CN110260795A - A kind of absolute displacement detection method based on increment absolute grating ruler - Google Patents
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Abstract
The invention discloses a kind of absolute displacement detection methods based on increment absolute grating ruler, comprising: by making marks on increment type grating scale, so that it is changed into a kind of increment absolute grating ruler, guarantees the uniqueness of every grid line；Acquisition raster image simultaneously denoises, and extracts the grid line of grating scale, establishes the neural network model of deep learning, each gate line of training, the neural network model after saving training；The mathematical model for establishing displacement detecting acquires the image of the grating scale of displacement to be checked and carries out the identification of grid line using trained neural network model, and the grid line number that will identify that feeds back to mathematical model, to calculate displacement detecting value.Increment type grating scale is changed into increment absolute grating ruler by the present invention, and the measurement method of displacement is the function of absolute grating ruler to be realized by incremental mode, also, significantly reduce the grating scale accuracy of manufacture.
Description
Technical Field
The invention relates to the technical field of displacement detection of grating scales, in particular to a method for changing incremental stripes into absolute codes and detecting the displacement of the absolute codes.
Background
With the continuous improvement of the integration level of microelectronic products, the requirements of higher speed and precision are put forward for a displacement detection system of microelectronic manufacturing equipment.
With the development of micro-machining technology, especially the continuous improvement of the integration level of microelectronic products, the sizes of the components of the micro-machining technology are reduced, and in the processes of machining and mounting the components, higher requirements are put forward on the positioning precision of microelectronic manufacturing equipment, and a precision measurement system is required to be developed towards the aspects of high precision and high speed. Therefore, the improvement of the displacement detection speed and precision of the grating ruler has great significance.
The grating scales used at present are mainly divided into an increment type and an absolute type. Where the incremental ruler is a grid of equally spaced bars, subdivision is achieved by the generation of moire fringes. Due to the contradiction between speed and precision, the step is easy to lose at high speed when the high-precision measurement is carried out. In order to solve the problems of step loss and power-on reset, people invent an absolute grating ruler, the position is coded, a group of codes is usually 200 mu m in length, and the requirements on grating lines and subdivision are high.
In addition, most Displacement Measurement systems are Based on the research of the traditional image processing method at present, the document "Real-Time Displacement Measurement of a Flexible Bridge Using Digital image processing technology" proposes to detect the Dynamic Displacement of the Bridge by Using the Digital image processing technology, and the document "A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using motion Extraction Algorithms" proposes to analyze the Vibration Displacement by Using a High-Speed Vision Sensor. Through the application of a digital image processing technology, the speed of measuring the displacement is effectively improved, the manufacturing requirement of the displacement sensor is reduced, the cost and the installation requirement are reduced, but the methods have physical model errors and environmental noise interference, so that the displacement measuring precision is greatly reduced. On the other hand, the document 'a grating nanometer measurement subdivision method with high resolution and high frequency response' proposes a grating nanometer measurement subdivision method with high resolution and high frequency response-dynamic tracking subdivision method, and obtains a frequency response speed of 100kHz when measuring with 5nm resolution, but the measurement allowable speed is only 0.5mm/s, and the measurement allowable speed cannot be improved.
Disclosure of Invention
The invention provides an absolute displacement detection method based on an incremental absolute grating ruler, which can effectively overcome the limitation (usually 20 mu m) of the grating manufacturing precision of the traditional grating measurement, greatly improve the operation speed of measurement feedback through a neural network model algorithm and achieve accurate displacement feedback and timely response under high acceleration in precision measurement.
In order to realize the task, the invention adopts the following technical scheme:
an absolute displacement detection method based on an incremental absolute grating ruler comprises the following steps:
step 1, marking on an incremental grating ruler to convert the incremental grating ruler into an incremental absolute grating ruler, so as to ensure the uniqueness of each grating pattern;
step 2, acquiring a grating image and denoising, extracting grating lines of a grating ruler, establishing a deep learning neural network model, training each grating line, and storing the trained neural network model;
and 3, establishing a displacement detection mathematical model, acquiring an image of the grating ruler to be detected for displacement, carrying out grating pattern recognition by using the trained neural network model, and feeding back the recognized grating pattern number to the mathematical model to calculate a displacement detection value.
Further, the mathematical model in step 3 is:
in the above formula, n represents the maximum grating pattern number in the common grating patterns in two adjacent grating ruler images, j represents the jth grating pattern of the grating ruler<n; d represents the grid distance of any adjacent grid lines, i represents the ith grating ruler image, uijRepresents the motion direction left boundary coordinates of the jth raster in the ith raster image, and ρ' represents the pixel size of the COMS camera.
Further, the step 1 of marking on the incremental grating scale converts the incremental grating scale into an incremental absolute grating scale, which specifically comprises:
a diagonal oblique line is carved on the short-stroke grating ruler from top left to bottom right, the oblique line is carved on the long-stroke grating ruler in a segmentation mode, each grid line is marked through the oblique line, and therefore each grid line has uniqueness, namely the grating ruler is converted into an absolute ruler.
Further, the acquiring a grating image and denoising in step 2 includes:
for a grating ruler on a carrier motion platform, firstly aligning a camera with a first grating of the grating ruler, then slowly moving the motion platform, collecting a plurality of grating images through the camera, finally, respectively storing the collected grating ruler images, and sequentially naming according to a positive integer sequence;
therefore, the noise is filtered by comparing the column vector of the picture with the threshold, and the specific formula is as follows:
where I '(: b) represents each column of pixel values of the pre-filtered raster image I' (u, v) and I (u, v) represents the filtered raster image.
Further, the extracting the grating lines of the grating ruler in the step 2 includes:
firstly, extracting all pixel points on a Y axis of a grating image, summing the pixel points, comparing the obtained value with a set threshold value, and if the value is greater than the threshold value, taking the position of the pixel as the initial boundary position of a characteristic window; then, in the X-axis direction of the grating image, taking m pixels behind the grating image as the width of the characteristic window, wherein m is the number of pixels corresponding to the grating width; extracting each grid line image by using the characteristic window, and simultaneously recording grid line numbers; and after the grating pattern image in the current grating pattern is extracted, reading the next grating pattern image, and sequentially extracting each grating pattern image according to the same method.
Further, the step 3 of collecting the image of the grating ruler to be detected for displacement and performing grating pattern recognition by using the trained neural network model, and feeding back the recognized grating pattern number to the mathematical model to calculate the displacement detection value includes:
for the grating ruler to be detected to be displaced, continuously acquiring grating ruler images by using a COMS camera in a moving state of the grating ruler; for each collected image, performing pre-processing of filtering and denoising on the grating ruler image by using TensorFlow software so as to extract grating lines; after image preprocessing, utilizing the trained network model in the step 2 to identify grid lines;
obtaining relative displacement by comparing the same grating in two continuous grating ruler images; the final displacement detection value is obtained by averaging the relative displacement difference between all the same grid lines in the two continuous images, so that the manufacturing error of the grating is overcome, the manufacturing requirement of the grating ruler is reduced, and the displacement detection is completed by obtaining the displacement information.
Compared with the prior art, the invention has the following technical characteristics:
1. by carving marks on the incremental grating ruler stripes, the incremental grating ruler is converted into an incremental absolute grating ruler, so that the contradiction between the speed and the precision of the incremental grating ruler is solved (in the high-precision measurement, the step is easy to lose at high speed), and the problem that the absolute grating ruler has high requirements on grating lines and subdivision is solved (usually, one group of codes is 200 mu m).
2. By introducing the neural network model, the influence of modeling errors and environmental errors on displacement measurement errors is reduced. Because each picture of the training set contains environmental factors and modeling factors, the displacement measurement precision and stability are improved.
3. Because the grating ruler has manufacturing errors in the manufacturing process, the displacement measurement of the scheme is realized by the pixel displacement of the motion between the same grating in two adjacent pictures, so that the manufacturing errors are eliminated, and the precision of the displacement measurement is improved.
4. Because the identification of the grating ruler by the neural network is realized by the shape of the grating image, the poorer the manufacturing precision of the grating ruler is, the easier the identification is, and thus, each grating is unique for a grating measurement system, so that the displacement measurement method realizes the function of an absolute grating ruler in an incremental mode, and the manufacturing precision of the grating ruler is greatly reduced.
5. The traditional displacement measurement system improves the resolution of the system by subdivision, but the subdivision method has the problems of accumulated error, poor precision and the like, and the resolution of the image is improved by the magnification factor of a lens and the pixel precision of an image sensor; the scheme is easy to obtain higher resolution ratio at lower cost.
6. In the practical application process, in order to prevent the frame loss of the COMS camera under the condition of high-speed movement, the number of the grid lines can be recorded by adopting a photoelectric sensor, and the image is subdivided and accurately positioned after the speed is slowed down.
Drawings
FIG. 1 is a long stroke grating scale;
FIG. 2 is a short stroke grating scale;
FIG. 3 is a schematic view of a feature window;
FIG. 4 is a grid-neural network model;
FIG. 5 is a displacement measurement principle;
FIG. 6 is a displacement measurement system;
fig. 7 is a schematic flow diagram of the present invention.
Detailed Description
Generally, the resolution of a single grating scale serving as a displacement sensor can reach the nanometer level, but due to the limitation of the scanning frequency of signal detection, the maximum allowable speed of single grating scale detection is often inversely proportional to the measurement resolution thereof, and in order to realize the precise measurement of the nanometer-level displacement, the system movement speed is limited by the limit speed of the grating displacement sensor (generally <200 mm/s); to realize displacement measurement of a system moving at high speed, the measurement step distance is limited by the resolution of the grating displacement sensor (generally >0.5 um).
The invention provides an incremental absolute grating ruler and a displacement detection method thereof for solving the problems, on one hand, the detection precision is improved (only related to the resolution of a camera and unrelated to the resolution of the grating ruler) through the subdivision of the image pixels of the grating ruler; on the other hand, the weight and the offset parameter of the neural network are downloaded into the FPGA, so that higher measurement speed (only related to the number of scanning frames of the camera and the processing speed of FPGA hardware) is achieved under high precision.
The invention relates to a technical scheme of an absolute displacement detection method based on an incremental absolute grating ruler, which specifically comprises the following steps:
step 1, marking on the incremental grating ruler to convert the incremental grating ruler into an incremental absolute grating ruler, so as to ensure the uniqueness of each grating pattern.
The specific type of marking on the grating ruler can be various, as long as each grating stripe (namely, black stripe in the grating ruler) has uniqueness after marking. In this embodiment, a simple method is used for marking:
for a short-stroke (<1000um) grating ruler, a diagonal oblique line is carved from top left to bottom right, and for a long-stroke (>1000um) grating ruler, the oblique line is carved in a subsection manner, each grating line is marked by the oblique line, so that each grating line has uniqueness, that is, the grating ruler is converted into an absolute ruler, zero resetting and subdivision operations are not needed in the detection process, and the response speed is accelerated; the grating ruler is characterized in that figures 1 and 2 show.
And 2, acquiring a grating image, denoising, extracting the grating lines of the grating ruler, establishing a deep learning neural network model, training each grating line, and storing the trained network model.
2.1 image acquisition
For the grating ruler on the carrier motion platform, firstly aligning a camera with a first grating of the grating ruler, then slowly moving the motion platform, collecting a plurality of batches of grating images through the camera, finally, respectively storing the collected grating ruler images, and sequentially naming according to a positive integer sequence.
2.2 image denoising
Because there is ambient noise interference in the grating image acquisition process, influences the extraction of grating line, consequently adopts the column vector with the picture and compares with the threshold value, filters the noise, and specific formula is as follows:
where I '(: b) represents each column of pixel values of the pre-filtered raster image I' (u, v) and I (u, v) represents the filtered raster image.
2.3 extraction of grid images through the characteristic Window
Positioning a characteristic window as shown in fig. 3, firstly, extracting all pixel points on a Y axis of a grating image, summing the pixel points, comparing the obtained value with a set threshold value, and if the value is greater than the threshold value, taking the position of the pixel as the initial boundary position of the characteristic window; then, in the X-axis direction of the grating image, taking m pixels behind the grating image as the width of the characteristic window, wherein m is the number of pixels corresponding to the grating width; extracting each grid line image by using the characteristic window, and simultaneously recording grid line numbers; and after the grating pattern image in the current grating pattern is extracted, reading the next grating pattern image, and sequentially extracting each grating pattern image according to the same method.
2.4 training grid
Taking the extracted grid pattern image as an input layer of a neural network model, and taking a grid pattern number corresponding to the grid pattern image as an output layer of the neural network model to train grid patterns; after training, the corresponding grid mark can be accurately identified, namely the grid mark is known to be the first grid mark.
The grid pattern neural network model is shown in fig. 4: an Artificial Neural network (Artificial Neural Networks) is an information processing system for simulating brain functions such as brain cell structures and functions, brain Neural structures, thinking processing problems and the like, and is used for searching new information representation, storage and processing modes from the perspective of simulating human brain intelligence. Generally, a neural network is composed of an input layer, a plurality of hidden layers and an output layer. The circle in fig. 4 can be regarded as a neuron (also referred to as a sensor). An important task in designing neural networks is to design the hiddenlayer, and the weights and offsets between neurons. Adding a small number of hidden layers to obtain a neural network SNN; the hidden layer is often a deep neural network DNN.
In a CNN network, an input variable j of n layers is represented as an output variable of a non-linear function of n-1 layers. The definition is as follows:
wherein,represents the kth output variable of the nth layer, f is the activation function,is the sum of all output variables of the n-1 layer,is the weight value from the ith node variable of the n-1 layer to the jth node variable of the n layer,is a bias of n layersShift variable, E is an error function, dkIs the target value of the kth node variable, ykFor the output of the hidden layer, ρ is the learning rate.
The scheme adopts three layers of ANN neural networks, namely an input layer, a hidden layer and an output layer, and weights and offsets are trained based on an Adam optimization algorithm; and storing the network model after training.
And 3, establishing a displacement detection mathematical model, acquiring an image of a grating ruler to be detected for displacement, carrying out grating pattern recognition by using the trained neural network model, and feeding back the recognized grating pattern number to the mathematical model to calculate a displacement detection value, thereby effectively overcoming inaccuracy and noise interference of a physical model.
For the grating ruler to be detected to be displaced, in the moving state of the grating ruler (the grating ruler and the COMS camera generate relative motion, the grating ruler can be moved, and the camera can also be moved), the COMS camera is adopted to continuously acquire grating ruler images; for each collected image, preprocessing such as filtering and denoising the grating ruler image by using TensorFlow software so as to extract grating lines conveniently; after image preprocessing, utilizing the trained network model in the step 2 to identify grid lines;
obtaining relative displacement by comparing the same grating in two continuous grating ruler images; the final displacement detection value is obtained by averaging the relative displacement difference between all the same grid lines in the two continuous images, so that the manufacturing error of the grating is overcome, the manufacturing requirement of the grating ruler is reduced, and the displacement detection is completed by obtaining the displacement information.
The traditional displacement detection of the grating ruler requires that the width of each grating line is consistent, and extremely high precision is ensured.
Moving measurement principle:
as shown in fig. 5, in the present scheme, when displacement detection is performed, a mathematical model S of displacement detection is established as follows; taking every two adjacent grating ruler images collected by a CMOS camera, and calculating a displacement detection value according to the following formula:
in the above formula, n represents the maximum grating pattern number in the common grating patterns in two adjacent grating ruler images, j represents the jth grating pattern of the grating ruler, i.e. the result identified by the network model, j<n; d represents the grid distance of any adjacent grid lines, i represents the ith grating ruler image, uijRepresents the motion direction left boundary coordinates of the jth raster in the ith raster image, and ρ' represents the pixel size of the COMS camera.
The displacement detection value can be calculated by the above formula, and then the maximum resolution of the grating ruler is as follows:
wherein X represents the magnification of the COMS camera.
Therefore, the scheme can realize displacement measurement with higher precision by using lower resolution.
In the scheme, the grating ruler grating displacement measurement system consists of five parts, namely an industrial camera, a grating ruler, a computer, a parallel light source and a neural network algorithm program (a program algorithm is loaded into FPGA hardware to accelerate the processing speed) based on Python and Tensflow. The industrial camera selects a high-speed camera with the resolution of 1280x1024 and the highest frame rate of 180 frames, and is used for acquiring and amplifying the moving picture of the grating ruler; the grating ruler selects an incremental grating ruler with the grating pitch of 50um and is used for detecting linear motion displacement; the computer selects 8GB memory with 3.4GHz main frequency for image processing and software operation. The displacement measurement system is shown in fig. 6, and the principle flow block is shown in fig. 7.
Claims (6)
1. An absolute displacement detection method based on an incremental absolute grating ruler is characterized by comprising the following steps:
step 1, marking on an incremental grating ruler to convert the incremental grating ruler into an incremental absolute grating ruler, so as to ensure the uniqueness of each grating pattern;
step 2, acquiring a grating image and denoising, extracting grating lines of a grating ruler, establishing a deep learning neural network model, training each grating line, and storing the trained neural network model;
and 3, establishing a displacement detection mathematical model, acquiring an image of the grating ruler to be detected for displacement, carrying out grating pattern recognition by using the trained neural network model, and feeding back the recognized grating pattern number to the mathematical model to calculate a displacement detection value.
2. The incremental absolute grating ruler-based absolute displacement detection method according to claim 1, wherein the mathematical model in step 3 is:
in the above formula, n represents the maximum grating pattern number in the common grating patterns in two adjacent grating ruler images, j represents the jth grating pattern of the grating ruler<n; d represents the grid distance of any adjacent grid lines, i represents the ith grating ruler image, uijRepresents the motion direction left boundary coordinates of the jth raster in the ith raster image, and ρ' represents the pixel size of the COMS camera.
3. The absolute displacement detection method based on the incremental absolute grating ruler of claim 1, wherein the incremental grating ruler is marked in the step 1 to be converted into the incremental absolute grating ruler, and specifically comprises the following steps:
a diagonal oblique line is carved on the short-stroke grating ruler from top left to bottom right, the oblique line is carved on the long-stroke grating ruler in a segmentation mode, each grid line is marked through the oblique line, and therefore each grid line has uniqueness, namely the grating ruler is converted into an absolute ruler.
4. The incremental absolute grating ruler-based absolute displacement detection method according to claim 1, wherein the step 2 of acquiring the grating image and denoising comprises:
for a grating ruler on a carrier motion platform, firstly aligning a camera with a first grating of the grating ruler, then slowly moving the motion platform, collecting a plurality of grating images through the camera, finally, respectively storing the collected grating ruler images, and sequentially naming according to a positive integer sequence;
therefore, the noise is filtered by comparing the column vector of the picture with the threshold, and the specific formula is as follows:
where I '(: b) represents each column of pixel values of the pre-filtered raster image I' (u, v) and I (u, v) represents the filtered raster image.
5. The incremental absolute grating ruler-based absolute displacement detection method according to claim 1, wherein the step 2 of extracting the grating lines of the grating ruler comprises:
firstly, extracting all pixel points on a Y axis of a grating image, summing the pixel points, comparing the obtained value with a set threshold value, and if the value is greater than the threshold value, taking the position of the pixel as the initial boundary position of a characteristic window; then, in the X-axis direction of the grating image, taking m pixels behind the grating image as the width of the characteristic window, wherein m is the number of pixels corresponding to the grating width; extracting each grid line image by using the characteristic window, and simultaneously recording grid line numbers; and after the grating pattern image in the current grating pattern is extracted, reading the next grating pattern image, and sequentially extracting each grating pattern image according to the same method.
6. The absolute displacement detection method based on the incremental absolute grating ruler of claim 1, wherein the step 3 of acquiring the image of the grating ruler to be detected for displacement, performing grating pattern recognition by using a trained neural network model, and feeding back the recognized grating pattern number to a mathematical model to calculate the displacement detection value comprises the following steps:
for the grating ruler to be detected to be displaced, continuously acquiring grating ruler images by using a COMS camera in a moving state of the grating ruler; for each collected image, performing pre-processing of filtering and denoising on the grating ruler image by using TensorFlow software so as to extract grating lines; after image preprocessing, utilizing the trained network model in the step 2 to identify grid lines;
obtaining relative displacement by comparing the same grating in two continuous grating ruler images; the final displacement detection value is obtained by averaging the relative displacement difference between all the same grid lines in the two continuous images, so that the manufacturing error of the grating is overcome, the manufacturing requirement of the grating ruler is reduced, and the displacement detection is completed by obtaining the displacement information.
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| CN111189396A (en) * | 2019-11-19 | 2020-05-22 | 广东工业大学 | Displacement detection method of incremental absolute grating ruler based on neural network |
| CN111189396B (en) * | 2019-11-19 | 2022-06-03 | 广东工业大学 | Displacement detection method of incremental absolute grating ruler based on neural network |
| CN111043988A (en) * | 2019-12-10 | 2020-04-21 | 东南大学 | Single stripe projection measurement method based on graphics and deep learning |
| CN111043988B (en) * | 2019-12-10 | 2021-04-23 | 东南大学 | Single stripe projection measurement method based on graphics and deep learning |
| CN113469039A (en) * | 2021-06-30 | 2021-10-01 | 李�杰 | High-speed direction recognition algorithm for grating ruler coding signals |
| CN113469039B (en) * | 2021-06-30 | 2022-07-05 | 李�杰 | High-speed direction identification method for grating ruler coding signals |
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