CN118596372A - Positioning tool for cutting crystal bar and crystal bar cutting method - Google Patents
Positioning tool for cutting crystal bar and crystal bar cutting method Download PDFInfo
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Abstract
The invention belongs to the technical field of sapphire processing, and discloses a positioning tool for cutting a crystal bar and a crystal bar cutting method, comprising the following steps: a base; the fixing frame is arranged at one end of the upper surface of the base and used for supporting the crystal bars; the pushing structure is arranged at the other end of the upper surface of the base and used for pushing the crystal bar to advance; the clamping and positioning mechanism is in sliding connection with the base and is used for clamping and fixing two sides of a position to be cut of the crystal bar; the control system is used for controlling the positioning tool to carry out self-adaptive clamping and positioning on the crystal bar; the control system includes: the system comprises a first data acquisition module, a first data processing module, a second data acquisition module and an analysis control module. According to the invention, the cutting size of the crystal bar is predicted according to the characteristics of the crystal bar to be cut, so that the cutting precision and quality can be remarkably improved; through accurate prediction and confirm clamping position and clamping force, reduce the adjustment time in the cutting process, promote whole production efficiency.
Description
Technical Field
The invention belongs to the technical field of sapphire processing, and particularly relates to a positioning tool for cutting a crystal bar and a crystal bar cutting method.
Background
Sapphire is a single crystal material, the main component of which is alumina, and because of its excellent physical and chemical properties, sapphire has been widely used in the fields of optics, electronics, semiconductors, etc. Sapphire crystal bars are commonly used for manufacturing LED substrates, optical windows and high-performance electronic devices, and the high hardness, high heat conductivity and excellent corrosion resistance of the sapphire crystal bars enable the sapphire crystal bars to be ideal materials for high-precision cutting processing, and the sapphire crystal bars need to be stably clamped and positioned by a positioning tool in the cutting process so as to ensure the cutting precision.
The utility model discloses a sapphire crystal bar cutting frock as publication No. CN105538527A, and it compresses tightly the location through putting the crystal bar in the constant head tank, reuse clamp plate, makes the crystal bar fixed firm, and the crystal bar can not beat and lead to cutting back terminal surface unevenness, edge to burst limit phenomenon when cutting, has improved processingquality and efficiency.
And if the Chinese patent with the publication number of CN219686170U discloses a positioning tool for cutting the crystal bar, the mechanical butt clamp of the fixed clamp head and the movable clamp head in the clamp mechanism is utilized, so that the positioning tool has quick and efficient butt clamp fixing performance, can reduce the operation strength of workers, has a stable butt clamp function, and can push the mechanical butt clamp of the fixed clamp head and the movable clamp head to have a firm and stable positioning clamping function through the combination of a transmission screw rod and a pushing sliding table.
The above patent also has the following problems:
When the crystal bar is positioned and cut, the processing purposes of the crystal bar are different due to different characteristics of the crystal bar, and the cutting sizes are different, and the crystal bar is not positioned accurately by the cutting sizes, so that the stability of the crystal bar in the cutting process is insufficient, the crystal bar is easy to shake, and the cutting quality and the cutting efficiency are affected; and current location frock when carrying out the centre gripping location to the crystal bar, can not be according to the characteristic of crystal bar self, self-adaptation clamping force, leads to crystal bar surface damage or internal stress to increase because of excessive centre gripping easily to in the cutting process, can not the positioning effect of real-time supervision crystal bar, intelligent degree is low, needs to improve.
Disclosure of Invention
The invention aims to provide a positioning tool for cutting a crystal bar, which is used for solving the technical problem in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme: positioning tool for cutting crystal bars, comprising: a base; the fixing frame is arranged at one end of the upper surface of the base and used for supporting the crystal bars; the pushing structure is arranged at the other end of the upper surface of the base and used for pushing the crystal bar to advance; the clamping and positioning mechanism is arranged between the fixing frame and the pushing structure, is connected with the base in a sliding manner and is used for clamping and fixing two sides of a position to be cut of the crystal bar; the control system is used for controlling the positioning tool to carry out self-adaptive clamping and positioning on the crystal bar;
the control system includes:
the first data acquisition module is used for acquiring a historical training data set of the positioning tool, wherein the historical training data set comprises crystal bar comprehensive information and cutting sizes;
The first data processing module is used for training a machine learning model for predicting the cutting size based on the historical training data set, collecting real-time comprehensive information of the crystal bar, and inputting the comprehensive information into the trained machine learning model to predict the cutting size;
the second data processing module is used for collecting historical cutting parameters and training a machine learning model for predicting clamping force based on the historical training data set and the historical cutting parameters;
The second data acquisition module is used for acquiring comprehensive influence information of the positioning tool in real time, wherein the comprehensive influence information comprises clamping force, vibration influence coefficient and clamping distance;
And the analysis control module is used for generating a clamping stability coefficient according to the comprehensive influence information, analyzing according to the clamping stability coefficient and generating an optimization instruction.
Preferably, the clamping and positioning mechanism comprises: the bottom plate is arranged on the base in a sliding manner; the first clamping structure is arranged on one side of the upper surface of the bottom plate and used for clamping and fixing the crystal bar; the second clamping structure is the same as the first clamping structure and is symmetrically arranged on the other side of the upper surface of the bottom plate with the first clamping structure; the bidirectional screw rod is rotatably arranged on the bottom plate and used for driving the first clamping structure and the second clamping structure to move; the screw nut is arranged on the bidirectional screw and is respectively connected with the first clamping structure and the second clamping structure; the driving motor is arranged on one side of the bottom plate, and the output end of the driving motor is fixedly connected with one end of the bidirectional screw rod.
Preferably, the first clamping structure includes: the clamping seat is arranged on the bottom plate in a sliding manner, and a V-shaped groove is formed in the upper surface of the clamping seat; the lower pressing block is arranged above the clamping seat, and a V-shaped groove is formed in the lower surface of the lower pressing block; the gate type frame is fixedly arranged at the upper end of the clamping seat; the first electric push rod is fixedly arranged on the portal frame, the extension end is fixedly connected with the top end of the lower pressing block and used for adjusting the distance between the lower pressing block and the clamping seat.
Preferably, the training predicts the machine learning model of the cut size as follows:
converting the collected comprehensive information of the crystal bar into a corresponding group of characteristic vectors;
Taking each group of feature vectors as input of the machine learning model, taking a cutting size corresponding to each group of crystal bar comprehensive information as output, taking a cutting size actually corresponding to each group of crystal bar comprehensive information as a prediction target, and taking a minimum loss function value of the machine learning model as a training target; and stopping training when the loss function value of the machine learning model is smaller than or equal to a preset target loss value.
Preferably, the training method of the machine learning model for predicting the clamping force comprises the following steps:
Converting the historical training data set and the historical cutting parameters into a corresponding set of feature vectors, taking each set of feature vectors as input of the machine learning model, taking a set of clamping forces corresponding to each set of historical training data set and the historical cutting parameters as output, taking a set of clamping forces actually corresponding to each set of historical training data set and the historical cutting parameters as a prediction target, and taking a minimized machine learning model loss function value as a training target; and stopping training when the machine learning model loss function value is smaller than or equal to a preset target loss value.
Preferably, the vibration influence coefficient is generated as follows:
Parameters related to the vibration influence coefficient include: vibration frequency, vibration amplitude, and vibration duration;
The vibration duration represents the time from when vibration starts to when sampling ends;
wherein Zd denotes a vibration influence coefficient, zp denotes a vibration frequency, zf denotes a vibration amplitude, zs denotes a vibration duration, Are all the weight coefficients of the two-dimensional space model,Are all greater than 0.
Preferably, the clamping stability coefficient is generated as follows:
wherein Wd represents a clamping stability coefficient, jc represents a clamping force, zd represents a vibration influence coefficient, and Jl represents a clamping distance; Are all the weight coefficients of the two-dimensional space model, Are all greater than 0.
Preferably, the manner of analyzing and generating the optimization command according to the clamping stability coefficient is as follows:
presetting a clamping stability coefficient threshold Wd 1;
if Wd is less than or equal to Wd 1, the clamping stability coefficient is less than or equal to the clamping stability coefficient threshold value, and the stability of the clamped crystal bar is poor, and at the moment, an optimization instruction is generated;
If Wd > Wd 1, the clamping stability coefficient is larger than the clamping stability coefficient threshold, the clamped stability of the crystal bar is good, and at the moment, an optimization instruction is not generated.
Preferably, the optimizing instruction includes:
setting threshold values corresponding to the clamping force and the clamping distance, and respectively marking the threshold values as the maximum clamping force and the minimum clamping distance;
setting the priority of the clamping distance higher than the priority of the clamping force;
When the clamping distance and the clamping force do not reach the respective corresponding threshold values, firstly reducing the clamping distance until the clamping stability coefficient is greater than the clamping stability coefficient threshold value, and stopping reducing the clamping distance;
When the clamping distance is reduced to the minimum clamping distance, the clamping stability coefficient is still smaller than or equal to the clamping stability coefficient threshold value, and then the clamping force is increased until the clamping stability coefficient is larger than the clamping stability coefficient threshold value, and the clamping force stops increasing.
A crystal bar cutting method is applied to the positioning tool for crystal bar cutting, and comprises the following steps:
Step one: placing the v end of the crystal bar to be cut on a push plate, and placing the other end of the crystal bar on a fixing frame;
Step two: collecting a historical training data set of the positioning tool, wherein the historical training data set comprises crystal bar comprehensive information and cutting sizes;
Step three: training a machine learning model for predicting the cutting size based on the historical training data set, collecting real-time comprehensive information of the crystal bar, inputting the comprehensive information into the machine learning model for predicting the cutting size after training, and moving the clamping and positioning mechanism to the corresponding position based on the predicted cutting size;
Step four: collecting historical cutting parameters, training a machine learning model for predicting clamping force based on a historical training data set and the historical cutting parameters, and clamping and fixing the crystal bar by using a clamping and positioning mechanism based on the predicted clamping force;
Step five: cutting the clamped crystal bar at the corresponding cutting point by using a cutting knife;
Step six: in the cutting process, acquiring comprehensive influence information of the positioning tool in real time, wherein the comprehensive influence information comprises clamping force, vibration influence coefficient and clamping distance;
Step seven: and generating a clamping stability coefficient according to the comprehensive influence information, analyzing according to the clamping stability coefficient and generating an optimization instruction.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. According to the invention, the cutting size of the crystal bar is predicted according to the length, diameter, shape, crystal orientation and crystal quality grade of the crystal bar to be cut, so that the cutting precision and quality can be remarkably improved, and the material utilization rate is optimized; the clamping position and the clamping force are accurately predicted and determined, so that the adjustment time in the cutting process is shortened, and the overall production efficiency is improved; and suitable clamping force can avoid excessive clamping to cause surface damage or internal stress increase of the crystal bar, and suitable clamping force and stable cutting process can reduce equipment abrasion.
2. According to the invention, the stability of the crystal bar in the cutting process can be fed back in real time by monitoring the clamping stability coefficient in the cutting process of the crystal bar in real time, and the stability of the crystal bar in the cutting process can be optimized and adjusted according to the real-time monitoring result, so that the influence of vibration on the cutting precision can be obviously reduced, smooth and smooth cutting surface is ensured, the shutdown adjustment time caused by vibration or instability is reduced, the clamping force is optimized and adjusted in real time, the damage to the crystal bar caused by excessive clamping can be prevented, the clamping distance is optimized and adjusted in real time, the vibration in the cutting process can be reduced, the safety of an operation environment is improved, and the cutting quality is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a perspective view of the present invention;
FIG. 2 shows a schematic diagram of the clamping and positioning mechanism of the present invention;
FIG. 3 shows a block diagram of embodiment 2 of the present invention;
fig. 4 shows a block diagram of embodiment 3 of the present invention.
Reference numerals: 100. a base; 200. a fixing frame; 201. a second electric push rod; 300. a pushing structure; 301. a hydraulic cylinder; 302. a push plate; 400. a clamping and positioning mechanism; 401. a bottom plate; 402. a first clamping structure; 402a, clamping seat; 402b, pressing down; 402c, a portal frame; 402d, a first electrical push rod; 403. a second clamping structure; 404. a bidirectional screw rod; 405. a lead screw nut; 406. the motor is driven.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a positioning tool for cutting a crystal bar includes: the crystal bar clamping device comprises a base 100, a fixing frame 200, a pushing structure 300 and a clamping and positioning mechanism 400, wherein the base 100 is integrally arranged in a rectangular plate shape, and the fixing frame 200 is arranged at one end of the upper surface of the base 100 and used for supporting a crystal bar; the pushing structure 300 is arranged at the other end of the upper surface of the base 100, the pushing structure 300 comprises a hydraulic cylinder 301 and a pushing plate 302, the extending end of the hydraulic cylinder 301 is fixedly connected with the pushing plate 302, the pushing plate 302 is integrally L-shaped, one end of a crystal bar to be clamped is supported, and the pushing structure 300 is integrally used for pushing the crystal bar to advance; the clamping and positioning mechanism 400 is arranged between the fixing frame 200 and the pushing structure 300, is connected with the base 100 in a sliding manner, and is used for clamping and fixing two sides of a position to be cut of the crystal bar;
In this embodiment, one end of the crystal bar to be clamped can be placed on the push plate 302, the other end of the crystal bar is placed on the fixing frame 200, and then the clamping and fixing mechanism 400 is used to clamp and fix two sides of the position of the crystal bar to be cut, so that the stability of clamping the cut crystal bar can be ensured.
Specifically, referring to fig. 1 and 2, the clamping and positioning mechanism 400 includes: the base plate 401, the first clamping structure 402, the second clamping structure 403, the bidirectional screw 404, the screw nut 405 and the driving motor 406, wherein the base plate 401 is integrally arranged in a rectangular plate shape, a sliding block is arranged at the bottom end of the base plate 401, a sliding groove is formed in the upper surface of the base 100, the base plate 401 is arranged on the base 100 in a sliding manner through the sliding block and the sliding groove, a second electric push rod 201 is arranged between the base plate 401 and the fixing frame 200, and the extending end of the second electric push rod 201 is fixedly connected with the base plate 401; the first clamping structure 402 is arranged on one side of the upper surface of the bottom plate 401 and is used for clamping and fixing the crystal bar; the second clamping structure 403 is the same as the first clamping structure 402 and is symmetrically arranged on the other side of the upper surface of the bottom plate 401 with the first clamping structure 402; a bi-directional screw 404 rotatably mounted on the base plate 401 for driving the first clamping structure 402 and the second clamping structure 403 to move; a screw nut 405 disposed on the bi-directional screw 404 and connected to the first clamping structure 402 and the second clamping structure 403, respectively; the driving motor 406 is arranged on one side of the bottom plate 401, and the output end of the driving motor is fixedly connected with one end of the bidirectional screw 404;
In this embodiment, the second electric push rod 201 may be extended or contracted to drive the bottom plate 401 to slide, so that the whole clamping and positioning mechanism 400 is moved to the cutting position of the crystal bar, and the two sides of the cutting point of the crystal bar are respectively clamped and fixed by the first clamping structure 402 and the second clamping structure 403, and meanwhile, the bidirectional screw 404 and the screw nut 405 can drive the first clamping structure 402 and the second clamping structure 403 to move in opposite directions or back to back, so that the distance between the two clamping points can be adjusted, so that the clamping points are close to the cutting point, and the stability of clamping can be further increased.
Further, referring to fig. 2, the first clamping structure 402 includes: the clamping seat 402a, the lower pressing block 402b, the portal frame 402c and the first electric push rod 402d, wherein the clamping seat 402a is arranged on the bottom plate 401 in a sliding manner, and a V-shaped groove is formed in the upper surface of the sliding seat; a lower pressing block 402b arranged above the clamping seat 402a, and a V-shaped groove is formed in the lower surface of the lower pressing block; a door frame 402c fixedly arranged at the upper end of the clamping seat 402 a; the first electric push rod 402d is fixedly arranged on the door frame 402c, and an extending end of the first electric push rod is fixedly connected with the top end of the lower pressing block 402b and is used for adjusting the distance between the lower pressing block 402b and the clamping seat 402 a.
In this embodiment, can place the crystal bar of waiting to centre gripping inside the V-arrangement recess on grip slipper 402a, then utilize first electric push rod 402d and extend to promote down briquetting 402b and move down for two V-arrangement recesses mutually support, and it is fixed to carry out the centre gripping to the crystal bar, in order to increase the stability that the crystal bar was held, reduces the crystal bar vibrations, is equipped with the shock pad on V-arrangement inslot wall, can protect the crystal bar, further increases the stability of crystal bar, simultaneously, still is equipped with pressure sensor on the V-arrangement inslot wall, can the real-time supervision crystal bar by the pressure of centre gripping.
Working principle: when the crystal bar to be cut needs to be clamped, one end of the crystal bar to be clamped is placed on the push plate 302, the other end of the crystal bar to be clamped is placed on the fixing frame 200, then the clamping and positioning mechanism 400 is moved according to the cutting size, the second electric push rod 201 is used for extending or shrinking to drive the bottom plate 401 to slide, accordingly the whole clamping and positioning mechanism 400 is moved to the cutting position of the crystal bar, the first electric push rod 402d and the extending pushing lower pressing block 402b are used for downwards moving, two V-shaped grooves are matched with each other to clamp and fix the crystal bar, the first clamping structure 402 and the second clamping structure 403 are used for respectively clamping and fixing two sides of a cutting point of the crystal bar, meanwhile, the two-way screw 404 and the screw nut 405 can drive the first clamping structure 402 and the second clamping structure 403 to move towards each other or away from each other, the distance between the two clamping points can be adjusted, the clamping points are close to the cutting point, the stability of clamping can be further increased, the cutting stability can not be guaranteed, and the cutting precision is high.
Example 2
Referring to fig. 3, in order to accurately control the clamping position and clamping force of the crystal bar and ensure cutting stability, the invention further comprises a first data acquisition module, a first data processing module and a second data processing module on the basis of embodiment 1, wherein the modules are connected in a wired/wireless manner to realize data transmission;
the first data acquisition module is used for acquiring a historical training data set of the positioning tool, wherein the historical training data set comprises crystal bar comprehensive information and cutting sizes;
specifically, the comprehensive information of the crystal bar comprises the length, diameter, shape, crystal orientation and crystal quality grade of the crystal bar to be cut;
the length of the crystal bar can be obtained in real time through a laser range finder; the diameter of the crystal bar can be obtained by scanning a laser scanner in real time;
The shape of the crystal bar comprises a cylindrical crystal bar, a hexagonal cylindrical crystal bar, a square cylindrical crystal bar, a conical crystal bar, a polyhedral crystal bar and the like;
The crystal orientation of the crystal bar comprises a C surface, an R surface, an A surface, an M surface and an N surface, and the crystal orientation of different crystal bars can influence the physical and chemical properties of the crystal bar, thereby influencing the processing and the application of the crystal bar;
The crystal quality grades of the crystal bar comprise grade A, grade B, grade C, grade D and grade E, and grade A, B, C, D, E is reduced in sequence, and the requirements of different applications on the crystal quality are different, so that the standards of the quality grades are also different;
the shape of the crystal bar, the crystal orientation of the crystal bar and the crystal quality grade of the crystal bar can be input through a screen page interacted with the positioning tool.
The first data processing module is used for training a machine learning model for predicting the cutting size based on the historical training data set, collecting real-time comprehensive information of the crystal bar, and inputting the comprehensive information into the trained machine learning model to predict the cutting size;
The machine learning model for predicting cut size is trained as follows:
converting the collected comprehensive information of the crystal bar into a corresponding group of characteristic vectors;
Taking each group of feature vectors as input of a machine learning model, taking a cutting size corresponding to each group of crystal bar comprehensive information as output, taking a cutting size actually corresponding to each group of crystal bar comprehensive information as a prediction target, and taking a minimum machine learning model loss function value as a training target; stopping training when the loss function value of the machine learning model is smaller than or equal to a preset target loss value;
the machine learning model may be one of an SVM regression, a random forest regression, a neural network regression, or the like.
The loss function value of the machine learning model is mean square error;
the mean square error is one of the usual loss functions by combining the loss functions The model is trained for the purpose of minimization, so that the machine learning model is better fitted with data, and the performance and accuracy of the model are improved;
the MSE in the loss function is a machine learning model loss function value, and x is a feature vector group number; m is the number of feature vector groups; y x is the cut size of the x-th set of eigenvector predictions, The cutting size actually corresponding to the x-th group of feature vectors;
Other model parameters of the machine learning model, target loss values, optimization algorithms, verification set proportion of training set test sets, optimization of loss functions and the like are all realized through actual engineering, and the model parameters are obtained after experimental optimization is continuously carried out.
According to the predicted cutting size, the clamping and positioning mechanism 400 is moved to a corresponding position, and the crystal bar to be cut is clamped and positioned, so that the positioning accuracy can be ensured, and the cutting effect is better;
for example, if the cutting size is 3cm, the position extending from one end of the ingot to the other end of the ingot is marked as a cutting point, and at this time, the clamping and positioning mechanism 400 may be moved to positions on both sides of the cutting point, so as to clamp and fix the ingot, and ensure stable clamping.
The second data processing module is used for collecting historical cutting parameters and training a machine learning model for predicting clamping force based on a historical training data set and the historical cutting parameters, wherein the historical cutting parameters comprise frequency and amplitude of vibration generated during cutting, and cutting environment temperature and humidity;
The vibration frequency is obtained as follows: the vibration sensor is arranged at the corresponding position of the positioning tool and used for collecting signals of vibration of the clamped crystal bar, wherein the vibration sensor can be one of an acceleration sensor, a speed sensor and a displacement sensor, the collected vibration signals are converted into digital signals for storage and analysis, the collected vibration data are preprocessed, the operations including filtering, noise reduction and the like are carried out so as to remove interference and extract effective vibration signals, fourier transform (or fast Fourier transform, FFT) is used for converting the time domain vibration signals into frequency domain signals, and main frequency components are searched in a frequency spectrum, namely the main frequency components of the vibration signals are vibration frequencies;
the vibration amplitude can be represented by calculating the root mean square value of the vibration signal, as follows:
wherein Zf represents vibration amplitude, T represents vibration signal acquisition time, and x (T) represents a sampling value of a vibration signal;
High frequency and large amplitude vibration can adversely affect the stability of the ingot, requiring higher clamping forces to maintain the ingot stationary.
The cutting environment temperature can be obtained through a temperature sensor, and the temperature change can cause thermal expansion and cold contraction of the crystal bar and equipment, so that the clamping force can be possibly required to be adjusted;
cutting environment humidity can be obtained through humidity transducer, and high humidity environment probably influences crystal bar surface friction, needs higher clamping force to keep stable.
The training method of the machine learning model for predicting the clamping force comprises the following steps:
converting the historical training data set and the historical cutting parameters into a corresponding group of feature vectors, taking each group of feature vectors as input of a machine learning model, taking a group of clamping forces corresponding to each group of historical training data set and the historical cutting parameters as output, taking a group of clamping forces actually corresponding to each group of historical training data set and the historical cutting parameters as a prediction target, and taking a minimized machine learning model loss function value as a training target; and stopping training when the loss function value of the machine learning model is smaller than or equal to a preset target loss value, wherein the machine learning model can be one of models such as support vector machine regression, random forest regression or neural network regression.
The machine learning model loss function value is the mean square error.
The mean square error is one of the usual loss functions by combining the loss functionsThe model is trained for the purpose of minimization, so that the machine learning model is better fitted with data, and the performance and accuracy of the model are improved;
MSE 1 in the loss function is a machine learning model loss function value, and s is a feature vector group number; q is the number of feature vector groups; w S is the clamping force predicted by the s-th group of feature vectors, The clamping force actually corresponding to the s-th group of feature vectors is obtained.
Other model parameters of the machine learning model, target loss values, optimization algorithms, verification set proportion of training set test sets, optimization of loss functions and the like are all realized through actual engineering, and the model parameters are obtained after experimental optimization is continuously carried out.
According to the predicted clamping force, the crystal bar to be cut is effectively clamped, so that the stability of the crystal bar can be guaranteed when the crystal bar is cut, and the cutting effect can be guaranteed to be good.
In the embodiment, the cutting size of the crystal bar is predicted according to the length, the diameter, the shape, the crystal orientation and the crystal quality grade of the crystal bar to be cut, so that the cutting precision and the quality can be remarkably improved, the material utilization rate is optimized, the production efficiency is improved, and the personalized requirements can be better adapted; the position for clamping and positioning the crystal bar is determined according to the predicted cutting size, so that the crystal bar is always at the optimal position in the cutting process, the displacement and deviation are reduced, the accurate clamping position can ensure that the crystal bar is stable and motionless in the cutting process, and the cutting precision and consistency are improved;
the clamping force of the crystal bar is predicted based on the historical training data set and the historical cutting parameters, so that the surface damage or the increase of the internal stress of the crystal bar caused by excessive clamping can be avoided, and the wear of equipment can be reduced due to proper clamping force and stable cutting process; through accurate prediction and confirm clamping position and clamping force, reduce the adjustment time in the cutting process, promote whole production efficiency.
Example 2
Referring to fig. 4, in order to further optimize the stability of clamping and positioning the ingot during the cutting process, the present invention further includes, based on embodiment 1:
the second data acquisition module is used for acquiring comprehensive influence information of the positioning tool in real time, wherein the comprehensive influence information comprises clamping force, vibration influence coefficient and clamping distance;
the clamping force is applied when the clamping mechanism clamps the crystal bar and can be obtained in real time through the pressure sensor;
specifically, the vibration influence coefficient is generated as follows:
Parameters related to the vibration influence coefficient include: vibration frequency, vibration amplitude, and vibration duration;
The vibration duration represents the time from when vibration starts to when sampling ends;
wherein Zd denotes a vibration influence coefficient, zp denotes a vibration frequency, zf denotes a vibration amplitude, zs denotes a vibration duration, Are all the weight coefficients of the two-dimensional space model,Are all greater than 0.
The higher the vibration frequency, the larger the vibration amplitude and the longer the vibration duration, the larger the influence of the vibration on the clamping stability of the crystal bar, and the smaller the influence on the clamping stability of the crystal bar;
specifically, the clamping distance represents the distance between the clamping point and the cutting point of the clamping mechanism, and can be obtained in real time through the laser range finder.
And the analysis control module is used for generating a clamping stability coefficient according to the comprehensive influence information, analyzing according to the clamping stability coefficient and generating an optimization instruction.
Specifically, the generation mode of the clamping stability coefficient is as follows:
wherein Wd represents a clamping stability coefficient, jc represents a clamping force, zd represents a vibration influence coefficient, and Jl represents a clamping distance; Are all the weight coefficients of the two-dimensional space model, Are all greater than 0.
It should be noted that, the larger the clamping force Jc is, the larger the clamping stability coefficient Wd is, the higher the clamping stability is, the larger the vibration influence coefficient Zd is, and the larger the clamping distance Jl is, the smaller the clamping stability coefficient Wd is, and the clamping stability is relatively lower.
It should be noted that, the formulas related in the above description are all formulas with dimensions removed and numerical values calculated, and are a formula closest to the actual situation obtained by software simulation by collecting a large amount of data, and weight coefficients in the formulas and various preset thresholds in the analysis process are set by those skilled in the art according to the actual situation or are obtained by simulation of a large amount of data; the size of the weight coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the weight coefficient depends on the number of sample data and the corresponding processing coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
The manner of analyzing and generating the optimization command according to the clamping stability coefficient is as follows:
presetting a clamping stability coefficient threshold Wd 1;
if Wd is less than or equal to Wd 1, the clamping stability coefficient is less than or equal to the clamping stability coefficient threshold value, and the stability of the clamped crystal bar is poor, and at the moment, an optimization instruction is generated;
If Wd > Wd 1, the clamping stability coefficient is larger than the clamping stability coefficient threshold, the clamped stability of the crystal bar is good, and at the moment, an optimization instruction is not generated.
Specifically, the optimizing instruction includes:
setting threshold values corresponding to the clamping force and the clamping distance, and respectively marking the threshold values as the maximum clamping force and the minimum clamping distance.
The priority of setting up the centre gripping distance is higher than the priority of centre gripping dynamics.
When the clamping distance and the clamping force do not reach the respective corresponding threshold values, the clamping distance is reduced first until the clamping stability coefficient is larger than the clamping stability coefficient threshold value, and the reduction of the clamping distance is stopped.
When the clamping distance is reduced to the minimum clamping distance, the clamping stability coefficient is still smaller than or equal to the clamping stability coefficient threshold value, and then the clamping force is increased until the clamping stability coefficient is larger than the clamping stability coefficient threshold value, and the clamping force stops increasing.
Through the setting of the priority, damage to the crystal bar caused by overlarge clamping force can be avoided as much as possible, and stable clamping and positioning are realized.
In this embodiment, through the centre gripping stability factor of real-time supervision crystal bar cutting in-process, can real-time feedback crystal bar in the stable condition of cutting, and optimize and adjust according to real-time supervision's result, can show the influence of reduction vibration to cutting accuracy, ensure that the cutting face is smooth and smooth, the shut down adjustment time that has reduced because vibration or instability arouses, real-time optimization adjustment clamping force, can prevent too the clamping and lead to the crystal bar to damage, the crystal bar that has also avoided the clamping force not enough to lead to shifts and damage simultaneously, and real-time optimization adjustment clamping distance, vibration in the cutting process can be reduced, operating environment's security has been improved, equipment trouble and incident because of vibration lead to have been reduced, the material waste because of unstable cutting has been reduced, and material utilization rate has been improved.
Example 4
A crystal bar cutting method is applied to the positioning tool for crystal bar cutting, and comprises the following steps:
step one: one end of the crystal bar to be cut is placed on the push plate 302, and the other end is placed on the fixing frame 200;
Step two: collecting a historical training data set of the positioning tool, wherein the historical training data set comprises crystal bar comprehensive information and cutting sizes;
Step three: training a machine learning model for predicting the cutting size based on the historical training data set, collecting real-time comprehensive information of the crystal bar, inputting the comprehensive information into the machine learning model for predicting the cutting size after training, and moving the clamping and positioning mechanism 400 to a corresponding position based on the predicted cutting size;
Step four: collecting historical cutting parameters, training a machine learning model for predicting clamping force based on a historical training data set and the historical cutting parameters, and clamping and fixing the crystal bar by using a clamping and positioning mechanism 400 based on the predicted clamping force;
Step five: cutting the clamped crystal bar at the corresponding cutting point by using a cutting knife;
Step six: in the cutting process, acquiring comprehensive influence information of the positioning tool in real time, wherein the comprehensive influence information comprises clamping force, vibration influence coefficient and clamping distance;
Step seven: and generating a clamping stability coefficient according to the comprehensive influence information, analyzing according to the clamping stability coefficient and generating an optimization instruction.
In this embodiment, when cutting the crystal bar, carry out accurate centre gripping location to near the cutting point to adopt appropriate clamping force, stability when can increasing the cutting, and in cutting process, real-time supervision centre gripping stability factor can real-time feedback crystal bar in the stable condition of cutting, and optimize and adjust according to real-time supervision's result, can show the influence that reduces vibration to cutting accuracy, guarantee that the cutting effect is good.
The present invention is not limited to the above-mentioned embodiments, and any person skilled in the art, based on the technical solution of the present invention and the inventive concept thereof, can be replaced or changed within the scope of the present invention.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. Positioning tool for cutting crystal bars, which is characterized by comprising:
A base (100);
the fixing frame (200) is arranged at one end of the upper surface of the base (100) and is used for supporting the crystal bars;
The pushing structure (300) is arranged at the other end of the upper surface of the base (100) and is used for pushing the crystal bar to advance;
the clamping and positioning mechanism (400) is arranged between the fixing frame (200) and the pushing structure (300) and is in sliding connection with the base (100) and used for clamping and fixing two sides of a position to be cut of the crystal bar;
the control system is used for controlling the positioning tool to carry out self-adaptive clamping and positioning on the crystal bar;
the control system includes:
the first data acquisition module is used for acquiring a historical training data set of the positioning tool, wherein the historical training data set comprises crystal bar comprehensive information and cutting sizes;
The first data processing module is used for training a machine learning model for predicting the cutting size based on the historical training data set, collecting real-time comprehensive information of the crystal bar, and inputting the comprehensive information into the trained machine learning model to predict the cutting size;
the second data processing module is used for collecting historical cutting parameters and training a machine learning model for predicting clamping force based on the historical training data set and the historical cutting parameters;
The second data acquisition module is used for acquiring comprehensive influence information of the positioning tool in real time, wherein the comprehensive influence information comprises clamping force, vibration influence coefficient and clamping distance;
And the analysis control module is used for generating a clamping stability coefficient according to the comprehensive influence information, analyzing according to the clamping stability coefficient and generating an optimization instruction.
2. A positioning fixture for cutting a crystal bar according to claim 1, wherein the clamping and positioning mechanism (400) comprises:
A bottom plate (401) slidably disposed on the base (100);
The first clamping structure (402) is arranged on one side of the upper surface of the bottom plate (401) and is used for clamping and fixing the crystal bar;
the second clamping structure (403) is identical to the first clamping structure (402) and is symmetrically arranged on the other side of the upper surface of the bottom plate (401) with the first clamping structure (402);
A bidirectional screw rod (404) rotatably mounted on the base plate (401) for driving the first clamping structure (402) and the second clamping structure (403) to move;
a screw nut (405) disposed on the bi-directional screw (404) and connected to the first clamping structure (402) and the second clamping structure (403), respectively;
The driving motor (406) is arranged on one side of the bottom plate (401), and the output end of the driving motor is fixedly connected with one end of the bidirectional screw rod (404).
3. A positioning tool for cutting a crystal bar according to claim 2, wherein the first clamping structure (402) comprises:
the clamping seat (402 a) is arranged on the bottom plate (401) in a sliding manner, and a V-shaped groove is formed in the upper surface of the clamping seat;
the lower pressing block (402 b) is arranged above the clamping seat (402 a), and a V-shaped groove is formed in the lower surface of the lower pressing block;
a gate frame (402 c) fixedly arranged at the upper end of the clamping seat (402 a);
The first electric push rod (402 d) is fixedly arranged on the portal frame (402 c), and the extending end is fixedly connected with the top end of the lower pressing block (402 b) and used for adjusting the distance between the lower pressing block (402 b) and the clamping seat (402 a).
4. A positioning tool for cutting a crystal bar according to claim 1 or 3, wherein the machine learning model for predicting the cutting size is trained as follows:
converting the collected comprehensive information of the crystal bar into a corresponding group of characteristic vectors;
Taking each group of feature vectors as input of the machine learning model, taking a cutting size corresponding to each group of crystal bar comprehensive information as output, taking a cutting size actually corresponding to each group of crystal bar comprehensive information as a prediction target, and taking a minimum loss function value of the machine learning model as a training target; and stopping training when the loss function value of the machine learning model is smaller than or equal to a preset target loss value.
5. The positioning fixture for cutting a crystal bar according to claim 4, wherein the training method of the machine learning model for predicting the clamping force comprises:
Converting the historical training data set and the historical cutting parameters into a corresponding set of feature vectors, taking each set of feature vectors as input of the machine learning model, taking a set of clamping forces corresponding to each set of historical training data set and the historical cutting parameters as output, taking a set of clamping forces actually corresponding to each set of historical training data set and the historical cutting parameters as a prediction target, and taking a minimized machine learning model loss function value as a training target; and stopping training when the machine learning model loss function value is smaller than or equal to a preset target loss value.
6. The positioning tool for cutting a crystal bar according to claim 5, wherein the vibration influence coefficient is generated by the following method:
Parameters related to the vibration influence coefficient include: vibration frequency, vibration amplitude, and vibration duration;
The vibration duration represents the time from when vibration starts to when sampling ends;
wherein Zd denotes a vibration influence coefficient, zp denotes a vibration frequency, zf denotes a vibration amplitude, zs denotes a vibration duration, Are all the weight coefficients of the two-dimensional space model,Are all greater than 0.
7. The positioning tool for cutting a crystal bar according to claim 6, wherein the clamping stability coefficient is generated by the following method:
wherein Wd represents a clamping stability coefficient, jc represents a clamping force, zd represents a vibration influence coefficient, and Jl represents a clamping distance; Are all the weight coefficients of the two-dimensional space model, Are all greater than 0.
8. The positioning tool for cutting a crystal bar according to claim 7, wherein the manner of analyzing and generating the optimization command according to the clamping stability coefficient is as follows:
presetting a clamping stability coefficient threshold Wd 1;
if Wd is less than or equal to Wd 1, the clamping stability coefficient is less than or equal to the clamping stability coefficient threshold value, and the stability of the clamped crystal bar is poor, and at the moment, an optimization instruction is generated;
If Wd > Wd 1, the clamping stability coefficient is larger than the clamping stability coefficient threshold, the clamped stability of the crystal bar is good, and at the moment, an optimization instruction is not generated.
9. The positioning tool for cutting a crystal bar according to claim 8, wherein the optimizing instruction comprises:
setting threshold values corresponding to the clamping force and the clamping distance, and respectively marking the threshold values as the maximum clamping force and the minimum clamping distance;
setting the priority of the clamping distance higher than the priority of the clamping force;
When the clamping distance and the clamping force do not reach the respective corresponding threshold values, firstly reducing the clamping distance until the clamping stability coefficient is greater than the clamping stability coefficient threshold value, and stopping reducing the clamping distance;
When the clamping distance is reduced to the minimum clamping distance, the clamping stability coefficient is still smaller than or equal to the clamping stability coefficient threshold value, and then the clamping force is increased until the clamping stability coefficient is larger than the clamping stability coefficient threshold value, and the clamping force stops increasing.
10. A method for cutting a crystal bar, which is characterized by being applied to the positioning tool for cutting a crystal bar according to any one of claims 1 to 9, and comprising the following steps:
Step one: one end of the crystal bar to be cut is placed on a push plate (302), and the other end of the crystal bar to be cut is placed on a fixing frame (200);
Step two: collecting a historical training data set of the positioning tool, wherein the historical training data set comprises crystal bar comprehensive information and cutting sizes;
step three: training a machine learning model for predicting cutting size based on a historical training data set, collecting real-time comprehensive information of the crystal bar, inputting the comprehensive information into the machine learning model for predicting cutting size after training, and moving the clamping and positioning mechanism (400) to a corresponding position based on the predicted cutting size;
step four: collecting historical cutting parameters, training a machine learning model for predicting clamping force based on a historical training data set and the historical cutting parameters, and clamping and fixing the crystal bar by using a clamping and positioning mechanism (400) based on the predicted clamping force;
Step five: cutting the clamped crystal bar at the corresponding cutting point by using a cutting knife;
Step six: in the cutting process, acquiring comprehensive influence information of the positioning tool in real time, wherein the comprehensive influence information comprises clamping force, vibration influence coefficient and clamping distance;
Step seven: and generating a clamping stability coefficient according to the comprehensive influence information, analyzing according to the clamping stability coefficient and generating an optimization instruction.
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