CN117672435A - Automatic fiber yarn layout method and system based on nanofiber preparation - Google Patents
Automatic fiber yarn layout method and system based on nanofiber preparation Download PDFInfo
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Abstract
The invention provides an automatic fiber yarn layout method and an automatic fiber yarn layout system based on nanofiber preparation, which relate to the technical field of fiber yarn layout, and the automatic fiber yarn layout method based on nanofiber preparation comprises the following steps: acquiring attribute parameters of fiber filaments and layout substrates and preparing nanofiber requirements; formulating a layout path and an error buffer zone of the fiber based on the acquired attribute parameters and the preparation requirements; laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and error buffer area; and carrying out real-time error detection on the fiber layout by an image analysis method, and adjusting the fiber layout based on an error detection result. The invention can better control errors and improve the stability of layout, and the layout method has higher layout efficiency and can ensure higher layout precision and stability.
Description
Technical Field
The invention relates to the technical field of fiber yarn layout, in particular to an automatic fiber yarn layout method and system based on nanofiber preparation.
Background
Nanofibers are a special type of fiber with a diameter on the order of nanometers, approximately between 1 nanometer and 1000 nanometers. A significant feature of such a fiber is that it possesses a very high specific surface area, which means that its surface area to volume ratio is very large, which also provides a very large active surface. Another important characteristic is their high flexibility, which enables them to adapt to various complex application environments and requirements. There are many methods for preparing nanofibers, including mechanical stretching, templating, self-assembly, etc., but the most common and effective is electrospinning. Electrospinning is a technique whose basic principle is to draw a polymer solution or melt from a spray head using a high voltage electric field and solidify into fibers in air or on a collector. In this process, the diameter and shape of the fibers can be precisely controlled by adjusting parameters of the electrospinning, such as electric field strength, distance from the spray head to the collector, diameter of the spray head, viscosity and concentration of the solution, and the like.
At present, nanofibers have great application potential in many fields, and in the biomedical field, they can be used as scaffolds for tissue engineering for manufacturing artificial skin, artificial bones, etc.; in the filtration and separation fields, they can effectively filter particulates and microorganisms due to their high specific surface area and minute pore size; in the energy field, they can be used to manufacture efficient batteries and supercapacitors.
However, in the process of preparing the nanofiber by using the fiber, the layout of the fiber needs to be subjected to path arrangement, but the conventional fiber layout method may generate errors in the process of laying the fiber, and due to the influence of factors such as attribute parameters of the fiber, precision of layout equipment, environmental factors and the like, the errors may be gradually accumulated, so that the accuracy of the layout is reduced, and the preparation quality of the nanofiber may be affected.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
In view of the above, the present invention provides an automatic fiber placement method and system based on nanofiber preparation, so as to solve the above-mentioned problems that errors may occur during fiber placement, and errors may gradually accumulate, resulting in reduced placement accuracy.
In order to solve the problems, the invention adopts the following specific technical scheme:
according to an aspect of the present invention, there is provided an automatic fiber placement method based on nanofiber preparation, comprising the steps of:
s1, acquiring attribute parameters of fiber yarns and a layout substrate and preparing nanofiber requirements;
S2, formulating a layout path and an error buffer zone of the fiber based on the acquired attribute parameters and the preparation requirements;
s3, laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and the error buffer area;
and S4, performing real-time error detection on the fiber layout by an image analysis method, and adjusting the fiber layout based on an error detection result.
Preferably, the making of the layout path and the error buffer zone of the fiber based on the acquired attribute parameters and the preparation requirements comprises the following steps:
s21, analyzing the acquired attribute parameters and the preparation requirements, and determining the layout requirements of the fiber filaments, wherein the layout requirements comprise the arrangement mode, density, direction and shape of the fiber filaments;
s22, setting a fiber layout path based on the determined fiber layout requirement and taking the shortest idle stroke path as an optimization target;
s23, setting an error buffer zone of the fiber yarn in the layout process based on the layout path of the fiber yarn.
Preferably, setting the fiber layout path based on the determined fiber layout requirement and with the shortest idle travel path as an optimization target includes the following steps:
S221, establishing a three-dimensional point cloud model of the nanofiber according to the layout requirements of the fiber filaments;
s222, analyzing a three-dimensional point cloud model of the nanofiber through a point cloud processing algorithm, finding out characteristic points of the three-dimensional point cloud model, taking the characteristic points as layout turning points, and building a graphic model based on the layout turning points;
s223, taking the shortest idle travel path as an optimization target, planning the path of the graphic model through a layout planning algorithm, and optimizing the path planning result through updating pheromone and heuristic information so as to ensure that the idle travel path is shortest;
s224, taking the optimization result of the path planning as the layout path of the fiber filaments.
Preferably, the three-dimensional point cloud model of the nanofiber is analyzed through a point cloud processing algorithm, the characteristic points of the three-dimensional point cloud model are found out and used as layout turning points, and the graphic model is built based on the layout turning points, and the method comprises the following steps:
s2221, carrying out filtering treatment on the three-dimensional point cloud model of the nanofiber;
s2222, setting a preset radius by taking each data point in the preprocessed three-dimensional point cloud model as a center, and establishing a cube neighborhood;
s2223, fitting a projection curved surface based on a least square method, and projecting each point cloud data in the neighborhood of the cube to the projection curved surface to obtain a projection point set;
S2224, performing covariance calculation on the obtained projection point set to obtain a covariance matrix corresponding to each projection point, and calculating a characteristic value and a characteristic vector of the covariance matrix;
s2225, judging whether each projection point is a boundary feature point of the nanofiber according to the correlation of the feature vectors, and storing the projection points meeting the linear correlation as feature points in a feature point set;
s2226, taking the feature points in the feature point set as layout turning points, connecting the layout turning points end to form edges, and building a graphic model.
Preferably, fitting a projection curved surface based on a least square method, and projecting each point cloud data in a cube neighborhood onto the projection curved surface to obtain a projection point set, wherein the method comprises the following steps of:
s22231, taking each point in the cube neighborhood as a neighborhood center;
s22232, for each neighborhood, fitting a projection curved surface of all points in the neighborhood of the cube by using a least square method;
s22233, taking the central point of the neighborhood center as an original point, taking two principal tangent vectors of the projection curved surface at the original point as an x axis and a y axis, and taking the function value of the projection curved surface as a z axis, and establishing a projection coordinate system;
s22234, for each point in the cube neighborhood, projecting the point to the fitted projection curved surface by a projective transformation method to obtain a projection point set.
Preferably, taking the shortest idle path as an optimization target, planning the path of the graphic model through a layout planning algorithm, and optimizing the path planning result through updating pheromone and heuristic information so as to ensure that the shortest idle path comprises the following steps:
s2231, calculating the distance between layout turning points according to the position of each layout turning point in the graphic model to obtain a distance matrix;
s2232, initializing parameters of a layout planning algorithm, including the quantity of fiber filaments, the volatilization rate of pheromones and the concentration of initial pheromones;
s2233, obtaining an initial layout path by using a greedy algorithm, and updating the pheromone concentration of the initial layout;
s2234, randomly placing fiber yarns on the edges of the graphic model, and selecting a next point as a next node of the path according to the pheromone concentration and heuristic information;
s2235, updating the concentration of the pheromone according to the length of the path and the volatilization rate of the pheromone after all the fiber filaments complete the complete path;
the calculation formula of the pheromone concentration is as follows:
；
in the method, in the process of the invention,f ij (t+1) is expressed in timetFiber yarn at +1iAnd fiber yarnjPheromone concentration on the path;
p represents the volatilization rate of the pheromone;
Δf ij Representing fiber filamentsiAnd fiber yarnjPheromones on the path;
f ij (t) Expressed in timetTime fiber yarniAnd fiber yarnjPheromone concentration on the path;
s2236, repeating the steps S2234-S2235 until the maximum iteration number is reached, and selecting the path with the largest pheromone as the optimal path.
Preferably, setting an error buffer zone of the fiber yarn in the layout process based on the layout path of the fiber yarn comprises the following steps:
s231, performing error source identification based on attribute parameters of the fiber yarns, a graphic model and layout equipment;
s232, carrying out quantization analysis on each identified error source, and determining the maximum deviation value of the error source;
s233, determining the range of the error buffer zone according to the determined maximum deviation difference and the set fiber layout path.
Preferably, the real-time error detection is performed on the fiber layout by an image analysis method, and the adjustment of the fiber layout based on the error detection result comprises the following steps:
s41, acquiring a real-time layout image of the fiber layout through the camera equipment;
s42, extracting real-time path information of the fiber yarn layout by using an image processing and analyzing technology according to the real-time layout image;
s43, comparing the extracted real-time path information with an optimal path to obtain an error value of the layout;
And S44, if the error value of the layout is positioned in the error buffer area, carrying out path adjustment on the layout of the fiber, and if the error value of the layout is positioned outside the error buffer area, ending the adjustment on the layout of the fiber and ending the layout of the fiber.
Preferably, comparing the extracted real-time path information with the optimal path to obtain an error value of the layout includes the steps of:
s431, aligning the extracted real-time path information with the starting point of the optimal path;
s432, respectively extracting features of the sub-paths corresponding to the real-time path information in the real-time path information and the optimal path, wherein the features comprise the length, curvature and direction of the path;
s433, similarity calculation is carried out on the characteristics of the real-time path information and the characteristics of the sub-paths corresponding to the real-time path information in the optimal path;
s434, determining an error value of the layout according to the similarity calculation result.
According to another aspect of the present invention, there is provided an automatic fiber filament arrangement system based on nanofiber preparation, comprising: the system comprises a data acquisition module, a path and buffer zone making module, a fiber yarn layout module and an error detection and adjustment module;
The data acquisition module is used for acquiring attribute parameters of the fiber filaments and the layout substrate and nanofiber preparation requirements;
the path and buffer area making module is used for making a layout path and an error buffer area of the fiber based on the acquired attribute parameters and the preparation requirements;
the fiber yarn layout module is used for laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and the error buffer area;
the error detection and adjustment module is used for carrying out real-time error detection on the fiber yarn layout through an image analysis method and adjusting the fiber yarn layout based on an error detection result.
The beneficial effects of the invention are as follows:
1. according to the invention, the layout requirements of the fiber yarns are analyzed, then the shortest idle stroke path is taken as an optimization target, and layout path planning is performed, so that the fiber yarn layout is more efficient, the layout time is saved, the fiber yarn layout is subjected to error detection by a real-time image analysis method, the fiber yarn layout is adjusted based on the error detection result, the layout paths can be optimized in real time, the layout errors are reduced, the layout precision is improved, an error buffer area is set according to the error source identification of the fiber yarn layout paths and layout equipment, the error control can be better performed, the layout stability is improved, and the layout method has higher layout efficiency and can also ensure higher layout precision and stability.
2. According to the invention, through deep analysis of attribute parameters and preparation requirements of the fiber yarns, and through establishment of a three-dimensional point cloud model of the nanofiber and characteristic point analysis, the layout requirements and layout paths of the fiber yarns can be more accurately determined, so that the quality and performance of products are improved, the shortest idle stroke path is taken as an optimization target, the layout paths of the fiber yarns can be effectively optimized by using a layout planning algorithm and a pheromone updating strategy, the production cost is reduced, the production efficiency is improved, errors possibly occurring in the layout process can be effectively controlled by setting an error buffer zone, and the precision and stability of the products are further improved.
3. The invention uses the camera equipment to obtain the real-time layout image, can monitor the layout state of the fiber yarn in real time, improves the production efficiency, extracts the real-time path information of the fiber yarn layout through the image processing and analysis technology, compares with the optimal path, can accurately obtain the error value of the layout, improves the production precision, can automatically adjust the layout of the fiber yarn according to the error value, reduces the manual intervention, reduces the production cost, and if the error value of the layout is positioned in the error buffer zone, carries out the path adjustment on the layout of the fiber yarn, and if the error value of the layout is positioned outside the error buffer zone, ends the adjustment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method for automatic layout of filaments based on nanofiber preparation in accordance with an embodiment of the present invention;
fig. 2 is a schematic block diagram of an automatic fiber filament placement system based on nanofiber preparation in accordance with an embodiment of the present invention.
In the figure:
1. a data acquisition module; 2. a path and buffer area making module; 3. a fiber yarn layout module; 4. and the error detection and adjustment module.
Detailed Description
In order to make the technical solutions in the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, based on the embodiments herein, which would be apparent to one of ordinary skill in the art without undue burden are intended to be within the scope of the present application.
According to the embodiment of the invention, an automatic fiber silk layout method and system based on nanofiber preparation are provided.
The invention will now be further described with reference to the accompanying drawings and detailed description, as shown in fig. 1, according to one embodiment of the invention, there is provided an automatic fiber filament arrangement method based on nanofiber preparation, comprising the steps of:
s1, acquiring attribute parameters of fiber yarns and a layout substrate and preparing nanofiber requirements;
specifically, the fiber filaments are prepared into nanofibers by an electrospinning technology, the fiber filament attribute parameters can be measured by using a scanning electron microscope or a transmission electron microscope to measure the diameter and shape of the fibers, and the precise measurement of the fiber diameter can be obtained by analyzing a microscope image.
The property parameters of the layout substrate include its surface roughness, surface energy, stiffness, pore size, etc. These parameters may be obtained by various measurement techniques, such as atomic force microscopy, X-ray photoelectron spectroscopy, and the like.
The preparation requirements of the nanofiber include a preparation device of the nanofiber, a preparation shape of the nanofiber, or an environment in which the nanofiber is prepared.
S2, formulating a layout path and an error buffer zone of the fiber based on the acquired attribute parameters and the preparation requirements;
as a preferred embodiment, the making of the layout path and the error buffer of the filament based on the acquired attribute parameters and the preparation requirements comprises the steps of:
s21, analyzing the acquired attribute parameters and the preparation requirements, and determining the layout requirements of the fiber filaments, wherein the layout requirements comprise the arrangement mode, density, direction, shape and the like of the fiber filaments;
the obtained fiber yarn was analyzed for physical and chemical property parameters such as diameter, length, strength, elastic modulus, melting point, etc. of the fiber to understand the basic properties of the fiber yarn.
The requirements of the preparation are analyzed, including the expected properties of the preparation (e.g., strength, softness, tensile properties, heat resistance, etc.), the intended use (e.g., insulation, filtration, composite, etc.), and the constraints of the manufacturing process (e.g., cost, time, equipment capacity, etc.).
Based on the above analysis, the layout requirements of the filaments are determined, including the arrangement (e.g., parallel, intersecting, spiral, random, etc.), density (the number or mass distribution of filaments in a unit volume or area), direction (the predominant orientation or distribution of orientation of the filaments), and shape (e.g., straight, curved, wavy, etc.).
S22, setting a fiber layout path based on the determined fiber layout requirement and taking the shortest idle stroke path as an optimization target;
as a preferred embodiment, setting the fiber placement path based on the determined fiber placement requirement and with the shortest idle travel path as the optimization objective, comprises the steps of:
s221, establishing a three-dimensional point cloud model of the nanofiber according to the layout requirements of the fiber filaments;
it should be noted that, according to the layout requirement, the arrangement mode, density, direction, shape and other information of the fiber filaments in the space are analyzed, then an empty point cloud model is initialized in the three-dimensional space, the point cloud model is composed of a series of points in the three-dimensional space, each point represents a possible fiber filament position, and according to the layout requirement, a point cloud meeting the requirement is generated; for example, if the layout requirement is that the filaments need to be arranged in a certain specific direction and density, then corresponding point clouds can be generated in the model according to the requirement, the points in each point cloud are considered as part of one filament, and the points are connected according to the shape and direction of the filament, so as to generate the three-dimensional point cloud model of the nanofiber.
S222, analyzing a three-dimensional point cloud model of the nanofiber through a point cloud processing algorithm, finding out characteristic points of the three-dimensional point cloud model, taking the characteristic points as layout turning points, and building a graphic model based on the layout turning points;
as a preferred embodiment, analyzing the three-dimensional point cloud model of the nanofiber through a point cloud processing algorithm, finding out characteristic points of the three-dimensional point cloud model and taking the characteristic points as layout turning points, and building a graphic model based on the layout turning points comprises the following steps:
s2221, carrying out filtering treatment on the three-dimensional point cloud model of the nanofiber;
it should be noted that, the filtering process is mainly used for smoothing the point cloud data and improving the quality of the data. Common filtering methods include fast bilateral filtering, gaussian filtering, median filtering, and the like.
S2222, setting a preset radius by taking each data point in the preprocessed three-dimensional point cloud model as a center, and establishing a cube neighborhood;
it should be noted that, a preset radius value is set, this value will be used to determine the size of the cube neighborhood, traverse each data point in the three-dimensional point cloud model, take each data point as the center, establish a cube neighborhood according to the preset radius, the side length of the cube neighborhood is twice the preset radius, and the center point is located at the center of the cube.
S2223, fitting a projection curved surface based on a least square method, and projecting each point cloud data in the neighborhood of the cube to the projection curved surface to obtain a projection point set;
as a preferred embodiment, fitting a projection curved surface based on a least square method, and projecting each point cloud data in a cube neighborhood onto the projection curved surface to obtain a projection point set, including the following steps:
s22231, taking each point in the cube neighborhood as a neighborhood center;
it should be noted that, each point in the cube neighborhood is traversed so that each point will be regarded as the center of a separate neighborhood, and for each point, a neighborhood centered on the point is set, and the size (i.e., radius) of the neighborhood is usually preset.
S22232, for each neighborhood, fitting a projection curved surface of all points in the neighborhood of the cube by using a least square method;
it should be noted that, first, coordinate data of all points in the neighborhood are collected, these data are used as input of a least square method to construct a model of a projection curved surface, and this model is usually a mathematical function, such as a linear function, a quadratic function, etc., and the parameters of the model are optimized by using the least square method with actual point cloud data as a target, so that the curved surface corresponding to the model is as close to the actual point cloud data as possible, and in the optimization process, the sum of squares of distances from all points to the curved surface of the model is usually minimized.
S22233, taking the central point of the neighborhood center as an original point, taking two principal tangent vectors of the projection curved surface at the original point as an x axis and a y axis, and taking the function value of the projection curved surface as a z axis, and establishing a projection coordinate system;
specifically, the projection surface function value refers to a mathematical function describing the shape of the surface in three-dimensional space, and in general, the projection surface may be represented by a function, which may be explicit or implicit, and the coordinate value of the z-axis is determined by the value of the function of the projection surface when the projection coordinate system is established.
Firstly, calculating a gradient vector of a projection curved surface at an origin, wherein the direction of the gradient vector points to the direction of the function ascending most rapidly at the origin, and in a multi-element function, the gradient is a vector and consists of partial derivatives;
then, two principal tangent vectors of the projection surface at the origin are found, which are orthogonal bases on the tangent plane, and can be obtained by calculating orthogonal vectors of the gradient vectors.
S22234, for each point in the cube neighborhood, projecting the point to the fitted projection curved surface by a projective transformation method to obtain a projection point set.
In particular, projective transformation is a commonly used method of dimension reduction of data, which can project high-dimensional data into a low-dimensional space, thereby reducing the dimension of the data and preserving the main features of the data.
It should be noted that, first, a projection operation is defined, which projects a point from its original position onto the fitted projection curved surface, traverses each point in the cube neighborhood, applies the projection operation defined above to each point, and then collects all the projection points together to obtain a projection point set.
S2224, performing covariance calculation on the obtained projection point set to obtain a covariance matrix corresponding to each projection point, and calculating a characteristic value and a characteristic vector of the covariance matrix;
in particular, a covariance matrix is a square matrix representing covariance between variables in a dataset, where the covariance matrix can be used to describe correlations between different variables and variances of the variables themselves, and where the covariance matrix can analyze relationships between different variables in the projection dataset to reveal the inherent structure and characteristics of the data.
S2225, judging whether each projection point is a boundary feature point of the nanofiber according to the correlation of the feature vectors, and storing the projection points meeting the linear correlation as feature points in a feature point set;
specifically, for each projection point, feature extraction is performed by a principal component analysis method, then feature vectors are calculated, for each pair of feature vector correlation coefficient metrics, linear correlation between feature vectors is measured, whether the linear correlation is satisfied can be judged by a preset threshold according to the correlation between feature vectors, and if the correlation between feature vectors exceeds the preset threshold, the corresponding projection point can be regarded as a boundary feature point of the nanofiber.
S2226, taking the feature points in the feature point set as layout turning points, connecting the layout turning points end to form edges, and building a graphic model.
Specifically, feature points in the feature point set are used as layout turning points, the feature points generally represent boundary features of nanofibers and can be used as nodes of a graphic model, the feature points are connected according to a certain sequence, edges are formed by connecting the feature points, the edges can be used for describing the shape and the structure of the nanofibers, and then the connected feature points and the formed edges are combined by a graph theory method to establish the graphic model.
S223, taking the shortest idle travel path as an optimization target, planning the path of the graphic model through a layout planning algorithm, and optimizing the path planning result through updating pheromone and heuristic information so as to ensure that the idle travel path is shortest;
specifically, the main idea of the layout planning algorithm is based on an ant colony algorithm, which is a heuristic algorithm, the inspiration is derived from the behavior of ants when searching food and establishing paths, the basic principle of the ant colony algorithm is to simulate the behavior of ants when searching and selecting paths, the ants mark paths by releasing pheromones, and the next node is selected according to the pheromone concentration and heuristic information. Accumulation of pheromones on paths affects the selection tendency of ants, while heuristic information provides an assessment and guidance of paths.
As a preferred embodiment, taking the shortest idle travel path as an optimization target, planning a path of a graphic model through a layout planning algorithm, and optimizing a path planning result through updating pheromone and heuristic information so as to minimize the idle travel path, wherein the method comprises the following steps of:
s2231, calculating the distance between layout turning points according to the position of each layout turning point in the graphic model to obtain a distance matrix;
it should be noted that, the coordinate position of each layout turning point in the graphic model is obtained, then the position of each layout turning point is represented by the coordinate values in the coordinate system, then the Euclidean distance of the layout turning points is calculated to determine the distance between the layout turning points, the calculated distances between the layout turning points are combined into a distance matrix, the distance matrix is a symmetric matrix, and each element represents the distance between the corresponding layout turning points.
S2232, initializing parameters of a layout planning algorithm, including the quantity of fiber filaments, the volatilization rate of pheromones and the concentration of initial pheromones;
it should be noted that, the number of the fiber filaments can be determined according to the preparation requirement and the image model; the pheromone volatility determines the degree of decay of the pheromone concentration in each iteration, with higher volatility resulting in rapid vanishing of the pheromone and lower volatility resulting in longer pheromone accumulation on the path.
S2233, obtaining an initial layout path by using a greedy algorithm, and updating the pheromone concentration of the initial layout;
specifically, a starting point is selected from the layout turning points as an initial node, then, starting from the starting point, a next node is selected according to a rule of closest distance to the starting point, so that the total length of the current path is minimum, the selected node is added to the path, and the pheromone concentration of the current path is updated.
S2234, randomly placing fiber yarns on the edges of the graphic model, and selecting a next point as a next node of the path according to the pheromone concentration and heuristic information;
it should be noted that heuristic information may provide guidance and constraints on path selection, helping algorithms to search for a better path more quickly and efficiently. It can be used to evaluate the nodes for quality, indicate the direction of the path, avoid invalid searches, etc.
S2235, updating the concentration of the pheromone according to the length of the path and the volatilization rate of the pheromone after all the fiber filaments complete the complete path;
the calculation formula of the pheromone concentration is as follows:
；
in the method, in the process of the invention,f ij (t+1) is expressed in timetFiber yarn at +1iAnd fiber yarnjPheromone concentration on the path;
p represents the volatilization rate of the pheromone;
Δf ij representing fiber filamentsiAnd fiber yarnjPheromones on the path;
f ij (t) Expressed in timetTime fiber yarniAnd fiber yarnjPheromone concentration on the path;
s2236, repeating the steps S2234-S2235 until the maximum iteration number is reached, and selecting the path with the largest pheromone as the optimal path.
S224, taking the optimization result of the path planning as the layout path of the fiber filaments.
S23, setting an error buffer zone of the fiber yarn in the layout process based on the layout path of the fiber yarn.
As a preferred embodiment, setting an error buffer zone of the filament in the laying-out process based on the layout path of the filament comprises the steps of:
s231, performing error source identification based on attribute parameters of the fiber yarns, a graphic model and layout equipment;
specifically, analyzing attribute parameters of the fiber yarn, including material characteristics, diameter, strength, elastic modulus and the like of the fiber yarn;
analyzing the graphic model, including the accuracy, the size, the curvature and the like of the graphic model;
analyzing characteristics of the layout equipment, including a layout robot, a sensor, a control system, and the like;
and then observing and analyzing the layout process of the fiber yarn through experiments and tests, identifying possible error sources, analyzing and processing data through collected experimental data and observation results, identifying the possible error sources, and evaluating the influence of the possible error sources on layout accuracy.
S232, carrying out quantization analysis on each identified error source, and determining the maximum deviation value of the error source;
it should be noted that, first, the identified error sources are classified according to their properties and influencing factors, relevant data and information are collected for each error source, then, the collected data are quantitatively analyzed by a statistical analysis method for each error source, and according to the result of the quantitative analysis, the deviation value of each error source is calculated, where the deviation value represents the maximum deviation degree of the error source.
S233, determining the range of the error buffer zone according to the determined maximum deviation difference and the set fiber layout path.
It should be noted that, first, the set fiber filament layout path is analyzed, including curve characteristics of the path, positions of turning points, layout ranges, and the like, and then the range of the error buffer is set according to the determined maximum deviation difference and the requirements of the layout path.
Specifically, through carrying out deep analysis to the attribute parameter and the preparation demand of cellosilk to and through establishing the three-dimensional point cloud model of nanofiber and carrying out characteristic point analysis, can confirm the overall arrangement demand and the overall arrangement route of cellosilk more accurately, and then improve quality and the performance of goods, regard idle stroke route shortest as the optimization target, through using overall arrangement planning algorithm and pheromone to update the tactics, can optimize the overall arrangement route of cellosilk effectively, reduce manufacturing cost and improvement production efficiency, through setting up the error buffer, can control the error that probably appears in the overall arrangement in-process effectively, further improve the precision and the stability of goods.
S3, laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and the error buffer area;
specifically, laying out the filaments on the substrate by a filament placement device according to the established placement path and error buffer comprises the steps of:
determining a starting point of fiber yarn layout according to the layout path;
selecting a layout device meeting the requirements of a layout path and an error buffer;
the filaments are laid out stepwise on the substrate using a layout apparatus according to the layout path.
And S4, performing real-time error detection on the fiber layout by an image analysis method, and adjusting the fiber layout based on an error detection result.
As a preferred embodiment, the real-time error detection of the fiber layout by the image analysis method and the adjustment of the fiber layout based on the error detection result comprise the following steps:
s41, acquiring a real-time layout image of the fiber layout through the camera equipment;
s42, extracting real-time path information of the fiber yarn layout by using an image processing and analyzing technology according to the real-time layout image;
it should be noted that, according to the real-time layout image, extracting real-time path information of the fiber yarn layout by using the image processing and analyzing technique includes the following steps:
Preprocessing the real-time layout image, including denoising, contrast enhancement, graying and the like, so as to improve the image quality and reduce the complexity of subsequent processing;
dividing fiber filaments from the background by using an image segmentation technology to obtain a segmentation result;
extracting path information of the fiber by calculating the center line of the fiber according to the segmentation result;
s43, comparing the extracted real-time path information with an optimal path to obtain an error value of the layout;
as a preferred embodiment, comparing the extracted real-time path information with the optimal path to obtain an error value of the layout includes the steps of:
s431, aligning the extracted real-time path information with the starting point of the optimal path;
it should be noted that, determining the starting points of the real-time path and the optimal path, translating the extracted real-time path to make the starting points coincide with the starting points of the optimal path, and then, calculating the distance between the starting points of the real-time path and the optimal path, and translating the real-time path along the distance.
S432, respectively extracting features of the sub-paths corresponding to the real-time path information in the real-time path information and the optimal path, wherein the features comprise the length, curvature, direction and the like of the paths;
It should be noted that, the path length may be obtained by accumulating the lengths of each segment; the curvature can be obtained by calculating the tangential direction change rate of each point on the path; the direction may be obtained by calculating the main direction of the path or the direction of the end point relative to the start point.
S433, similarity calculation is carried out on the characteristics of the real-time path information and the characteristics of the sub-paths corresponding to the real-time path information in the optimal path;
it should be noted that, the features of the real-time path information and the sub-path features of the optimal path are respectively formed into a feature vector, and then the manhattan distance of each point in the real-time path information and the sub-path is calculated, the manhattan distance can be used for reflecting the similarity of the two paths, and the smaller the distance is, the higher the similarity is.
S434, determining an error value of the layout according to the similarity calculation result.
Specifically, the manhattan distance calculated as described above is used as the error value of the layout.
And S44, if the error value of the layout is positioned in the error buffer area, carrying out path adjustment on the layout of the fiber, and if the error value of the layout is positioned outside the error buffer area, ending the adjustment on the layout of the fiber and ending the layout of the fiber.
Specifically, the real-time layout image is obtained by using the camera equipment, so that the layout state of the fiber yarn can be monitored in real time, the production efficiency is improved, the real-time path information of the fiber yarn layout is extracted through the image processing and analysis technology, and then compared with the optimal path, the error value of the layout can be accurately obtained, the production precision is improved, the layout of the fiber yarn can be automatically adjusted according to the error value, the manual intervention is reduced, the production cost is reduced, if the error value of the layout is positioned in the error buffer zone, the path adjustment is performed on the layout of the fiber yarn, and if the error value of the layout is positioned outside the error buffer zone, the adjustment is finished.
As shown in fig. 2, according to another embodiment of the present invention, there is provided an automatic fiber filament arrangement system based on nanofiber preparation, comprising: the system comprises a data acquisition module 1, a path and buffer zone making module 2, a fiber yarn layout module 3 and an error detection and adjustment module 4;
the data acquisition module 1 is used for acquiring attribute parameters of the fiber yarns and the layout substrate and nanofiber preparation requirements;
The path and buffer area making module 2 is used for making a layout path and an error buffer area of the fiber yarn based on the acquired attribute parameters and the preparation requirements;
the fiber yarn layout module 3 is used for laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and the error buffer zone;
the error detection and adjustment module 4 is used for carrying out real-time error detection on the fiber filament layout through an image analysis method and adjusting the fiber filament layout based on an error detection result.
In summary, by means of the above technical scheme of the invention, the layout requirements of the fiber filaments are analyzed, and then the layout path planning is performed by taking the shortest idle path as an optimization target, so that the fiber filament layout is more efficient, the layout time is saved, the error detection is performed on the fiber filament layout by a real-time image analysis method, the layout of the fiber filaments is adjusted based on the error detection result, the layout path can be optimized in real time, the layout error is reduced, the layout precision is improved, the error buffer area is set according to the fiber filament layout path and the error source identification of the layout equipment, the error control can be better performed, the layout stability is improved, and the layout method has higher layout efficiency and can ensure higher layout precision and stability; according to the invention, through deep analysis of attribute parameters and preparation requirements of the fiber yarns, and through establishment of a three-dimensional point cloud model of the nanofiber and characteristic point analysis, the layout requirements and layout paths of the fiber yarns can be more accurately determined, so that the quality and performance of products are improved, the shortest idle stroke path is taken as an optimization target, the layout paths of the fiber yarns can be effectively optimized by using a layout planning algorithm and a pheromone updating strategy, the production cost is reduced, the production efficiency is improved, errors possibly occurring in the layout process can be effectively controlled by setting an error buffer zone, and the precision and stability of the products are further improved; the invention uses the camera equipment to obtain the real-time layout image, can monitor the layout state of the fiber yarn in real time, improves the production efficiency, extracts the real-time path information of the fiber yarn layout through the image processing and analysis technology, compares with the optimal path, can accurately obtain the error value of the layout, improves the production precision, can automatically adjust the layout of the fiber yarn according to the error value, reduces the manual intervention, reduces the production cost, and if the error value of the layout is positioned in the error buffer zone, carries out the path adjustment on the layout of the fiber yarn, and if the error value of the layout is positioned outside the error buffer zone, ends the adjustment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The automatic fiber yarn layout method based on nanofiber preparation is characterized by comprising the following steps of:
s1, acquiring attribute parameters of fiber yarns and a layout substrate and preparing nanofiber requirements;
S2, formulating a layout path and an error buffer zone of the fiber based on the acquired attribute parameters and the preparation requirements;
s3, laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and the error buffer area;
and S4, performing real-time error detection on the fiber layout by an image analysis method, and adjusting the fiber layout based on an error detection result.
2. The automatic fiber placement method according to claim 1, wherein the step of creating a fiber placement path and an error buffer based on the acquired attribute parameters and the preparation requirements comprises the steps of:
s21, analyzing the acquired attribute parameters and the preparation requirements, and determining the layout requirements of the fiber filaments, wherein the layout requirements comprise the arrangement mode, density, direction and shape of the fiber filaments;
s22, setting a fiber layout path based on the determined fiber layout requirement and taking the shortest idle stroke path as an optimization target;
s23, setting an error buffer zone of the fiber yarn in the layout process based on the layout path of the fiber yarn.
3. The automatic fiber placement method according to claim 2, wherein the setting of the fiber placement path based on the determined fiber placement requirement and with the shortest idle path as the optimization target comprises the steps of:
S221, establishing a three-dimensional point cloud model of the nanofiber according to the layout requirements of the fiber filaments;
s222, analyzing a three-dimensional point cloud model of the nanofiber through a point cloud processing algorithm, finding out characteristic points of the three-dimensional point cloud model, taking the characteristic points as layout turning points, and building a graphic model based on the layout turning points;
s223, taking the shortest idle travel path as an optimization target, planning the path of the graphic model through a layout planning algorithm, and optimizing the path planning result through updating pheromone and heuristic information so as to ensure that the idle travel path is shortest;
s224, taking the optimization result of the path planning as the layout path of the fiber filaments.
4. The automatic layout method of fiber yarn based on nanofiber preparation according to claim 3, wherein the three-dimensional point cloud model of nanofiber is analyzed by a point cloud processing algorithm, the characteristic points of the three-dimensional point cloud model are found out and used as layout turning points, and the graphic model is built based on the layout turning points, and the method comprises the following steps:
s2221, carrying out filtering treatment on the three-dimensional point cloud model of the nanofiber;
s2222, setting a preset radius by taking each data point in the preprocessed three-dimensional point cloud model as a center, and establishing a cube neighborhood;
S2223, fitting a projection curved surface based on a least square method, and projecting each point cloud data in the neighborhood of the cube to the projection curved surface to obtain a projection point set;
s2224, performing covariance calculation on the obtained projection point set to obtain a covariance matrix corresponding to each projection point, and calculating a characteristic value and a characteristic vector of the covariance matrix;
s2225, judging whether each projection point is a boundary feature point of the nanofiber according to the correlation of the feature vectors, and storing the projection points meeting the linear correlation as feature points in a feature point set;
s2226, taking the feature points in the feature point set as layout turning points, connecting the layout turning points end to form edges, and building a graphic model.
5. The automatic fiber yarn layout method based on nanofiber preparation according to claim 4, wherein the fitting of the projection curved surface based on the least square method and the projection of each point cloud data in the cube neighborhood onto the projection curved surface to obtain the projection point set comprises the following steps:
s22231, taking each point in the cube neighborhood as a neighborhood center;
s22232, for each neighborhood, fitting a projection curved surface of all points in the neighborhood of the cube by using a least square method;
S22233, taking the central point of the neighborhood center as an original point, taking two principal tangent vectors of the projection curved surface at the original point as an x axis and a y axis, and taking the function value of the projection curved surface as a z axis, and establishing a projection coordinate system;
s22234, for each point in the cube neighborhood, projecting the point to the fitted projection curved surface by a projective transformation method to obtain a projection point set.
6. The automatic layout method of fiber yarn based on nanofiber manufacturing according to claim 3, wherein the path planning of the graphic model by using the shortest idle path as an optimization target and through a layout planning algorithm, and optimizing the path planning result by updating pheromone and heuristic information so as to minimize the idle path comprises the following steps:
s2231, calculating the distance between layout turning points according to the position of each layout turning point in the graphic model to obtain a distance matrix;
s2232, initializing parameters of a layout planning algorithm, including the quantity of fiber filaments, the volatilization rate of pheromones and the concentration of initial pheromones;
s2233, obtaining an initial layout path by using a greedy algorithm, and updating the pheromone concentration of the initial layout;
s2234, randomly placing fiber yarns on the edges of the graphic model, and selecting a next point as a next node of the path according to the pheromone concentration and heuristic information;
S2235, updating the concentration of the pheromone according to the length of the path and the volatilization rate of the pheromone after all the fiber filaments complete the complete path;
the calculation formula of the pheromone concentration is as follows:；
in the method, in the process of the invention,f ij (t+1) is expressed in timetFiber yarn at +1iAnd fiber yarnjPheromone concentration on the path;
p represents the volatilization rate of the pheromone;
Δf ij representing fiber filamentsiAnd fiber yarnjPheromones on the path;
f ij (t) Expressed in timetTime fiber yarniAnd fiber yarnjPheromone concentration on the path;
s2236, repeating the steps S2234-S2235 until the maximum iteration number is reached, and selecting the path with the largest pheromone as the optimal path.
7. The automatic fiber placement method according to claim 4, wherein the setting of the error buffer area of the fiber during the placement process based on the placement path of the fiber comprises the following steps:
s231, performing error source identification based on attribute parameters of the fiber yarns, a graphic model and layout equipment;
s232, carrying out quantization analysis on each identified error source, and determining the maximum deviation value of the error source;
s233, determining the range of the error buffer zone according to the determined maximum deviation difference and the set fiber layout path.
8. The automatic fiber placement method according to claim 6, wherein the real-time error detection of fiber placement by image analysis and the adjustment of fiber placement based on the error detection result comprises the steps of:
s41, acquiring a real-time layout image of the fiber layout through the camera equipment;
s42, extracting real-time path information of the fiber yarn layout by using an image processing and analyzing technology according to the real-time layout image;
s43, comparing the extracted real-time path information with an optimal path to obtain an error value of the layout;
and S44, if the error value of the layout is positioned in the error buffer area, carrying out path adjustment on the layout of the fiber, and if the error value of the layout is positioned outside the error buffer area, ending the adjustment on the layout of the fiber and ending the layout of the fiber.
9. The automatic layout method of fiber yarn based on nanofiber manufacturing according to claim 8, wherein said comparing the extracted real-time path information with the optimal path to obtain the error value of the layout comprises the steps of:
s431, aligning the extracted real-time path information with the starting point of the optimal path;
S432, respectively extracting features of the sub-paths corresponding to the real-time path information in the real-time path information and the optimal path, wherein the features comprise the length, curvature and direction of the path;
s433, similarity calculation is carried out on the characteristics of the real-time path information and the characteristics of the sub-paths corresponding to the real-time path information in the optimal path;
s434, determining an error value of the layout according to the similarity calculation result.
10. An automatic fiber placement system based on nanofiber preparation for realizing the automatic fiber placement method based on nanofiber preparation according to any one of claims 1 to 9, characterized in that the automatic fiber placement system based on nanofiber preparation comprises: the system comprises a data acquisition module, a path and buffer zone making module, a fiber yarn layout module and an error detection and adjustment module;
the data acquisition module is used for acquiring attribute parameters of the fiber filaments and the layout substrate and nanofiber preparation requirements;
the path and buffer area making module is used for making a layout path and an error buffer area of the fiber yarn based on the acquired attribute parameters and the preparation requirements;
the fiber yarn layout module is used for laying out the fiber yarns on the substrate through fiber yarn layout equipment according to the formulated layout path and the error buffer area;
The error detection and adjustment module is used for carrying out real-time error detection on the fiber layout through an image analysis method and adjusting the fiber layout based on an error detection result.
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