Method for detecting and analyzing failure of paint coating of automobile part by using microscope

Abstract

The invention discloses a method for detecting and analyzing failure of a paint coating of an automobile part by utilizing a microscope, and relates to the field of automobile coating technology detection, the method establishes a set of logic progressive analysis process, and comprises the following steps: integrating background information such as basic information, service history and process parameters of the part; carrying out macroscopic observation and optical microscope detection, and analyzing a failure part; a scanning electron microscope is adopted to obtain a microstructure and determine a failure mode; an energy dispersion spectrometer is used for analyzing element components of a characteristic area, and the core of the method is that background information such as failure modes and element components obtained through microscopic detection, service history and technological parameters is subjected to cross validation, and a logic chain which is mutually verified is constructed to determine failure reasons. Through the logic progressive standardized process, visual tracing of failure causes is realized, and the accuracy and reliability of diagnosis are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automobile painting technology detection, and in particular to a method for detecting and analyzing paint coating failure of automobile parts by using a microscope. BACKGROUND

[0002] The base material of automobile parts is mainly steel. In a variety of service environments, it will continuously contact water, ultraviolet rays and various chemical media, and at the same time, it will also bear mechanical friction or scratching, so it is prone to corrosion and damage. The paint coating system forms a physical barrier on the surface of the base material, effectively isolating the base material from the external environment, and plays a key role in inhibiting corrosion and bearing mechanical damage. In addition, the coating gives the parts rich appearance effects, meeting the market's decorative needs.

[0003] Therefore, automobile paint coatings are widely used on various parts from the exterior of the vehicle body, such as doors, bumpers, chassis, to the interior, such as instrument panels, control panels, etc. However, due to the influence of various factors such as the quality of the paint itself, coating process control and post-service working conditions, these coatings may appear failure phenomena such as blistering, peeling, cracking, etc. during use, which makes them lose their existing protection and decoration functions. Accurate detection and analysis of the failure causes is the core link of product quality control and process improvement.

[0004] Currently, various instrument analysis methods are often used for failure analysis in the industry. For example, although infrared spectroscopy can characterize the type of paint, the presence of pigments or unknown additives often interferes with the analysis of the spectrum, leading to unclear conclusions; gas chromatography can be used to analyze residual solvents or batch problems, but its effectiveness is severely dependent on whether the same batch of paint samples is retained on the production site, limiting its application range; and ion exchange chromatography can analyze inorganic salts that cause corrosion or blistering, but the sample pretreatment process is quite complex and time-consuming. Similarly, gel chromatography for detecting coating degradation is limited to soluble samples, and has limitations in applicability.

[0005] These common analysis methods are often not systematic in practice, and the analysis process is easily affected by the type of paint, the storage of samples, and generally has the problems of complex sample preparation and long analysis period, resulting in low overall analysis efficiency. More importantly, they usually provide macroscopic chemical component information, which is difficult to directly associate the failure phenomenon with specific microscopic physical structure or interface state, so that the analysis conclusion often stays at the speculation level, lacking direct and convincing evidence. SUMMARY

[0006] The purpose of the present application is to provide a method for detecting and analyzing the failure of automobile part paint coating by using a microscope, aiming to solve the problems in the prior art that the failure analysis method is not systematic, the analysis process is limited and the efficiency is not high, especially it is difficult to establish a direct correlation between macroscopic failure phenomena and microscopic physical evidence, thereby leading to insufficient accuracy and reliability of the diagnostic conclusion.

[0007] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0008] The present application provides a method for detecting and analyzing the failure of automobile part paint coating by using a microscope, which establishes a set of logical progressive analysis process from macro to micro, from morphology to composition, from phenomenon to background. The method comprises:

[0009] S100, systematic information integration: collecting the basic information, service history and environmental conditions, substrate type, original design coating system information, and coating production process parameters of the automobile part, and determining the specific location of the failure phenomenon.

[0010] S200, macroscopic observation and image recording: based on the specific location of the failure phenomenon determined, using a magnifying glass to detect the appearance of the failure site at the specific location, and using a high-definition camera to take a photo of the failure area with a ruler, recording the macroscopic morphology of the failure site.

[0011] S300, optical microscope detection: based on the macroscopic morphology of the failure site, the surface, back of the spall and cross-section of the failure site are analyzed to obtain the coating layer structure, interface bonding state, actual thickness and macroscopic defect information of the failure site.

[0012] S400, microscopic morphology detection and failure mode determination: based on the macroscopic defect information of the failure site and the area that cannot be clearly determined due to insufficient resolution of the optical microscope, a scanning electron microscope is used to obtain the microscopic morphology image of the failure site, and the failure mode is determined based on the microscopic morphology image.

[0013] S500, micro-area composition analysis: based on the microscopic morphology image of the failure site obtained in the S400 step, an energy dispersive spectrometer attached to the scanning electron microscope is used to analyze the elemental composition of the feature area presented in the microscopic morphology image which is directly related to the failure phenomenon.

[0014] S600, comprehensive diagnosis of failure mechanism: the failure mode determined in the S400 step and the micro-area elemental composition determined in the S500 step are cross-verified with the service history, environmental conditions and coating production process parameters of the automobile part collected in the S100 step, a mutual verification failure mechanism logical chain is constructed, and other unrelated failure possibilities are excluded, thereby determining the failure cause.

[0015] The core innovation of the present application is the final failure mechanism comprehensive diagnosis (S600). Instead of simply listing data, the physical evidence (such as failure mode, micro-area element composition) obtained by microscopic detection is cross-verified with the background information (such as service environment, production process) collected in advance. By constructing a logic chain that confirms each other, interference factors and false evidence can be effectively excluded, so as to lock the root cause and improve the accuracy and reliability of failure analysis.

[0016] Preferably, in the S400 step, the determination of the failure mode specifically includes: observing the micro-morphology image of the failure site, if the failure site presents a granular structure or rough morphology, it is determined as cohesive failure; if the failure site presents a smooth and smooth morphology, it is determined as interfacial adhesion failure.

[0017] In one specific embodiment, the method is used to analyze coating peeling failure, and the S500 step includes: energy dispersive spectrometer analysis of the surface exposed after the coating of the failure site peels off and the back of the coating fragments peeled off from the failure site, and the physical level of peeling is determined by comparing the element composition of the two analysis positions.

[0018] Further, if the unexpected impurity elements are detected on both the surface exposed after the coating of the failure site peels off and the back of the coating fragments peeled off from the failure site, the failure is attributed to contaminants or process residues. The impurity elements can include silicon, sodium, potassium, chlorine or sulfur elements.

[0019] Further, if the characteristic elements of the underlying coating or substrate plating layer are detected on the back of the coating fragments peeled off from the failure site, the failure is attributed to the cohesive failure of the underlying coating or substrate plating layer. The characteristic elements can be zinc elements in zinc-rich primer or zinc elements in galvanized steel plate galvanized layer.

[0020] In another specific embodiment, the method is used to analyze corrosion failure, and the S500 step includes: energy dispersive spectrometer analysis of the corrosion products of the failure site, and the characteristic elements such as chlorine or sulfur in the corrosion products are used to trace the chemical medium that triggers corrosion.

[0021] Preferably, the method is used to analyze shrinkage hole coating defects or protruding coating defects, the S400 step includes locating foreign matter in the center area of the defect using the scanning electron microscope, and the S500 step includes identifying the element composition of the foreign matter using the energy dispersive spectrometer to trace the source of pollution.

[0022] Further, if silicon element is detected in the center of the shrinkage coating defect, the pollution source is determined as silicon-containing substance; if crystal composed of phosphorus and zinc elements is detected in the center of the protrusion coating defect, the defect is determined as phosphating crystal particle formed in the phosphating process.

[0023] Preferably, in the S300 step, the cross-section analysis of the failure site includes: preparing a cross-section sample by cutting, embedding, grinding and polishing, and confirming the coating layered structure under an optical microscope, evaluating the interface bonding state, and measuring the thickness of each coating.

[0024] Preferably, in the S100 step, the service history information includes at least one of thermodynamic factor, chemical factor, mechanical factor and radiation factor; and the coating system information of the original design includes the chemical species and the design thickness range of each coating.

[0025] Through the above scheme, the following beneficial technical effects are obtained:

[0026] The present application cross-verify the physical evidence obtained by microscopic detection, i.e. the determined failure mode and the determined micro-area chemical composition, with the background information such as service history and process parameters collected in the early stage; this analysis method of mutual verification logical chain changes the traditional mode of relying on single evidence for inference, can effectively exclude the misjudgment caused by accidental factors or false evidence, so as to ensure that the final failure cause conclusion has high accuracy and reliability.

[0027] The present application can deeply analyze the scale to micron or even nanometer level through microscopic morphology detection, and accurately locate the starting point of failure, such as the physical level of interface separation or the foreign particle in the defect center; the micro-area composition analysis can identify the elements in situ in the characteristic area, and directly associate the macro coating failure phenomenon (such as peeling, corrosion or shrinkage) with the specific physical evidence (such as interface pollutants, corrosive elements or foreign impurities) at the microscopic level, so that the failure cause tracing process is intuitive and reliable.

[0028] The present application solidifies the complex failure analysis process into a standardized operation from information integration to macroscopic observation, and then to microscopic detection and comprehensive diagnosis; this process ensures that the analysis work is done step by step, avoids arbitrary or repetitive testing due to unclear detection ideas, and ensures that all key information and evidence can be systematically collected and evaluated. This not only improves the work efficiency of single analysis, but also ensures the consistency of analysis process and the comparability of results between different cases. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of a method for detecting and analyzing the failure of automobile part paint coating by using a microscope according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0030] The technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0031] Figure 1 is a flowchart of a method for detecting and analyzing paint coating failure of an automobile part using a microscope according to one or more embodiments of the present application.

[0032] The method for detecting and analyzing paint coating failure of an automobile part includes the following steps as shown in Figure 1

[0033] Step S100, background investigation is performed on the coating failure part. Basic information of the part is collected, including part name, part number and service life. Service history and environmental conditions of the part are recorded. The type of the substrate to which the coating is attached is confirmed. Information of the original designed coating system is obtained, including the types of primer, midcoat, topcoat and clearcoat. Coating production process parameters are sorted out. The specific position of the failure phenomenon on the vehicle or assembly part is recorded.

[0034] Step S200, the part is observed and photographed using a magnifying glass and a high-definition camera. The magnifying glass is used to detect the appearance of the failure site. The high-definition camera is used to take photos of the failure area from different angles with light source. A ruler is placed in the same focal plane as the sample during photographing, and the morphology and size of the failure characteristics are recorded.

[0035] Step S300, the failure part is detected using an optical microscope. The coating surface defects and the backside state of the exfoliation are observed. The coating cross-section sample is prepared, and the coating layered structure, interface bonding state and internal compactness are observed. The actual thickness of each coating is measured.

[0036] Step S400, the failure part is detected using a scanning electron microscope. The sample surface is scanned using a focused electron beam to obtain a micro-morphology image of the failure area. Micro-cracks, pores, particle morphology and fracture characteristics are observed.

[0037] Step S500, the failure part is detected using SEM-EDS method. Based on the observation of the morphology by the scanning electron microscope, the micro-area element composition analysis is performed using an energy dispersive spectrometer. The energy dispersive spectrometer is based on the analysis of characteristic X-rays, and the wavelength of the characteristic X-rays follows the Bragg law of crystal diffraction:

[0038] ; ​

[0039] wherein, d is the diffraction order, d is the interplanar spacing of the detector crystal, is the angle between the incident X-ray and the crystal plane.

[0040] Elemental concentration obtained by energy dispersive spectrometer The calculation formula is:

[0041]

[0042] wherein, is the mass concentration of element A in the sample, is the characteristic X-ray intensity of element A collected from the sample micro area, is the characteristic X-ray intensity collected from the pure A element standard sample under the same test conditions, is the matrix effect correction factor which comprehensively considers the atomic number effect, absorption effect and fluorescence effect.

[0043] Step S600, determining the failure reason of the failed component according to the failure mode and the monitoring analysis result. Integrating background information, macroscopic morphology, microstructure and elemental composition data. Building a failure mechanism logical chain to determine the cause of coating failure.

[0044] The following is a detailed description of each step of the application.

[0045] Before starting any physical detection, step S100 conducts a systematic background investigation and information collection on the coating failure component. This step aims to establish a complete information framework for failure analysis, which provides direction guidance and judgment basis for subsequent physical detection, and becomes the basis for final comprehensive diagnosis.

[0046] The collected content first includes the basic information of the component, i.e. the accurate name of the component, the part number and the service life since it was put into use. The service life of the component is the basic data for evaluating the degree of influence of natural aging, fatigue accumulation or long-term environmental effect of the coating.

[0047] At the same time, the service history and environmental conditions of the component are recorded in detail. The environmental conditions here are external driving factors leading to coating failure, which can specifically include but are not limited to:

[0048] Thermodynamic factors, for example, the highest and lowest working temperatures experienced by the component during use, the temperature cycle frequency and temperature rise and fall rate caused by day and night temperature difference or equipment start-stop (such as engine compartment);

[0049] ​Chemical factors, such as average and maximum humidity of the environment where the part is located, whether it is in a high-salt environment for a long time (such as coastal areas or winter snow-melting agent use areas), whether it is exposed to a specific pH environment (such as acid rain in industrial areas), and whether it has been in direct contact with specific chemical solvents (such as fuel oil, lubricating oil, brake fluid, glass water, or strong cleaning agent);

[0050] Mechanical factors, such as whether the part is subjected to continuous vibration, sudden impact, high-speed stone impact from the road surface, or scratching with other objects during use;

[0051] Radiation factors, such as the cumulative time and intensity of exposure to ultraviolet light. These information helps to preliminarily infer the failure mode, such as blistering of the coating in a high-salt environment indicating permeation corrosion, and coating chalking under long-term ultraviolet radiation indicating photo-degradation aging.

[0052] Identify the type of substrate to which the coating is attached, such as cold-rolled steel, galvanized steel, aluminum alloy, or engineering plastics such as ABS / PC. The nature of the substrate directly determines the choice of coating system and the adhesion mechanism. Identifying the substrate type is crucial for determining whether the failure occurs in the coating, at the interface between the coating and the substrate, or in the substrate itself. For example, failure occurring on a galvanized steel sheet requires special attention to the state of the galvanized layer itself and possible interfacial chemical reactions.

[0053] Obtain the coating system information of the original design. This information constitutes a "design benchmark" and includes the chemical types of each coating from the bottom to the top (such as epoxy primer, polyurethane intermediate coating, and acrylic topcoat) and the design thickness range. In subsequent testing, by comparing the actual measured coating structure with this design benchmark, process deviations in production can be quickly identified, such as coating missing, incorrect number of layers, or thickness out of specification.

[0054] Organize key process information during the coating production process. These information are the core basis for tracing manufacturing problems. Specifically, it can be further divided into: substrate pretreatment process, including the specific methods and quality control standards of each process such as degreasing, washing, phosphating, or passivation; coating spraying process, such as the spraying method used (air spraying, electrostatic spraying), and the environmental temperature and humidity during spraying; coating curing process, including the curing temperature curve and curing time of each coating. Any deviation in these process parameters directly affects the final coating performance, for example, insufficient pretreatment leads to residual contaminants at the interface, and insufficient curing temperature leads to low cross-linking density of the coating, poor mechanical properties, and poor weather resistance.

[0055] Finally, the exact physical location of the failure on the vehicle or assembly component is recorded accurately, with photos or drawings attached. Different locations of a component on the vehicle correspond to completely different micro-environments. For example, the failure of the underbody shield mainly considers the stone impact resistance and corrosion resistance, while the failure of the roof focuses more on the ultraviolet resistance and acid rain resistance. The location information directly links the environmental factors with the failure phenomenon, providing a stronger direction for analysis.

[0056] All the collected information is integrated to establish a dedicated analysis file for this failure case, which will run through all subsequent physical detection steps, for ready reference, comparison and verification, to ensure that the final diagnosis conclusion is based on a solid foundation of comprehensive information and physical evidence.

[0057] After collecting and integrating the background information in step S100, step S200 conducts macroscopic observation and image recording of the failed component. This step obtains intuitive physical evidence of failure characteristics through standardized operations, and provides the basis for area selection for subsequent microscopic analysis.

[0058] A stereomicroscope or a portable magnifying glass with a magnification range of 10-80 times is used to detect the failure area. The focus of observation is on the macroscopic morphological characteristics of the failure mode, such as the boundary shape of coating peeling, the size and distribution density of blisters, the direction and width of cracks, and the extent and uniformity of coating discoloration or pulverization. Preliminary determination of these macroscopic characteristics, combined with background information, can initially narrow down the scope of failure cause investigation.

[0059] To achieve standardized recording of failure characteristics, a high-definition industrial camera is used for image acquisition. In image acquisition, the configuration of the light source is a key link. The use of a ring shadowless lamp can obtain uniform illumination for observing the overall appearance of the failure. The use of a side light source with adjustable angle can highlight the depth of the small undulations, textures and cracks on the coating surface by creating shadows on the sample surface. By shooting from different angles such as vertical and inclined, the three-dimensional characteristics of the defects can be recorded comprehensively.

[0060] To ensure the objectivity and measurability of image recording, a ruler must be placed in the same focal plane as the failure area of the sample during shooting. This ruler provides an accurate size reference for subsequent analysis, making it possible to quantify or semi-quantify the size of defects and their distribution density. The high-definition image with the ruler generated in this step serves as the initial physical evidence of the failure analysis file, providing a macroscopic comparison benchmark for all subsequent analyses.

[0061] On the basis of macroscopic observation and image recording, step S300 uses an optical microscope to conduct microscopic detection of the failure area at a medium magnification, to obtain detailed information about the coating structure and defects. This step establishes a link between macroscopic phenomena and microscopic structure by observing the coating surface, the back of the spall, and the coating cross-section in multiple dimensions.

[0062] First, the surface of the coating failure area is observed to check for pinholes, shrinkage holes, microcracks, powder particles, or blisters visible under an optical microscope. For spalling failure, the coating fragments that have fallen off are collected and placed with their backs up on the stage for observation, focusing on foreign matter attached to the back, such as rust products, mud and gravel, oil stains, or metal debris from the substrate. These observations help to preliminarily determine whether the failure is due to defects in the coating itself or external contamination or substrate damage.

[0063] The analysis of the coating cross-section is the core of this step. A sample is taken from the failure area or a representative location, and a cross-section is prepared for observation through standard metallographic sample preparation procedures such as cutting, mounting, grinding, and polishing. For the preparation process of the metallographic sample, those skilled in the art can refer to the standard method for implementation, and the specific operation is a known technology in the art, which will not be described here. Under the microscope, the actual layered structure of the coating can be confirmed to be consistent with the design reference, the interface bonding state between the coatings can be evaluated to be tight, and the presence of bubbles, cavities, and other defects that affect the density within the coating can be checked.

[0064] The image analysis software integrated with the microscope is used to measure the cross-sectional image to obtain the actual thickness of each coating . The calculation is based on the following formula:

[0065] ;

[0066] In the formula, is the length of the coating cross-section in the vertical direction to the surface measured in the micrograph, is the effective magnification of the microscope imaging. Comparing the measured value with the design thickness range can directly determine whether there are process problems of excessive or insufficient film thickness.

[0067] To enhance the image contrast between different coatings or between the coating and the substrate, a chemical indicator can be used for selective coloring. For example, using the property of phenolphthalein indicator to present red in an alkaline environment, the alkaline residue of the pretreatment process or the coating with alkalinity such as phosphating can be identified. For example, using the principle that chelating agents such as fuscopore can react with specific metal ions (such as zinc ions) to form colored complexes, the zinc-plated layer or zinc-rich primer layer can be colored to clearly distinguish its interface with the steel substrate or other coatings. This method combines chemical recognition with optical imaging to improve the accuracy of the analysis of complex coating systems.

[0068] When the resolution of optical microscope is not sufficient to reveal the fine features of the failure, step S400 employs scanning electron microscope (SEM) for high resolution microtopography detection. The core of this step is to utilize the high magnification imaging capability of SEM to analyze the microtopography of the failure fracture, and to determine the root physical mode of the failure according to specific topographic features.

[0069] The working principle of scanning electron microscope is to scan the sample surface with a focused high-energy electron beam, and generate images with high resolution and large depth of field by collecting secondary electron or backscattered electron signals. This technology can clearly observe sub-micron structural details, and plays a decisive role in failure analysis.

[0070] The present method establishes a corresponding diagnostic relationship between micro features and failure modes by observing the topography of the failure fracture.

[0071] For peeling or cracking failure, the fracture of the failed part is directly placed under SEM for observation. If the fracture is observed to exhibit a granular structure or a rough topography with unevenness, and its feature is that the pigment or filler particles in the internal coating or the metal grains in the internal galvanized layer are exposed, it indicates that the fracture occurs in the internal single coating. This fracture mode is determined as cohesive failure. The occurrence of cohesive failure directly points to the mechanical performance deficiency or internal defects of the coating itself.

[0072] If the fracture is observed to exhibit a relatively smooth topography, and its feature is that a layer of coating is completely separated from another layer or from the surface of the substrate, and the back surface topography of the peeled coating is an exact negative replication of the underlying interface, it indicates that the fracture occurs at the interface between the two different layers. This fracture mode is determined as interfacial adhesion failure. The occurrence of interfacial adhesion failure directly points to the insufficient interlayer bonding force, and the reason needs to be further determined in combination with subsequent composition analysis.

[0073] In addition, SEM can also be used to observe the initiation and propagation path of microcracks, as well as to evaluate the dispersion and agglomeration state of pigment and filler particles in the coating. These topographic information all provide key physical evidence for the comprehensive diagnosis of the subsequent steps.

[0074] After the failure topography is preliminarily determined by scanning electron microscope (SEM), step S500 utilizes the energy dispersive spectrometer (EDS) integrated with SEM for interface positioning and attribution analysis of peeling failure. This step combines topography observation with micro-area composition analysis, accurately compares the element composition on both sides of the failure interface, determines the specific physical level of peeling occurrence, and reveals the chemical reasons leading to the loss of adhesion.

[0075] The core of this analysis method is to detect the elements at two key locations: one is the surface exposed after the coating peeling off from the part, and the other is the back of the peeled-off coating fragments.

[0076] When performing the peeling failure analysis, if the SEM morphology observation in the previous step S400 determines that the interface adhesion is damaged, the focus of the EDS analysis is to identify the abnormal elements on the interface. The surface of the substrate exposed after the coating peeling off and the back of the peeled-off coating are analyzed by EDS respectively. If unexpected impurity elements are detected at the interface, it indicates that the failure is related to contaminants or residues of the previous treatment process. For example, the detection of silicon (Si) element usually indicates silicon oil or release agent contamination; the detection of alkali metal elements such as sodium (Na) and potassium (K) indicates the residue of alkaline substances such as degreasing tank liquid in the previous treatment; the detection of chlorine (Cl) and sulfur (S) elements indicates that the failure is related to the penetration of chloride and sulfide corrosion media in the environment. By identifying these characteristic elements, the failure cause can be directly attributed to specific contamination sources or process defects.

[0077] If the SEM morphology observation in the previous step S400 determines that the internal cohesion is damaged, the EDS analysis is used to confirm the specific coating where the fracture occurs. For example, in the case of epoxy coating peeling off from zinc-rich primer, EDS analysis is performed on the back of the epoxy coating fragments. If a large amount of zinc (Zn) element is detected, it proves that the fracture does not occur at the interface between the epoxy coating and the zinc-rich primer, but occurs within the zinc-rich primer coating itself. This accurately determines the failure mode as internal cohesion failure of the zinc-rich primer. Similarly, for coating peeling off on a galvanized steel plate, if zinc element is detected on both the back of the peeled coating and the exposed substrate surface, it proves that the internal cohesion failure occurs in the galvanized layer, and the cause can be traced back to the quality problem of the galvanized layer itself, such as excessive thickness leading to excessive internal stress.

[0078] Through the above joint diagnosis of morphology and composition, this method analyzes the peeling failure from macroscopic phenomenon description to microscopic physical positioning and chemical attribution. By cross-verifying the elements on both sides of the failure interface, it can clearly distinguish between adhesion failure and internal cohesion failure, and accurately locate the layer where the fracture occurs, providing direct and solid evidence for the final failure mechanism diagnosis.

[0079] The scanning electron microscope is used to observe the micro-morphology of the corrosion products, such as filamentous, lamellar or loose granular structure. At the same time, the initiation position of corrosion, such as micro-cracks or holes in the coating, and the path of corrosion along the substrate are observed. These morphology information provides intuitive basis for judging the type of corrosion (such as filamentous corrosion) and the corrosion process.

[0080] On the basis of the morphology observation, the energy dispersive spectrometer is used to identify the element composition of the corrosion product and its surrounding area. Through the point analysis or surface scanning analysis of the corrosion product, its chemical composition can be determined.

[0081] If the main component of the corrosion product is the base metal element (such as iron, zinc or aluminum) and oxygen element, and the chlorine (Cl) element signal is detected in the product, it can be determined that the failure is induced by chloride. The corrosion medium usually comes from the deicing agent used in winter or the salt spray in the marine environment.

[0082] Similarly, if sulfur (S) element is detected in the corrosion product mainly composed of metal oxides, it can be determined that the failure is related to the acidic environment or sulfide, which can be traced back to the acid rain in the industrial area or the sulfide pollutants in the atmosphere.

[0083] For the corrosion failure occurring in high temperature environment, if EDS analysis shows that the oxidation layer is rich in elements such as vanadium (V), sodium (Na) or sulfur (S), it can be determined that hot corrosion such as sulfate or vanadate has occurred.

[0084] By identifying the characteristic elements in the corrosion product, this method directly links the macroscopic service environment information collected in step S100 with the chemical composition of the micro area, thereby providing direct evidence for the mechanism analysis of corrosion failure.

[0085] For the film forming defects such as shrinkage cavity formed on the surface of the coating, this method also uses SEM-EDS combined technology to trace the pollution source. Such defects are usually caused by pollutants with low surface tension before the paint solidification.

[0086] Using a scanning electron microscope, find the target defect (such as shrinkage cavity) at low magnification, and then gradually increase the magnification to observe the fine morphology of the center area of the defect. For typical shrinkage cavity defects, there is usually a foreign particle or residue that causes the coating film to move away from the center position.

[0087] After the foreign matter is precisely located by SEM, the energy dispersive spectrometer is used for point analysis to obtain its element composition. By identifying the characteristic elements of the foreign matter, it can be associated with a specific pollution source.

[0088] For example, if silicon (Si) element is detected in the center of the shrinkage cavity, the pollution source is determined to be derived from silicon-containing substances in the production environment or process, such as silicone oil, release agent, sealant or aerosol of some personal care products.

[0089] If the regular crystal-like morphology is observed in the protrusion defect center on the coating surface, and EDS analysis shows that it is mainly composed of phosphorus (P) and zinc (Zn) elements, then the defect is determined to be an excessive or agglomerated phosphating crystal particle formed in the phosphating process of the substrate pretreatment.

[0090] By combining the micro-morphology observation of the defect with the element composition identification of the central foreign matter, the method can directly link the macro coating defect with the specific chemical contaminant at the micro level, providing accurate evidence for tracing the source of contamination and improving production processes or environmental control

[0091] For coating failure caused by mechanical wear or high temperature environment, the method also uses SEM-EDS combined technology for mechanism determination.

[0092] For wear failure, the micro-morphology of the wear area is observed using a scanning electron microscope to determine the specific type of wear. If the morphology presents directional grooves or scratches, it is determined to be abrasive wear. On this basis, the energy dispersive spectrometer is used to analyze the elements inside the groove or the wear surface. If embedded hard particles are detected in the groove, and the main component is identified by EDS as silicon (Si), then the cause of the wear can be attributed to the sand particles in the environment. If characteristic metal elements from the cooperating moving parts are detected on the wear surface, it can be determined that the failure is adhesive wear or material transfer.

[0093] For coating failure caused by high temperature, a scanning electron microscope can be used to observe the micro-cracking, blistering or powdering phenomena caused by thermal degradation of the coating. The energy dispersive spectrometer is used to detect composition changes. For example, by comparing the oxygen (O) element content of the failure surface with the non-failure area or the coating interior, the degree of oxidation of the coating can be quantitatively evaluated. In addition, by performing EDS line scanning or area scanning analysis on the coating cross-section, element migration phenomena occurring at high temperatures can be revealed. For example, a concentration gradient of zinc (Zn) element in the substrate diffusing to the primer layer can be detected, which proves that high temperature has destroyed the original interface structure between the coating and the substrate, resulting in a decrease in adhesion.

[0094] Step S600 is a final comprehensive analysis of the information and data obtained from all the preceding detection steps to determine the unique and exclusive failure cause. The core innovation of the method is to build a complete logical reasoning framework from physical evidence to final conclusion, that is, to systematically correlate background information, macro-morphology, microstructure and element composition data to form a mutually corroborating failure logic chain.

[0095] This comprehensive diagnostic process is not simply a list of data, but follows a rigorous logical verification and exclusion process.

[0096] First, the physical evidence obtained in the previous steps is integrated. For example, the chemical composition of the failure interface determined by SEM-EDS in step S500 is compared with the fracture micro-morphology observed in step S400 and the coating cross-section structure and thickness measured in step S300 to form a preliminary physical judgment about the failure mode.

[0097] Then, this physical judgment is cross-verified with the background information collected in step S100. For example, if SEM-EDS detects chlorine (Cl) elements at the failure interface and determines that it is chloride-induced corrosion. At this time, the service environment recorded in the background information needs to be consulted. If the component is indeed long-term service in coastal areas or roads frequently using de-icing agents in winter, the environmental factors and physical evidence form a confirmation, and the reliability of the failure path is enhanced. On the contrary, if the service environment does not have the condition to introduce chloride, the source of chlorine elements needs to be re-considered, such as whether it may come from some pollution in the production process.

[0098] Through the above mutual confirmation, a complete failure analysis logic is constructed. Taking a specific coating peeling case as an example:

[0099] Macroscopic observation (S200): It is found that the coating peels off in pieces.

[0100] SEM morphology observation (S400): It is determined that the fracture morphology of the back of the peeled coating is a smooth plane, which belongs to interface adhesion failure.

[0101] SEM-EDS composition analysis (S500): Analysis of the back of the peeled coating and the exposed surface of the substrate shows that no expected primer layer elements are detected, but a large amount of silicon (Si) elements are found on both surfaces.

[0102] Optical microscope analysis (S300): Observation of the coating cross-section shows that the coating system is complete, and the thickness of each layer is within the design specification, but there is a small gap between the bottom layer and the substrate.

[0103] Background information (S100): Looking back at the production process, it is found that the turnover rack of this batch of components used a silicon-containing release agent before coating, and the cleaning process was incomplete.

[0104] Based on the above information, the following analysis logic can be constructed: the silicon oil pollution in the production link (background information) causes the attachment of low surface energy pollutants (EDS evidence) on the substrate surface, which prevents the primer from effectively wetting and combining with the substrate (optical microscope evidence), and finally causes adhesion failure at the contaminated substrate / primer interface (SEM morphology evidence) due to stress during service, which manifests as macroscopic coating peeling.

[0105] This method also uses the method of elimination. In the aforementioned case, since the coating thickness is normal, there are no internal defects, and the failure occurs at the interface of the bottom layer, it can be ruled out that the internal stress is too large due to excessive film thickness, the cohesion of the coating is insufficient, or the adhesion between the coatings is poor.

[0106] Finally, based on this evidence-based, logically closed-loop analysis path, the final diagnostic conclusion about the coating failure of the part is drawn, and clear guidance is provided for subsequent process improvement or quality control

[0107] The application will be described in more detail below with reference to specific embodiments.

[0108] Example 1:

[0109] This example relates to the analysis of a failure part with delamination of an epoxy coating from a zinc-rich primer.

[0110] According to step S100, collect background information, the coating system of the part is an epoxy topcoat applied on a zinc-rich primer. According to step S200, use a magnifying glass to observe the peeled epoxy coating fragments, and there is no obvious, uniform thick layer of zinc-rich primer attached to the back.

[0111] Since macroscopic and optical observation cannot determine the failure layer, according to steps S400 and S500, the peeled epoxy coating fragments are placed in a scanning electron microscope (SEM) for high magnification morphology observation and energy dispersive spectroscopy (EDS) composition analysis. First, use EDS to analyze the back of the epoxy coating, and the results detect a significant zinc (Zn) element signal, which proves that the fracture indeed occurs in the zinc-rich primer layer or its surface, and is directly related to the primer layer. Then, use SEM to observe the form of zinc element, and find that the zinc powder is not in the expected close-packed state, but in the form of uniform but discontinuous point particles distributed in the epoxy base.

[0112] Finally, according to step S600, comprehensive diagnosis is carried out. The "uniform but discontinuous point form" of zinc powder in the SEM image is a typical morphology characteristic caused by dry spraying or excessive spraying. This spraying process defect makes the zinc-rich primer fail to form a continuous and dense film during curing, resulting in a large number of micro-pores in the coating and a significant decrease in cohesive strength. Therefore, although EDS detects zinc on the back of the epoxy coating, it is not an interface adhesion failure, but due to the poor curing of the zinc-rich primer, its cohesive strength is insufficient to resist the stress generated during the curing or use of the topcoat, resulting in cohesive failure within the primer layer. Conclusion: the root cause of the coating delamination failure is the dry spraying process defect in the production of the zinc-rich primer, which results in insufficient cohesive strength.

[0113] (Note: If in another similar case, SEM image observation shows that the zinc powder is in amorphous powder form, and EDS analysis shows that the oxygen content is abnormally high, it can be determined that the zinc-rich primer surface has been oxidized before the topcoat is applied, generating a poor adhesion zinc oxide layer, resulting in a decrease in adhesion.)

[0114] Example 2:

[0115] This example relates to the analysis of a batch of galvanized steel sheets for automotive use after the completion of the electrophoretic coating, on which the surface of the coating presents protruding defects.

[0116] According to step S100, the background information is collected, the production process of the galvanized steel sheet is: cleaning, phosphating, epoxy cathodic electrophoretic coating. The defects are found after all the process flows are completed. The production workshop environment is clean, which can initially rule out the introduction of external particle pollution during the coating process. According to step S200, using a magnifying glass and an optical microscope, the defects are observed to be isolated, hard protrusions on the surface of the coating.

[0117] Take the sample with defects and place it directly in the scanning electron microscope (SEM) for detection according to steps S400 and S500. The SEM image shows that under each protrusion, there is a loose granular structure composed of a large number of fine crystals, which lifts the electrophoretic paint film above. EDS point analysis of the granular accumulation shows that the energy spectrum is mainly composed of phosphorus (P) and zinc (Zn) elements.

[0118] Finally, according to step S600, a comprehensive diagnosis is made. The EDS analysis results (P and Zn elements) clearly indicate that the granular material is zinc phosphate. Combined with the background process information, the material comes from the phosphating process before electrophoresis. Its loose and accumulated morphology indicates that abnormal crystal growth occurs during phosphating, forming oversized or agglomerated zinc phosphate crystals. These abnormal phosphating crystals adhere to the surface of the galvanized substrate, and although the subsequent electrophoretic coating covers them, it cannot hide their physical profile, thus forming protruding defects on the surface of the final coating. The conclusion is that the root cause of the protruding defects on the surface of the coating is the improper control of the phosphating process in the substrate pretreatment stage, resulting in the generation of abnormal zinc phosphate crystals.

[0119] Example 3:

[0120] This example relates to the analysis of a failed part with a large area of epoxy coating delamination from a galvanized steel sheet.

[0121] According to step S200, macroscopic observation is performed, and it is found that the back of the peeled epoxy coating fragments has a bright metallic luster, indicating that a large amount of zinc layer has been peeled off with the coating.

[0122] To precisely locate the layer where the fracture occurred, a cross-section metallographic sample was prepared from the sample taken from the peeling boundary according to step S300. The cross-section sample was placed in a scanning electron microscope (SEM) and observed and measured according to steps S400 and S500. The SEM cross-section image clearly shows that the epoxy coating itself is intact and dense, but the fracture path is not at the interface between the epoxy coating and the galvanized layer, but entirely within the galvanized layer. The thickness of each layer in the cross-section was measured using the image analysis software integrated with the microscope, and it was found that the thickness of the galvanized layer of the sample was significantly higher than the upper limit of the design specification.

[0123] Finally, comprehensive diagnosis was performed according to step S600. The SEM cross-section image directly proves that the failure mode is the cohesive failure of the galvanized layer itself. The thickness measurement result reveals the root cause. For the galvanized layer, when its thickness is too large, the internal stress generated by the crystallization process will significantly increase, and at the same time, its own toughness and mechanical strength (i.e., cohesive strength) will decrease with the increase of the thickness. Therefore, when the over-thick galvanized layer bears the shrinkage stress generated during the curing of the overlying epoxy coating, or bears the thermal stress and mechanical stress during subsequent use, its own strength is not enough to resist these stresses, and thus cracking occurs at the thinnest intermediate layer, causing the overlying part of the galvanized layer to peel off together with the epoxy coating. Conclusion: the root cause of the coating delamination is that the galvanized layer of the galvanized steel sheet is too thick, resulting in a decrease in its cohesive strength.

[0124] Example 4:

[0125] This example relates to the analysis of a fading problem of an aluminum coil coating for building exterior walls.

[0126] According to step S100, the background information was collected, and the surface coating of the aluminum coil was a topcoat formed by cross-linking melamine-formaldehyde and polyester resin. According to step S200, macroscopic observation was performed, and it was found that the coating fading phenomenon was unevenly distributed, and in the areas where the fading was more serious, there was a sticky feeling when touched by hand, and dust particles and fragments were embedded on the surface.

[0127] A sample was taken from the faded area and placed in a scanning electron microscope (SEM) for detection and analysis according to steps S400 and S500. The SEM image shows that the coating surface of the faded area is rougher than that of the non-faded area, and a large number of foreign fine particles are embedded on the surface. EDS point analysis of these embedded particles showed that they were rich in silicon (Si) element, indicating that they were mainly dust or sand particles in the environment.

[0128] Finally, a comprehensive diagnosis is made according to step S600. Physical evidence (SEM-EDS) shows that the direct appearance of discoloration is related to a large amount of silicon-containing contaminants adhered to the surface of the coating. However, the ability of the coating to adhere contaminants itself reflects the abnormality of its surface state. For a fully cured crosslinked coating designed for outdoor use, its surface should have sufficient hardness and anti-sticking property. The phenomenon that the current coating is prone to stick contaminants directly points to its insufficient crosslinking degree. The coating with low crosslinking degree has a low glass transition temperature, and after being heated by sunlight, the surface of the coating will become soft and sticky, thereby firmly sticking dust particles in the air. These contaminants not only affect the appearance, but also the moisture and chemicals contained therein will further accelerate the degradation of the coating base material (polyester resin) in cooperation with ultraviolet light, ultimately showing discoloration and performance degradation of the coating. Conclusion: the root cause of the discoloration of the coating is the low crosslinking degree of the topcoat, which leads to poor weather resistance of the coating, the surface becomes sticky under the action of light and heat, adsorbs contaminants and accelerates the aging degradation of itself.

[0129] Finally, it should be pointed out that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting and analyzing paint coating failures on automotive parts using a microscope, characterized in that, include: S100, Systematic Information Integration: Collect basic information, service history and environmental conditions, substrate type, coating system information of the original design, coating production process parameters of the automotive parts, and determine the specific location of the failure phenomenon; S200. Macroscopic observation and image recording: Based on the specific location of the determined failure phenomenon, use a magnifying glass to perform visual inspection of the failure part at the specific location, and use a high-definition camera with a ruler to take photos of the failure area and record the macroscopic morphology of the failure part. S300, Optical Microscope Inspection: Analyze the macroscopic morphology of the failed part, and analyze the surface, back side of the peeling material and cross-section of the failed part to obtain the coating layer structure, interface bonding state, actual thickness and macroscopic defect information of the failed part; S400. Microscopic morphology detection and failure mode determination: For the macroscopic defect information of the failure site and the areas that cannot be clearly identified due to insufficient resolution of the optical microscope, a scanning electron microscope is used to acquire the microscopic morphology image of the failure site, and the failure mode is determined based on the microscopic morphology image. S500, Micro-area composition analysis: Based on the microscopic morphology image of the failure site obtained in step S400, the elemental composition analysis of the characteristic regions in the microscopic morphology image that are directly related to the failure phenomenon is performed using the energy dispersive spectroscopy instrument attached to the scanning electron microscope. The characteristic regions include the failure interface, corrosion products, or foreign inclusions in the defect center. S600 Comprehensive Failure Mechanism Diagnosis: The failure modes identified in step S400 and the elemental composition identified in step S500 are cross-validated with the service history, environmental conditions, and painting production process parameters of the automotive parts collected in step S100. A mutually corroborating logical chain of failure mechanisms is constructed, and other unrelated failure possibilities are ruled out, thereby determining the cause of failure.

2. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 1, characterized in that, In step S400, the determination of the failure mode specifically includes: By observing the microscopic morphology of the failed area, if the failed area exhibits a granular structure or a rough morphology, it is determined to be cohesive failure; if the failed area exhibits a flat and smooth morphology, it is determined to be interfacial adhesion failure.

3. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 1, characterized in that, In step S500, the failure interface includes the surface exposed after peeling and the back side of the coating fragment. Energy dispersive spectroscopy is used to analyze the surface exposed after the coating peels off at the failure site and the back side of the coating fragment peeled off at the failure site. The elemental composition of the two analysis locations is compared to determine the physical level at which the peeling occurred.

4. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 3, characterized in that, If unexpected impurity elements are detected on both the exposed surface after the coating peels off at the failure site and on the back side of the coating fragments peeled off at the failure site, the failure is attributed to contaminants or process residues; the impurity elements include silicon, sodium, potassium, chlorine, or sulfur.

5. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 3, characterized in that, If characteristic elements of the underlying coating or substrate plating are detected on the back side of the coating fragment peeled off at the failure site, the failure is attributed to cohesive failure of the underlying coating or substrate plating; the characteristic element is zinc in zinc-rich primer or zinc in the zinc plating layer of galvanized steel sheet.

6. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 1, characterized in that, The S500 step includes: performing energy dispersive spectroscopy analysis on the corrosion products of the failed site, and identifying chlorine or sulfur characteristic elements in the corrosion products to trace the chemical medium that caused the corrosion.

7. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 1, characterized in that, For pinhole coating defects or protruding coating defects, step S400 includes using the scanning electron microscope to locate foreign objects in the center region of the defect; The S500 step includes using the energy dispersive spectroscopy instrument to identify the elemental composition of the foreign substance for tracing the source of pollution.

8. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 7, characterized in that, If silicon is detected at the center of the pinhole coating defect, the source of contamination is identified as a silicon-containing substance. If crystals composed of phosphorus and zinc are detected at the center of the raised coating defect, the defect is identified as phosphating crystal particles formed during the phosphating process.

9. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 1, characterized in that, In step S300, the cross-sectional analysis of the failed location includes: Cross-sectional samples were prepared by cutting, embedding, grinding and polishing, and the coating layer structure was confirmed under an optical microscope to evaluate the interfacial bonding state and measure the thickness of each coating.

10. The method for detecting and analyzing paint coating failure of automotive parts using a microscope according to claim 1, characterized in that, In step S100, the service history information includes: At least one of the following: thermodynamic factors, chemical factors, mechanical factors, and radiation factors; The information on the original coating system design includes the chemical composition and design thickness range of each coating.

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