Defect identification and early warning system in additive manufacturing process

Abstract

The invention discloses a defect identification and early warning system in an additive manufacturing process, and the system comprises a molten pool image data collection module which forms a time sequence molten pool image data matrix; the molten pool image data preprocessing module is used for forming a preprocessed molten pool image data matrix; the Harlisia eagle algorithm defect area positioning module is used for carrying out global search by utilizing an improved Harlisia eagle algorithm and outputting a defect candidate area molten pool image data matrix; the fractal segmentation algorithm micro-crack defect fine identification module is used for obtaining a micro-crack defect area molten pool image data matrix after fine segmentation; the micro-crack defect feature extraction and quantitative evaluation module is used for forming a micro-crack defect quantitative evaluation result; and the microcrack defect early warning module triggers a real-time early warning feedback mechanism. According to the method, the professional and technical thresholds required by traditional modeling are reduced, and the universality and convenience of three-dimensional modeling are improved.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a defect recognition and early warning system in an additive manufacturing process. Background Art

[0002] With the rapid development of additive manufacturing technology, metal 3D printing, as an important branch of it, has been widely used in aerospace, medical equipment and high-end manufacturing fields. Compared with traditional subtractive manufacturing technology, metal additive manufacturing can achieve precise forming of complex structures and has significant advantages in material utilization and design freedom. However, since additive manufacturing involves high energy input and complex melt pool dynamics, a variety of defects are prone to occur during the manufacturing process, such as microcracks, pores, and unfused areas. These defects directly affect the mechanical properties, fatigue life and reliability of the finished parts. Therefore, how to identify and warn these defects in real time during the additive manufacturing process has become an important challenge in current research and industrial applications.

[0003] At present, the mainstream additive manufacturing defect detection methods mainly include two categories: offline detection and online detection. Offline detection usually uses X-ray tomography, metallographic analysis, and ultrasonic detection to perform high-precision internal structure analysis of finished products, but there are two main problems: First, the detection cycle is long and cannot meet the needs of real-time monitoring during the production process; second, the detection cost is high and it is difficult to apply it to industrial production on a large scale. Therefore, the industry is gradually shifting to online detection technology in order to identify and control defects in real time during the manufacturing process.

[0004] Online detection technology mainly relies on optical monitoring, thermal imaging analysis and data-driven modeling methods. Among them, optical monitoring technology uses high-speed cameras to capture changes in the state of the molten pool and analyze potential signs of defects. It can more intuitively reflect the dynamic characteristics of the molten pool during the manufacturing process. However, it has poor robustness under complex backgrounds and is easily disturbed by environmental noise and metal surface reflection characteristics, resulting in low recognition accuracy. In addition, thermal imaging analysis technology can monitor the temperature field distribution of the molten pool through infrared thermal imagers and infer the location of potential defects. However, due to the accuracy of the sensor and changes in ambient temperature, there are limitations such as high misjudgment rate and difficulty in accurately quantifying defects.

[0005] In recent years, data-driven intelligent analysis methods, such as deep learning and machine learning, have been widely studied in the field of additive manufacturing defect detection. Neural network models are usually trained based on large-scale experimental data to achieve automatic defect recognition. However, the main defects of this type of method include: First, it is highly dependent on high-quality training data and is difficult to generalize to additive manufacturing processes under different materials and process parameters; second, the computational complexity is high and it is difficult to meet real-time requirements; third, there is a lack of in-depth understanding of the defect formation mechanism, making it difficult to carry out effective early warning and process optimization.

[0006] In summary, the existing defect detection technology in the additive manufacturing process still has great deficiencies in real-time, accuracy, anti-interference and process controllability, and it is difficult to meet the stringent requirements of the high-end manufacturing field for product quality and consistency. Therefore, there is an urgent need for a more efficient and accurate defect identification and early warning method to improve the reliability of the additive manufacturing process and product performance. Summary of the invention

[0007] One purpose of the present invention is to propose a defect recognition and early warning system in the additive manufacturing process. The present invention reduces the professionalism and technical threshold required for traditional modeling and improves the universality and convenience of three-dimensional modeling.

[0008] A defect recognition and early warning system in an additive manufacturing process according to an embodiment of the present invention includes: A molten pool image data acquisition module is used to obtain visible light molten pool image data and temperature field molten pool image data in the metal additive manufacturing process to form a time series molten pool image data matrix; A molten pool image data preprocessing module is used to preprocess the time series molten pool image data matrix to form a preprocessed molten pool image data matrix; Harris Eagle algorithm defect area positioning module, which is used to perform global search based on the preprocessed molten pool image data matrix using the improved Harris Eagle algorithm to determine the candidate areas of microcrack defects and output the molten pool image data matrix of the defect candidate areas; The micro-crack defect fine recognition module of the fractal segmentation algorithm is used to calculate the local grayscale difference characteristics of the micro-crack defect candidate area based on the molten pool image data matrix of the defect candidate area, and perform local threshold segmentation on the micro-crack defect candidate area to obtain the molten pool image data matrix of the micro-crack defect area after fine segmentation; The microcrack defect feature extraction and quantitative evaluation module is used to extract the geometric and morphological features of microcracks based on the finely segmented molten pool image data matrix of the microcrack defect area to form a quantitative evaluation result of microcrack defects; The microcrack defect early warning module is used to trigger a real-time early warning feedback mechanism based on the quantitative evaluation results of microcrack defects.

[0009] A defect recognition and early warning method in an additive manufacturing process is applied to a defect recognition and early warning system in an additive manufacturing process, comprising the following steps: S1. During the metal additive manufacturing process, the molten pool is monitored in real time using imaging equipment to obtain a continuous time series molten pool image data matrix; S2, preprocessing the time series molten pool image data matrix to obtain a preprocessed molten pool image data matrix; S3, using the Harris Eagle algorithm to perform a global search on the preprocessed molten pool image data matrix, automatically locate the candidate area of ​​microcrack defects in the molten pool image data matrix, and form a molten pool image data matrix of defect candidate areas; S4, applying a fractal segmentation algorithm to analyze the fractal characteristics of microcrack defects in the molten pool image data of the defect candidate area, and obtaining a molten pool image data matrix of the microcrack defect area after fine segmentation; S5. Extracting geometric and morphological features of microcracks based on the finely segmented molten pool image data matrix of the microcrack defect area to form a quantitative evaluation result of microcrack defects; S6. Based on the quantitative evaluation results of microcrack defects, a real-time early warning feedback mechanism is triggered.

[0010] Optionally, the S1 specifically includes the following steps: S11. During the metal additive manufacturing process, the molten pool is monitored in real time using an imaging device, wherein the imaging device includes a high-speed camera and an infrared thermal imager, wherein the high-speed camera is used to obtain visible light molten pool image data of the molten pool area, and the infrared thermal imager is used to collect temperature field molten pool image data of the molten pool area, and the obtained visible light molten pool image data and temperature field molten pool image data are synchronously stored to form multimodal molten pool image data; S12, fusing the collected multi-modal melt pool image data to construct a time series melt pool image data matrix :

[0011] in, is the time series melt pool image data matrix, is the visible light melt pool image data matrix, is the temperature field melt pool image data matrix, represents the time series frame index, Represents the image space index.

[0012] Optionally, the S2 specifically includes the following steps: S21, the time series molten pool image data matrix Image noise removal is performed, and median filtering is used to process the time series melt pool image data matrix The image data of each frame is denoised to eliminate the random noise interference caused by high temperature gasification in the molten pool environment, and the molten pool image data matrix after preliminary denoising is obtained. ; S22, the molten pool image data matrix after the preliminary denoising Grayscale normalization is performed to map the grayscale value of each frame of the molten pool image data in the molten pool image data matrix to a uniform interval [0,1], and the grayscale normalized molten pool image data matrix is ​​obtained. ; S23, the molten pool image data matrix after grayscale normalization The contrast of each frame of the molten pool image is enhanced to enhance the grayscale difference between the microcrack area and the molten pool background area, highlight the characteristics of the crack area, and form a contrast-enhanced molten pool image data matrix ; S24, contrast-enhanced melt pool image data matrix The edge filtering is applied to each frame of the molten pool image. The edge-preserving Gaussian bilateral filter is used to enhance the clarity of the crack area boundary to form a preprocessed molten pool image data matrix. .

[0013] Optionally, S3 specifically includes the following steps: S31. In the metal additive manufacturing process, based on the pre-processed melt pool image data matrix Initialize the hawk group position matrix of the Harris Hawk algorithm , the initial position of the eagle group individuals in the melt pool image data matrix is ​​expressed as:

[0014] in, Indicates The initial position coordinates of the eagle group individuals in the melt pool image data matrix, is the total number of eagle individuals, is the two-dimensional spatial coordinate of the kth eagle group individual, indicating the specific horizontal and vertical coordinates of the individual in the melt pool image data matrix; S32, establish the individual fitness function of the eagle group, the individual fitness function of the eagle group is calculated by the grayscale difference between the current position of the eagle group individual and the average grayscale of the background of the molten pool image in its neighborhood:

[0015] in, is the fitness function value of the kth eagle group individual at the tth iteration, For the The iteration The individual position coordinates of the eagle group, The individual position coordinates of the eagle group The neighborhood window of set size for the center, is the preprocessed melt pool image data matrix Individual position coordinates of the eagle group The gray value at Neighborhood window The total number of pixels within is the preprocessed melt pool image data matrix In the neighborhood window Inner pixel The gray value at ; S33. Construct individual memory weights of eagle groups based on historical search information of eagle groups , update the individual positions of the eagle group :

[0016] in, For the The first iteration The individual position coordinates of the eagle group, Memory weights for individual eagles:

[0017] in, is the fitness function value of the i-th eagle group individual at the t-th iteration; Eagle group individual memory weight According to the difference between the current fitness function value of the eagle group and the fitness function value of the historical eagle group, the position of the eagle group individuals is moved to the area with higher grayscale difference; S34. Build eagle group search strategy to automatically determine indicators :

[0018] in, Automatically determine indicators for eagle group search strategies, is the highest individual fitness function value of the eagle group, is the total number of individuals in the swarm, when the swarm search strategy automatically determines the index Less than the preset threshold When , the eagle group search automatically switches to the local refinement mode, otherwise it switches to the global exploration mode; S35. In the global exploration mode, the individual positions of the eagle group are updated as follows:

[0019] in, is the next iteration position of the eagle group individuals in the global exploration mode, is the current optimal individual position of the eagle group, represents the position of an individual eagle group randomly selected from the current search space at the t + 1th iteration, and is a random factor in the interval [0,1]; S36. In the local fine mode, the individual positions of the eagle group are constrained by the temperature field of the molten pool. When the gray value of the temperature field molten pool image data corresponding to the individual position of the eagle group Above the temperature abnormality threshold When , the individual positions of the eagle group are updated to:

[0020] in, is the next iteration position of the eagle group individuals in the local exploration mode, is the gray value at the current position coordinate of the eagle group individual in the temperature field melt pool image data matrix, representing the temperature field intensity of the current position coordinate, is the abnormal temperature threshold of the molten pool, is the temperature field constraint coefficient; S37. After the eagle group search is completed, select the optimal eagle group individual position of the final iteration As the center position of the microcrack defect candidate area, a fixed-size molten pool image area is intercepted with the center position of the microcrack defect candidate area as the center to form a molten pool image data matrix of the defect candidate area .

[0021] Optionally, the S4 specifically includes the following steps: S41, based on the defect candidate area melt pool image data matrix Calculate the local grayscale difference characteristics of each defect candidate area in the defect candidate area molten pool image data matrix :

[0022] in, Indicates the position coordinates of the defect candidate area The local grayscale difference characteristics of the defect candidate area centered on Represents the position coordinates of the defect candidate area in the molten pool image data matrix of the defect candidate area The gray value at is the overall average gray value of the defect candidate area, are the number of rows and columns of the local window respectively; S42, local grayscale difference features Adaptive fractal scaling Dynamic adjustment based on local grayscale difference characteristics:

[0023] in, The defect candidate area is the position coordinate of the defect candidate area Adaptive fractal scaling at is the initial standard fractal box size, is the adaptive adjustment coefficient; S43, based on adaptive fractal scale Calculate the local fractal dimension of the melt pool image data matrix in the defect candidate area :

[0024] in, Indicates the position coordinates of the defect candidate area The local fractal dimension at , Represented on a fractal scale Position coordinates of the candidate area of ​​the lower coverage defect The minimum number of boxes required at the location; S44, based on the local fractal dimension and local grayscale difference features Determine the adaptive segmentation threshold of microcrack defect area :

[0025] in, is the position coordinate of the defect candidate area The adaptive segmentation threshold of the microcrack defect area, , is the weight coefficient; S45, using adaptive segmentation threshold of microcrack defect area The molten pool image data matrix of the defect candidate area Perform local threshold binary segmentation to obtain the molten pool image data matrix of the microcrack defect area after fine segmentation : .

[0026] Optionally, the S5 specifically includes the following steps: S51, based on the molten pool image data matrix of the microcrack defect area after fine segmentation Construct a microcrack boundary detection operator to extract the boundary contour point set of the microcrack area :

[0027] in, Represents the boundary contour point set of the microcrack defect area, Represents the melt pool image data matrix of the microcrack defect area after fine segmentation The gradient of is the edge detection threshold; S52. Calculate the length of microcrack defects , the minimum circumscribed rectangle method is used to fit the boundary points of the microcrack area and define the microcrack length is the major axis of the minimum enclosing rectangle:

[0028] in, represents the length of the microcrack defect, and are any two points in the microcrack boundary contour, Represents the Euclidean distance between two points; S53, using the normal projection method to calculate the average width of the microcrack centerline direction :

[0029] in, represents the average width of microcrack defects, is the coordinate of the positive boundary point extending outward from the g-th sampling point along the normal direction to the crack edge, The g-th sampling point shrinks inward along the normal direction to the negative boundary point coordinate of the crack edge. is the total number of sampling points on the center line of the microcrack; S54, based on the molten pool image data matrix of the microcrack defect area after fine segmentation Extract the crack edge contour of the microcrack defect area and determine the crack morphology characteristics , the crack morphology is described by the crack edge contour curvature characteristics:

[0030] in, is the crack morphology characteristic, reflecting the degree of curvature of the crack area contour. , Respectively represent The first-order derivatives in the horizontal and vertical directions of the edge contour points, , Respectively represent The second-order derivatives in the horizontal and vertical directions of the edge contour points, Represents the total number of points sampled on the crack edge contour; S55, based on the length of microcracks ,width and crack morphology characteristics , calculate the severity score of microcrack defects :

[0031] in, , and is the weight coefficient; S56. Score based on the severity of microcrack defects Set the microcrack defect level, and the defect level is divided according to: Defect grade classification

[0032] in, and They are the low-level and high-level severity thresholds of microcrack defects in additive manufacturing, respectively, forming the quantitative evaluation results of microcrack defects in additive manufacturing.

[0033] The beneficial effects of the present invention are: (1) The present invention introduces adaptive memory weights and temperature field constraint mechanisms to make the defect area search more accurate. By constructing the fitness function of the eagle group individuals and combining the temperature field molten pool data, the search mode is dynamically adjusted to ensure that the eagle group individuals can focus on the high grayscale difference area, thereby improving the accuracy of microcrack detection.

[0034] (2) The present invention aims at detecting microcrack defects in the additive manufacturing process, adopts a fractal segmentation algorithm, calculates local grayscale difference characteristics and fractal dimensions, and adaptively adjusts the crack segmentation threshold to achieve accurate extraction of microcracks. It can adaptively adjust the segmentation strategy under different complex backgrounds, and is particularly suitable for scenes with large changes in microcrack size. In addition, the fractal segmentation algorithm combined with adaptive scale adjustment makes the extraction of crack boundaries smoother, which can effectively reduce pseudo-crack interference.

[0035] (3) Based on the identification of microcrack defects, the present invention establishes an intelligent early warning system based on defect severity scoring, and combines the process parameter optimization model to achieve dynamic control of defect formation. Based on the quantitative defect evaluation results, the key process parameters of laser power and scanning speed are dynamically adjusted to reduce the probability of defect generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a defect identification and early warning system in an additive manufacturing process proposed by the present invention. DETAILED DESCRIPTION

[0037] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0038] refer to Figure 1, a defect recognition and early warning system in an additive manufacturing process, comprising: A molten pool image data acquisition module is used to obtain visible light molten pool image data and temperature field molten pool image data in the metal additive manufacturing process to form a time series molten pool image data matrix; A molten pool image data preprocessing module is used to preprocess the time series molten pool image data matrix to form a preprocessed molten pool image data matrix; Harris Eagle algorithm defect area positioning module, which is used to perform global search based on the preprocessed molten pool image data matrix using the improved Harris Eagle algorithm to determine the candidate areas of microcrack defects and output the molten pool image data matrix of the defect candidate areas; The micro-crack defect fine recognition module of the fractal segmentation algorithm is used to calculate the local grayscale difference characteristics of the micro-crack defect candidate area based on the molten pool image data matrix of the defect candidate area, and perform local threshold segmentation on the micro-crack defect candidate area to obtain the molten pool image data matrix of the micro-crack defect area after fine segmentation; The microcrack defect feature extraction and quantitative evaluation module is used to extract the geometric and morphological features of microcracks based on the finely segmented molten pool image data matrix of the microcrack defect area to form a quantitative evaluation result of microcrack defects; The microcrack defect early warning module is used to trigger a real-time early warning feedback mechanism based on the quantitative evaluation results of microcrack defects.

[0039] A defect recognition and early warning method in an additive manufacturing process is applied to a defect recognition and early warning system in an additive manufacturing process, comprising the following steps: S1. During the metal additive manufacturing process, the molten pool is monitored in real time using imaging equipment to obtain a continuous time series molten pool image data matrix; S2, preprocessing the time series molten pool image data matrix to obtain a preprocessed molten pool image data matrix; S3, using the Harris Eagle algorithm to perform a global search on the preprocessed molten pool image data matrix, automatically locate the candidate area of ​​microcrack defects in the molten pool image data matrix, and form a molten pool image data matrix of defect candidate areas; S4, applying a fractal segmentation algorithm to analyze the fractal characteristics of microcrack defects in the molten pool image data of the defect candidate area, and obtaining a molten pool image data matrix of the microcrack defect area after fine segmentation; S5. Extracting geometric and morphological features of microcracks based on the finely segmented molten pool image data matrix of the microcrack defect area to form a quantitative evaluation result of microcrack defects; S6. Based on the quantitative evaluation results of microcrack defects, if the severity score exceeds the set threshold, different levels of warnings are triggered, including visual alarms, production parameter adjustment suggestions and automated control strategies. Combined with the molten pool temperature field data, the main influencing factors of crack generation are analyzed, and the additive manufacturing process parameters are optimized, including laser power, scanning speed and powder feeding rate. The system dynamically adjusts the process parameters based on real-time data feedback to inhibit crack propagation and improve forming quality and stability.

[0040] In this implementation, S1 specifically includes the following steps: S11. During the metal additive manufacturing process, the molten pool is monitored in real time using an imaging device, the imaging device including a high-speed camera and an infrared thermal imager, the high-speed camera is used to obtain visible light molten pool image data of the molten pool area, the infrared thermal imager is used to collect temperature field molten pool image data of the molten pool area, and the obtained visible light molten pool image data and temperature field molten pool image data are synchronously stored to form multimodal molten pool image data; S12, fusing the collected multi-modal melt pool image data to construct a time series melt pool image data matrix :

[0041] in, is the time series melt pool image data matrix, is the visible light melt pool image data matrix, is the temperature field melt pool image data matrix, represents the time series frame index, Represents the image space index.

[0042] This implementation method uses a high-speed camera and an infrared thermal imager in the acquisition of molten pool image data to achieve multimodal fusion of visible light information and temperature field information of the molten pool, improve data integrity and accuracy, synchronize time series processing to ensure data continuity and traceability, and use an adaptive sampling strategy to optimize data storage and improve spatiotemporal resolution, thereby providing high-quality input data for subsequent microcrack defect detection, enhancing the robustness and real-time performance of the detection system, effectively reducing false detections and missed detections caused by environmental changes, and improving quality control capabilities in the additive manufacturing process.

[0043] In this implementation, S2 specifically includes the following steps: S21, time series melt pool image data matrix Image noise removal is performed, and median filtering is used to process the time series melt pool image data matrix The image data of each frame is denoised to eliminate the random noise interference caused by high temperature gasification in the molten pool environment, and the molten pool image data matrix after preliminary denoising is obtained. ; S22, the molten pool image data matrix after preliminary denoising Grayscale normalization is performed to map the grayscale value of each frame of the molten pool image data in the molten pool image data matrix to a uniform interval [0,1], and the grayscale normalized molten pool image data matrix is ​​obtained. ; S23, the molten pool image data matrix after grayscale normalization The contrast of each frame of the molten pool image is enhanced to enhance the grayscale difference between the microcrack area and the molten pool background area, highlight the characteristics of the crack area, and form a contrast-enhanced molten pool image data matrix ; S24, contrast-enhanced melt pool image data matrix The edge filtering is applied to each frame of the molten pool image. The edge-preserving Gaussian bilateral filter is used to enhance the clarity of the crack area boundary to form a preprocessed molten pool image data matrix. .

[0044] The molten pool image data preprocessing method proposed in this embodiment improves image clarity, enhances the contrast and recognizability of microcrack areas through noise removal, grayscale normalization, contrast enhancement and edge filtering. The adaptive edge filtering technology can effectively suppress background noise while maintaining crack boundary details and optimizing the input quality of subsequent algorithms. The time series molten pool image data matrix storage structure ensures data stability, improves the accuracy of dynamic crack detection, lays the foundation for accurate positioning and morphological analysis of microcracks, and improves the overall performance of the detection system.

[0045] In this implementation, S3 specifically includes the following steps: S31. In the metal additive manufacturing process, based on the pre-processed melt pool image data matrix Initialize the hawk group position matrix of the Harris Hawk algorithm , the initial position of the eagle group individuals in the melt pool image data matrix is ​​expressed as:

[0046] in, Indicates The initial position coordinates of the eagle group individuals in the melt pool image data matrix, is the total number of eagle individuals, is the two-dimensional spatial coordinate of the kth eagle group individual, indicating the specific horizontal and vertical coordinates of the individual in the melt pool image data matrix; S32, establish the individual fitness function of the eagle group, the individual fitness function of the eagle group is calculated by the grayscale difference between the current position of the eagle group individual and the average grayscale of the background of the molten pool image in its neighborhood:

[0047] in, is the fitness function value of the kth eagle group individual at the tth iteration, For the The iteration The individual position coordinates of the eagle group, The individual position coordinates of the eagle group The neighborhood window of set size for the center, is the preprocessed melt pool image data matrix Individual position coordinates of the eagle group The gray value at Neighborhood window The total number of pixels within is the preprocessed melt pool image data matrix In the neighborhood window Inner pixel The gray value at ; It is used to calculate the fitness of the eagle group individuals to the microcrack defect area during the search process, that is, to measure the difference between the pixel grayscale value of the current individual position and the average grayscale value of its neighborhood area. A larger fitness value indicates that the position may be in the microcrack area, while a smaller fitness value indicates that it is in the background area. Through the formula, the Harris Eagle algorithm can give priority to selecting areas with higher fitness as potential microcrack defect areas during the search process, thereby improving the accuracy and stability of defect detection.

[0048] S33. Construct individual memory weights of eagle groups based on historical search information of eagle groups , update the individual positions of the eagle group :

[0049] in, For the The iteration The individual position coordinates of the eagle group, Memory weights for individual eagles:

[0050] in, is the fitness function value of the i-th eagle group individual at the t-th iteration; Eagle group individual memory weight According to the difference between the current fitness function value of the eagle group and the fitness function value of the historical eagle group, the position of the eagle group individuals is moved to the area with higher grayscale difference; S34. Build eagle group search strategy to automatically determine indicators :

[0051] in, Automatically determine indicators for eagle group search strategies, is the highest individual fitness function value of the eagle group, is the total number of individuals in the swarm, when the swarm search strategy automatically determines the index Less than the preset threshold When , the eagle group search automatically switches to the local refinement mode, otherwise it switches to the global exploration mode; S35. In the global exploration mode, the individual positions of the eagle group are updated as follows:

[0052] in, is the next iteration position of the eagle group individuals in the global exploration mode, is the current optimal individual position of the eagle group, represents the position of an individual eagle group randomly selected from the current search space at the t + 1th iteration, and is a random factor in the interval [0,1]; Under the action of the formula, the eagle group individuals can explore the microcrack defect area on a global scale, improving the adaptability and robustness of the Harris Eagle algorithm in the complex environment of the metal additive manufacturing molten pool.

[0053] S36. In the local fine mode, the individual positions of the eagle group are constrained by the temperature field of the molten pool. When the gray value of the temperature field molten pool image data corresponding to the individual position of the eagle group Above the temperature abnormality threshold When , the individual positions of the eagle group are updated to:

[0054] in, is the next iteration position of the eagle group individuals in the local exploration mode, is the gray value at the current position coordinate of the eagle group individual in the temperature field melt pool image data matrix, representing the temperature field intensity of the current position coordinate. is the abnormal temperature threshold of the molten pool, is the temperature field constraint coefficient; By introducing temperature field data, the influence of the molten pool environment on microcrack identification is enhanced, making the Eagle Swarm search more accurate, effectively reducing false detections and missed detections, and improving the quality monitoring capabilities in the metal additive manufacturing process.

[0055] S37. After the eagle group search is completed, select the optimal eagle group individual position of the final iteration As the center position of the microcrack defect candidate area, a fixed-size molten pool image area is intercepted with the center position of the microcrack defect candidate area as the center to form a molten pool image data matrix of the defect candidate area .

[0056] This implementation method uses an improved Harris Eagle algorithm to automatically locate microcrack defects, constructs an adaptive fitness function based on the molten pool image features and temperature field data, improves the search accuracy of defective areas, and proposes Eagle group search history memory weights and temperature field constraint strategies to enable the algorithm to have stronger global exploration and local optimization capabilities in a complex molten pool environment. The dynamic search mode switching mechanism improves search efficiency, avoids falling into local optimality, and improves the stability and accuracy of microcrack detection.

[0057] In this implementation, S4 specifically includes the following steps: S41, based on the defect candidate area melt pool image data matrix Calculate the local grayscale difference characteristics of each defect candidate area in the defect candidate area molten pool image data matrix :

[0058] in, Indicates the position coordinates of the defect candidate area The local grayscale difference characteristics of the defect candidate area centered on Represents the position coordinates of the defect candidate area in the molten pool image data matrix of the defect candidate area The gray value at is the overall average gray value of the defect candidate area, are the number of rows and columns of the local window respectively; The local grayscale difference feature formula is used to calculate the local grayscale difference feature of each pixel in the defect candidate area. The local grayscale difference reflects the grayscale change of the molten pool image in the microcrack area, which helps to distinguish the crack area from the normal molten pool area.

[0059] S42, local grayscale difference features Adaptive fractal scaling Dynamic adjustment based on local grayscale difference characteristics:

[0060] in, The defect candidate area is the position coordinate of the defect candidate area Adaptive fractal scaling at is the initial standard fractal box size, is the adaptive adjustment coefficient; S43, based on adaptive fractal scale Calculate the local fractal dimension of the melt pool image data matrix in the defect candidate area :

[0061] in, Indicates the position coordinates of the defect candidate area The local fractal dimension at , Represented on a fractal scale Position coordinates of the candidate area of ​​the lower coverage defect The minimum number of boxes required at the location; S44, based on the local fractal dimension and local grayscale difference features Determine the adaptive segmentation threshold of microcrack defect area :

[0062] in, is the position coordinate of the defect candidate area The adaptive segmentation threshold of the microcrack defect area, , is the weight coefficient; S45, using adaptive segmentation threshold of microcrack defect area The molten pool image data matrix of the defect candidate area Perform local threshold binary segmentation to obtain the molten pool image data matrix of the microcrack defect area after fine segmentation : .

[0063] This implementation method accurately segments the defect candidate area based on the improved adaptive fractal segmentation algorithm. An adaptive fractal scale calculation method driven by local grayscale difference characteristics is proposed to make the fractal dimension calculation more targeted and improve the fine-grained extraction capability of the crack area. The adaptive threshold segmentation strategy combines the local fractal dimension and grayscale characteristics to improve the recognition accuracy of the crack area, effectively distinguish the molten pool texture from the real microcracks, and improve the accuracy of microcrack segmentation.

[0064] In this implementation, S5 specifically includes the following steps: S51, based on the molten pool image data matrix of the microcrack defect area after fine segmentation Construct a microcrack boundary detection operator to extract the boundary contour point set of the microcrack area :

[0065] in, Represents the boundary contour point set of the microcrack defect area, Represents the melt pool image data matrix of the microcrack defect area after fine segmentation The gradient of is the edge detection threshold; S52. Calculate the length of microcrack defects , the minimum circumscribed rectangle method is used to fit the boundary points of the microcrack area and define the microcrack length is the major axis of the minimum enclosing rectangle:

[0066] in, represents the length of the microcrack defect, and are any two points in the microcrack boundary contour, Represents the Euclidean distance between two points; S53, using the normal projection method to calculate the average width of the microcrack centerline direction :

[0067] in, represents the average width of microcrack defects, is the coordinate of the positive boundary point extending outward from the g-th sampling point along the normal direction to the crack edge, The g-th sampling point shrinks inward along the normal direction to the negative boundary point coordinate of the crack edge. is the total number of sampling points on the center line of the microcrack; S54, based on the molten pool image data matrix of the microcrack defect area after fine segmentation Extract the crack edge contour of the microcrack defect area and determine the crack morphology characteristics , the crack morphology is described by the crack edge contour curvature characteristics:

[0068] in, is the crack morphology characteristic, reflecting the degree of curvature of the crack area contour. , Respectively represent The first-order derivatives in the horizontal and vertical directions of the edge contour points, , Respectively represent The second-order derivatives in the horizontal and vertical directions of the edge contour points, Represents the total number of points sampled on the crack edge contour; In practical applications, the higher the crack curvature, the more dramatic the change in the crack edge, which may be a microcrack caused by rapid growth or molten pool fluctuation. The value can be used for crack severity scoring, assisting in process parameter optimization, and preventing further crack propagation.

[0069] S55, based on the length of microcracks ,width and crack morphology characteristics , calculate the severity score of microcrack defects :

[0070] in, , and is the weight coefficient; S56. Score based on the severity of microcrack defects Set the microcrack defect level, and the defect level is divided according to: Defect grade classification

[0071] in, and They are the low-level and high-level severity thresholds of microcrack defects in additive manufacturing, respectively, forming the quantitative evaluation results of microcrack defects in additive manufacturing.

[0072] This implementation method uses a multi-feature fusion method to perform defect quantitative analysis on the microcrack area after fine segmentation. The crack length and width are accurately calculated through boundary contour extraction, minimum circumscribed rectangle fitting, and normal projection methods. At the same time, the crack edge contour curvature characteristics are combined to enhance the defect morphology description capability, build a comprehensive severity scoring function, and perform graded evaluation on crack defects to improve the scientificity and accuracy of microcrack severity determination. The defect quantitative analysis results can be used for real-time early warning and process optimization, and improve the intelligent quality control capability of metal additive manufacturing processes.

[0073] Embodiment 1: During the night shift production on March 15, 2024, the additive manufacturing workshop of an aviation manufacturing company was using laser selective melting technology to print Inconel718 nickel-based alloy turbine blades. The operator started the printing task at 22:37. The system set the process parameters as laser power 400W, scanning speed 800mm / s, layer thickness 30μm, and scanning spacing 0.1mm. Since turbine blades are key components of aircraft engines, their mechanical properties requirements are extremely high. Therefore, any slight microcracks will cause the parts to be scrapped and even affect flight safety.

[0074] When printing reached the 134th layer (time: 23:02), the defect recognition and early warning system of the present invention began to detect abnormal signals. The system monitored abnormal grayscale changes in the molten pool area (the local grayscale difference reached 42.6%) and temperature distribution shifted (the local temperature gradient abnormally increased by 6.2%), which were precursors to the formation of microcracks.

[0075] The real-time data is recorded as follows: Number of layers: 134; coordinates of abnormal area: (X: 23.7mm, Y: 18.4mm); abnormal grayscale change: enhanced by 42.6% (normal range is 20%-30%); temperature anomaly value: local temperature gradient increased by 6.2%; abnormal duration: 3.7 seconds; eagle group fitness function peak: 0.91 (exceeding the defect threshold of 0.85).

[0076] At 23:02:14, the system automatically triggered the Harris Hawk Optimizer (HHO) algorithm to conduct a detailed search of the defect area and confirmed that there was a potential risk of microcracks in the area. To prevent the defect from further expanding, the system automatically adjusted the process parameters based on the defect prediction model: The laser power was reduced by 3% (from 400W to 388W); the scanning speed was increased by 50mm / s (from 800mm / s to 850mm / s); the scanning strategy was optimized, the number of remelting times was reduced, and the direction of the laser path was adjusted.

[0077] After the adjustment, the system continued to monitor the area, and the results were as follows: Layer 136 (time: 23:07): abnormal grayscale change dropped to 28.4% (returned to normal range); Layer 138 (time: 23:12): temperature gradient returned to the standard value (±1.3%); Eagle group fitness function dropped to 0.79 (below the defect threshold).

[0078] After printing was completed, an X-ray tomography (XCT) scan was performed at 03:42 and compared with samples produced using traditional fixed process parameters, the following differences were found:

[0079] In addition, XCT testing found that the fatigue life of turbine blades produced using the method of the present invention was increased by about 27% in high-cycle fatigue testing, proving the effectiveness of the defect identification and early warning system.

[0080] The method of the present invention can identify abnormal conditions in the molten pool during additive manufacturing in real time, quickly adjust process parameters, and reduce the incidence of defects. Compared with traditional methods, the defect detection rate is higher, and process optimization and adjustment can be performed within 1.2 seconds, effectively reducing the generation of microcracks and improving part quality and production stability.

[0081] The present invention introduces adaptive memory weights and temperature field constraint mechanisms to make the defect area search more accurate. By constructing the individual fitness function of the eagle group and combining the temperature field molten pool data, the search mode is dynamically adjusted to ensure that the individual eagle group can focus on the high grayscale difference area, thereby improving the accuracy of microcrack detection.

[0082] The present invention aims at the detection of microcrack defects in the additive manufacturing process, adopts a fractal segmentation algorithm, calculates the local grayscale difference characteristics and fractal dimension, adaptively adjusts the crack segmentation threshold, and realizes accurate extraction of microcracks. The segmentation strategy can be adaptively adjusted under different complex backgrounds, which is particularly suitable for scenes with large changes in microcrack size. In addition, the fractal segmentation algorithm combined with adaptive scale adjustment makes the extraction of crack boundaries smoother, which can effectively reduce pseudo-crack interference.

[0083] Based on the identification of microcrack defects, the present invention establishes an intelligent early warning system based on defect severity scoring, and combines the process parameter optimization model to realize dynamic control of defect formation. Based on the quantitative evaluation results of defects, the key process parameters of laser power and scanning speed are dynamically adjusted to reduce the probability of defect generation.

[0084] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A defect recognition and early warning system in an additive manufacturing process, characterized in that: include: A molten pool image data acquisition module is used to obtain visible light molten pool image data and temperature field molten pool image data in the metal additive manufacturing process to form a time series molten pool image data matrix; A molten pool image data preprocessing module is used to preprocess the time series molten pool image data matrix to form a preprocessed molten pool image data matrix; Harris Eagle algorithm defect area positioning module, which is used to perform global search based on the preprocessed molten pool image data matrix using the improved Harris Eagle algorithm to determine the candidate areas of microcrack defects and output the molten pool image data matrix of the defect candidate areas; The micro-crack defect fine recognition module of the fractal segmentation algorithm is used to calculate the local grayscale difference characteristics of the micro-crack defect candidate area based on the molten pool image data matrix of the defect candidate area, and perform local threshold segmentation on the micro-crack defect candidate area to obtain the molten pool image data matrix of the micro-crack defect area after fine segmentation; The microcrack defect feature extraction and quantitative evaluation module is used to extract the geometric and morphological features of microcracks based on the finely segmented molten pool image data matrix of the microcrack defect area to form a quantitative evaluation result of microcrack defects; The microcrack defect early warning module is used to trigger a real-time early warning feedback mechanism based on the quantitative evaluation results of microcrack defects.

2. A defect recognition and early warning method in an additive manufacturing process, applied to a defect recognition and early warning system in an additive manufacturing process as claimed in claim 1, characterized in that: The following steps are involved: S1. During the metal additive manufacturing process, the molten pool is monitored in real time using imaging equipment to obtain a continuous time series molten pool image data matrix; S2, preprocessing the time series molten pool image data matrix to obtain a preprocessed molten pool image data matrix; S3, using the Harris Eagle algorithm to perform a global search on the preprocessed molten pool image data matrix, automatically locate the candidate area of ​​microcrack defects in the molten pool image data matrix, and form a molten pool image data matrix of defect candidate areas; S4, applying a fractal segmentation algorithm to analyze the fractal characteristics of microcrack defects in the molten pool image data of the defect candidate area, and obtaining a molten pool image data matrix of the microcrack defect area after fine segmentation; S5. Extracting geometric and morphological features of microcracks based on the finely segmented molten pool image data matrix of the microcrack defect area to form a quantitative evaluation result of microcrack defects; S6. Based on the quantitative evaluation results of microcrack defects, a real-time early warning feedback mechanism is triggered.

3. A defect identification and early warning method in an additive manufacturing process according to claim 2, characterized in that: The S1 specifically includes the following steps: S11. During the metal additive manufacturing process, the molten pool is monitored in real time using an imaging device, wherein the imaging device includes a high-speed camera and an infrared thermal imager, wherein the high-speed camera is used to obtain visible light molten pool image data of the molten pool area, and the infrared thermal imager is used to collect temperature field molten pool image data of the molten pool area, and the obtained visible light molten pool image data and temperature field molten pool image data are synchronously stored to form multimodal molten pool image data; S12, fusing the collected multi-modal melt pool image data to construct a time series melt pool image data matrix : in, is the time series melt pool image data matrix, is the visible light melt pool image data matrix, is the temperature field melt pool image data matrix, represents the time series frame index, Represents the image space index.

4. The defect identification and early warning method in the additive manufacturing process according to claim 3, characterized in that: The S2 specifically includes the following steps: S21, the time series molten pool image data matrix Image noise removal is performed, and median filtering is used to process the time series melt pool image data matrix The image data of each frame is denoised to eliminate the random noise interference caused by high temperature gasification in the molten pool environment, and the molten pool image data matrix after preliminary denoising is obtained. ; S22, the molten pool image data matrix after the preliminary denoising Grayscale normalization is performed to map the grayscale value of each frame of the molten pool image data in the molten pool image data matrix to a uniform interval [0,1], and the grayscale normalized molten pool image data matrix is ​​obtained. ; S23, the molten pool image data matrix after grayscale normalization The contrast of each frame of the molten pool image is enhanced to enhance the grayscale difference between the microcrack area and the molten pool background area, highlight the characteristics of the crack area, and form a contrast-enhanced molten pool image data matrix ; S24, contrast-enhanced melt pool image data matrix The edge filtering is applied to each frame of the molten pool image. The edge-preserving Gaussian bilateral filter is used to enhance the clarity of the crack area boundary to form a preprocessed molten pool image data matrix. .

5. A defect identification and early warning method in an additive manufacturing process according to claim 4, characterized in that: The S3 specifically includes the following steps: S31. In the metal additive manufacturing process, based on the pre-processed melt pool image data matrix Initialize the hawk group position matrix of the Harris Hawk algorithm , the initial position of the eagle group individuals in the melt pool image data matrix is ​​expressed as: in, Indicates The initial position coordinates of the eagle group individuals in the melt pool image data matrix, is the total number of eagle individuals, is the two-dimensional spatial coordinate of the kth eagle group individual, indicating the specific horizontal and vertical coordinates of the individual in the melt pool image data matrix; S32, establish the individual fitness function of the eagle group, the individual fitness function of the eagle group is calculated by the grayscale difference between the current position of the eagle group individual and the average grayscale of the background of the molten pool image in its neighborhood: in, is the fitness function value of the kth eagle group individual at the tth iteration, For the The first iteration The individual position coordinates of the eagle group, The individual position coordinates of the eagle group The neighborhood window of set size for the center, is the preprocessed melt pool image data matrix Individual position coordinates of the eagle group The gray value at Neighborhood window The total number of pixels within is the preprocessed melt pool image data matrix In the neighborhood window Inner pixel The gray value at ; S33. Construct individual memory weights of eagle groups based on historical search information of eagle groups , update the individual positions of the eagle group : in, For the The first iteration The individual position coordinates of the eagle group, Memory weights for individual eagles: in, is the fitness function value of the i-th eagle group individual at the t-th iteration; Eagle group individual memory weight According to the difference between the current fitness function value of the eagle group and the fitness function value of the historical eagle group, the position of the eagle group individuals is moved to the area with higher grayscale difference; S34. Build eagle group search strategy to automatically determine indicators : in, Automatically determine indicators for eagle group search strategies, is the highest individual fitness function value of the eagle group, is the total number of individuals in the swarm, when the swarm search strategy automatically determines the index Less than the preset threshold When , the eagle group search automatically switches to the local refinement mode, otherwise it switches to the global exploration mode; S35. In the global exploration mode, the individual positions of the eagle group are updated as follows: in, is the next iteration position of the eagle group individuals in the global exploration mode, is the current optimal individual position of the eagle group, represents the position of an individual eagle group randomly selected from the current search space at the t + 1th iteration, and is a random factor in the interval [0,1]; S36. In the local fine mode, the individual positions of the eagle group are constrained by the temperature field of the molten pool. When the gray value of the temperature field molten pool image data corresponding to the individual position of the eagle group Above the temperature abnormality threshold When , the individual positions of the eagle group are updated to: in, is the next iteration position of the eagle group individuals in the local exploration mode, is the gray value at the current position coordinate of the eagle group individual in the temperature field melt pool image data matrix, representing the temperature field intensity of the current position coordinate. is the abnormal temperature threshold of the molten pool, is the temperature field constraint coefficient; S37. After the eagle group search is completed, select the optimal eagle group individual position of the final iteration As the center position of the microcrack defect candidate area, a fixed-size molten pool image area is intercepted with the center position of the microcrack defect candidate area as the center to form a molten pool image data matrix of the defect candidate area .

6. A defect identification and early warning method in an additive manufacturing process according to claim 5, characterized in that: The S4 specifically comprises the following steps: S41, based on the defect candidate area melt pool image data matrix Calculate the local grayscale difference characteristics of each defect candidate area in the defect candidate area molten pool image data matrix : in, Indicates the position coordinates of the defect candidate area The local grayscale difference characteristics of the defect candidate area centered on Represents the position coordinates of the defect candidate area in the molten pool image data matrix of the defect candidate area The gray value at is the overall average gray value of the defect candidate area, are the number of rows and columns of the local window respectively; S42, local grayscale difference features Adaptive fractal scaling Dynamic adjustment based on local grayscale difference characteristics: in, The defect candidate area is the position coordinate of the defect candidate area Adaptive fractal scaling at is the initial standard fractal box size, is the adaptive adjustment coefficient; S43, based on adaptive fractal scale Calculate the local fractal dimension of the melt pool image data matrix in the defect candidate area : in, Indicates the position coordinates of the defect candidate area The local fractal dimension at , Represented on a fractal scale Position coordinates of the candidate area of ​​the lower coverage defect The minimum number of boxes required at the location; S44, based on the local fractal dimension and local grayscale difference features Determine the adaptive segmentation threshold of microcrack defect area : in, is the position coordinate of the defect candidate area The adaptive segmentation threshold of the microcrack defect area, , is the weight coefficient; S45, using adaptive segmentation threshold of microcrack defect area The molten pool image data matrix of the defect candidate area Perform local threshold binarization segmentation to obtain the molten pool image data matrix of the microcrack defect area after fine segmentation : 。 7. A defect identification and early warning method in an additive manufacturing process according to claim 6, characterized in that: The S5 specifically includes the following steps: S51, based on the molten pool image data matrix of the microcrack defect area after fine segmentation Construct a microcrack boundary detection operator to extract the boundary contour point set of the microcrack area : in, Represents the boundary contour point set of the microcrack defect area, Represents the melt pool image data matrix of the microcrack defect area after fine segmentation The gradient of is the edge detection threshold; S52. Calculate the length of microcrack defects , the minimum circumscribed rectangle method is used to fit the boundary points of the microcrack area and define the microcrack length is the major axis of the minimum enclosing rectangle: in, represents the length of the microcrack defect, and are any two points in the microcrack boundary contour, Represents the Euclidean distance between two points; S53, using the normal projection method to calculate the average width of the microcrack centerline direction : in, represents the average width of microcrack defects, is the coordinate of the positive boundary point extending outward from the g-th sampling point along the normal direction to the crack edge, The g-th sampling point shrinks inward along the normal direction to the negative boundary point coordinate of the crack edge. is the total number of sampling points on the center line of the microcrack; S54, based on the molten pool image data matrix of the microcrack defect area after fine segmentation Extract the crack edge contour of the microcrack defect area and determine the crack morphology characteristics , the crack morphology is described by the crack edge contour curvature characteristics: in, is the crack morphology characteristic, reflecting the degree of curvature of the crack area contour. , Respectively represent The first-order derivatives in the horizontal and vertical directions of the edge contour points, , Respectively represent The second-order derivatives in the horizontal and vertical directions of the edge contour points, Represents the total number of points sampled on the crack edge contour; S55, based on the length of microcracks ,width and crack morphology characteristics , calculate the severity score of microcrack defects : in, , and is the weight coefficient; S56. Score based on the severity of microcrack defects Set the microcrack defect level, and the defect level is divided according to: Defect grade classification in, and They are the low-level and high-level severity thresholds of microcrack defects in additive manufacturing, respectively, forming the quantitative evaluation results of microcrack defects in additive manufacturing.

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