collect the material information of the target mold, read the predetermined loss correction scheme, and correct the preliminary target ray detection result in combination with the collected target material information and other technical means, which reduces the influence of material characteristics on the detection result and achieves the technical effect of improving the accuracy and reliability of the detection result.
[0026] In a possible implementation, to obtain the target detection result, step S300 further includes step S310 of judging whether the target mold meets the first predetermined constraint based on the target material information. Specifically, the material information of the target mold includes the composition, proportion, etc. of the material. The first predetermined constraint is set based on the detection purpose and the actual use of the target mold. For example, if the target mold needs to have a certain strength and wear resistance, then the metal content is an important indicator, and the first predetermined constraint can be set as the metal content being greater than or equal to 60%. Compare the material information of the target mold with the first predetermined constraint to judge whether the target mold meets the first predetermined constraint. Step S320, if it meets, retrieve the first predetermined plan in the predetermined loss correction plan. Specifically, when the target mold meets the first predetermined constraint, trigger the process of retrieving the predetermined loss correction plan. The predetermined loss correction plan contains various correction strategies for different situations, and these correction strategies are formulated according to factors such as the material, structure, and defect type of the mold. Search for the first predetermined plan that matches the first predetermined constraint in the predetermined loss correction plan. Step S330, activate the ultrasonic flaw detection module according to the first predetermined plan to perform internal defect detection on the target mold to obtain the target ultrasonic detection result. Specifically, under the guidance of the first predetermined plan, activate the ultrasonic flaw detection module. Ultrasonic flaw detection is a non-destructive testing method. By emitting ultrasonic waves into the material and receiving their reflected signals, defects and abnormalities inside the material can be detected. For molds with a high metal content, ultrasonic flaw detection can effectively detect defects such as cracks and pores inside the mold. Use the ultrasonic flaw detection module to perform an internal scan on the target mold, detect possible defects, record and analyze the ultrasonic flaw detection data to obtain the target ultrasonic detection result. Step S340, if it does not meet, retrieve the second predetermined plan in the predetermined loss correction plan. Specifically, when the target mold does not meet the first predetermined constraint, trigger the process of retrieving the second predetermined plan, and search for the second predetermined plan that matches the current situation in the predetermined loss correction plan, that is, for non-metal molds or molds with a low metal content, other types of detection methods need to be used. Step S350, activate the penetrant flaw detection module according to the second predetermined plan to perform internal defect detection on the target mold to obtain the target penetrant detection result. Specifically, under the guidance of the second predetermined plan, activate the penetrant flaw detection module. Penetrant flaw detection is a non-destructive testing method suitable for non-metal materials and materials with a low metal content, and can effectively detect cracks and pores on the surface of the material. Use the penetrant flaw detection module to perform a surface detection on the target mold, search for possible defects, record and analyze the penetrant flaw detection data to obtain the target penetrant detection result. Step S360, correct the target ray detection result according to the target ultrasonic detection result or the target penetrant detection result to obtain the target detection result.Specifically, analyze the target ultrasonic inspection results or target penetrant inspection results to determine the specific defect types and locations of the target mold. According to the defect types and locations, correct the target radiographic inspection results to eliminate or reduce errors and obtain the target inspection results. Although radiographic inspection can detect defects inside the target mold, due to the limitations of its principle and technology, there are sometimes errors or deficiencies. This implementation method corrects the radiographic inspection results by combining the results of multiple inspection methods, achieving the technical effect of improving the accuracy and reliability of inspection.
[0027] In a possible implementation, after obtaining the target ultrasonic detection result, step S330 further includes step S331 of traversing and analyzing the first material type by calling the material acoustic property database to obtain the first acoustic property information, where the first material type is any one of the multiple material types with proportion identifiers. Specifically, access the material acoustic property database, which is a database storing the acoustic properties of different materials. These properties determine the characteristics of ultrasonic wave propagation in materials. Select the first material type from the material acoustic property database, traverse and analyze the first material type, and obtain the acoustic property information related to the first material type, such as sound velocity, attenuation coefficient, impedance, etc. Step S332, obtain the target acoustic feedback coefficient of the target mold according to the first acoustic feedback coefficient obtained by adjusting the first initial acoustic feedback coefficient obtained by analyzing the first acoustic property information in combination with the first proportion. Specifically, according to the first acoustic property information obtained in step S331, calculate the first initial acoustic feedback coefficient, which represents the propagation characteristics of ultrasonic waves in the first material type. Since the target mold contains multiple material components, adjust the first initial acoustic feedback coefficient in combination with the first proportion (i.e., the proportion of the first material type in the target mold). The adjusted acoustic feedback coefficient is the first acoustic feedback coefficient. For other material types in the target mold, use the same method to obtain the second acoustic feedback coefficient, the third acoustic feedback coefficient,... Finally, perform a weighted average on the adjusted acoustic feedback coefficients of all material types to obtain the target acoustic feedback coefficient of the target mold. The target acoustic feedback coefficient is a parameter used to describe the degree of influence on ultrasonic wave propagation in the target mold. Step S333, perform a feedback adjustment on the target ultrasonic detection result by weighting the target acoustic feedback coefficient. Specifically, use the target acoustic feedback coefficient as a weighting factor to perform a weighting process on the target ultrasonic detection result. Through the weighting process, perform a feedback adjustment on the target ultrasonic detection result to eliminate or reduce the error caused by the material acoustic properties. Due to the differences in the acoustic properties of different materials, ultrasonic waves will be absorbed, scattered, and interfered to varying degrees during propagation. These factors cause certain errors in the ultrasonic flaw detection results. This implementation eliminates or reduces these errors by performing a feedback adjustment on the ultrasonic detection result by weighting the target acoustic feedback coefficient, achieving the technical effect of making the ultrasonic detection result more accurate and reliable.
[0028] In a possible implementation, to obtain the first acoustic feedback coefficient, step S332 further includes step S3321 of extracting and analyzing the first acoustic characteristic information based on a predetermined acoustic characteristic index to obtain the first initial acoustic feedback coefficient. The first acoustic characteristic information includes the first absorption rate, the first scattering rate, and the first propagation speed of the ultrasonic wave by the first material type. Specifically, a predetermined acoustic characteristic index is set in advance. The acoustic characteristic index is a series of parameters used to analyze and describe the acoustic performance of materials, including but not limited to: the absorption rate, scattering rate, propagation speed, etc. of the material. Based on the predetermined acoustic characteristic index, the first acoustic characteristic information related to the first material type is extracted from the material acoustic characteristic database, including the first absorption rate, the first scattering rate, and the first propagation speed of the ultrasonic wave by the first material type. The first acoustic characteristic information is analyzed to determine their influence on the propagation of the ultrasonic wave in the material. For example, the absorption rate determines the degree to which the ultrasonic wave is absorbed in the material, the scattering rate affects the propagation direction of the ultrasonic wave, and the propagation speed determines the propagation time of the ultrasonic wave in the material. Based on the analysis results, combined with a predetermined calculation model or formula, the first initial acoustic feedback coefficient is calculated. The first initial acoustic feedback coefficient reflects the basic feedback of the ultrasonic wave propagation in a single material type (the first material type) without considering the material ratio. Step S3322 is to perform a weighted calculation on the first initial acoustic feedback coefficient using the first ratio as a weight coefficient to obtain the first acoustic feedback coefficient, where the first ratio refers to the ratio of the first material type matched based on the multiple material types with ratio identifiers in the target mold. Specifically, the material composition of the target mold is analyzed by methods such as experimental measurement, material analysis, or design documents to determine the ratio of the first material type in the target mold, that is, the first ratio. The first ratio is used as a weight coefficient to perform a weighted calculation on the first initial acoustic feedback coefficient, that is, to combine the initial acoustic feedback coefficient with the material ratio to obtain a more realistic acoustic feedback coefficient, that is, the first acoustic feedback coefficient. The first acoustic feedback coefficient not only considers the acoustic characteristics of the first material type itself but also the actual ratio of the first material type in the target mold, and more accurately reflects the propagation of the ultrasonic wave in the target mold. Since different materials have different absorption, scattering, and propagation speeds for ultrasonic waves, and these characteristics affect the effect of ultrasonic detection, this implementation obtains the first initial acoustic feedback coefficient by extracting and analyzing the first absorption rate, the first scattering rate, and the first propagation speed of the ultrasonic wave by the first material type, accurately reflecting the propagation of the ultrasonic wave in the target mold, and achieving the technical effect of improving the accuracy of obtaining the first acoustic feedback coefficient.
[0029] In the foregoing, with reference to Figure 1 A method for intelligent detection of internal defects of a mold according to an embodiment of the present invention is described in detail. Next, with reference toFigure 2 Describe an intelligent detection device for internal defects of a mold according to an embodiment of the present invention.
[0030] An intelligent detection device for internal defects of a mold according to an embodiment of the present invention is used to solve the technical problem of low accuracy and reliability of the detection results in the existing detection of internal defects of molds, and achieve the technical effect of improving the accuracy and reliability of the detection results. An intelligent detection device for internal defects of a mold includes: a target ray detection result acquisition module 10, a target material information acquisition module 20, and a target detection result acquisition module 30.
[0031] The target ray detection result acquisition module 10 is used to activate the ray flaw detection module to detect internal defects of the target mold and obtain the target ray detection result; The target material information acquisition module 20 is used to collect the target material information of the target mold, and the target material information includes multiple material types with proportion identifiers; The target detection result acquisition module 30 is used to read a predetermined loss correction scheme and correct the target ray detection result in combination with the multiple material types with proportion identifiers to obtain the target detection result.
[0032] Next, the specific configuration of the target ray detection result acquisition module 10 will be described in detail. As described above, to obtain the target ray detection result, the target ray detection result acquisition module 10 may further include: a ray flaw detection module configuration unit for storing the predetermined ray detection information of the target mold in the ray flaw detection module; a target ray detection information acquisition unit for acquiring the target ray detection information of the target mold detected by the ray flaw detection module; a comparison unit for comparing the target ray detection information with the predetermined ray detection information to obtain a comparison deviation; and a target ray detection result acquisition unit for obtaining the target ray detection result according to the comparison deviation.
[0033] Among them, to obtain the comparison deviation, the comparison unit may further include: a first die block extraction subunit configured to extract a first die block from a set of target die blocks obtained by dividing the target die, where the first die block is mapped to a first detection point; a first ray attenuation curve acquisition subunit configured to obtain a first ray attenuation curve of the first detection point by traversing the predetermined ray detection information; a second ray attenuation curve acquisition subunit configured to obtain a second ray attenuation curve of the first detection point by traversing the target ray detection information; a feature extraction subunit configured to sequentially perform feature extraction on the first ray attenuation curve and the second ray attenuation curve based on predetermined attenuation curve features to obtain a first curve feature parameter and a second curve feature parameter respectively; and a feature deviation index addition subunit configured to add a first curve feature deviation index obtained by comparing the first curve feature parameter and the second curve feature parameter to the comparison deviation.
[0034] Among them, the feature extraction subunit may further include: a predetermined attenuation curve feature construction micro-unit, where the predetermined attenuation curve features include curve width, curve height, signal height difference between two end points, signal height peak value, signal height valley value, time corresponding to the signal peak value, and time corresponding to the signal valley value.
[0035] Next, the specific configuration of the target detection result acquisition module 30 will be described in detail. As described above, to obtain the target detection result, the target detection result acquisition module 30 may further include: a judgment unit configured to judge whether the target die meets a first predetermined constraint based on the target material information; a first predetermined scheme retrieval unit configured to retrieve a first predetermined scheme in the predetermined loss correction scheme if it meets the requirement; a target ultrasonic detection result acquisition unit configured to activate an ultrasonic flaw detection module to perform internal defect detection on the target die according to the first predetermined scheme to obtain a target ultrasonic detection result; a second predetermined scheme retrieval unit configured to retrieve a second predetermined scheme in the predetermined loss correction scheme if it does not meet the requirement; a target penetrant detection result acquisition unit configured to activate a penetrant flaw detection module to perform internal defect detection on the target die according to the second predetermined scheme to obtain a target penetrant detection result; and a correction unit configured to correct the target ray detection result according to the target ultrasonic detection result or the target penetrant detection result to obtain the target detection result.
[0036] After obtaining the target ultrasonic detection result, the target ultrasonic detection result acquisition unit may further include: a first acoustic characteristic information acquisition subunit configured to traverse and analyze a first material type by invoking a material acoustic characteristic database to obtain first acoustic characteristic information, where the first material type is any one of the multiple material types with proportion identifiers; a target acoustic feedback coefficient acquisition subunit configured to obtain a target acoustic feedback coefficient of the target mold according to a first acoustic feedback coefficient obtained by adjusting a first initial acoustic feedback coefficient obtained by analyzing the first acoustic characteristic information in combination with a first proportion; and a feedback adjustment subunit configured to perform feedback adjustment on the target ultrasonic detection result by weighting the target acoustic feedback coefficient.
[0037] After obtaining the first acoustic feedback coefficient, the target acoustic feedback coefficient acquisition subunit may further include: a first initial acoustic feedback coefficient acquisition micro-unit configured to extract and analyze the first acoustic characteristic information based on a predetermined acoustic characteristic index to obtain the first initial acoustic feedback coefficient, where the first acoustic characteristic information includes a first absorption rate, a first scattering rate, and a first propagation speed of the first material type with respect to ultrasonic waves; and a first acoustic feedback coefficient acquisition micro-unit configured to perform weighted calculation on the first initial acoustic feedback coefficient by using the first proportion as a weight coefficient to obtain the first acoustic feedback coefficient, where the first proportion refers to the proportion of the first material type matched based on the multiple material types with proportion identifiers in the target mold.
[0038] An intelligent detection device for internal defects of a mold provided by an embodiment of the present invention can execute an intelligent detection method for internal defects of a mold provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.
[0039] Although various references are made to certain modules in the device according to the embodiments of the present application, any number of different modules can be used and run on a user terminal and / or a server. The included respective units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.
[0040] The above specific embodiments do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the protection scope of this application. In some cases, the actions or steps recited in this application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.
Claims
1. An intelligent detection method for internal defects of a mold, characterized in that: include: Activate the X-ray flaw detection module to perform internal defect detection on the target mold and obtain the target X-ray detection result; Collecting target material information of the target mold, wherein the target material information includes a plurality of material types with proportion marks; The predetermined loss correction scheme is read, and the target ray detection result is corrected in combination with the multiple material types with proportional identification to obtain the target detection result.
2. The method according to claim 1, characterized in that The process of obtaining the target ray detection results includes: The radiographic flaw detection module stores predetermined radiographic detection information of the target mold; Acquire target ray detection information of the target mold detected by the ray flaw detection module; Comparing the target ray detection information with the predetermined ray detection information to obtain a comparison deviation; The target ray detection result is obtained according to the comparison deviation.
3. The method according to claim 2, characterized in that The process of obtaining the contrast deviation includes: Extracting a first mold block from a target mold block set obtained by dividing the target mold, wherein the first mold block is mapped to a first detection point; Obtaining a first ray attenuation curve of the first detection point by traversing the predetermined ray detection information; Obtaining a second ray attenuation curve of the first detection point by traversing the target ray detection information; Based on the predetermined attenuation curve characteristics, feature extraction is performed on the first ray attenuation curve and the second ray attenuation curve in sequence to obtain first curve feature parameters and second curve feature parameters respectively; A first curve characteristic deviation index obtained by comparing the first curve characteristic parameter and the second curve characteristic parameter is added to the comparison deviation.
4. The method according to claim 3, characterized in that The predetermined attenuation curve characteristics include curve width, curve height, difference in signal height between two end points, signal height peak, signal height valley, time corresponding to signal peak, and time corresponding to signal valley.
5. The method according to claim 1, characterized in that The process of obtaining target detection results includes: Determining whether the target mold meets a first predetermined constraint based on the target material information; If it is in compliance, calling the first predetermined plan among the predetermined loss correction plans; According to the first predetermined scheme, the ultrasonic flaw detection module is activated to perform internal defect detection on the target mold to obtain a target ultrasonic detection result; If it does not meet the requirements, calling the second predetermined plan in the predetermined loss correction plan; According to the second predetermined scheme, the penetrant flaw detection module is activated to perform internal defect detection on the target mold to obtain a target penetrant detection result; The target radiographic detection result is corrected according to the target ultrasonic detection result or the target penetration detection result to obtain the target detection result.
6. The method according to claim 5, characterized in that After obtaining the target ultrasonic test results, including: Calling a material acoustic property database to perform traversal analysis on a first material type to obtain first acoustic property information, wherein the first material type is any one of the multiple material types with proportion identifiers; Obtaining a target acoustic feedback coefficient of the target mold according to a first acoustic feedback coefficient obtained by adjusting a first initial acoustic feedback coefficient obtained by analyzing the first acoustic characteristic information in combination with a first ratio; The target acoustic feedback coefficient is weighted to perform feedback adjustment on the target ultrasonic detection result.
7. The method according to claim 6, characterized in that The process of obtaining the first acoustic feedback coefficient includes: Extracting and analyzing the first acoustic characteristic information based on a predetermined acoustic characteristic index to obtain the first initial acoustic feedback coefficient; Using the first ratio as a weight coefficient to perform weighted calculation on the first initial acoustic feedback coefficient to obtain the first acoustic feedback coefficient; The first ratio refers to the ratio of the first material type matched based on the multiple material types with ratio identifiers in the target mold; The first acoustic characteristic information includes a first absorption rate, a first scattering rate, and a first propagation speed of the first material type to ultrasonic waves.
8. An intelligent detection device for internal defects of a mold, characterized in that: The device is used to implement the intelligent detection method for internal defects of a mold according to any one of claims 1 to 7, and the device comprises: A target radiographic detection result acquisition module, which is used to activate the radiographic flaw detection module to perform internal defect detection on the target mold to obtain a target radiographic detection result; A target material information acquisition module, the target material information acquisition module is used to acquire target material information of the target mold, the target material information includes a plurality of material types with proportion marks; The target detection result acquisition module is used to read a predetermined loss correction scheme, and correct the target ray detection result in combination with the multiple material types with proportional identification to obtain the target detection result.