CN118465220B - High-detection-precision nondestructive detection method and system for weld joint - Google Patents
High-detection-precision nondestructive detection method and system for weld joint Download PDFInfo
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Abstract
The invention discloses a high-detection-precision nondestructive detection method and a high-detection-precision nondestructive detection system for a welding seam, which relate to the technical field of welding seam detection of an airplane structure, wherein in the operation of the system, data of a welding seam area of a machine body, including surface characteristic information, material attribute data and a welding seam defect part condition of a welding part, are collected through an ultrasonic probe or an X-ray detector and transmitted to a preprocessing module, the collected data are preprocessed, the preprocessed data are subjected to characteristic extraction, and a first data set, a second data set and a third data set are subjected to quantitative analysis, are combined and then are calculated to obtain: and the nondestructive testing index Gjjd is used for comparing the nondestructive testing index Gjjd obtained by the characteristic extraction module with a preset qualified threshold value X and a preset qualified threshold value Z, performing defect detection and identification, displaying the detected result of the weld defects of the airframe to operators, and simultaneously generating a detailed testing report to provide a reference basis for aircraft maintenance and repair.
Description
Technical Field
The invention relates to the technical field of welding seam detection of aircraft structures, in particular to a high-detection-precision welding seam nondestructive detection method and a high-detection-precision welding seam nondestructive detection system.
Background
Welding is a crucial process in the high-tech fields of aerospace and aviation. The welding joins the various components of the aircraft structure directly to the safety and reliability of the aircraft. However, due to the complexity of the welding process and the extremely high requirements of aerospace engineering on weld quality, conventional visual inspection and destructive inspection methods have failed to meet the requirements of nondestructive inspection of the weld. Therefore, there is an urgent need for a high detection accuracy weld nondestructive inspection system to ensure weld quality and aircraft safety. Such a system needs to be able to accurately and reliably detect possible minor defects in the weld, and to be able to provide detailed detection reports, providing important basis for maintenance and repair of the aircraft.
However, the conventional weld detection method has some limitations, such as depending on manual visual detection, omission and misjudgment are easy to occur, and destructive detection can damage welded parts, so that real-time monitoring cannot be performed. In addition, the traditional method cannot comprehensively and efficiently evaluate the weld joint, and only defects on the surface can be found, so that deep hidden defects are difficult to find. Therefore, the traditional method has the problems of low detection efficiency, low accuracy and the like, and cannot meet the high requirement on welding quality in the aerospace field.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a high-detection-precision nondestructive detection method and a high-detection-precision nondestructive detection system for a welding line, and solves the problems in the background art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the welding seam nondestructive testing system with high detection precision comprises a data acquisition module, a preprocessing module, a characteristic extraction module, a defect detection module and a result display module;
The data acquisition module is used for acquiring data of a welding seam area of the machine body through the ultrasonic probe or the X-ray detector, including surface characteristic information, material attribute data and a welding seam defect part condition of the welding part, and transmitting the data to the preprocessing module as a first data set, a second data set and a third data set;
The preprocessing module is used for preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and characteristic of a salient weld, and the characteristic comprises edge, shape, color and size;
the feature extraction module is responsible for carrying out feature extraction on the preprocessed data, carrying out quantitative analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd;
the defect detection module is used for comparing the nondestructive detection index Gjjd obtained by the feature extraction module with a preset qualified threshold X and a preset qualified threshold Z to detect and identify defects and evaluate the quality of welding seams;
The result display module is used for displaying the detected weld defect result of the airplane body to an operator in the form of an image, a report or an acoustic prompt, and generating a detailed detection report including quality assessment, defect type, position and size of the weld, so that a reference basis is provided for maintenance and repair of the airplane.
Preferably, the data acquisition module comprises a data acquisition unit;
the data acquisition unit is used for acquiring surface information of a welding seam of the machine body through the high-resolution camera and the laser three-dimensional scanner to obtain a first data set, acquiring attribute data of materials through the mass spectrometer and the thermal conductivity measuring instrument to obtain a second data set, acquiring defect conditions of the welding seam through the ultrasonic probe and the ultrasonic detector to obtain a third data set, and transmitting the third data set to the preprocessing module after finishing;
The first data set includes: surface roughness Bmcc, edge definition Byqx, surface texture density Bmwl, and curvature Hfql of the weld;
the second data set includes: a material elastic modulus Txml, a material thermal conductivity Rdlz, a material density Clmd, and a material melting point Clrd;
the third data set includes: air hole density Qkmd, crack length Lwcd, inclusion size Jzcd, unfused area Wrhmj, and metal liquefaction defect level Jsyh.
Preferably, the preprocessing module comprises a data preprocessing unit;
The data preprocessing unit is used for preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and weld seam salient feature, the feature comprises edge, shape, color and size, and unnecessary frequency components are removed to keep weld seam feature signals.
Preferably, the feature extraction module comprises a quantization analysis unit and a feature calculation unit;
The quantization analysis unit is used for extracting characteristics of the preprocessed data, performing quantization analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd, surface characteristic coefficient Bmtz, material property coefficient Clsx and weld defect coefficient Hfqx;
The nondestructive testing index Gjjd is obtained by calculating the following formula:
；
wherein Bmtz denotes a surface characteristic coefficient, clsx denotes a material property coefficient, hfqx denotes a weld defect coefficient, q, w, and e denote proportionality coefficients of the surface characteristic coefficient Bmtz, the material property coefficient Clsx, and the weld defect coefficient Hfqx, respectively;
Wherein, ，，And (2) andR represents a first correction constant.
Preferably, the surface characteristic coefficient Bmtz is obtained by calculating the following formula:
；
wherein Bmcc denotes surface roughness, byqx denotes edge sharpness, bmwl denotes surface texture density, hfql denotes curvature of a weld, and t, y, u, and i denote surface roughness Bmcc, edge sharpness Byqx, surface texture density Bmwl, and proportionality coefficients of curvature Hfql of the weld, respectively;
Wherein, ，，，And (2) andO represents the second correction constant.
Preferably, the material property coefficient Clsx is obtained by calculation using the following formula:
；
Wherein Txml denotes a material elastic modulus, rdlz denotes a material thermal conductivity, clmd denotes a material density, clrd denotes a material melting point, and p, a, s, and d denote proportionality coefficients of the material elastic modulus Txml, the material thermal conductivity Rdlz, the material density Clmd, and the material melting point Clrd, respectively;
Wherein, ，，，And (2) andF represents a third correction constant.
Preferably, the weld defect coefficient Hfqx is obtained by calculating the following formula:
；
Wherein Qkmd denotes a pore density, lwcd denotes a crack length, jzcd denotes an inclusion size, wrhmj denotes an unfused region area, jsyh denotes a metal liquefaction defect degree, g, h, j, k and c denote scaling coefficients of the pore density Qkmd, the crack length Lwcd, the inclusion size Jzcd, the unfused region area Wrhmj and the metal liquefaction defect degree Jsyh, respectively;
Wherein, ，，，，And L represents a fourth correction constant.
Preferably, the defect detection module comprises a weld assessment unit and a weld identification unit;
The weld joint evaluation unit is used for performing defect detection by comparing the nondestructive detection index Gjjd acquired by the characteristic extraction module with a preset qualified threshold X and a preset qualified threshold Z, and acquiring a grade evaluation scheme:
the nondestructive testing index Gjjd is less than or equal to a preset qualification threshold X, a first grade evaluation is obtained, the welding seam state is judged to be a qualification grade, no additional adjustment is needed, and maintenance and calibration are carried out on welding machine equipment in a fixed period;
The method comprises the steps of obtaining a second level evaluation, judging that the state of a welding seam is a warning level which is lower than 20% of a qualified standard, carrying out real-time monitoring, analyzing factors which lead to the quality of the welding seam not reaching the qualified standard, improving pertinently, optimizing welding process parameters, improving welding environment and improving the technical level of a welder, carrying out welding seam quality monitoring and evaluation in a fixed period, and adjusting the welding process according to the real-time condition to effectively control and improve the quality of the welding seam;
The method comprises the steps of presetting a qualified threshold Z which is less than or equal to a nondestructive testing index Gjjd, obtaining a third level evaluation, judging that a welding line is of a unqualified level, and carrying out re-welding or repairing, and carrying out comprehensive detection and improvement on a welding process aiming at the welding line which is detected to be unqualified;
The weld joint identification unit is responsible for carrying out defect detection and identification again on the weld joint which cannot be directly judged through the nondestructive detection index, carrying out defect analysis on the weld joint by utilizing the existing data characteristics and model, wherein the defect analysis content comprises detection pores, cracks and unfused areas, and re-evaluating the quality of the weld joint according to the type and the size of the defect.
Preferably, the result display module comprises a defect result display unit;
The defect result display unit is used for marking the detected weld defects on the image, highlighting the defect parts so that operators can intuitively know the problems of the weld, generating detailed detection reports including quality assessment, defect types, positions and sizes of the weld, providing reference basis for subsequent maintenance and repair, and reporting the detection results of the weld to the operators in a voice prompt mode so as to know the state of the weld in real time in work.
A high-detection-precision nondestructive detection method for a welding line comprises the following steps:
Step one: acquiring data of a welding seam area of a machine body through an ultrasonic probe or an X-ray detector, wherein the data comprise surface characteristic information, material attribute data and a welding seam defect part condition of a welding part, and the data are used as a first data set, a second data set and a third data set and transmitted to a preprocessing module;
step two: preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and characteristic of a protruding weld joint, and the characteristic comprises edge, shape, color and size;
Step three: extracting features of the preprocessed data, carrying out quantitative analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd;
Step four: comparing the nondestructive testing index Gjjd obtained by the feature extraction module with a preset qualified threshold X and a preset qualified threshold Z, performing defect detection and identification, and evaluating the quality of the welding seam;
Step five: displaying the detected weld defect result of the airplane body to an operator in the form of an image, a report or an acoustic prompt, and simultaneously generating a detailed detection report including quality assessment, defect type, position and size of the weld, and providing reference basis for maintenance and repair of the airplane.
(III) beneficial effects
The invention provides a high-detection-precision nondestructive detection method and a high-detection-precision nondestructive detection system for a welding line, which have the following beneficial effects:
(1) When the system operates, data of a welding line area of a machine body, including surface characteristic information, material attribute data and a welding line defect part condition of a welding part, are collected through an ultrasonic probe or an X-ray detector and transmitted to a preprocessing module, the collected data are preprocessed, the preprocessed data are subjected to characteristic extraction, the first data set, the second data set and the third data set are subjected to quantitative analysis, and after combination, calculation is carried out, so that the system is obtained: and the nondestructive testing index Gjjd is used for comparing the nondestructive testing index Gjjd obtained by the characteristic extraction module with a preset qualified threshold value X and a preset qualified threshold value Z, performing defect detection and identification, displaying the detected result of the weld defects of the airframe to operators, and simultaneously generating a detailed testing report to provide a reference basis for aircraft maintenance and repair.
(2) And acquiring data of a welding seam area of the machine body, including surface characteristic information, material attribute data and welding seam defect conditions, by using an ultrasonic probe or an X-ray detector. The optimized effect may include more accurate acquisition of data of the welded component, improved efficiency and comprehensiveness of data acquisition, and preprocessing of the acquired data, including filtering, noise removal, signal enhancement, etc., to highlight the characteristics of the weld. After optimization, the interference and noise of the data can be reduced, and the accuracy of subsequent feature extraction and defect detection is improved. And carrying out feature extraction and quantitative analysis on the preprocessed data, and calculating a nondestructive testing index Gjjd. After optimization, the accuracy and the effectiveness of extracting weld joint features can be improved, and more reliable data support is provided for subsequent defect detection.
(3) And comparing the nondestructive testing index Gjjd obtained by the characteristic extraction module with a preset qualified threshold value, performing defect detection and identification, and evaluating the quality of the welding line. After optimization, the accuracy and reliability of defect detection can be improved, the misjudgment rate is reduced, the quality of the welding seam is ensured to meet the requirement, the detected welding seam defect result is displayed to an operator in the form of an image, a report or an audible prompt, and a detailed detection report is generated. After optimization, the visual degree and the understandability of the detection result can be improved, a more accurate reference basis is provided for aircraft maintenance and repair, and meanwhile, the working efficiency is improved and human errors are reduced.
Drawings
FIG. 1 is a block diagram of a high detection accuracy nondestructive weld joint detection system according to the present invention;
FIG. 2 is a schematic diagram of the steps of a method for nondestructive testing of a weld with high detection accuracy according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Welding is a crucial process in the high-tech fields of aerospace and aviation. The welding joins the various components of the aircraft structure directly to the safety and reliability of the aircraft. However, due to the complexity of the welding process and the extremely high requirements of aerospace engineering on weld quality, conventional visual inspection and destructive inspection methods have failed to meet the requirements of nondestructive inspection of the weld. Therefore, there is an urgent need for a high detection accuracy weld nondestructive inspection system to ensure weld quality and aircraft safety. Such a system needs to be able to accurately and reliably detect possible minor defects in the weld, and to be able to provide detailed detection reports, providing important basis for maintenance and repair of the aircraft.
However, the conventional weld detection method has some limitations, such as depending on manual visual detection, omission and misjudgment are easy to occur, and destructive detection can damage welded parts, so that real-time monitoring cannot be performed. In addition, the traditional method cannot comprehensively and efficiently evaluate the weld joint, and only defects on the surface can be found, so that deep hidden defects are difficult to find. Therefore, the traditional method has the problems of low detection efficiency, low accuracy and the like, and cannot meet the high requirement on welding quality in the aerospace field.
Example 1
The invention provides a high-detection-precision weld joint nondestructive detection system, referring to FIG. 1, comprising a data acquisition module, a preprocessing module, a feature extraction module, a defect detection module and a result display module;
The data acquisition module is used for acquiring data of a welding seam area of the machine body through the ultrasonic probe or the X-ray detector, including surface characteristic information, material attribute data and a welding seam defect part condition of the welding part, and transmitting the data to the preprocessing module as a first data set, a second data set and a third data set;
The preprocessing module is used for preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and characteristic of a salient weld, and the characteristic comprises edge, shape, color and size;
the feature extraction module is responsible for carrying out feature extraction on the preprocessed data, carrying out quantitative analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd;
the defect detection module is used for comparing the nondestructive detection index Gjjd obtained by the feature extraction module with a preset qualified threshold X and a preset qualified threshold Z to detect and identify defects and evaluate the quality of welding seams;
The result display module is used for displaying the detected weld defect result of the airplane body to an operator in the form of an image, a report or an acoustic prompt, and generating a detailed detection report including quality assessment, defect type, position and size of the weld, so that a reference basis is provided for maintenance and repair of the airplane.
In this embodiment, data of a weld joint region of a machine body, including surface feature information, material attribute data and a weld joint defect condition of a welding part, are collected through an ultrasonic probe or an X-ray detector, and are transmitted to a preprocessing module, the collected data are preprocessed, feature extraction is performed on the preprocessed data, quantization analysis is performed on a first data set, a second data set and a third data set, and after combination, calculation is performed, so that the obtained data are obtained: the nondestructive testing index Gjjd is used for comparing the nondestructive testing index Gjjd obtained by the characteristic extraction module with a preset qualified threshold value X and a preset qualified threshold value Z, performing defect detection and identification, evaluating the quality of a welding line, displaying the detected result of the welding line defect of the machine body to an operator in the form of an image, a report or an acoustic prompt, and simultaneously generating a detailed testing report, wherein the detailed testing report comprises the quality evaluation, the defect type, the position and the size of the welding line, and provides a reference basis for aircraft maintenance and repair.
Example 2
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically: the data acquisition module comprises a data acquisition unit;
the data acquisition unit is used for acquiring surface information of a welding seam of the machine body through the high-resolution camera and the laser three-dimensional scanner to obtain a first data set, acquiring attribute data of materials through the mass spectrometer and the thermal conductivity measuring instrument to obtain a second data set, acquiring defect conditions of the welding seam through the ultrasonic probe and the ultrasonic detector to obtain a third data set, and transmitting the third data set to the preprocessing module after finishing;
The first data set includes: surface roughness Bmcc, edge definition Byqx, surface texture density Bmwl, and curvature Hfql of the weld;
the second data set includes: a material elastic modulus Txml, a material thermal conductivity Rdlz, a material density Clmd, and a material melting point Clrd;
the third data set includes: air hole density Qkmd, crack length Lwcd, inclusion size Jzcd, unfused area Wrhmj, and metal liquefaction defect level Jsyh.
The preprocessing module comprises a data preprocessing unit;
The data preprocessing unit is used for preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and weld seam salient feature, the feature comprises edge, shape, color and size, and unnecessary frequency components are removed to keep weld seam feature signals.
In this embodiment, by acquiring surface information using a high-resolution camera and a laser three-dimensional scanner, acquiring material property data using a mass spectrometer and a thermal conductivity meter, and acquiring weld defect conditions using an ultrasonic probe and an ultrasonic detector, more accurate and comprehensive data can be obtained. The method is beneficial to improving the accuracy and reliability of subsequent processing steps, divides the acquired data into three data sets, and comprises weld surface characteristics, material properties and weld defect conditions, so that the system can comprehensively know the state of the weld from multiple dimensions. The method is favorable for the system to evaluate the quality of the welding line and detect the potential defects more accurately, the preprocessing unit carries out the processes of filtering, denoising, signal enhancement and the like on the acquired data, the characteristic information of the welding line can be extracted effectively, unnecessary interference factors are removed, the quality and usability of the data are improved, the characteristics of the welding line such as edges, shapes, colors and the like are highlighted in the preprocessing process, the relevant characteristics of the welding line are extracted more accurately in the follow-up steps, the accuracy and the reliability of detection are improved, the unnecessary frequency components are removed by the preprocessing unit, the characteristic signals of the welding line are reserved, the follow-up characteristic extraction and defect detection can be carried out more effectively, and the performance and the reliability of the system are improved.
Example 3
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically: the feature extraction module comprises a quantitative analysis unit and a feature calculation unit;
The quantization analysis unit is used for extracting characteristics of the preprocessed data, performing quantization analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd, surface characteristic coefficient Bmtz, material property coefficient Clsx and weld defect coefficient Hfqx;
The nondestructive testing index Gjjd is obtained by calculating the following formula:
；
wherein Bmtz denotes a surface characteristic coefficient, clsx denotes a material property coefficient, hfqx denotes a weld defect coefficient, q, w, and e denote proportionality coefficients of the surface characteristic coefficient Bmtz, the material property coefficient Clsx, and the weld defect coefficient Hfqx, respectively;
Wherein, ，，And (2) andR represents a first correction constant.
The surface characteristic coefficient Bmtz is obtained by calculation according to the following formula:
；
wherein Bmcc denotes surface roughness, byqx denotes edge sharpness, bmwl denotes surface texture density, hfql denotes curvature of a weld, and t, y, u, and i denote surface roughness Bmcc, edge sharpness Byqx, surface texture density Bmwl, and proportionality coefficients of curvature Hfql of the weld, respectively;
Wherein, ，，，And (2) andO represents the second correction constant.
The material property coefficient Clsx is obtained by calculation according to the following formula:
；
Wherein Txml denotes a material elastic modulus, rdlz denotes a material thermal conductivity, clmd denotes a material density, clrd denotes a material melting point, and p, a, s, and d denote proportionality coefficients of the material elastic modulus Txml, the material thermal conductivity Rdlz, the material density Clmd, and the material melting point Clrd, respectively;
Wherein, ，，，And (2) andF represents a third correction constant.
The weld defect coefficients Hfqx are obtained by calculation according to the following formula:
；
Wherein Qkmd denotes a pore density, lwcd denotes a crack length, jzcd denotes an inclusion size, wrhmj denotes an unfused region area, jsyh denotes a metal liquefaction defect degree, g, h, j, k and c denote scaling coefficients of the pore density Qkmd, the crack length Lwcd, the inclusion size Jzcd, the unfused region area Wrhmj and the metal liquefaction defect degree Jsyh, respectively;
Wherein, ，，，，And L represents a fourth correction constant.
Example 4
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically: the defect detection module comprises a weld joint evaluation unit and a weld joint identification unit;
The weld joint evaluation unit is used for performing defect detection by comparing the nondestructive detection index Gjjd acquired by the characteristic extraction module with a preset qualified threshold X and a preset qualified threshold Z, and acquiring a grade evaluation scheme:
the nondestructive testing index Gjjd is less than or equal to a preset qualification threshold X, a first grade evaluation is obtained, the welding seam state is judged to be a qualification grade, no additional adjustment is needed, and maintenance and calibration are carried out on welding machine equipment in a fixed period;
The method comprises the steps of obtaining a second level evaluation, judging that the state of a welding seam is a warning level which is lower than 20% of a qualified standard, carrying out real-time monitoring, analyzing factors which lead to the quality of the welding seam not reaching the qualified standard, improving pertinently, optimizing welding process parameters, improving welding environment and improving the technical level of a welder, carrying out welding seam quality monitoring and evaluation in a fixed period, and adjusting the welding process according to the real-time condition to effectively control and improve the quality of the welding seam;
The method comprises the steps of presetting a qualified threshold Z which is less than or equal to a nondestructive testing index Gjjd, obtaining a third level evaluation, judging that a welding line is of a unqualified level, and carrying out re-welding or repairing, and carrying out comprehensive detection and improvement on a welding process aiming at the welding line which is detected to be unqualified;
The weld joint identification unit is responsible for carrying out defect detection and identification again on the weld joint which cannot be directly judged through the nondestructive detection index, carrying out defect analysis on the weld joint by utilizing the existing data characteristics and model, wherein the defect analysis content comprises detection pores, cracks and unfused areas, and re-evaluating the quality of the weld joint according to the type and the size of the defect.
The defect detection module comprises a weld joint evaluation unit and a weld joint identification unit;
The weld joint evaluation unit is used for performing defect detection by comparing the nondestructive detection index Gjjd acquired by the characteristic extraction module with a preset qualified threshold X and a preset qualified threshold Z, and acquiring a grade evaluation scheme:
the nondestructive testing index Gjjd is less than or equal to a preset qualification threshold X, a first grade evaluation is obtained, the welding seam state is judged to be a qualification grade, no additional adjustment is needed, and maintenance and calibration are carried out on welding machine equipment in a fixed period;
The method comprises the steps of obtaining a second level evaluation, judging that the state of a welding seam is a warning level which is lower than 20% of a qualified standard, carrying out real-time monitoring, analyzing factors which lead to the quality of the welding seam not reaching the qualified standard, improving pertinently, optimizing welding process parameters, improving welding environment and improving the technical level of a welder, carrying out welding seam quality monitoring and evaluation in a fixed period, and adjusting the welding process according to the real-time condition to effectively control and improve the quality of the welding seam;
The method comprises the steps of presetting a qualified threshold Z which is less than or equal to a nondestructive testing index Gjjd, obtaining a third level evaluation, judging that a welding line is of a unqualified level, and carrying out re-welding or repairing, and carrying out comprehensive detection and improvement on a welding process aiming at the welding line which is detected to be unqualified;
The weld joint identification unit is responsible for carrying out defect detection and identification again on the weld joint which cannot be directly judged through the nondestructive detection index, carrying out defect analysis on the weld joint by utilizing the existing data characteristics and model, wherein the defect analysis content comprises detection pores, cracks and unfused areas, and re-evaluating the quality of the weld joint according to the type and the size of the defect.
In this embodiment, the quality of the weld seam can be accurately estimated by comparing the nondestructive testing index Gjjd with a preset qualification threshold, and the classification estimation scheme can be classified according to the result. The method is beneficial to determining the qualification of the welding line, finding potential quality problems in time, and giving out warning and monitoring in real time when the nondestructive testing index is between preset qualification thresholds. This allows the operator to take timely action, analyze factors that lead to weld quality problems, and improve targeted.
Example 5
Referring to fig. 2, a high-detection-precision nondestructive detection method for a weld joint is specifically shown: the method comprises the following steps:
Step one: acquiring data of a welding seam area of a machine body through an ultrasonic probe or an X-ray detector, wherein the data comprise surface characteristic information, material attribute data and a welding seam defect part condition of a welding part, and the data are used as a first data set, a second data set and a third data set and transmitted to a preprocessing module;
step two: preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and characteristic of a protruding weld joint, and the characteristic comprises edge, shape, color and size;
Step three: extracting features of the preprocessed data, carrying out quantitative analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd;
Step four: comparing the nondestructive testing index Gjjd obtained by the feature extraction module with a preset qualified threshold X and a preset qualified threshold Z, performing defect detection and identification, and evaluating the quality of the welding seam;
Step five: displaying the detected weld defect result of the airplane body to an operator in the form of an image, a report or an acoustic prompt, and simultaneously generating a detailed detection report including quality assessment, defect type, position and size of the weld, and providing reference basis for maintenance and repair of the airplane.
In the embodiment, the effective control and improvement of the weld quality are facilitated by optimizing the welding process parameters, improving the environmental conditions and improving the technical level. The weld joint identification unit is responsible for carrying out defect detection and identification on the weld joint which cannot be directly judged by the nondestructive detection index. By using the existing data characteristics and models, the defects of the welding line, including pores, cracks, unfused areas and the like, can be rapidly and accurately analyzed, and the quality of the welding line can be re-estimated. By comprehensively detecting and improving the unqualified weld, the system can identify the problems existing in the welding process and provide targeted improvement measures. The method is beneficial to improving the quality and the reliability of the welding seam and reducing the occurrence of unqualified welding seams, thereby reducing the maintenance and repair cost of the aircraft and ensuring the safety and the reliability of the aircraft.
Specific examples:
suppose we have a certain welding line nondestructive testing system, the following specific parameter values are acquired through the data acquisition unit:
a first data set:
surface roughness Bmcc =0.25, edge definition Byqx =0.32, surface texture density Bmwl =0.28, weld curvature Hfql =0.21;
a second data set:
Material elastic modulus Txml =210, material thermal conductivity Rdlz =50, material density Clmd =7.85, material melting point Clrd =15.38;
Third data set:
Air hole density Qkmd =0.005, crack length Lwcd =2.3, inclusion size Jzcd =0.1, unfused region area Wrhmj =150, metal liquefaction defect level Jsyh =0.08
Corresponding proportional coefficient ：q=0.2,w=0.20,e=0.23,t=0.21,y=0.16,u=0.29,i=0.16,p=0.18,a=0.18,s=0.15,d=0.18,g=0.15,h=0.18,j=0.18,k=0.12,c=0.18;
First correction constant: r=0.08, second correction constant: o=0.53, third correction constant: f=0.34, fourth correction constant: l=0.55;
Bmtz=[(0.25×0.21)+(0.32×0.16)+(0.28×0.29)+(0.21×0.16)]+0.53=1；
Clsx= [(210×0.18)+(50×0.18)+(7.85×0.15)+(15.38×0.18)]+0.34=82；
Hfqx=[(0.005×0.15)+(2.3×0.18)+(0.1×0.18)+(150×0.18)+(0.08×0.18)]+0.55=28；
Gjjd =[(1×0.2)+(82×0.20)+(28×0.23)]/(0.2+0.20+0.23)+0.08=35；
Setting a preset threshold value X as 30, setting a preset threshold value Z as 50, presetting a qualified threshold value X < a nondestructive testing index Gjjd < a preset qualified threshold value Z, acquiring a second level evaluation, judging that the state of the welding seam is a warning level which is lower than a qualified standard by 20%, carrying out real-time monitoring, analyzing factors which lead to the quality of the welding seam not reaching the qualified standard, pertinently improving welding process parameters, improving welding environment and improving the technical level of a welder, carrying out welding seam quality monitoring and evaluation in a fixed period, and adjusting the welding process according to the real-time condition so as to effectively control and improve the quality of the welding seam.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A high detection accuracy's welding seam nondestructive test system, its characterized in that: the device comprises a data acquisition module, a preprocessing module, a feature extraction module, a defect detection module and a result display module;
The data acquisition module is used for acquiring data of a welding seam area of the machine body through the ultrasonic probe or the X-ray detector, including surface characteristic information, material attribute data and a welding seam defect part condition of the welding part, and transmitting the data to the preprocessing module as a first data set, a second data set and a third data set;
The preprocessing module is used for preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and characteristic of a salient weld, and the characteristic comprises edge, shape, color and size;
the feature extraction module is responsible for carrying out feature extraction on the preprocessed data, carrying out quantitative analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd;
the defect detection module is used for comparing the nondestructive detection index Gjjd obtained by the feature extraction module with a preset qualified threshold X and a preset qualified threshold Z to detect and identify defects and evaluate the quality of welding seams;
The result display module is used for displaying the detected weld defect result of the machine body to an operator in the form of an image, a report or an acoustic prompt, and generating a detailed detection report including quality assessment, defect type, position and size of the weld so as to provide reference basis for aircraft maintenance and repair;
the data acquisition module comprises a data acquisition unit;
the data acquisition unit is used for acquiring surface information of a welding seam of the machine body through the high-resolution camera and the laser three-dimensional scanner to obtain a first data set, acquiring attribute data of materials through the mass spectrometer and the thermal conductivity measuring instrument to obtain a second data set, acquiring defect conditions of the welding seam through the ultrasonic probe and the ultrasonic detector to obtain a third data set, and transmitting the third data set to the preprocessing module after finishing;
The first data set includes: surface roughness Bmcc, edge definition Byqx, surface texture density Bmwl, and curvature Hfql of the weld;
the second data set includes: a material elastic modulus Txml, a material thermal conductivity Rdlz, a material density Clmd, and a material melting point Clrd;
the third data set includes: air hole density Qkmd, crack length Lwcd, inclusion size Jzcd, unfused area Wrhmj, and metal liquefaction defect level Jsyh;
the feature extraction module comprises a quantitative analysis unit and a feature calculation unit;
The quantization analysis unit is used for extracting characteristics of the preprocessed data, performing quantization analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd, surface characteristic coefficient Bmtz, material property coefficient Clsx and weld defect coefficient Hfqx;
The nondestructive testing index Gjjd is obtained by calculating the following formula:
；
wherein Bmtz denotes a surface characteristic coefficient, clsx denotes a material property coefficient, hfqx denotes a weld defect coefficient, q, w, and e denote proportionality coefficients of the surface characteristic coefficient Bmtz, the material property coefficient Clsx, and the weld defect coefficient Hfqx, respectively;
Wherein, ，，And (2) andR represents a first correction constant;
The surface characteristic coefficient Bmtz is obtained by calculation according to the following formula:
；
wherein Bmcc denotes surface roughness, byqx denotes edge sharpness, bmwl denotes surface texture density, hfql denotes curvature of a weld, and t, y, u, and i denote surface roughness Bmcc, edge sharpness Byqx, surface texture density Bmwl, and proportionality coefficients of curvature Hfql of the weld, respectively;
Wherein, ，，，And (2) andO represents a second correction constant;
the material property coefficient Clsx is obtained by calculation according to the following formula:
；
Wherein Txml denotes a material elastic modulus, rdlz denotes a material thermal conductivity, clmd denotes a material density, clrd denotes a material melting point, and p, a, s, and d denote proportionality coefficients of the material elastic modulus Txml, the material thermal conductivity Rdlz, the material density Clmd, and the material melting point Clrd, respectively;
Wherein, ，，，And (2) andF represents a third correction constant;
the weld defect coefficients Hfqx are obtained by calculation according to the following formula:
；
Wherein Qkmd denotes a pore density, lwcd denotes a crack length, jzcd denotes an inclusion size, wrhmj denotes an unfused region area, jsyh denotes a metal liquefaction defect degree, g, h, j, k and c denote scaling coefficients of the pore density Qkmd, the crack length Lwcd, the inclusion size Jzcd, the unfused region area Wrhmj and the metal liquefaction defect degree Jsyh, respectively;
Wherein, ，，，，And (2) andL represents a fourth correction constant.
2. The high detection accuracy weld nondestructive testing system of claim 1, wherein: the preprocessing module comprises a data preprocessing unit;
The data preprocessing unit is used for preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and weld seam salient feature, the feature comprises edge, shape, color and size, and unnecessary frequency components are removed to keep weld seam feature signals.
3. The high detection accuracy weld nondestructive testing system of claim 1, wherein: the defect detection module comprises a weld joint evaluation unit and a weld joint identification unit;
The weld joint evaluation unit is used for performing defect detection by comparing the nondestructive detection index Gjjd acquired by the characteristic extraction module with a preset qualified threshold X and a preset qualified threshold Z, and acquiring a grade evaluation scheme:
the nondestructive testing index Gjjd is less than or equal to a preset qualification threshold X, a first grade evaluation is obtained, the welding seam state is judged to be a qualification grade, no additional adjustment is needed, and maintenance and calibration are carried out on welding machine equipment in a fixed period;
The method comprises the steps of obtaining a second level evaluation, judging that the state of a welding seam is a warning level which is lower than 20% of a qualified standard, carrying out real-time monitoring, analyzing factors which lead to the quality of the welding seam not reaching the qualified standard, improving pertinently, optimizing welding process parameters, improving welding environment and improving the technical level of a welder, carrying out welding seam quality monitoring and evaluation in a fixed period, and adjusting the welding process according to the real-time condition to effectively control and improve the quality of the welding seam;
The method comprises the steps of presetting a qualified threshold Z which is less than or equal to a nondestructive testing index Gjjd, obtaining a third level evaluation, judging that a welding line is of a unqualified level, and carrying out re-welding or repairing, and carrying out comprehensive detection and improvement on a welding process aiming at the welding line which is detected to be unqualified;
The weld joint identification unit is responsible for carrying out defect detection and identification again on the weld joint which cannot be directly judged through the nondestructive detection index, carrying out defect analysis on the weld joint by utilizing the existing data characteristics and model, wherein the defect analysis content comprises detection pores, cracks and unfused areas, and re-evaluating the quality of the weld joint according to the type and the size of the defect.
4. The high detection accuracy weld nondestructive testing system of claim 1, wherein: the result display module comprises a defect result display unit;
The defect result display unit is used for marking the detected weld defects on the image, highlighting the defect parts so that operators can intuitively know the problems of the weld, generating detailed detection reports including quality assessment, defect types, positions and sizes of the weld, providing reference basis for subsequent maintenance and repair, and reporting the detection results of the weld to the operators in a voice prompt mode so as to know the state of the weld in real time in work.
5. The high-detection-precision nondestructive detection method for the weld joint, which comprises the high-detection-precision nondestructive detection system for the weld joint, and is characterized in that: the method comprises the following steps:
Step one: acquiring data of a welding seam area of a machine body through an ultrasonic probe or an X-ray detector, wherein the data comprise surface characteristic information, material attribute data and a welding seam defect part condition of a welding part, and the data are used as a first data set, a second data set and a third data set and transmitted to a preprocessing module;
step two: preprocessing the acquired data, wherein the preprocessing mode comprises filtering, noise removal, signal enhancement, image parameter adjustment and characteristic of a protruding weld joint, and the characteristic comprises edge, shape, color and size;
Step three: extracting features of the preprocessed data, carrying out quantitative analysis on the first data set, the second data set and the third data set, and calculating after combination to obtain: nondestructive testing index Gjjd;
Step four: comparing the nondestructive testing index Gjjd obtained by the feature extraction module with a preset qualified threshold X and a preset qualified threshold Z, performing defect detection and identification, and evaluating the quality of the welding seam;
Step five: displaying the detected weld defect result of the airplane body to an operator in the form of an image, a report or an acoustic prompt, and simultaneously generating a detailed detection report including quality assessment, defect type, position and size of the weld, and providing reference basis for maintenance and repair of the airplane.
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