CN111468989B - Five-axis linkage numerical control manipulator polishing control system and method - Google Patents
Five-axis linkage numerical control manipulator polishing control system and method Download PDFInfo
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- CN111468989B CN111468989B CN202010238238.3A CN202010238238A CN111468989B CN 111468989 B CN111468989 B CN 111468989B CN 202010238238 A CN202010238238 A CN 202010238238A CN 111468989 B CN111468989 B CN 111468989B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B1/00—Processes of grinding or polishing; Use of auxiliary equipment in connection with such processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B29/00—Machines or devices for polishing surfaces on work by means of tools made of soft or flexible material with or without the application of solid or liquid polishing agents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B41/00—Component parts such as frames, beds, carriages, headstocks
- B24B41/04—Headstocks; Working-spindles; Features relating thereto
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B51/00—Arrangements for automatic control of a series of individual steps in grinding a workpiece
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
- B25J11/005—Manipulators for mechanical processing tasks
- B25J11/0065—Polishing or grinding
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Abstract
The invention belongs to the technical field of machining, and discloses a five-axis linkage numerical control manipulator polishing control system and a method, which comprises a parameter configuration module, a monitoring module, a data acquisition module, a storage module, an angle adjusting module, a central control module, a polishing force pneumatic loading module, a polishing parameter optimization module and a quality detection module; the parameter configuration module comprises: the device comprises a numerical value input unit, a mode setting unit, a mode selection unit and an instruction generation unit. The monitoring module monitors the polishing process in real time through the camera; the data acquisition module detects and acquires various data parameters in the polishing process through a plurality of sensors with different functions. The invention can realize accurate and fast tracking variable polishing force control in the polishing process, control corresponding polishing force in real time and effectively meet the force control requirement of polishing processing; on the premise of ensuring the polishing quality, the invention improves the working efficiency.
Description
Technical Field
The invention belongs to the technical field of machining, and particularly relates to a five-axis linkage numerical control manipulator polishing control system and method.
Background
At present, due to the fact that people have increasingly strict requirements on the surface quality of products in recent years, polishing is more and more widely applied to surface processing, and taking mold processing as an example, the working hours of polishing account for 30% -40% of the whole mold manufacturing cycle. The polishing process adopts a softer tool for processing, the cutting amount is relatively small, the material removal rate is low, so the polishing process is usually used as a supplementary process after grinding in the actual production, and is widely applied to plastic molds with higher appearance requirements and surface treatment before anodes of certain pan alloy products as a processing method for improving the surface smoothness, and the current surface finish polishing process can improve the coarse sugar degree to more than 0.025 mu m.
Because machine polishing is different from manual polishing, manual polishing has intelligent regulation and an autonomous judgment function, a mechanical arm can only operate according to a set program, and the normal polishing force needs to be correspondingly changed according to the curvature change of different polishing points on an optical curved surface, so that the polishing contact pressure can be kept constant, and the constant material removal rate is obtained to ensure the surface quality of a workpiece. Therefore, the automatic polishing machine needs to control the pose of the polishing tool and the polishing force at the same time, and the polishing force needs to change in size and direction according to the curvature and the normal vector change of the polishing surface. At present, the research and development of a polishing processing control system are mostly focused on polishing residence time control and constant-force polishing control, the research on the variable polishing force control along with a processing track is less, the variable polishing force control which can be accurately and quickly tracked cannot be realized, and the force control requirement of polishing processing cannot be met. In the conventional polishing process, the polishing parameters are usually set to be constant values according to the surface of the workpiece, but if the surface of the workpiece is not uniform, the constant polishing parameters cause under-polishing phenomenon on the area with large material removal amount, so that the polishing efficiency is reduced, and the quality of the processed surface is influenced.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the research and development of the existing polishing control system mostly focuses on polishing residence time control and constant-force polishing control, and the variable polishing force control which can accurately and quickly track can not be realized, and the force control requirement of polishing processing can not be met.
(2) For uneven workpieces, the constant polishing parameters can cause under-polishing phenomena for areas with large material removal amount, so that the polishing efficiency is reduced, and the quality of the processed surface is influenced.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a five-axis linkage numerical control manipulator polishing control system and a five-axis linkage numerical control manipulator polishing control method.
The invention is realized in such a way that a polishing control method of a five-axis linkage numerical control manipulator comprises the following steps:
firstly, setting operation parameters of a system through input equipment in a parameter configuration module, and carrying out five-axis linkage numerical control mechanical arm polishing operation;
step two, in the polishing operation process of the five-axis linkage numerical control manipulator, the monitoring module monitors the polishing process in real time through a camera; the data acquisition module detects and acquires various data parameters in the polishing process through a plurality of sensors with different functions and transmits the acquired data to the central control module for processing;
step three, according to the acquired data information, the central control module controls the angle adjusting module to adjust the three-dimensional angle of the polishing head through the driving motor; controlling a polishing force loading module to monitor the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in real time when the control is not carried out, and controlling the polishing force to track to be close to a set value;
step four, a user in the quality detection module collects images of the surface of the polished workpiece, and the qualified rate of the polished workpiece is judged by identifying image parameters; the polishing parameter optimization module optimizes the set process parameters according to the qualification rate in the quality detection module to obtain the optimal parameters in the uneven workpiece surface processing process; the storage module stores preset parameters, collected images and detection information through a storage.
Further, in the first step, the method for performing feature extraction and identification on the polishing process image collected by the monitoring module includes:
adjusting the image gray level in the polishing process, and adjusting the specific illumination of the image; calculating the gradient of each pixel by using an image pixel method; dividing the polishing process image into a plurality of small areas according to the calculated pixel gradient;
calculating gradient histograms of a plurality of small regions, and performing statistics;
and combining the small regions according to the feature data required to be extracted, so as to obtain the corresponding image feature description.
Further, in the second step, the method for performing data fusion on each data parameter acquired by the data acquisition module in the polishing process by using the sensors with different functions comprises the following steps:
establishing corresponding data samples for data acquired by a plurality of sensors, extracting corresponding characteristic numerical values according to each type of sensor, and establishing corresponding characteristic vectors;
identifying the established characteristic vectors through a self-adaptive neural network, and performing labeling interpretation;
establishing the relevance between the feature vectors through a relevance algorithm according to the feature vectors marked and explained;
and synthesizing the data detected by each sensor by using a fusion algorithm according to the label interpretation and the relevance of the feature vector.
Further, in the third step, the adjusting method adopted by the angle adjusting module includes:
judging X, Y, Z axes of the polishing machine tool according to a right-hand rectangular Cartesian coordinate system, judging the positive direction of B, C rotation coordinate axes according to a right-hand spiral rule, determining a Z axis, and then determining an X axis and a Y axis;
receiving a control parameter generated by a parameter configuration module, and setting a three-dimensional coordinate system of a target position according to the control parameter;
and converting the motion track data of the tool in the workpiece coordinate into a value in a machine tool coordinate system, and moving the coordinate position of the tool to a set target position.
Further, in the third step, the following steps are specifically adopted to control the polishing force in the polishing force loading module to track to be close to the set value:
1) the measured value of the force is measured by a force sensor arranged between the cylinder and the polishing tool and is fed back to a PLC controller, and a sliding average filtering PID algorithm is realized in the PLC;
2) calculating the duty ratio of a PWM signal according to a set value and an actual measurement value, and controlling the gas flow reaching the cylinder through the high-speed switch valve to enable the output force of the cylinder piston to approach a force set value;
3) when the polishing position changes, the polishing force can quickly respond and track to a force set value according to the requirement changing along with the track, and the force fluctuation in the loading process is within an error allowable range.
Further, in the fourth step, the optimization method of the polishing parameter optimization module specifically adopts the following steps:
step A, inputting ideal material removal amount and surface roughness;
b, the controller searches for the optimal polishing parameters corresponding to the removal of the required material and the improvement of the surface roughness by adopting a genetic algorithm;
step C, in step B, the genetic algorithm repeatedly modifies the population of the single solution, randomly selects individuals from the current population, and generates next generation of individuals by taking the individuals as parents;
step D, feeding back the predicted polishing performance parameters to a genetic algorithm through a neural network;
step E, according to the expected adaptability value, the genetic algorithm uses the expected and expected polishing performance to evaluate an objective function;
and F, outputting the optimal polishing parameters if the expected adaptability value is reached, and otherwise, turning to the step C.
Further, before optimization, the polishing parameter optimization module establishes a polishing process model through artificial intelligence of a neural network, and the polishing process model is established by the following specific steps:
carrying out a plurality of groups of polishing experiments under different polishing parameters, and calculating polishing performance parameters; carrying out normalization processing on the polishing parameters and the polishing effect, and carrying out effective training and optimization;
training the neural network by using the experimental results, simulating to obtain a polishing process, and testing a prediction result by using the untrained experimental results;
if the error between the predicted and actual measurements of the neural network meets the required modeling criteria, the neural network is saved as a model of the polishing process.
Further, in the fourth step, the method for classifying the preset parameters, the collected images and the detection information by the storage module includes:
extracting corresponding data characteristics and setting corresponding classification standards according to corresponding preset parameters, acquired images and detection information, and constructing corresponding classification models;
and carrying out Bayesian network inference according to the classification model, calculating the conditional probability of the class nodes, and classifying the classification data.
Another object of the present invention is to provide a five-axis linkage numerical control manipulator polishing control system for implementing the five-axis linkage numerical control manipulator polishing control method, the five-axis linkage numerical control manipulator polishing control system including:
the parameter configuration module is connected with the central control module and is used for setting the operation parameters of the system through input equipment;
the monitoring module is connected with the central control module and is used for monitoring the polishing process in real time through the camera; the process of carrying out feature extraction and identification on the polishing process image collected by the monitoring module comprises the following steps: adjusting the image gray level in the polishing process, and adjusting the specific illumination of the image; calculating the gradient of each pixel by using an image pixel method; dividing the polishing process image into a plurality of small areas according to the calculated pixel gradient; calculating gradient histograms of a plurality of small regions, and performing statistics; combining a plurality of small areas according to the set feature data needing to be extracted to obtain corresponding image feature description;
the data acquisition module is connected with the central control module and is used for detecting and acquiring various data parameters in the polishing process through a plurality of sensors with different functions and transmitting the acquired data to the central control module for processing; the data fusion process of the data acquisition module on various data parameters acquired by a plurality of sensors with different functions in the polishing process is as follows: establishing corresponding data samples for data acquired by a plurality of sensors, extracting corresponding characteristic numerical values according to each type of sensor, and establishing corresponding characteristic vectors; identifying the established characteristic vectors through a self-adaptive neural network, and performing labeling interpretation; establishing the relevance between the feature vectors through a relevance algorithm according to the feature vectors marked and explained; synthesizing data detected by each sensor by using a fusion algorithm according to the label interpretation and the relevance of the feature vector;
the storage module is connected with the central control module and used for storing preset parameters, acquired images and detection information through the storage; the process of classifying the preset parameters, the collected images and the detection information by the storage module is as follows: extracting corresponding data characteristics and setting corresponding classification standards according to corresponding preset parameters, acquired images and detection information, and constructing corresponding classification models; carrying out Bayesian network inference according to the classification model, calculating the conditional probability of class nodes, and classifying the classification data;
the angle adjusting module is connected with the central control module and is used for adjusting the three-dimensional angle of the polishing head through the driving motor;
the central control module is connected with the parameter configuration module, the monitoring module, the data acquisition module, the storage module, the angle adjustment module, the polishing force pneumatic loading module, the polishing parameter optimization module and the quality detection module, controls each module through incremental PID, adds a sliding average filtering algorithm in the incremental PID control process and filters real-time process variables adjusted by PID;
the polishing force loading module is connected with the central control module and is used for monitoring the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in an uncontrolled manner in real time and controlling the polishing force to track to be close to a set value;
the polishing parameter optimization module is connected with the central control module and used for optimizing the set process parameters to obtain the optimal parameters in the uneven workpiece surface processing process; inputting ideal material removal amount and surface roughness; the controller employs a genetic algorithm to find optimal polishing parameters corresponding to the desired material removal and surface roughness improvement; repeatedly modifying the population of a single solution by a genetic algorithm, randomly selecting individuals from the current population, and generating next generation individuals by taking the individuals as parents; feeding back the predicted polishing performance parameters to a genetic algorithm through a neural network; the genetic algorithm uses the expected and desired polishing performance to evaluate an objective function based on the desired fitness value; outputting an optimal polishing parameter if a desired fitness value is reached;
and the quality detection module is connected with the central control module, and a user acquires images of the surface of the polished workpiece and judges the qualified rate of the polished workpiece by identifying image parameters.
Further, the parameter configuration module comprises:
the numerical value input unit is used for inputting and adjusting set numerical values through external input equipment;
the mode setting unit is used for dividing a plurality of groups of different setting values into different working modes;
the mode selection unit is used for displaying a plurality of different working modes through an interactive interface and selecting the working mode corresponding to the set parameter according to the required parameter;
the instruction generating unit is used for generating a mode setting signal corresponding to the production program instruction and transmitting the setting signal to the central control module.
By combining all the technical schemes, the invention has the advantages and positive effects that:
(1) the parameter configuration module of the invention sets the operation parameters of the system through the input device; the monitoring module monitors the polishing process in real time through the camera; the data acquisition module detects and acquires various data parameters in the polishing process through a plurality of sensors with different functions and transmits the acquired data to the central control module for processing; the storage module stores preset parameters, collected images and detection information through a memory; the angle adjusting module adjusts the three-dimensional angle of the polishing head through the driving motor; the polishing force loading module monitors the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in real time when the control is not carried out, and controls the polishing force to track to be close to a set value; and the polishing parameter optimization module optimizes the set process parameters to obtain the optimal parameters in the uneven workpiece surface machining process. And in the quality detection module, a user acquires an image of the surface of the polished workpiece, and the qualification rate of the polished workpiece is judged by identifying image parameters. The invention can realize accurate and fast tracking variable polishing force control in the polishing process, control corresponding polishing force in real time and effectively meet the force control requirement of polishing processing; on the premise of ensuring the polishing quality, the invention improves the working efficiency.
(2) The method for extracting and identifying the characteristics of the polishing process image acquired by the monitoring module can effectively acquire the quality of the polished surface and find the quality problem in time.
(3) The data acquisition module can effectively fuse the acquired data and is beneficial to finishing the information processing of the required decision and evaluation tasks.
(4) The angle adjusting module adopts an adjusting method, and can adjust the polishing angle of the manipulator in time according to the acquired data.
(5) When the force set value is changed, the pneumatic polishing force loading system can quickly respond to enable the polishing force to track to the set value, so that the control of the variable polishing force along with the motion trail is realized, the dynamic performance of the pneumatic polishing force loading system can be effectively improved by adopting the PID control of the sliding average filter, the phenomenon that the polishing force is reduced along with the motion due to the influence of external factors in the polishing process is eliminated, and the polishing force is kept near the set value; in the force loading process, the maximum deviation is within 5 percent, the error fluctuation range is within +/-1N, the fluctuation range is small, and the polishing device has enough stability and accuracy and meets the force control requirement of polishing processing. A polishing algorithm suitable for processing uneven surfaces is provided based on a Neural Network (NNW) and a Genetic Algorithm (GA), and the ideal removal amount and the surface roughness improvement value of a processed workpiece are used as objective functions to optimize polishing parameters, so that the polishing efficiency is improved, and the surface quality of the processed workpiece is ensured.
Drawings
Fig. 1 is a schematic structural view of a five-axis linkage numerical control manipulator polishing control system provided by an embodiment of the invention.
Fig. 2 is a schematic structural diagram of a parameter configuration module according to an embodiment of the present invention.
Fig. 3 is a flowchart of a polishing control method of a five-axis linkage numerically controlled manipulator according to an embodiment of the present invention.
Figure 4 is a flowchart of a method for controlling polishing force tracking to a value near a set point in a polishing force loading module according to an embodiment of the present invention.
FIG. 5 is a flowchart of a polishing parameter optimization module optimization method according to an embodiment of the present invention.
In the figure: 1. a parameter configuration module; 2. a monitoring module; 3. a data acquisition module; 4. a storage module; 5. an angle adjustment module; 6. a central control module; 7. a polishing force loading module; 8. a polishing parameter optimization module; 9. a quality detection module; 11. a numerical value input unit; 12. a mode setting unit; 13. a mode selection unit; 14. an instruction generating unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a five-axis linkage numerical control manipulator polishing control system and a five-axis linkage numerical control manipulator polishing control method, and the invention is described in detail below with reference to the attached drawings.
As shown in fig. 1, a five-axis linkage numerical control manipulator polishing control system provided in an embodiment of the present invention includes:
and the parameter configuration module 1 is connected with the central control module and is used for setting the operation parameters of the system through input equipment.
And the monitoring module 2 is connected with the central control module and is used for monitoring the polishing process in real time through the camera.
And the data acquisition module 3 is connected with the central control module and used for detecting and acquiring various data parameters in the polishing process through a plurality of sensors with different functions and transmitting the acquired data to the central control module for processing.
And the storage module 4 is connected with the central control module and is used for storing preset parameters, acquired images and detection information through a memory.
And the angle adjusting module 5 is connected with the central control module and is used for adjusting the three-dimensional angle of the polishing head through the driving motor.
And the central control module 6 is connected with the parameter configuration module, the monitoring module, the data acquisition module, the storage module, the angle adjustment module, the polishing force pneumatic loading module, the polishing parameter optimization module and the quality detection module, controls the modules through incremental PID, adds a sliding average filtering algorithm in the incremental PID control process and filters real-time process variables adjusted by the PID.
And the polishing force loading module 7 is connected with the central control module and is used for monitoring the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in an uncontrolled manner in real time and controlling the polishing force to track to be close to a set value.
And the polishing parameter optimization module 8 is connected with the central control module and is used for optimizing the set process parameters to obtain the optimal parameters in the uneven workpiece surface machining process.
And the quality detection module 9 is connected with the central control module, and a user acquires images of the surface of the polished workpiece and judges the qualified rate of the polished workpiece by identifying image parameters.
As shown in fig. 2, a parameter configuration module 1 provided in an embodiment of the present invention includes:
and the numerical value input unit 11 is used for performing input adjustment on the set numerical value through an external input device.
A mode setting unit 12, configured to divide a plurality of different sets of setting values into different operating modes.
And the mode selection unit 13 is used for displaying a plurality of different working modes through an interactive interface, and selecting the working mode corresponding to the set parameter according to the required parameter.
The instruction generating unit 14 generates a mode setting signal corresponding to the production program instruction, and transmits the setting signal to the central control module.
As shown in fig. 3, a method for controlling polishing of a five-axis linkage numerically controlled manipulator according to an embodiment of the present invention includes:
s101: and setting the operation parameters of the system through input equipment in the parameter configuration module, and performing five-axis linkage numerical control mechanical arm polishing operation.
S102: in the polishing operation process of the five-axis linkage numerical control manipulator, the monitoring module monitors the polishing process in real time through a camera; the data acquisition module detects and acquires various data parameters in the polishing process through a plurality of sensors with different functions and transmits the acquired data to the central control module for processing.
S103: according to the acquired data information, the central control module controls the angle adjusting module to adjust the three-dimensional angle of the polishing head through the driving motor; and controlling a polishing force loading module to monitor the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in real time when the control is not carried out, and controlling the polishing force to track to be close to a set value.
S104: a user in the quality detection module acquires an image of the surface of the polished workpiece and judges the qualified rate of the polished workpiece by identifying image parameters; the polishing parameter optimization module optimizes the set process parameters according to the qualification rate in the quality detection module to obtain the optimal parameters in the uneven workpiece surface processing process; the storage module stores preset parameters, collected images and detection information through a storage.
In S101 provided by the embodiment of the present invention, a method for performing feature extraction and identification on an image of a polishing process acquired by a monitoring module 2 includes:
adjusting the image gray level in the polishing process, and adjusting the specific illumination of the image; calculating the gradient of each pixel by using an image pixel method; dividing the polishing process image into a plurality of small areas according to the calculated pixel gradient;
calculating gradient histograms of a plurality of small regions, and performing statistics;
and combining the small regions according to the feature data required to be extracted, so as to obtain the corresponding image feature description.
In S102 provided in the embodiment of the present invention, a method for performing data fusion on data parameters acquired by a plurality of sensors with different functions in a polishing process by a data acquisition module 3 includes:
establishing corresponding data samples for data acquired by a plurality of sensors, extracting corresponding characteristic numerical values according to each type of sensor, and establishing corresponding characteristic vectors;
identifying the established characteristic vectors through a self-adaptive neural network, and performing labeling interpretation;
establishing the relevance between the feature vectors through a relevance algorithm according to the feature vectors marked and explained;
and synthesizing the data detected by each sensor by using a fusion algorithm according to the label interpretation and the relevance of the feature vector.
In S103 provided by the embodiment of the present invention, the adjusting method adopted by the angle adjusting module 5 includes:
the X, Y, Z axes of the polishing machine tool are judged according to a right-hand rectangular Cartesian coordinate system, the positive direction of the B, C rotation coordinate axes is judged according to a right-hand spiral rule, the Z axis is determined, and then the X axis and the Y axis are determined.
And receiving the control parameters generated by the parameter configuration module, and setting a three-dimensional coordinate system of the target position according to the control parameters.
And converting the motion track data of the tool in the workpiece coordinate into a value in a machine tool coordinate system, and moving the coordinate position of the tool to a set target position.
In S103 provided by the embodiment of the present invention, the following steps are specifically adopted to control the polishing force tracking in the polishing force loading module 7 to be close to the set value:
s201: the measured force value is measured by a force sensor arranged between the cylinder and the polishing tool and fed back to the PLC controller, and a moving average filtering PID algorithm is realized in the PLC.
S202: and calculating the duty ratio of the PWM signal according to the set value and the measured value, and controlling the gas flow reaching the cylinder through the high-speed switch valve to enable the output force of the cylinder piston to approach the force set value.
S203: when the polishing position changes, the polishing force can quickly respond and track to a force set value according to the requirement changing along with the track, and the force fluctuation in the loading process is within an error allowable range.
In S104 provided in the embodiment of the present invention, the optimization method of the polishing parameter optimization module 8 specifically includes the following steps:
s301: the desired material removal and surface roughness are input.
S302: the controller employs a genetic algorithm to find the optimal polishing parameters corresponding to the desired material removal and surface roughness improvement.
S303: in step S202, the genetic algorithm iteratively modifies the population of individual solutions and randomly selects individuals from the current population and generates next generation individuals as parents.
S304: and feeding back the predicted polishing performance parameters to the genetic algorithm through a neural network.
S305: the genetic algorithm uses the expected and desired polishing performance to evaluate an objective function based on the desired fitness value.
S306: if the desired fitness value is reached, the optimal polishing parameters are output, otherwise the process goes to step S203.
In S104 provided in the embodiment of the present invention, before the polishing parameter optimization module 8 performs optimization, a polishing process model is first established through artificial intelligence of a neural network, and the specific steps of establishing the polishing process model are as follows:
carrying out a plurality of groups of polishing experiments under different polishing parameters, and calculating polishing performance parameters; and carrying out normalization processing on the polishing parameters and the polishing effect, and carrying out effective training and optimization.
And training the neural network by using the experimental results, simulating to obtain a polishing process, and testing a prediction result by using the untrained experimental results.
If the error between the predicted and actual measurements of the neural network meets the required modeling criteria, the neural network is saved as a model of the polishing process.
In S104 provided in the embodiment of the present invention, the method for classifying the preset parameters, the acquired image, and the detection information by the storage module 4 includes:
extracting corresponding data characteristics and setting corresponding classification standards according to corresponding preset parameters, acquired images and detection information, and constructing corresponding classification models;
and carrying out Bayesian network inference according to the classification model, calculating the conditional probability of the class nodes, and classifying the classification data.
The working principle of the invention is as follows: and setting the operation parameters of the system through input equipment in the parameter configuration module 1, and performing five-axis linkage numerical control mechanical arm polishing operation. In the polishing operation process of the five-axis linkage numerical control manipulator, the monitoring module 2 monitors the polishing process in real time through a camera; the data acquisition module 3 detects and acquires various data parameters in the polishing process through a plurality of sensors with different functions, and transmits the acquired data to the central control module for processing.
According to the acquired data information, the central control module 6 controls the angle adjusting module 5 to adjust the three-dimensional angle of the polishing head through the driving motor; and the polishing force control loading module 7 monitors the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in real time when the control is not carried out, and controls the polishing force to track to be close to a set value. The quality detection module 9 is used for collecting images of the polished workpiece surface by a user and judging the qualified rate of the polished workpiece by identifying image parameters; the polishing parameter optimization module 8 optimizes the set process parameters according to the qualification rate in the quality detection module to obtain the optimal parameters in the uneven workpiece surface processing process; the storage module 4 stores the preset parameters, the collected images and the detection information through a memory.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A polishing control method of a five-axis linkage numerical control manipulator is characterized by comprising the following steps:
firstly, setting operation parameters of a system through input equipment in a parameter configuration module, and carrying out five-axis linkage numerical control mechanical arm polishing operation;
step two, in the polishing operation process of the five-axis linkage numerical control manipulator, the monitoring module monitors the polishing process in real time through a camera; performing feature extraction and identification on the polishing process image acquired by the monitoring module; the data acquisition module detects, acquires and fuses various data parameters in the polishing process through a plurality of sensors with different functions, and transmits the acquired data to the central control module for processing;
step three, according to the acquired data information, the central control module controls the angle adjusting module to adjust the three-dimensional angle of the polishing head through the driving motor; the polishing force loading module monitors the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in real time when the control is not carried out, and controls the polishing force to track to be close to a set value;
step four, a user in the quality detection module collects images of the surface of the polished workpiece, and the qualified rate of the polished workpiece is judged by identifying image parameters; the polishing parameter optimization module optimizes the set process parameters according to the qualification rate in the quality detection module to obtain the optimal parameters in the uneven workpiece surface processing process; the storage module stores and classifies the preset parameters, the collected images and the detection information through a memory;
in the second step, the method for performing feature extraction and identification on the polishing process image acquired by the monitoring module comprises the following steps:
adjusting the image gray level in the polishing process, and adjusting the specific illumination of the image; calculating the gradient of each pixel by using an image pixel method; dividing the polishing process image into a plurality of small areas according to the calculated pixel gradient;
calculating gradient histograms of a plurality of small regions, and performing statistics;
combining a plurality of small areas according to the set feature data needing to be extracted to obtain corresponding image feature description;
in the second step, the method for performing data fusion on each data parameter acquired by the data acquisition module in the polishing process of the sensors with different functions comprises the following steps:
establishing corresponding data samples for data acquired by a plurality of sensors, extracting corresponding characteristic numerical values according to each type of sensor, and establishing corresponding characteristic vectors;
identifying the established characteristic vectors through a self-adaptive neural network, and performing labeling interpretation;
establishing the relevance between the feature vectors through a relevance algorithm according to the feature vectors marked and explained;
and synthesizing the data detected by each sensor by using a fusion algorithm according to the label interpretation and the relevance of the feature vector.
2. The polishing control method of the five-axis linkage numerical control manipulator according to claim 1, wherein in the third step, the angle adjusting module adopts an adjusting method comprising the following steps:
judging X, Y, Z axes of the polishing machine tool according to a right-hand rectangular Cartesian coordinate system, judging the positive direction of B, C rotation coordinate axes according to a right-hand spiral rule, determining a Z axis, and then determining an X axis and a Y axis;
receiving a control parameter generated by a parameter configuration module, and setting a three-dimensional coordinate system of a target position according to the control parameter;
and converting the motion track data of the tool in the workpiece coordinate into a value in a machine tool coordinate system, and moving the coordinate position of the tool to a set target position.
3. The polishing control method of the five-axis linkage numerically controlled mechanical arm according to claim 1, wherein in the third step, the following steps are specifically adopted when the controlled polishing force in the polishing force loading module is tracked to the vicinity of the set value:
1) the measured value of the force is measured by a force sensor arranged between the cylinder and the polishing tool and is fed back to a PLC controller, and a sliding average filtering PID algorithm is realized in the PLC;
2) calculating the duty ratio of a PWM signal according to a set value and an actual measurement value, and controlling the gas flow reaching the cylinder through the high-speed switch valve to enable the output force of the cylinder piston to approach a force set value;
3) when the polishing position changes, the polishing force can quickly respond and track to a force set value according to the requirement changing along with the track, and the force fluctuation in the loading process is within an error allowable range.
4. The polishing control method of the five-axis linkage numerical control manipulator according to claim 1, wherein in the fourth step, the optimization method of the polishing parameter optimization module specifically adopts the following steps:
step A, inputting ideal material removal amount and surface roughness;
b, the controller searches for the optimal polishing parameters corresponding to the removal of the required material and the improvement of the surface roughness by adopting a genetic algorithm;
step C, in step B, the genetic algorithm repeatedly modifies the population of the single solution, randomly selects individuals from the current population, and generates next generation of individuals by taking the individuals as parents;
step D, feeding back the predicted polishing performance parameters to a genetic algorithm through a neural network;
step E, according to the expected adaptability value, the genetic algorithm uses the expected and expected polishing performance to evaluate an objective function;
and F, outputting the optimal polishing parameters if the expected adaptability value is reached, and otherwise, turning to the step C.
5. The polishing control method of the five-axis linkage numerically controlled manipulator according to claim 4, wherein before optimization, the polishing parameter optimization module establishes the polishing process model through artificial intelligence of a neural network, and the specific steps of establishing the polishing process model are as follows:
carrying out a plurality of groups of polishing experiments under different polishing parameters, and calculating polishing performance parameters; carrying out normalization processing on the polishing parameters and the polishing effect, and carrying out effective training and optimization;
training the neural network by using the experimental results, simulating to obtain a polishing process, and testing a prediction result by using the untrained experimental results;
if the error between the predicted and actual measurements of the neural network meets the required modeling criteria, the neural network is saved as a model of the polishing process.
6. A method for controlling polishing of a five-axis linkage numerically controlled manipulator according to claim 1, wherein in the fourth step, the method for classifying the preset parameters, the collected images and the detected information by the storage module comprises:
extracting corresponding data characteristics and setting corresponding classification standards according to corresponding preset parameters, acquired images and detection information, and constructing corresponding classification models;
and carrying out Bayesian network inference according to the classification model, calculating the conditional probability of the class nodes, and classifying the classification data.
7. A five-axis linkage numerically controlled robot polishing control system that implements the five-axis linkage numerically controlled robot polishing control method according to any one of claims 1 to 6, characterized by comprising:
the parameter configuration module is connected with the central control module and is used for setting the operation parameters of the system through input equipment;
the monitoring module is connected with the central control module and is used for monitoring the polishing process in real time through the camera; the process of carrying out feature extraction and identification on the polishing process image collected by the monitoring module comprises the following steps: adjusting the image gray level in the polishing process, and adjusting the specific illumination of the image; calculating the gradient of each pixel by using an image pixel method; dividing the polishing process image into a plurality of small areas according to the calculated pixel gradient; calculating gradient histograms of a plurality of small regions, and performing statistics; combining a plurality of small areas according to the set feature data needing to be extracted to obtain corresponding image feature description;
the data acquisition module is connected with the central control module and is used for detecting and acquiring various data parameters in the polishing process through a plurality of sensors with different functions and transmitting the acquired data to the central control module for processing; the data fusion process of the data acquisition module on various data parameters acquired by a plurality of sensors with different functions in the polishing process is as follows: establishing corresponding data samples for data acquired by a plurality of sensors, extracting corresponding characteristic numerical values according to each type of sensor, and establishing corresponding characteristic vectors; identifying the established characteristic vectors through a self-adaptive neural network, and performing labeling interpretation; establishing the relevance between the feature vectors through a relevance algorithm according to the feature vectors marked and explained; synthesizing data detected by each sensor by using a fusion algorithm according to the label interpretation and the relevance of the feature vector;
the storage module is connected with the central control module and used for storing preset parameters, acquired images and detection information through the storage; the process of classifying the preset parameters, the collected images and the detection information by the storage module is as follows: extracting corresponding data characteristics and setting corresponding classification standards according to corresponding preset parameters, acquired images and detection information, and constructing corresponding classification models; carrying out Bayesian network inference according to the classification model, calculating the conditional probability of class nodes, and classifying the classification data;
the angle adjusting module is connected with the central control module and is used for adjusting the three-dimensional angle of the polishing head through the driving motor;
the central control module is connected with the parameter configuration module, the monitoring module, the data acquisition module, the storage module, the angle adjustment module, the polishing force pneumatic loading module, the polishing parameter optimization module and the quality detection module, controls each module through incremental PID, adds a sliding average filtering algorithm in the incremental PID control process and filters real-time process variables adjusted by PID;
the polishing force loading module is connected with the central control module and is used for monitoring the polishing force tracking change condition which is adjusted by adding a sliding average filtering PID control algorithm and the polishing force tracking change condition in an uncontrolled manner in real time and controlling the polishing force to track to be close to a set value;
the polishing parameter optimization module is connected with the central control module and used for optimizing the set process parameters to obtain the optimal parameters in the uneven workpiece surface processing process; inputting ideal material removal amount and surface roughness; the controller employs a genetic algorithm to find optimal polishing parameters corresponding to the desired material removal and surface roughness improvement; repeatedly modifying the population of a single solution by a genetic algorithm, randomly selecting individuals from the current population, and generating next generation individuals by taking the individuals as parents; feeding back the predicted polishing performance parameters to a genetic algorithm through a neural network; the genetic algorithm uses the expected and desired polishing performance to evaluate an objective function based on the desired fitness value; outputting an optimal polishing parameter if a desired fitness value is reached;
and the quality detection module is connected with the central control module, and a user acquires images of the surface of the polished workpiece and judges the qualified rate of the polished workpiece by identifying image parameters.
8. The five-axis linked numerically controlled robot polishing control system of claim 7, wherein the parameter configuration module comprises:
the numerical value input unit is used for inputting and adjusting set numerical values through external input equipment;
the mode setting unit is used for dividing a plurality of groups of different setting values into different working modes;
the mode selection unit is used for displaying a plurality of different working modes through an interactive interface and selecting the working mode corresponding to the set parameter according to the required parameter;
the instruction generating unit is used for generating a mode setting signal corresponding to the production program instruction and transmitting the setting signal to the central control module.
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