CN107942929B - Control method of numerical control lathe based on neural network computing technology - Google Patents
Control method of numerical control lathe based on neural network computing technology Download PDFInfo
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- CN107942929B CN107942929B CN201711332883.6A CN201711332883A CN107942929B CN 107942929 B CN107942929 B CN 107942929B CN 201711332883 A CN201711332883 A CN 201711332883A CN 107942929 B CN107942929 B CN 107942929B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/19—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/34—Director, elements to supervisory
- G05B2219/34093—Real time toolpath generation, no need for large memory to store values
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Abstract
The invention discloses a control method of a numerical control lathe based on a neural network computing technology, belongs to the field of automatic control, and comprises the numerical control lathe and a neural computing server for computing a machining track of a cutter of the numerical control lathe, so that the problem that the numerical control lathe automatically generates a machining code is solved, the manpower is saved, and the machining efficiency of the numerical control lathe is improved.
Description
Technical Field
The invention belongs to the field of automatic control.
Background
Writing code has been considered to be a relatively complex matter, automation has not been well advanced to this field at present, and logic processing and the like involved in the automation is required to be manually operated at any time. With the development of neural network algorithms and the increase of human demands, the automation of the block becomes a necessary content for realization. Neural network algorithms have been implemented to recognize the shape, sound, logic, etc. contained in the object and then map them to the code, which shortens the process of writing the code.
At present, numerically controlled lathes on the market are all manually written with processing programs, a fully automatic process is not realized, and the optimal path for machining the cutter cannot be automatically judged according to a neural network algorithm.
Disclosure of Invention
The invention aims to provide a control method of a numerical control lathe based on a neural network computing technology, which solves the problem that the numerical control lathe automatically generates a machining code, saves manpower and improves the machining efficiency of the numerical control lathe.
In order to achieve the purpose, the invention adopts the following technical scheme:
the control system of the numerical control lathe based on the neural network computing technology comprises the numerical control lathe and a neural computing server used for computing the machining track of the numerical control lathe tool, and the neural computing server is connected with the numerical control lathe.
The control method of the numerical control lathe based on the neural network computing technology comprises the following steps:
step 1: firstly, establishing a control system of the numerical control lathe based on the neural network computing technology as claimed in claim 1;
step 2: establishing a three-dimensional model of a processing space according to the size of the processing space of the numerical control lathe, inputting three-dimensional coordinate data of the three-dimensional model of the processing space into a neural computer server, and inputting a preset value of the times of neural network training into the neural computer server;
and step 3: the neural computer server simulates a three-dimensional model of a machining space of the numerical control lathe according to the input three-dimensional coordinate data of the three-dimensional model of the machining space, and determines the origin coordinates of a lathe clamp and the origin coordinates of a lathe tool in the three-dimensional model of the machining space;
and 4, step 4: dividing the three-dimensional model of the processing space into a plurality of minimum square spaces according to 1 unit length, taking 8 vertexes of the minimum square spaces as base points, and recording the coordinates of the base points of all the base points;
and 5: establishing a part three-dimensional model of a part to be machined, inputting three-dimensional coordinate data of the part three-dimensional model into a neural computer server, simulating the part three-dimensional model by the neural computer server, and determining a clamping original point coordinate of a part clamped by a lathe clamp;
step 6: the neural computer server automatically puts the three-dimensional model of the part into the three-dimensional model of the machining space, and enables the clamping original point to coincide with the original point of the lathe clamp;
and 7: the neural computer server judges the part outline of the part three-dimensional model in the machining space three-dimensional model according to the difference between the color of the part three-dimensional model and the machining space three-dimensional model, and determines the three-dimensional coordinate point of the part outline;
and 8: a processing worker selects a point from three-dimensional coordinate points of the part outline as a target point through a neural computer server, and the neural computer server records the three-dimensional coordinate of the target point;
and step 9: the neural computer server finds out all paths of the tool moving to the target point through a neural network computing method according to the following steps:
step 9-1: returning the lathe tool in the three-dimensional model of the processing space of the neural computer server to the original point coordinate of the lathe tool;
step 9-2: the neural computer server takes the point where the lathe tool is located as an original vertex, finds out 8 minimum square spaces taking the original vertex as the vertex as a motion space, takes all base points in the motion space as track base points, and randomly selects one track base point from all track base points as a moving point through a random number generation function;
step 9-3: the neural computer server simulates the lathe tool to move to the coordinate of the moving point;
step 9-4: judging whether the coordinates of the moving point are the coordinates of the target point: if yes, executing step 9-5; otherwise, storing the coordinates of the moving point and executing the step 9-2;
step 9-5: the neural computer server collects the coordinates of all the moving points into the moving track of the cutter and records the moving track;
and 9-6: judging whether the preset times of neural network training are finished or not: if yes, executing step 10; if not, executing the step 9-1;
step 10: the neural computer server combines all the moving tracks of the cutter together, and finds out the crossed intersection points among all the different moving tracks according to the moving tracks of the cutter;
step 11: the neural computer server counts the passing times of all the cross points passing through different moving tracks;
step 12: the neural computer server screens the cross points with the most passing times as the optimal cross points according to the passing times;
step 13: the neural computer server finds out the optimal path of the tool moving to the target point through a neural network calculation method according to the following steps:
step 13-1: returning the lathe tool in the three-dimensional model of the processing space of the neural computer server to the original point coordinate of the lathe tool;
step 13-2: the neural computer server takes the point where the lathe tool is located as an original vertex, finds out 8 minimum square spaces taking the original vertex as the vertex as a motion space, and finds out all optimal cross points in the motion space;
step 13-3: judging whether the motion space has an optimal intersection point: if yes, executing step 13-4; if not, executing the step 13-1;
step 13-4: the neural computer server simulates the movement of a lathe tool to the coordinate of the optimal intersection point;
step 13-5: judging whether the coordinates of the optimal intersection point are the coordinates of the target point: if yes, executing step 9-5; if not, storing the coordinates of the optimal intersection point, and executing the step 9-2;
step 13-6: the neural computer server collects the coordinates of all the optimal intersections into the optimal movement track of the cutter, and records the optimal movement track;
step 13-7: judging whether the preset times of neural network training are finished or not: if yes, go to step 14; if not, executing the step 13-1;
step 14: the neural computer server calculates the length of each optimal movement track;
step 15: selecting the optimal moving track with the shortest length as the processing track of the cutter by the neural computer server;
step 16: the neural computer server automatically generates a processing code of the numerical control lathe according to the processing track of the cutter;
and step 17: and the neural computer server displays the processing code of the numerical control lathe, and after a processing person confirms the processing code, the neural computer server transmits the processing code to a controller of the numerical control lathe to finish processing.
The control method of the numerical control lathe based on the neural network computing technology adopts the neural network computing technology to automatically compute the motion trail of the cutter of the numerical control lathe and generate the processing code according to the motion trail of the cutter, thereby solving the problem that the numerical control lathe automatically generates the processing code, saving the manpower and improving the processing efficiency of the numerical control lathe.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a flow chart of the present invention for finding all paths for the tool to move to the target point;
fig. 3 is a flow chart of the present invention for finding an optimal path for the tool to move to a target point.
Detailed Description
Example 1:
the control system of the numerical control lathe based on the neural network computing technology comprises the numerical control lathe and a neural computing server used for computing the machining track of the numerical control lathe tool, and the neural computing server is connected with a controller of the numerical control lathe.
Example 2:
the method for controlling a numerically controlled lathe based on the neural network computing technology shown in fig. 1 is implemented on the basis of the control system of the numerically controlled lathe based on the neural network computing technology described in embodiment 1, and includes the following steps:
step 1: firstly, establishing a control system of the numerical control lathe based on the neural network computing technology as claimed in claim 1;
step 2: establishing a three-dimensional model of a processing space according to the size of the processing space of the numerical control lathe, inputting three-dimensional coordinate data of the three-dimensional model of the processing space into a neural computer server, and inputting a preset value of the times of neural network training into the neural computer server;
and step 3: the neural computer server simulates a three-dimensional model of a machining space of the numerical control lathe according to the input three-dimensional coordinate data of the three-dimensional model of the machining space, and determines the origin coordinates of a lathe clamp and the origin coordinates of a lathe tool in the three-dimensional model of the machining space;
and 4, step 4: dividing the three-dimensional model of the processing space into a plurality of minimum square spaces according to 1 unit length, taking 8 vertexes of the minimum square spaces as base points, and recording the coordinates of the base points of all the base points;
and 5: establishing a part three-dimensional model of a part to be machined, inputting three-dimensional coordinate data of the part three-dimensional model into a neural computer server, simulating the part three-dimensional model by the neural computer server, and determining a clamping original point coordinate of a part clamped by a lathe clamp;
step 6: the neural computer server automatically puts the three-dimensional model of the part into the three-dimensional model of the machining space, and enables the clamping original point to coincide with the original point of the lathe clamp;
and 7: the neural computer server judges the part outline of the part three-dimensional model in the machining space three-dimensional model according to the difference between the color of the part three-dimensional model and the machining space three-dimensional model, and determines the three-dimensional coordinate point of the part outline;
and 8: a processing worker selects a point from three-dimensional coordinate points of the part outline as a target point through a neural computer server, and the neural computer server records the three-dimensional coordinate of the target point;
as shown in fig. 2, step 9: the neural computer server finds out all paths of the tool moving to the target point through a neural network computing method according to the following steps:
step 9-1: returning the lathe tool in the three-dimensional model of the processing space of the neural computer server to the original point coordinate of the lathe tool;
step 9-2: the neural computer server takes the point where the lathe tool is located as an original vertex, finds out 8 minimum square spaces taking the original vertex as the vertex as a motion space, takes all base points in the motion space as track base points, and randomly selects one track base point from all track base points as a moving point through a random number generation function;
step 9-3: the neural computer server simulates the lathe tool to move to the coordinate of the moving point;
step 9-4: judging whether the coordinates of the moving point are the coordinates of the target point: if yes, executing step 9-5; otherwise, storing the coordinates of the moving point and executing the step 9-2;
step 9-5: the neural computer server collects the coordinates of all the moving points into the moving track of the cutter and records the moving track;
and 9-6: judging whether the preset times of neural network training are finished or not: if yes, executing step 10; if not, executing the step 9-1;
step 10: the neural computer server combines all the moving tracks of the cutter together, and finds out the crossed intersection points among all the different moving tracks according to the moving tracks of the cutter;
step 11: the neural computer server counts the passing times of all the cross points passing through different moving tracks;
step 12: the neural computer server screens the cross points with the most passing times as the optimal cross points according to the passing times;
as shown in fig. 3, step 13: the neural computer server finds out the optimal path of the tool moving to the target point through a neural network calculation method according to the following steps:
step 13-1: returning the lathe tool in the three-dimensional model of the processing space of the neural computer server to the original point coordinate of the lathe tool;
step 13-2: the neural computer server takes the point where the lathe tool is located as an original vertex, finds out 8 minimum square spaces taking the original vertex as the vertex as a motion space, and finds out all optimal cross points in the motion space;
step 13-3: judging whether the motion space has an optimal intersection point: if yes, executing step 13-4; if not, executing the step 13-1;
step 13-4: the neural computer server simulates the movement of a lathe tool to the coordinate of the optimal intersection point;
step 13-5: judging whether the coordinates of the optimal intersection point are the coordinates of the target point: if yes, executing step 9-5; if not, storing the coordinates of the optimal intersection point, and executing the step 9-2;
step 13-6: the neural computer server collects the coordinates of all the optimal intersections into the optimal movement track of the cutter, and records the optimal movement track;
step 13-7: judging whether the preset times of neural network training are finished or not: if yes, go to step 14; if not, executing the step 13-1;
step 14: the neural computer server calculates the length of each optimal movement track;
step 15: selecting the optimal moving track with the shortest length as the processing track of the cutter by the neural computer server;
step 16: the neural computer server automatically generates a processing code of the numerical control lathe according to the processing track of the cutter;
and step 17: and the neural computer server displays the processing code of the numerical control lathe, and after a processing person confirms the processing code, the neural computer server transmits the processing code to a controller of the numerical control lathe to finish processing.
The control method of the numerical control lathe based on the neural network computing technology adopts the neural network computing technology to automatically compute the motion trail of the cutter of the numerical control lathe and generate the processing code according to the motion trail of the cutter, thereby solving the problem that the numerical control lathe automatically generates the processing code, saving the manpower and improving the processing efficiency of the numerical control lathe.
Various other modifications and changes may be made by those skilled in the art based on the above-described technical solutions and concepts, and all such modifications and changes should fall within the scope of the claims of the present invention.
Claims (1)
1. The control method of the numerical control lathe based on the neural network computing technology is characterized by comprising the following steps of: the device comprises a numerical control lathe and a neural calculation server for calculating the processing track of a tool of the numerical control lathe, wherein the neural calculation server is connected with the numerical control lathe;
the method comprises the following steps:
step 1: firstly, establishing a control system of a numerical control lathe based on a neural network computing technology;
step 2: establishing a three-dimensional model of a processing space according to the size of the processing space of the numerical control lathe, inputting three-dimensional coordinate data of the three-dimensional model of the processing space into a neural computer server, and inputting a preset value of the times of neural network training into the neural computer server;
and step 3: the neural computer server simulates a three-dimensional model of a machining space of the numerical control lathe according to the input three-dimensional coordinate data of the three-dimensional model of the machining space, and determines the origin coordinates of a lathe clamp and the origin coordinates of a lathe tool in the three-dimensional model of the machining space;
and 4, step 4: dividing the three-dimensional model of the processing space into a plurality of minimum square spaces according to 1 unit length, taking 8 vertexes of the minimum square spaces as base points, and recording the coordinates of the base points of all the base points;
and 5: establishing a part three-dimensional model of a part to be machined, inputting three-dimensional coordinate data of the part three-dimensional model into a neural computer server, simulating the part three-dimensional model by the neural computer server, and determining a clamping original point coordinate of a part clamped by a lathe clamp;
step 6: the neural computer server automatically puts the three-dimensional model of the part into the three-dimensional model of the machining space, and enables the clamping original point to coincide with the original point of the lathe clamp;
and 7: the neural computer server judges the part outline of the part three-dimensional model in the machining space three-dimensional model according to the difference between the color of the part three-dimensional model and the machining space three-dimensional model, and determines the three-dimensional coordinate point of the part outline;
and 8: a processing worker selects a point from three-dimensional coordinate points of the part outline as a target point through a neural computer server, and the neural computer server records the three-dimensional coordinate of the target point;
and step 9: the neural computer server finds out all paths of the tool moving to the target point through a neural network computing method according to the following steps:
step 9-1: returning the lathe tool in the three-dimensional model of the processing space of the neural computer server to the original point coordinate of the lathe tool;
step 9-2: the neural computer server takes the point where the lathe tool is located as an original vertex, finds out 8 minimum square spaces taking the original vertex as the vertex as a motion space, takes all base points in the motion space as track base points, and randomly selects one track base point from all track base points as a moving point through a random number generation function;
step 9-3: the neural computer server simulates the lathe tool to move to the coordinate of the moving point;
step 9-4: judging whether the coordinates of the moving point are the coordinates of the target point: if yes, executing step 9-5; otherwise, storing the coordinates of the moving point and executing the step 9-2;
step 9-5: the neural computer server collects the coordinates of all the moving points into the moving track of the cutter and records the moving track;
and 9-6: judging whether the preset times of neural network training are finished or not: if yes, executing step 10; if not, executing the step 9-1;
step 10: the neural computer server combines all the moving tracks of the cutter together, and finds out the crossed intersection points among all the different moving tracks according to the moving tracks of the cutter;
step 11: the neural computer server counts the passing times of all the cross points passing through different moving tracks;
step 12: the neural computer server screens the cross points with the most passing times as the optimal cross points according to the passing times;
step 13: the neural computer server finds out the optimal path of the tool moving to the target point through a neural network calculation method according to the following steps:
step 13-1: returning the lathe tool in the three-dimensional model of the processing space of the neural computer server to the original point coordinate of the lathe tool;
step 13-2: the neural computer server takes the point where the lathe tool is located as an original vertex, finds out 8 minimum square spaces taking the original vertex as the vertex as a motion space, and finds out all optimal cross points in the motion space;
step 13-3: judging whether the motion space has an optimal intersection point: if yes, executing step 13-4; if not, executing the step 13-1;
step 13-4: the neural computer server simulates the movement of a lathe tool to the coordinate of the optimal intersection point;
step 13-5: judging whether the coordinates of the optimal intersection point are the coordinates of the target point: if yes, executing step 9-5; if not, storing the coordinates of the optimal intersection point, and executing the step 9-2;
step 13-6: the neural computer server collects the coordinates of all the optimal intersections into the optimal movement track of the cutter, and records the optimal movement track;
step 13-7: judging whether the preset times of neural network training are finished or not: if yes, go to step 14; if not, executing the step 13-1;
step 14: the neural computer server calculates the length of each optimal movement track;
step 15: selecting the optimal moving track with the shortest length as the processing track of the cutter by the neural computer server;
step 16: the neural computer server automatically generates a processing code of the numerical control lathe according to the processing track of the cutter;
and step 17: and the neural computer server displays the processing code of the numerical control lathe, and after a processing person confirms the processing code, the neural computer server transmits the processing code to a controller of the numerical control lathe to finish processing.
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