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WO2020118659A1 - Système de détection de défaut structural et procédé de détection de défaut structural - Google Patents

Système de détection de défaut structural et procédé de détection de défaut structural Download PDF

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Publication number
WO2020118659A1
WO2020118659A1 PCT/CN2018/121109 CN2018121109W WO2020118659A1 WO 2020118659 A1 WO2020118659 A1 WO 2020118659A1 CN 2018121109 W CN2018121109 W CN 2018121109W WO 2020118659 A1 WO2020118659 A1 WO 2020118659A1
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WIPO (PCT)
Prior art keywords
photoelectric sensor
defect detection
sound wave
light
neural network
Prior art date
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Ceased
Application number
PCT/CN2018/121109
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English (en)
Chinese (zh)
Inventor
王星泽
舒远
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Heren Keji Shenzhen LLC
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Heren Keji Shenzhen LLC
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Priority to PCT/CN2018/121109 priority Critical patent/WO2020118659A1/fr
Priority to CN201880071366.4A priority patent/CN111316093A/zh
Publication of WO2020118659A1 publication Critical patent/WO2020118659A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/12—Analysing solids by measuring frequency or resonance of acoustic waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4481—Neural networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
    • G01N2021/4173—Phase distribution
    • G01N2021/4186—Phase modulation imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/02—Indexing codes associated with the analysed material
    • G01N2291/028—Material parameters
    • G01N2291/0289—Internal structure, e.g. defects, grain size, texture

Definitions

  • the invention relates to the technical field of device detection, in particular to a structural defect detection system and a structural defect detection method.
  • assembly defects on the surface of an object can be detected by a visual method, for example, by image processing technology, and an automatic optical inspection (Automatic Inspection (AOI) method) method is commonly used.
  • AOI Automatic Inspection
  • the inventor has found through research that because the automatic optical inspection only inspects the appearance, it cannot see through the internal structure of the product, and therefore cannot see through the product, so it cannot detect all the actual defects of the product.
  • X-ray inspection in the prior art can better detect and image internal assembly defects of products than automatic optical inspection.
  • X-rays have radioactive hazards.
  • the inventors found through research that for complex multi-layer internal structures, penetrating X-rays are difficult to distinguish the three-dimensional structure inside the product, and there are areas in the product that are blocked. The assembly defects cannot be accurately located. The position of the X-ray inspection makes it impossible to achieve a good inspection effect when facing the defect inspection of the PCB board of microelectronic devices with various assembly methods.
  • an acoustic wave signal whose frequency continuously changes is applied to the surface of the measured object from all directions, and the structural defect of the measured object is subjected to forced vibration due to the action of simple harmonics, as the frequency of the acoustic wave signal continues Increase, when the frequency of a certain acoustic wave signal is equal to or close to the natural frequency of the internal defect part of the measured object, resonance will occur at the defect, and the amplitude of vibration will be the largest at this time, so a large Off-surface displacement.
  • the structural defect detection system includes a laser, a beam splitter, a beam expander, a semi-transparent mirror, an acoustic wave generator, an acoustic wave frequency adjuster, an imaging lens, a photoelectric sensor, and a computer;
  • the acoustic wave generator is connected to the acoustic wave frequency adjuster, the acoustic wave frequency adjuster is connected to the computer; the photoelectric sensor is connected to the computer;
  • the computer sends a frequency control signal to the sonic frequency regulator, and the frequency control signal is transmitted to the sonic wave generator after the digital-to-analog (D/A) conversion of the sonic frequency regulator; the sonic wave generator sends out frequency control Acoustic signal corresponding to the signal;
  • D/A digital-to-analog
  • the laser light emitted by the laser passes through a beam splitter, a beam expander, an imaging lens, and a semi-transparent mirror to form an interference optical path;
  • the interference optical path includes that the laser emits laser light, and the laser beam is first split by the beam splitter to form object light and reference light; the object light becomes parallel after being expanded by the beam expander Light is projected onto the object to be measured; diffuse reflection light is generated on the surface of the object to be measured, and the transmission of the diffuse reflection light through the imaging lens and the half mirror is received by the photoelectric sensor; the reference light passes through The transflective mirror is received by the photoelectric sensor after being reflected; the optical path of the structural defect detection system is adjusted so that the optical paths of the object light and the reference light are equal; the reference light passes through the transflective mirror After the reflection, the diffuse reflected light is simultaneously projected on the photoelectric sensor to form a speckle interference field; the speckle interference field is digitized by the photoelectric sensor to generate a speckle image;
  • the photoelectric sensor transmits the generated speckle image to the computer; the computer calculates the phase change of the speckle image to obtain the vibration waveform distribution of the measured object under the excitation of sound waves of different frequencies; further, a computer can also be used Calculate the phase difference diagram of the measured object under the excitation of sound waves of different frequencies.
  • the photoelectric sensor is a CCD photoelectric sensor or a CMOS photoelectric sensor
  • the acoustic wave generator is a voltage-controlled acoustic wave generator, including a power amplifier and a speaker.
  • the structural defect detection method includes a training phase and a detection phase
  • the training phase includes:
  • the phase difference map of the sample of the measured object constitutes a training data set for training a neural network, and there is a correspondence between the detection state in the training data set and the phase difference map;
  • Extract multiple phase difference maps and their corresponding detection states from the training data set use the phase difference maps as input features of the neural network, and use the detection states as output features of the neural network, using the The input feature and the output feature train the neural network to obtain a neural network model of the relationship between the phase difference map of the measured object and the defect of the measured object;
  • the detection phase includes:
  • the output layer of the neural network outputs a detection state, and the detection state is an output feature of the neural network.
  • the defined detection status includes no defects, pores, deformation, and other defects.
  • the neural network is a deep neural network based on deep learning.
  • obtaining the phase difference map specifically includes:
  • the sound wave generator sends out sound wave signals of different frequencies
  • the laser light emitted by the laser forms an interference optical path;
  • the interference optical path forms a speckle interference field on the photoelectric sensor.
  • the speckle interference field is digitally processed by the photoelectric sensor to generate a speckle image and transmitted to the computer;
  • phase map of the surface deformation of the measured object under different sound wave frequencies obtained by the phase shift method at different sound wave frequencies is subtracted between the phase maps to obtain the surface of the measured object at different sound wave frequencies Deformed phase difference diagram.
  • the interference optical path includes: the laser light emitted by the laser is first split by a beam splitter to form object light and reference light; the object light is expanded by the beam expander to become parallel light and projected onto the measured object On the object; diffuse reflection light is generated on the surface of the measured object, and the diffuse reflection light is received by the photoelectric sensor through the imaging lens and the transflective mirror; the reference light is reflected by the transflective mirror, and the The diffuse reflected light is simultaneously projected on the photoelectric sensor to form a speckle interference field.
  • the number of phase difference maps of multiple samples of the object to be tested is greater than or equal to 1000 for each type of defect; the number of phase maps is greater than or equal to 3.
  • the speckles obtained by the stimulated vibration of the internal defects of the measured object can be precisely measured and calculated by the speckle obtained by the interference of the coherent laser beam irradiated on the surface of the measured object, without the need to phase the speckle image Reconstruct the real image of the object, but directly calculate the vibration displacement of the interference speckle with the frequency of the sound wave signal, which can indirectly solve the forced vibration waveform of the measured object under the active stress of the sound wave.
  • the intensity and frequency of the sound wave can be set to a more suitable range to adapt to various structural defects. Different material properties, structural distribution and different ways of connecting and assembling different devices will produce different vibration signal distributions.
  • the vibration signal distribution can be A high-precision speckle image is used to measure the phase difference map, and then an artificial neural network is used to detect and identify the defect occurrence area and defect type.
  • the technical solution of the present invention is a non-contact, high-precision, online, real-time Non-destructive testing method.
  • FIG. 1 is a schematic diagram of a structural defect detection system in the present invention
  • FIG. 2 is a schematic diagram of acquiring a phase difference graph when the sound wave frequency is changed in the present invention
  • FIG. 3 is a schematic diagram of defect detection using a neural network in the present invention.
  • the present invention discloses a structural defect detection system, which includes: a laser 1, a beam splitter 2, a beam expander 3, a semi-transparent mirror 7, an acoustic wave generator 4, an acoustic wave Frequency adjuster 5, imaging lens 6, photoelectric sensor 9, computer 8; wherein, the sound wave generator 4 is connected to the sound wave frequency adjuster 5, the sound wave frequency adjuster 5 is connected to the computer 8; The photoelectric sensor 9 is connected to the computer 8; wherein, the sound wave generator includes a power amplifier and a speaker.
  • the laser light source of the laser 1 emits coherent laser light; the coherent laser light is first split by the beam splitter 2 to form object light and reference light; the object light is expanded by the beam expander 3 to become parallel light and projected onto the object to be measured As a result, diffusely reflected light containing deformation or vibration information of the measured object is received by the photo sensor 9 after passing through the imaging lens;
  • the photoelectric sensor is a CCD photoelectric sensor or a CMOS photoelectric sensor, and the photoelectric sensor uses a non-imaging method to detect the speckle image obtained by interference at high speed, without the need for a complicated optical lens to perform speckle Imaging
  • the speckle image is transmitted to the computer 8 connected to the photoelectric sensor 9; the computer 8 is used to calculate the phase change of the speckle image, so that the measured object is different Vibration waveform distribution under frequency sound wave excitation;
  • the vibration waveforms of various regions of the measured object are mainly affected by two aspects: first, it is related to the frequency of the sound wave excited by the sound wave generator and the resonance caused by the defect of the measured object; second, it is related to the material of the measured object itself and various micro
  • the connection mode of the electronic device is related; the above information can be used to effectively judge and detect whether there are any defects such as deformation, wrinkling, cracks, etc. during the assembly process.
  • the computer 8 sends a frequency control signal to the sound wave frequency regulator 5, and the frequency control signal is transmitted to the sound wave generator 4 after the digital-to-analog (D/A) conversion of the sound wave frequency regulator 5; the sound wave generator 4 sends out Acoustic signal corresponding to frequency control signal.
  • D/A digital-to-analog
  • the acoustic wave generator 4 is a voltage-controlled acoustic wave generator, including a power amplifier and a speaker; the acoustic wave generator 4 can generate the acoustic wave signal required by the control signal, and the acoustic wave signal is amplified by the power amplifier It can meet the frequency response and sound intensity required by the defect detection task, and send it to the speaker to emit the corresponding sound wave, so as to realize the continuous frequency broadband sound wave scanning.
  • the principle that the internal structure defects of the measured object can be detected is: the sound wave generator outputs a broadband sound wave signal whose frequency continuously changes, and the sound wave signal acts on the surface of the measured object from different directions.
  • the internal defect site is subjected to forced vibration due to the action of simple harmonics.
  • the defect site There will be resonance phenomenon, at this time the vibration amplitude is the largest, so there will be a large amplitude of off-surface displacement in the surface defect part of the object; at the same time, considering the structural safety of each object to be tested, the excitation of the sound wave intensity and excitation Effectively design the position to avoid the damage of the measured object caused by the excessive sound wave intensity.
  • the above method is called broadband sound wave scanning excitation.
  • the photoelectric sensor continuously captures the speckle image formed by the interference of the object light and the reference light, and calculates the package phase distribution map of the object under the deformation state through the multiple speckle images,
  • the image processing technology is used to detect and identify the defect area and the non-defect area of the measured object from the phase distribution change diagram reflecting the defect information, thereby performing fast and accurate defect detection processing on the measured object.
  • the phase difference map of the surface deformation of the measured object can obtain the phase difference map of the surface deformation of the measured object at different sound wave frequencies f.
  • the abnormality of the phase difference map on the distribution of different regions can determine whether the defect exists and the approximate location of the defect.
  • a method for classifying, training, and detecting a phase difference map that can indirectly reflect structural defects using a neural network is disclosed; wherein, the neural network is a deep neural network based on deep learning;
  • the deep neural network based on deep learning can obtain hierarchical visual features from the input phase difference map through unsupervised and supervised learning methods, thereby providing a more effective defect detection scheme.
  • phase difference map needs to be obtained in both training and detection stages:
  • the computer sends a frequency control signal to the sound wave frequency regulator, and the frequency control signal is transmitted to the sound wave generator after being converted by the digital-to-analog (D/A) of the sound wave frequency regulator; the sound wave generator sends out the sound wave signal corresponding to the frequency control signal ;
  • D/A digital-to-analog
  • the laser light source of the laser emits coherent laser light;
  • the coherent laser light is first split by the beam splitter to form the object light and the reference light;
  • the object light is expanded by the beam expander to become parallel light and projected onto the object to be measured, thereby ,
  • the diffuse reflected light containing the deformation or vibration information of the measured object is received by the photoelectric sensor after passing through the imaging lens;
  • the speckle image is transmitted to the computer connected to the photoelectric sensor; the phase change of the speckle image is calculated by the computer to obtain the vibration waveform of the measured object under the excitation of sound waves of different frequencies distributed;
  • the photoelectric sensor continuously captures the speckle image formed by the interference between the object light and the reference light, and calculates the package phase distribution map of the measured object in the deformed state through the multiple speckle images ;
  • the phase map of the surface deformation of the object under different sound waves obtained by the phase shift method at different sound wave frequencies is obtained by subtracting the phase maps to obtain different sound wave frequencies
  • the phase difference graph of the surface deformation of the measured object is obtained by subtracting the phase maps to obtain different sound wave frequencies.
  • the definition of non-defect, stomatal, deformation, and others is the detection state, that is, the output characteristics include no defect, stomatal, deformation, and others;
  • phase difference maps and their corresponding detection states from the training data set; use the phase difference maps that indirectly reflect structural defects as the input features of the neural network input layer, and use the detection states as the output features of the neural network output layer, Using the input features and output features to train the neural network, a neural network model of the relationship between the phase difference map of the measured object and the defects of the measured object is obtained.
  • the output layer of the neural network outputs a detection state, wherein the detection state is an output characteristic of the neural network; the detection state includes no defects, pores, deformation, and others.
  • the external excitation method can be changed.
  • thermal loading, Vacuum loading, electromagnetic excitation loading and other methods through the analysis of deformation, to obtain information about defects.
  • the defect characteristics and distribution of some of the measured objects are very complicated.
  • the parameters loaded by external excitation sources, such as loading time, intensity, and uniformity, are all parameters that can be adjusted by the structural defect detection system.
  • the technical scheme of the present invention can detect assembly defects of complex and small electronic devices inside consumer electronic products, and is a non-contact, high-precision, online, and real-time non-destructive detection method.
  • the technical solution of the present invention stimulates the measured object to induce forced vibration through active sound waves, can detect the deformation, vibration, impact, stiffness and strength of various construction machinery and equipment, and controls the product quality inspection and optimization of the production process parameters It is an advantageous detection tool.
  • the technical solution of the present invention can realize rapid and online detection of laser welding, glue bonding quality, and bonding quality of composite materials by monitoring the displacement change and deformation of objects with the excitation source, effectively characterizing and evaluating the structure Of the bonding quality, and evaluate the relationship between loading time and deformation.
  • the technical solution of the present invention can analyze the structural characteristics of objects, thereby making it more widely used, such as the monitoring of the operating status of industrial production equipment, the monitoring of gas leakage, the monitoring of the deformation of machined parts, etc.
  • the above monitoring results can be used for Industrial big data analysis in the Internet of Things industry.

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  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Biochemistry (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Acoustics & Sound (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Abstract

L'invention concerne un système de détection de défaut structural comprenant : un laser (1), un diviseur de faisceau (2), un dilatateur de faisceau (3), un miroir semi-transparent (7), un générateur d'ondes sonores (4), un dispositif de réglage de fréquence d'onde sonore (5), une lentille d'imagerie (6), un capteur photoélectrique (9) et un ordinateur (8). L'ordinateur (8) commande le générateur d'ondes sonores (4) pour envoyer un signal d'onde sonore. La lumière laser émise par le laser (1) forme un trajet de lumière d'interférence. Le trajet de lumière d'interférence forme un champ d'interférence de granularité sur le capteur photoélectrique (9) pour générer une image de granularité. Le capteur photoélectrique (9) transmet l'image de granularité à l'ordinateur (8) pour effectuer une détection de défaut d'un objet détecté. De plus, l'invention concerne en outre un procédé de détection de défaut structural. Au moyen de la solution technique, il est possible de détecter un défaut d'assemblage de petits dispositifs électroniques complexes à l'intérieur d'un produit électronique grand public, et le procédé de détection de défaut structural est un procédé de test non destructif, sans contact, de haute précision, en ligne et en temps réel.
PCT/CN2018/121109 2018-12-14 2018-12-14 Système de détection de défaut structural et procédé de détection de défaut structural Ceased WO2020118659A1 (fr)

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PCT/CN2018/121109 WO2020118659A1 (fr) 2018-12-14 2018-12-14 Système de détection de défaut structural et procédé de détection de défaut structural
CN201880071366.4A CN111316093A (zh) 2018-12-14 2018-12-14 结构缺陷检测系统及结构缺陷检测方法

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