Nondestructive testing method for automobile power battery shell micropores based on synchrotron radiation X-ray phase imaging
By using synchrotron X-ray phase imaging technology, the problems of poor signal-to-noise ratio and defect risk assessment in the detection of micropores in automotive power battery casings have been solved, achieving high-sensitivity detection and early risk warning. It is applicable to non-destructive testing of automotive power battery casings, fuel cell membranes, aerospace composite material components, precision castings, and electronic packaging devices.
Patent Information
- Application Number
- CN202511817669.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-03-17
AI Technical Summary
Existing technologies cannot effectively detect micron-sized pores in the casing of automotive power batteries, resulting in poor signal-to-noise ratio, a contradiction between spatial resolution and detection sensitivity, and an inability to assess defect risks or predict their evolution trend under external stress.
Synchrotron X-ray phase imaging technology is used to irradiate the target area with a high-coherence X-ray beam and collect scattered light intensity matrix data. Phase inversion and sparsity constraint optimization are then performed to reconstruct the electron cloud density distribution. Combined with local gradient and connectivity graphs, abnormal regions are identified, and defects are classified and risks are assessed.
It achieves highly sensitive detection of micropores, can clearly identify micropores that cannot be seen by traditional methods, quantify their failure risk, and provide early warning and adaptive optimization detection system.
Smart Images
Figure CN121678729A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of photoelectric detection / image recognition / non-destructive testing technology, and in particular to a non-destructive testing method for micropores in the casing of an automotive power battery using synchrotron radiation X-ray phase imaging. Background Technology
[0002] The integrity of the battery casing is crucial to the battery's safety and lifespan. During the compression molding process, defects such as microscopic pores and shrinkage cavities can easily occur inside the casing. These defects, especially early micron-sized pores, can expand into cracks under the thermal stress and internal pressure of battery charge-discharge cycles, leading to electrolyte leakage, casing rupture, or even serious safety accidents such as thermal runaway.
[0003] Traditional inspection methods, such as visual inspection and water testing, cannot detect internal defects. Industrial DR and CT X-ray imaging technologies are currently the mainstream non-destructive testing methods. They are based on the difference in the degree of attenuation of X-rays after passing through materials to form images. For battery casings, the material density is uniform, and the difference in absorption coefficient between micropores and the substrate material is extremely small, resulting in insufficient contrast in traditional X-ray absorption imaging, making them difficult to identify.
[0004] The closest existing solution is industrial CT scanning and 3D reconstruction technology based on microfocus X-ray sources. This solution emits a cone-shaped beam of X-rays through an X-ray tube, penetrating the battery casing, and the attenuated projection image is received by a flat panel detector. By rotating the sample, projection data from hundreds or thousands of angles are acquired, and algorithms such as filtering and backprojection are used to reconstruct a 3D density distribution map inside the sample. Operators identify and segment regions with a density lower than the matrix material by setting grayscale thresholds, and calculate their size and location.
[0005] Disadvantages of existing technology: Insensitive to subtle density changes: Traditional absorption imaging relies on significant differences in atomic number or density between defects and the matrix. For micropores in battery casings, the density contrast is extremely low, resulting in a poor signal-to-noise ratio, and tiny defects are easily overwhelmed by noise.
[0006] The contradiction between spatial resolution and detection sensitivity: To improve the detection capability of micron-sized pores, extremely high spatial resolution is required. This usually requires the use of microfocal sources and sacrifices detection efficiency, resulting in slow detection speed and difficulty in meeting production line cycle time.
[0007] Unable to effectively assess defect risk: It can only provide the location and apparent size of pores, but cannot quantify key characteristics that affect failure risk, such as the stress concentration state at the pore edges and the connectivity with the surrounding material, and it is difficult to predict their evolution trend under external stress. Summary of the Invention
[0008] This invention aims to address the technical problems of existing methods for detecting defects in automotive power battery casings, such as insensitivity to slight density changes, contradiction between spatial resolution and detection sensitivity, and inability to effectively assess defect risks. It provides a non-destructive testing method for micropores in automotive power battery casings using synchrotron X-ray phase imaging.
[0009] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A non-destructive testing method for micropores in the casing of an automotive power battery using synchrotron X-ray phase imaging includes the following steps: Step S1: Construction of uniform irradiation field and data acquisition; A highly coherent X-ray beam is generated, and after energy filtering and wavefront correction, a uniform irradiation field is formed to irradiate the battery casing sample, and scattered light intensity matrix data from multiple angles are collected. Step S2: Phase inversion and electron cloud density reconstruction; Phase inversion and sparsity constraint optimization were performed on the scattered light intensity matrix data to reconstruct high-precision electron cloud density distribution data inside the sample, and a connectivity graph reflecting density continuity was constructed based on this data. Step S3: Feature fusion and anomaly region identification; Local gradient and frequency perturbation features are extracted from electron cloud density distribution data and fused with connectivity graphs to construct fused feature data. Abnormal regions are then accurately identified based on preset thresholds. Step S4: Defect classification and risk assessment; The identified porosity defects are classified by feature, and their failure risk level is assessed based on their perturbation amplitude, local connectivity and density change trend, generating a visualized risk distribution map; Step S5: Parameter feedback and system optimization; The detection results are compared with a database of known defects. The feature extraction and risk assessment parameters are dynamically updated based on the accuracy of the comparison. The updated parameters are then fed back to the irradiation field control step S1 to achieve adaptive optimization of the detection system.
[0010] In the above technical solution, in step S1, the highly coherent X-ray beam is generated by a synchrotron radiation source or by an X-ray source combined with a micro-focusing tube phase contrast imaging.
[0011] In the above technical solution, step S2 specifically includes: The collected angle-scattered light intensity matrix I(θ, x, y) is input into an iterative phase retrieval algorithm; For the preliminary phase distribution Perform Laplacian regularization to suppress noise and highlight real boundaries, and then obtain the preliminary distribution ρ(x, y, z) of electron cloud density through the phase-density conversion relationship; Perform local frequency compensation on ρ(x, y, z) to enhance the high-frequency details of the micropore edges and output the optimized electron cloud density distribution ρ_opt; Based on ρ_opt, calculate the density gradient between each voxel and its neighboring voxels, and construct a density connectivity relationship graph G=(V, E), where the nodes V represent spatial positions and the weights of the edges E represent the density gradient values between adjacent positions.
[0012] In the above technical solution, in step S2, the phase is solved by using the transmission intensity equation method.
[0013] In the above technical solution, step S3 is specifically as follows: Feature extraction: Use the designed convolution kernel to perform convolution operations on ρ_opt to extract the local density change rate at each position ; At the same time, perform a short-time Fourier transform on ρ_opt to extract the local frequency perturbation amplitude f_pert; Combine into a local feature vector; Feature fusion: Fuse the local feature vector with the gradient weight data w_grad in the connectivity relationship graph to form fused feature data ; Anomaly determination: Set the determination threshold: If [[ID=2C]]and w_grad<T_conn, then determine that this area is a pore defect; Among them, T_grad is the preset critical value for determining whether the local density change rate constitutes a defect boundary; T_conn is the preset critical value for determining whether the local connectivity in the density connectivity relationship graph constitutes an isolated defect.
[0014] In the above technical solution, step S4 is specifically as follows: Defect classification: According to the spatial distribution pattern of and the complexity of f_pert, classify the defects: Isolated pore: Showing a closed ring shape; Network-like pore group: w_grad shows potential connectivity paths between multiple defects; Risk assessment: Construct a risk feature matrix M_risk: Perturbation amplitude A_pert: The maximum value at the pore edge ; Connectivity C_conn: The clustering degree of pores in the connectivity relationship graph; Trend of change T_trend: Analyze the statistical trend of stomatal characteristics by scanning multiple samples from the same batch or the same area multiple times; Set threshold ranges for A_pert, C_conn, and T_trend. If all three exceed the limits, the defect is judged as a high-risk defect. Finally, output the failure risk level and three-dimensional coordinates of each defect. Generate a visual risk distribution map: map risk levels to different colors, render the map in the 3D reconstruction model, and show the location and degree of danger of defects.
[0015] In the above technical solution, in step S4, classification is performed by a support vector machine or a lightweight convolutional neural network.
[0016] The present invention has the following beneficial effects: The present invention provides a non-destructive testing method for micropores in the casing of automotive power batteries using synchrotron X-ray phase imaging. This method utilizes the high coherence and phase contrast imaging principle of synchrotron X-rays to image phase changes caused by electron cloud density perturbations. Its sensitivity to the internal microstructure of low atomic number materials is several orders of magnitude higher than that of absorption imaging, and it can clearly identify micropores that cannot be seen by traditional methods, thus improving detection sensitivity.
[0017] The non-destructive testing method for micropores in the casing of automotive power batteries using synchrotron X-ray phase imaging of the present invention not only detects the presence of pores, but also quantitatively assesses and classifies the stability and expansion risk of pores by analyzing features such as the electron cloud density gradient and connectivity around the pores, thereby achieving early risk warning.
[0018] The non-destructive testing method for micropores in automotive power battery casings using synchrotron X-ray phase imaging of the present invention feeds the test results back to the production process, providing accurate data support for adjusting injection molding process parameters, reducing defect generation from the source, and forming a closed-loop optimization. Attached Figure Description
[0019] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0020] Figure 1 This is a schematic diagram illustrating the method steps of a specific embodiment of the non-destructive testing method for micropores in the casing of an automotive power battery using synchrotron X-ray phase imaging according to the present invention. Detailed Implementation
[0021] The present invention will now be described in detail with reference to the accompanying drawings.
[0022] The present invention provides a non-destructive testing method for micropores in the casing of automotive power batteries using synchrotron X-ray phase imaging, such as... Figure 1 As shown, it includes the following steps: Step S1: A highly coherent X-ray beam is generated by a synchrotron radiation source. After energy screening and wavefront correction, a uniform irradiation field is formed to irradiate the battery casing sample and collect scattered light intensity matrix data from multiple angles.
[0023] Step S2: Perform phase inversion and sparsity constraint optimization on the scattered light intensity matrix data to reconstruct high-precision electron cloud density distribution data inside the sample, and construct a connectivity graph reflecting density continuity based on this data.
[0024] Step S3: Extract local gradient and frequency perturbation features from electron cloud density distribution data, and fuse them with connectivity graph to construct fused feature data. Based on preset thresholds, accurately identify abnormal regions.
[0025] Step S4: Classify the identified porosity defects by feature and assess their failure risk level based on their perturbation amplitude, local connectivity and density change trend, and generate a visualized risk distribution map.
[0026] Step S5: Compare the detection results with the known defect database, dynamically update the feature extraction and risk assessment parameters based on the comparison accuracy, and feed the updated parameters back to the irradiation field control step in step S1 to achieve adaptive optimization of the detection system.
[0027] The following details the non-destructive testing method for micropores in the casing of automotive power batteries using synchrotron X-ray phase imaging, based on the present invention.
[0028] Step S1: Construction of uniform irradiation field and data acquisition; A high-brightness, highly coherent initial beam of X-rays is generated using a synchrotron radiation source.
[0029] Photons of specific energies are filtered out using a dual-crystal monochromator to ensure good penetration through the battery casing.
[0030] Adaptive optics are used to correct the wavefront of the beam, eliminate aberrations, and form an illumination field with uniform phase in the sample area.
[0031] When the battery casing sample is placed in a uniform phase irradiation field, the X-rays interact with the electron cloud inside the sample, producing a scattering pattern that carries structural information.
[0032] An angular step scan is performed during sample rotation using a high-resolution detector to record a series of scattering images, forming an angular scattered light intensity matrix I(θ, x, y).
[0033] In other specific embodiments, on production lines that do not require extremely high resolution, a laboratory X-ray source combined with a microfocus tube grating-based phase contrast imaging technique can be used as an alternative to synchrotron radiation sources to reduce costs and achieve production line integration.
[0034] Step S2: Phase inversion and electron cloud density reconstruction; Input the collected I(θ, x, y) into an iterative-based phase retrieval algorithm. This algorithm repeatedly iterates between the real space and the Fourier space to recover the lost phase information from the intensity information, obtaining a preliminary phase distribution .
[0035] Perform Laplacian regularization on to suppress noise and highlight real boundaries, and then obtain a preliminary distribution of the electron cloud density ρ(x, y, z) through the phase-density conversion relationship.
[0036] Perform local frequency compensation on ρ(x, y, z) to enhance high-frequency details such as the edges of micro pores, and output the optimized electron cloud density distribution ρ_opt.
[0037] Based on ρ_opt, calculate the density gradient of each voxel with its neighboring voxels, and construct a density connectivity relationship graph G=(V, E), where the nodes V represent spatial positions, and the weights of the edges E represent the density gradient values between adjacent positions. Low-weight edges indicate that there is a density mutation here, which may be the pore boundary.
[0038] In other specific embodiments, in step S2, in addition to the iterative phase retrieval method, the transport-of-intensity equation method can also be used to directly solve the phase, which is faster in calculation and applicable to samples with regular shapes and uniform thicknesses.
[0039] Step S3: Feature fusion and abnormal region identification; Feature extraction: Use the designed convolution kernel to perform convolution operations on ρ_opt to extract the local density change rate at each position . At the same time, perform a short-time Fourier transform on ρ_opt to extract the local frequency perturbation amplitude f_pert. Combine into a local feature vector.
[0040] Feature fusion: Fuse the above local feature vector with the gradient weight data w_grad in the connectivity relationship graph G to form fusion feature data .
[0041] Abnormal determination: Set a determination threshold: If and w_grad < T_conn, then determine that this region is a pore defect; Wherein, T_grad is a preset critical value used to determine whether the local density change rate constitutes a defect boundary; T_conn is a preset critical value used to determine whether the local connectivity in the density connectivity graph constitutes an isolated defect.
[0042] This dual criterion can effectively distinguish between real pores and image noise.
[0043] Step S4: Defect classification and risk assessment; Defect classification: based on Based on the spatial distribution pattern and the complexity of f_pert, defects are classified: Isolated pores: It is a closed loop with uniform and low f_pert inside.
[0044] Network of pores: w_grad shows the potential connectivity paths between multiple defects.
[0045] Risk assessment: Constructing the risk characteristic matrix M_risk: Disturbance amplitude A_pert: Stomatal edge The larger the value, the more severe the stress concentration.
[0046] Connectivity C_conn: The degree of clustering of pores in the connectivity graph G. The higher the connectivity, the greater the risk of forming through cracks.
[0047] Trend of change T_trend: Analyze the statistical trend of stomatal characteristics by scanning multiple samples from the same batch or the same area multiple times.
[0048] Define threshold ranges for A_pert, C_conn, and T_trend. If all three exceed their limits, the defect is considered high-risk. Finally, output the failure risk level and three-dimensional coordinates of each defect.
[0049] Visualization: Risk levels are mapped to different colors and rendered in the 3D reconstruction model to generate a "Battery Casing Pore Risk Distribution Map", which intuitively shows the location and degree of danger of defects.
[0050] In other specific embodiments, in the defect classification in step S4, a support vector machine or a lightweight convolutional neural network can be introduced to replace rule-based classification in order to improve the classification accuracy of defects with complex shapes.
[0051] Step S5: Parameter feedback and system optimization; The defect types and risk levels detected in this study are compared with the results of offline destructive testing or high-confidence testing to calculate the classification accuracy and risk prediction accuracy of this study.
[0052] If the accuracy decreases, the convolution kernel parameters in step S3 and the risk judgment threshold in step S4 are automatically adjusted using optimization algorithms such as gradient descent.
[0053] These updated parameters are fed back to the wavefront correction system in step S1 to fine-tune the phase control parameters, so that the next round of irradiation field can better highlight the most common defect features of the current batch of shells, forming an adaptive detection closed loop.
[0054] The synchrotron radiation X-ray phase imaging technology upon which this invention is based has been widely applied in materials science, life science and other fields in large-scale synchrotron radiation facilities at home and abroad, and its physical principles and engineering implementation are mature.
[0055] A polypropylene model containing pre-defined micropores was simulated using X-ray phase-contrast imaging simulation software based on Wave Propagation. The results show that: Under the same radiation dose, the phase contrast image of the method of the present invention can clearly distinguish all the preset pores, while in the traditional absorption contrast image, pores below 20 μm are basically masked by noise.
[0056] Through the processing flow of this invention, the three-dimensional morphology of pores was successfully reconstructed, and isolated pores and potentially connected pore networks were accurately distinguished based on the connectivity diagram.
[0057] The risk assessment module correctly assigned a higher risk level to the pores that simulated stress concentration.
[0058] These simulation results preliminarily demonstrate the effectiveness and superior performance of the technical solution of the present invention in detecting micropores in automotive battery casings.
[0059] Detection principle: Based on phase contrast and electron cloud density analysis, it has extremely high sensitivity to micropores.
[0060] Detection capability: It can clearly identify micropores of 10μm or even smaller, and can detect early density disturbance regions that have not yet formed cavities.
[0061] Information dimensions: Provides multi-dimensional features such as defect type, connectivity with the matrix, and degree of edge stress concentration.
[0062] Output: Defect risk classification distribution map, which directly guides quality judgment and maintenance decisions.
[0063] System adaptability: It has self-learning and parameter feedback capabilities, and can continuously optimize to adapt to the testing needs of different production lines and different batches of products.
[0064] The synchrotron X-ray phase imaging method for non-destructive testing of micropores in automotive power battery casings of the present invention, with slight adjustments, can be used for non-destructive testing of the following products: Fuel cell membrane: Detect the uniformity and integrity of its internal micropores.
[0065] Aerospace composite material components: Detecting microscopic defects such as interlayer porosity and fiber breakage.
[0066] Precision castings: Detecting shrinkage cavities and micro-shrinkage pores in titanium alloy and aluminum alloy castings.
[0067] Electronic packaging devices: Detecting pores inside the molding compound and chip bonding delamination.
[0068] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging, characterized in that, The method comprises the following steps: Step S1: uniform irradiation field construction and data acquisition; A high-coherence X-ray beam is generated, energy-filtered and wavefront-corrected to form a uniform irradiation field, which irradiates the battery shell sample and collects multi-angle scattering light intensity matrix data; Step S2: phase inversion and electron cloud density reconstruction; The scattering light intensity matrix data is subjected to phase inversion and sparse constraint optimization to reconstruct high-precision sample internal electron cloud density distribution data, and a connected relationship graph reflecting density continuity is constructed accordingly; Step S3: feature fusion and abnormal area identification; Local gradient and frequency disturbance features are extracted from the electron cloud density distribution data, and are fused with the connected relationship graph to construct fusion feature data, and abnormal areas are accurately identified based on a preset threshold; Step S4: defect classification and risk assessment; The identified pore defects are classified by features, and their failure risk levels are assessed based on their disturbance amplitude, local connectivity and density change trend, and a visual risk distribution graph is generated; Step S5: parameter feedback and system optimization; The detection results are compared with the known defect database, the feature extraction and risk assessment parameters are dynamically updated according to the comparison accuracy, and the updated parameters are fed back to the irradiation field regulation link of step S1 to realize adaptive optimization of the detection system.
2. The method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging according to claim 1, characterized in that, In step S1, the high-coherence X-ray beam is generated by a synchrotron radiation source or by an X-ray source in combination with a micro-focus tube phase contrast imaging.
3. The method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging according to claim 1, characterized in that, Step S2 specifically comprises: The collected angle scattering light intensity matrix I(θ, x, y) is input into an iterative phase recovery algorithm; The preliminary phase distribution The Laplace regularization is performed to suppress the noise and highlight the real boundary, and then the phase-density conversion relationship is used to obtain the preliminary electron cloud density distribution ρ(x, y, z); Local frequency compensation is performed on ρ(x, y, z) to enhance the high-frequency details of the micro-pore edges, and an optimized electron cloud density distribution ρ_opt is output; Based on ρ_opt, the density gradient of each voxel and its neighborhood voxels is calculated to construct a density connectivity graph G=(V, E), wherein the node V represents the spatial position, and the weight of the edge E represents the density gradient value between adjacent positions.
4. The method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging according to claim 1, characterized in that, In step S2, the transmission intensity equation method is used to solve the phase.
5. The method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging according to claim 3, characterized in that, Step S3 specifically comprises: Feature extraction: use the designed convolution kernel to convolve ρ_opt, extract the local density change rate of each position At the same time, the short-time Fourier transform is performed on ρ_opt to extract the local frequency disturbance amplitude f_pert; and Combined into a local feature vector Feature fusion: the local feature vector is fused with the gradient weight data w_grad in the connectivity graph to form fused feature data ; Abnormality determination: set the determination threshold as follows: If and w_grad < T_conn, then the region is determined to be a void defect. wherein T_grad is a preset threshold for determining whether the local density change rate constitutes a defect boundary; T_conn is a preset threshold for determining whether the local connectivity in the density connectivity graph constitutes an isolated defect.
6. The method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging according to claim 5, characterized in that, Step S4 specifically comprises: Defect classification: According to the spatial distribution pattern and the complexity of f_pert, the defects are classified: Isolated Pores: In closed loops; Reticular pore group: w_grad shows the potential connectivity path between multiple defects; Risk assessment: construct a risk feature matrix M_risk: Perturbation amplitude A_pert: maximum value of the pore edge ; Connectivity C_conn: the clustering degree of the pores in the connectivity graph; Trend T_trend: by scanning multiple samples of the same batch or the same area multiple times, the statistical change trend of the pore features is analyzed; Set threshold intervals for A_pert, C_conn and T_trend, if all three exceed the limits, it is determined as a high-risk defect; finally output the failure risk level and three-dimensional coordinates of each defect; Generate a visual risk distribution graph: map the risk level to different colors and render in the three-dimensional reconstruction model to display the defect location and danger level.
7. The method for non-destructive testing of micro-pores of a power battery case of an automobile by synchrotron X-ray phase imaging according to claim 5, characterized in that, In step S4, classification is performed by a support vector machine or a light-weight convolutional neural network.