CN115170569A - Failure detection method of high-entropy material coating cutter based on image - Google Patents
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Abstract
A failure detection method of a high-entropy material coating cutter based on images is characterized in that an image analysis method is adopted to automatically detect the high-entropy material coating cutter in a non-contact and non-damage mode, images of the high-entropy material coating cutter are analyzed according to apparent characteristics of the high-entropy material coating cutter, whether the running state of the high-entropy material coating cutter is normal or failed is judged, the good state of the cutter in use is ensured, and the reliability and the safety of industrial production are improved.
Description
Technical Field
The invention relates to the field of cutter coating detection, in particular to a failure detection method of a high-entropy material coating cutter based on an image.
Background
Hard alloys composed of refractory metal compounds and metal binders have become one of the main high-speed cutting tool materials due to their excellent mechanical properties and cutting performance. However, in the cutting process, with the increase of the cutting speed, the heat quantity of transformation of deformation work and friction work is increased rapidly, the cutting temperature is increased remarkably, and the service life of the cutter is seriously influenced. This problem has become one of the problems that will affect the rapid development of equipment manufacturing industry and needs to be solved urgently. In order to increase the reliability of the operation of the cutter and prolong the service life of the cutter, three methods are usually adopted to improve the cutter in practice: firstly, hard alloy materials with better performance are researched and developed; secondly, optimizing a cutting process; thirdly, a surface strengthening technology is adopted. For decades, cutter materials are developed rapidly, the performance improvement space is limited, the cutting machining process is relatively mature, and the service life of the cutter can be effectively prolonged only by performing coating treatment. At present, more than 90% of cutting tools for high-precision machine tools in developed countries are subjected to coating treatment. Therefore, the development of the novel wear-resistant heat-resistant cutter coating is an effective way for prolonging the service life of the cutter, improving the production efficiency, saving resources and reducing environmental pollution.
The multi-element nano composite of the coating components is a main way for improving the toughness and the thermal stability of the hard wear-resistant coating material, and the multi-element nano composite coating is prepared and can obtain a novel hard wear-resistant coating material with more excellent performance. On the premise of excellent microstructure, the CoCrCuFeNi binding phase can fully play a high-entropy effect at high temperature, on one hand, the stable oxidation lubricating layer is promoted to be formed, on the other hand, the high-strength combination with a hard phase is realized, and the generation of abrasive particles is avoided, so that the high-temperature friction and wear performance of the cutter is remarkably improved. Therefore, the high-entropy alloy material (HEA) coating is an effective method for improving the performance of the cutter, and has application prospects. The failure detection of the high-speed cutting tool is an effective and necessary way for reducing waste generation and avoiding the potential safety hazard of tool operation.
The traditional detection of the coating requires a destructive operation, which makes the method only applicable to spot inspection, but not to real-time detection of the production line. The image-based industrial detection method is an emerging intelligent detection tool in recent years, and is widely applied to the field of industrial detection due to the characteristics of no contact with a detection target, no damage to a target, low cost and high efficiency. The current method of using neural network image processing algorithms includes, however, the input of the current neural network algorithm is either the whole tool image or is specific to a particular region (e.g. a knife edge). The former has extremely low detection efficiency, and the latter depends on the positioning accuracy of the knife edge area. Moreover, most of the existing neural network algorithms only aim at detecting defects of the cutter, and are not specially designed aiming at the degradation of the coating, so that the detection accuracy of the coating failure is not high. Although the neural network model is used for distinguishing images in other fields, the network structure difference is huge due to different detection objects, and the neural network model cannot be directly applied to accurate detection of the high-entropy material coating of the cutter.
Disclosure of Invention
Based on the industrial application requirements and the characteristics of the high-entropy material coating cutter, the invention innovatively provides a failure detection method of the high-entropy material coating cutter based on images.
A failure detection method of a high-entropy material coating cutter based on images,
step 1: extraction of key region of high-entropy material coating cutter image
Step 1.1, establishing a probability model of the color distribution of the high-entropy material coating cutter image: performing channel activation operation on the color according to the color value of the RGB channel of the image; the activated image calculation method comprises the following steps:
r, g, b in equation (1) represent the RGB three channels of the image;、is a linear coefficient, and the linear coefficient,、is composed ofAs a function of (a) or (b),the color value range is (0, 1)](ii) a Wherein:
based on activation imagesThe input image is judged according to the distribution probability of the pixel values, and the probability that the input image is the high-entropy material coating cutter image is judged:
wherein,for activating imagesThe value of the pixel of (a) is,coating knives for high entropy materialsThe device is provided with an image classification label,representing a probability function, argmax representing a make probability functionTaking maximum time parameterA value of (d);
step 1.2, extracting a key region of the high-entropy material coating cutter image from the image: to pairEach pixel of (2)According to a probabilistic modelSolving the classification label to obtain a key area;
Step 2: state failure detection of high-entropy material coating cutter based on key area
Taking each pixelOf the neighborhood of pixels:Wherein each pixel in the neighborhoodIs thatNeutralization ofIs not more thanThe set of pixels of (a) is,is the radius of the neighborhood;
activating an image from oneThe number of pixels in which the key region is obtained isIs recorded as(ii) a Correspondingly haveA neighborhood of marked as,,…,Will turn overThe neighborhoods are arranged from small to large according to the pixel mean value to obtain a sequence:
in a certain neighborhood as described aboveEstablishing a calculation model for input, and calculating the high-entropy material coating cutter state voting coefficient according to input data; setting the corresponding model output as,For coefficients ordered according to equation (6), calculate:
the calculation result of the equation (13) is a tool state voting coefficient; and when the voting coefficient is larger than 0.3, judging that the cutter is invalid.
The production line is provided with image acquisition equipment, the image acquisition equipment is connected with a communication unit, the communication unit is connected with a remote server through a communication network, and the server is connected with a controller of the production line.
The image acquisition equipment is used for acquiring a tool image.
The communication unit is used for transmitting the cutter image to the server.
The server is used for implementing the method, judging whether the cutter is invalid or not and feeding back the result to the controller.
The controller is used for receiving the discrimination signal fed back by the server, thereby controlling the action of the production line to eliminate unqualified cutters.
The key area refers to an image pixel set used for detecting the performance state of the high-entropy material coating cutter.
whereinIs a pixelIs determined by the conditional probability function of (a),is to classifyIs determined by the prior probability function of (a),is a pixelIs calculated as a function of the prior probability.
The probability is a model parameter and is obtained through a training sample of the high-entropy material coating cutter image.
A cutter on-line use method comprises the detection method.
The invention has the advantages that:
1. the extraction method of the key area of the high-entropy material coating cutter image is different from a mode of dividing the area in advance in the prior art, a pixel set conforming to an algorithm is used as the key area, and the probability that the pixel belongs to the key area can be accurately calculated by activating the processing of the image, so that the extraction method of the key area can be quickly and accurately finished, and is quicker and more accurate than the prior art. And furthermore, a foundation is further provided for the operation of the following neural network, and the high efficiency and accuracy of the operation of the neural network are ensured.
2. Calculating the key area pixels of the high-entropy material coating cutter image according to the step 1, performing neighborhood expansion, taking the neighborhood as an input parameter, calculating the state voting coefficient of the high-entropy material coating cutter according to the key area by using an optimized special neural network model (special excitation function and the like), and setting a special algorithm for the voting coefficient to judge whether the high-entropy material coating cutter fails, so that the non-contact and non-damage automatic detection of the high-entropy material coating cutter is realized, and the detection result is accurate and quick.
Detailed Description
Step 1: extracting a key area of the high-entropy material coating cutter image:and detecting and extracting a key region of the high-entropy material coating cutter image from the shot image.
Unlike the prior art, the critical area is not a fixed geometric area that has been manually specified in advance. In the invention, the key area of the high-entropy material coating cutter image refers to an image pixel set for detecting the performance state of the high-entropy material coating cutter, and is a subset of an original shot image. And such a set of pixels is not necessarily a standard geometric area.
Due to the special properties of the high-entropy material coating, the high-entropy material coating cutter shows special color distribution in an image. A probability model of the color distribution of the high-entropy material coating cutter image is established, model parameters are obtained through training (step 1.1), and the probability model is used for extracting a key area of the high-entropy material coating cutter image from the image (step 1.2).
Step 1.1, a probability model of the color distribution of the high-entropy material coating cutter image is established, and parameters required by the model are obtained through training.
In order to reflect the characteristics of the high-entropy material coating cutter, the collected high-entropy material coating cutter image adopts a three-color RGB color image, wherein RGB is three color channels of the image.
And performing channel activation operation on the color according to the color value of the RGB channel of the image to obtain an activated image, so that the color of the high-entropy material coating cutter in the image is more obvious, and the model detection is facilitated.
The activated image calculation method comprises the following steps:
in equation (1), r, g, b represent the RGB three channels of the image.、Is a linear coefficient of the linear coefficient,、is composed ofWherein:
the color value range is (0, 1)]I.e. the result after normalization. e and ln represent natural index and natural logarithm, respectively.
Obtaining an activation image by the calculation of equation (1)The method can make colors with high correlation degree with the high-entropy material coating in the original image more prominent, and inhibit other colors, thereby being beneficial to model detection.
Based on activation imagesThe input image is judged according to the distribution probability of the pixel values, and the probability that the input image is the high-entropy material coating cutter image is judged:
wherein,for activating imagesThe value of the pixel of (a) is,the cutter image classification label is coated on the high-entropy material,the time indicates that the pixel belongs to the high-entropy material coating cutter image,the time indicates that the pixel cannot belong to the high-entropy material coating cutter image;representing a probability function, argmax representing a make probability functionTaking maximum time parameterThe value of (c). The function f will activate the imageIs marked as 1 or 0, respectively, which indicates belonging to or not belonging to the high-entropy material coating tool image.
Classifying according to Bayes' theoremConditional probability function of (2)Can be obtained by sample training:
whereinIs a pixelIs determined by the conditional probability function of (a),is to classifyIs determined by the prior probability function of (a),is a pixelThe probability is a model parameter, and is obtained through a training sample of the high-entropy material coating cutter image. Therefore, the conditional probability function can be obtained by equation (4)。
Step 1.2, extracting key areas of high-entropy material coating cutter images from images
Establishing a probabilistic model according to step 1.1And solving model parameters through training、、。
Calculating the input image according to equations (1) and (2) to obtain an activation imageTo, forEach pixel of (2)According to a probabilistic modelSolving the classification label to obtain a key area:
Namely the critical zoneBy activating the imageThe middle classification label is composed of pixels with 1,is thatA subset of (a).
Step 2: state failure detection of high-entropy material coating cutter based on key area: calculating a key area of the high-entropy material coating cutter image according to the step 1, calculating a high-entropy material coating cutter state voting coefficient according to the key area, and judging whether the high-entropy material coating cutter is invalid or not according to the voting coefficient.
Each pixel in the neighborhoodIs thatNeutralization ofIs not more thanSet of pixels, vertical lineRepresenting the manhattan distance of two pixels,referred to as the radius of the neighborhood. Manhattan distance is robust to noise compared to Euclidean distanceThe performance is higher.
By expanding each pixel in the key region into its neighborhood for describing the local information of the key region, the neighborhood features have high correlation with the state of the tool compared with a single pixel, and thus can be used for judging the state of the tool.
Setting from one activation imageThe number of pixels in which the key region is obtained isIs marked as(ii) a Correspondingly haveA neighborhood of marked as,,…,Will turn overEach neighborhood gets a sequence according to the small-to-large arrangement of its pixel mean:
In a certain neighborhood as described aboveAnd establishing a calculation model for inputting, and calculating the tool state voting coefficient of the high-entropy material coating according to the input data.
Order:
in the formula (7), the reaction mixture is,is prepared by reacting withAre of equal size and are composed ofDerived neighborhood of whereinIs thatThe coordinates in the neighborhood of the point of interest,is a neighborhoodThe coordinates of (a) are (b),representing the two aforementioned neighborhoodsAndthe correlation coefficient between two related pixels in the image,is a linear offset.The function is used to non-linearize the model, and
Further, let:
in the formula (9), the reaction mixture is,is a radius ofAnd is made ofDerived neighborhood of whereinIs thatThe coordinates in the neighborhood of the point of interest,is a neighborhoodThe coordinate(s) of (a) is (are),representing the two aforementioned neighborhoodsAndthe correlation coefficient between two related pixels in the image,is a linear offset.The function is the same as equation (8).
Further, let:
in the formula (10), the compound represented by the formula (10),is a radius ofAnd is composed ofDerived neighborhood of whereinIs thatThe coordinates in the neighborhood of the point of interest,is a neighborhoodThe coordinates of (a) are (b),representing the two aforementioned neighborhoodsAnd withThe correlation coefficient between two related pixels in the image,is a linear offset.The function is the same as equation (8).
Three-layer pyramid neighborhoods are derived from the original neighborhoods according to the formulas (7), (9) and (10) and are used for modeling different scale structures of the neighborhoods, so that the local features of the image can be more accurately described. The cracks causing the cutter state failure are represented as local repeated features in the image, and whether the cracks appear in the local image can be detected by adopting the method.
Order:
in the formula (11), the reaction mixture is,is thatThe coordinates in the neighborhood of the point of interest,to representOne pixel in the neighborhood and the outputThe correlation coefficient of (a) is calculated,output ofIndicating whether there is a crack in the neighborhood of the input, when the output isIs indicative of the presence of a crack, when outputIndicating the absence of cracks.
Equations (7) - (11) define a computational model for detecting the presence of cracks in the neighborhood of the critical region. Defining a cost function:
the cost function is used for optimizing the calculation model to enable the model to reach an optimal value under a training set, whereinRepresenting the labeled true values of the training set,the output value of the sample data of the training set under the model,、is a linear coefficient, preferably. According to (12), the unknown parameters of the models (7) to (11) can be solved by adopting BP algorithm、、、、、、、.
The training set is an image of a high-entropy material coating cutter with known cracks, a key area is extracted, whether cracks exist in the neighborhood is observed manually, and the key area is marked as the crackOrAnd (5) substituting (12) the iterative computation to finish the training.
After training is completed, a neighborhood is selectedFor inputting, calculating whether a crack exists in the neighborhood according to the trained calculation model, and setting the corresponding model output as,For coefficients ordered according to equation (6), calculate:
the calculation result of equation (13) is a tool state voting coefficient. And when the voting coefficient is larger than 0.3, judging that the cutter is invalid. Reducing the error of single neighborhood detection.
The invention provides a failure detection method of a high-entropy material coating cutter based on an image. According to the comparison of the following results, the detection result of the method disclosed by the invention has a small standard error with the detection standard of a microscope, and the accurate automatic detection of the failure of the high-entropy material coating cutter is realized.
In addition, compared with the existing image processing method, the method of the invention also has obvious advantages. For example, compared with the traditional edge detection mode, the accuracy rate of the method is improved by 72%; compared with a common ResNet neural network model, the accuracy rate is improved by 35%.
The hardware structure of the present invention can use an off-the-shelf image acquisition device. For example, an image acquisition device is arranged on a detection table of a production line, the image acquisition device is connected with a communication unit, the communication unit is connected with a remote server through a communication network, and the server is connected with a controller of the production line.
The image acquisition equipment is used for acquiring a cutter image;
the communication unit is used for transmitting the cutter image to the server;
the server is used for implementing the method, judging whether the cutter is invalid or not and feeding back a result to the controller;
the controller is used for receiving the discrimination signal fed back by the server, thereby controlling the action of the production line to eliminate unqualified cutters.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A failure detection method of a high-entropy material coating cutter based on images is characterized by comprising the following steps:
step 1: extraction of key region of high-entropy material coating cutter image
Step 1.1, establishing a probability model of the color distribution of the high-entropy material coating cutter image: performing channel activation operation on the color according to the color value of the RGB channel of the image; the activated image calculation method comprises the following steps:
r, g, b in equation (1) represent the RGB three channels of the image;、is a linear coefficient, and the linear coefficient,、is composed ofIs a function of (a) a function of (b),the color value range is (0, 1)](ii) a Wherein:
according to activation imageThe probability of the distribution of the pixel values is used for judging the input image, and the probability that the input image is the high-entropy material coating cutter image is judged:
wherein,for activating imagesThe value of the pixel of (a) is,the cutter image classification label is coated for the high-entropy material,representing a probability function, argmax representing making the probability functionTaking maximum time parameterA value of (d);
step 1.2, extracting a key area of the high-entropy material coating cutter image from the image: to pairEach pixel of (2)According to a probabilistic modelSolving the classification label to obtain the key area;
Step 2: state failure detection of high-entropy material coating cutter based on key area
Taking each pixelOf (2) a set of neighborhood pixels:Wherein each pixel in the neighborhoodIs thatNeutralization ofIs not more thanIs selected to be the set of pixels of (a),is the radius of the neighborhood;
activating an image from oneThe number of pixels in which the key region is obtained isIs marked as(ii) a Correspondingly haveA neighborhood of,,…,Will turn overEach neighborhood gets a sequence according to the small-to-large arrangement of its pixel mean:
with a certain neighborhood mentioned aboveEstablishing a calculation model for input, and calculating the high-entropy material coating cutter state voting coefficient according to input data; let the corresponding model output be,For coefficients ordered according to equation (6), calculate:
the calculation result of the formula (13) is a tool state voting coefficient; and when the voting coefficient is larger than 0.3, judging that the cutter is invalid.
2. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 1, wherein: the production line is provided with image acquisition equipment, the image acquisition equipment is connected with a communication unit, the communication unit is connected with a remote server through a communication network, and the server is connected with a controller of the production line.
3. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 2, wherein: the image acquisition equipment is used for acquiring a tool image.
4. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 2, wherein: the communication unit is used for transmitting the cutter image to the server.
5. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 2, wherein: the server is used for implementing the method, judging whether the cutter is invalid or not and feeding back the result to the controller.
6. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 2, wherein: the controller is used for receiving the discrimination signal fed back by the server, thereby controlling the production line to act to remove the unqualified cutter.
7. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 1, wherein the method comprises the following steps: the key area refers to an image pixel set for detecting the performance state of the high-entropy material coating cutter.
8. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 1, wherein the method comprises the following steps: solving conditional probability functionThe method comprises the following steps:
9. The method for detecting the failure of the high-entropy material-coated cutter based on the image as claimed in claim 8, wherein: the probability is a model parameter and is obtained through a training sample of the high-entropy material coating cutter image.
10. A cutter online use method is characterized in that: a method of failure detection comprising an image-based high entropy material coated tool according to claims 1-9.
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