CN111474898B - Method for optimizing processing technological parameters of free-form surface - Google Patents

Method for optimizing processing technological parameters of free-form surface Download PDF

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CN111474898B
CN111474898B CN202010303397.7A CN202010303397A CN111474898B CN 111474898 B CN111474898 B CN 111474898B CN 202010303397 A CN202010303397 A CN 202010303397A CN 111474898 B CN111474898 B CN 111474898B
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张定华
夏卫红
陈冰
肖敏
张莹
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Northwestern Polytechnical University
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    • 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
    • G05B19/4097—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 using design data to control NC machines, e.g. CAD/CAM
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Abstract

The invention discloses a method for optimizing the processing technological parameters of a free-form surface, which mainly comprises the following steps: 1. firstly, the process parameters are preliminarily optimized through fuzzy control on-line regulation and control. 2. And screening the unqualified optimized area through space-time mapping. And mapping the machining process signal as a function of the machining position, drawing a machining process data cloud picture, visually analyzing the machining process data through constraint conditions, screening areas with unqualified machining requirements, and optimizing the unqualified areas again. 3. And (5) learning and updating the process parameters off line. And completing single process parameter optimization through online process parameter regulation and control and unqualified screening, and performing offline learning and accumulation on the optimized process parameters, wherein the process parameters accumulated through learning are used for the processing process of the next part. Through the continuous iterative learning cycle process, the technological parameters are continuously accumulated and updated, so that the optimization of the machining technological parameters of the free-form surface type parts is realized.

Description

Method for optimizing processing technological parameters of free-form surface
Technical Field
The invention relates to the technical field of machining, in particular to a free-form surface machining process parameter optimization method which is used for free-form surface machining process parameter optimization. And more particularly to a free-form surface machining process parameter optimization method based on process data space-time mapping.
Background
With the rapid development of the aviation and aerospace industries in China, intelligent manufacturing becomes a necessary trend for the development of the manufacturing industry. The free curved surface type part is a core part of an aeroengine, the processing quality of the free curved surface type part directly determines the working performance of the aeroengine, and due to the importance of the free curved surface type part and the special structural material, numerical control milling processing is the mainstream technology for manufacturing the free curved surface type part at present. The free curved surface type part of the aircraft engine has a complex structure, uses titanium alloy and other materials with the characteristics of corrosion resistance and high strength, has multiple processing procedures, causes low processing efficiency and is difficult to ensure the processing quality. In the whole milling process of the free-form surface type part, the process parameters are key factors influencing the processing quality and efficiency, so that the reasonable selection of the process parameters is an important way for improving the processing efficiency and the part quality. However, the machining process of the free-form surface type part is a complex dynamic process with high nonlinearity and strong time variability, the machining working condition changes along with the cutting process, but the capability of a numerical control machine tool control system for identifying and processing the uncertain condition in the cutting process is low, and the numerical control machine tool control system cannot be correspondingly adjusted along with the change of the cutting process, so that in order to avoid or reduce the abnormity caused by the fact that the machine tool cannot process the uncertain condition of the machining process, conservative cutting parameters are generally selected in actual machining, thereby restricting the performance of the machine tool to a certain extent, failing to effectively exert the capability of the machine tool, causing low machining efficiency and difficult guarantee of the machining quality.
The invention patent (CN106020132A) discloses a rough machining feed speed optimization method based on field measured cutting force data and offline optimization. The method comprises the steps of measuring cutting force data in an actual field, reversely calculating the cutting depth of a corresponding point on a processing track, fitting an original geometric model of a processed blank part, and further realizing the optimization of the feeding speed of the blank part by a cutting parameter off-line optimization method. However, in the method, the initial geometric model of the blank is obtained through trial cutting of the first part of a batch of parts, under the condition that the same batch of blanks are not uniform, the obtained initial geometric model of the blank cannot adapt to all blanks, a machine tool cannot handle the uncertainty condition of the machining process, and a mature cutting force model is adopted during the obtaining of the cutting depth, and parameter calibration is carried out through a dynamometer, so that the method has high requirements on equipment, great operation difficulty and large amount of preparation work is needed in the early stage to carry out subsequent optimization work. Therefore, the method is optimized for processing the same batch of relatively uniform simple blanks, does not consider time-varying working conditions, uses a mature model, and cannot optimize the processing task of the free-curved surface type part under the strong time-varying working conditions.
Disclosure of Invention
The invention provides a technological parameter optimization method specially aiming at a strong time-varying working condition processing process of free-form surface parts, and aims to solve the problems that the processing efficiency of a processing task of the existing strong time-varying working condition of the free-form surface parts, which is provided by the background art, is low, and the processing quality is difficult to guarantee.
The method regulates and controls the process parameters on line through fuzzy control, screens the optimized unqualified regions through space-time mapping, optimizes the unqualified regions again, and continuously screens the cyclic optimization process to enable the process parameters to be continuously accumulated and updated off line, so that the optimization of the milling process parameters is realized.
The technical scheme of the invention is as follows:
the method for optimizing the processing technological parameters of the free-form surface is characterized by comprising the following steps: the method comprises the following steps:
step 1: fuzzy control on-line regulation and control process parameters:
step 1.1: extracting processing process data required by optimizing process parameters; the processing process data is obtained by calculation according to the acquired processing process constraint parameters;
step 1.2: converting the processing process data extracted in the step 1.1 into a fuzzy language value;
step 1.3: reasoning the processing process data converted into the fuzzy language value through a set fuzzy rule to obtain corresponding fuzzy control quantity;
step 1.4: defuzzifying the fuzzy control quantity obtained by the fuzzy reasoning to obtain the accurate quantity of the process parameter to be executed finally;
step 2: screening unqualified optimized areas through space-time mapping:
step 2.1: mesh division is carried out on the curved surface according to the u and v directions of the curved surface parameters, and the number of parameter lines is determined according to processing position data actually acquired by processing: in the curved surface machining process, the v parameter direction is the milling direction, the number of parameter lines is recorded as num1, the number of u parameter lines is smaller than the number of cutting lines, data are guaranteed to exist in each grid, and the number is recorded as num 2;
step 2.2: dividing the acquired processing constraint parameters into data blocks, wherein the processing constraint parameter data corresponding to each grid is data of a short time domain, and the average amplitude value in the short time domain is adopted to represent the data in the short time domain; numbering the short time domain average amplitude of the constraint parameter in the processing process according to the parameter plane grid numbering sequence, and recording the average amplitude in each grid as M ij Forming process constraint parametersAn average amplitude table;
step 2.3: according to the simultaneity of the acquisition of the constraint parameters of the processing position and the processing process, mapping the data of the constraint parameters of the processing process into a function of the processing position by utilizing time synchronization, and realizing the one-to-one correspondence of the number of the curved surface mesh and the number of the average amplitude of the constraint parameters of the processing process;
step 2.4: for the actual value P of the process constraint parameter in each grid ij And a target value P obj Calculating difference value, and screening out unsatisfied products
Figure BDA0002454870210000031
Wherein η is a set deviation threshold;
step 2.5: the screened unqualified area is subjected to fuzzy control on-line regulation and control of process parameters again according to the step 1, then unqualified area screening is carried out again, and the like, until the qualified processing process parameters of the whole area are obtained;
and step 3: off-line learning and updating of process parameters:
completing single process parameter optimization through the step 1 and the step 2; off-line learning and accumulation are carried out on the optimized technological parameters, and the technological parameters after learning and accumulation are used for the processing process of the next part; through the continuous iterative learning cycle process, the technological parameters are continuously accumulated and updated, so that the optimization of the machining technological parameters of the free-form surface type parts is realized.
Further, in step 1.1, the process data is the error between the actual value and the target value of the process constraint parameter and the error change rate.
Further, in step 1.1, the constraint parameter of the machining process is the power of the spindle.
Further, in step 1.2, the accurate processing procedure data obtained in step 1.1 is converted into a fuzzy language value according to a quantization factor; wherein the quantization factor is obtained according to the following process:
assuming that the universe of discourse of the process data is [ -x, + x ], and the universe of fuzzy sets of process data is { -n, -n +1, …, 0, …, n-1, n +1}, the quantization factor of the process data is:
Figure BDA0002454870210000041
advantageous effects
The invention has the beneficial effects that:
(1) through on-line regulation and control of process parameters, the numerical control machine tool can adapt to the situation of strong time-varying working conditions in the machining process of free-form surface parts, various uncertain factors are treated, and machining quality is improved.
(2) The required data of the processing process are directly read from the numerical control system, and additional measuring equipment is not needed, so that the requirement of the optimization process on the processing equipment is low, the preparation period is short, and the data are stable.
(3) And the machining result is visually analyzed through the space-time mapping relation between the machining process data and the part machining position, and the machining state is visually known so as to carry out next optimization work.
(4) And the unqualified optimized area is screened and optimized again through the constraint condition, so that repeated global optimization is not needed each time, and the processing efficiency is improved.
(5) Through off-line accumulation and updating of the process parameters, the optimized process parameters are learned and accumulated each time, the real-time updating of the process parameters is kept, and the application range is wider.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of the optimization of parameters of the free-form surface machining process of the present invention;
FIG. 2 is a diagram of the fuzzy control on-line process parameter control process of the present invention;
FIG. 3 is a schematic diagram of the actual value and the reference value of the actual spindle power for machining according to the present invention;
FIG. 4 is a schematic diagram of the present invention for dividing power deviation regions by threshold;
FIG. 5 is a flow chart of a process parameter learning loop and iterative optimization of the present invention.
Detailed Description
The method mainly adjusts and controls the process parameters on line through fuzzy control, screens the optimized unqualified regions through space-time mapping, optimizes the unqualified regions again, and continuously screens the cyclic optimization process to enable the process parameters to be continuously accumulated and updated off line, so that the optimization of the milling process parameters is realized.
The invention specifically comprises the following steps:
(1) and 3, fuzzy control on-line regulation and control of process parameters. Fuzzification of input quantity, reading processing process data required by optimizing process parameters from a numerical control system, and converting the input data into a required domain range through a quantization factor; fuzzy reasoning, which is used for reasoning the control action to be taken corresponding to the input data through a fuzzy rule; and (4) resolving the fuzzy, and converting the fuzzy quantity obtained by fuzzy inference into the accurate quantity to be executed finally by the system.
(2) And screening the unqualified optimized area through space-time mapping. Establishing a space-time mapping relation between the machining process constraint parameters and the machining positions of the parts, simultaneously reading the machining position coordinates and the machining process constraint parameters required by optimization from the numerical control system, mapping the machining process constraint parameters into a function of the machining positions, and drawing a machining process constraint parameter cloud chart; screening unqualified optimized areas, visually analyzing the constraint parameters of the processing process through optimized constraint conditions, and screening areas with unqualified processing requirements; and optimizing the unqualified optimized area again, performing fuzzy control on the screened unqualified optimized area to regulate and control the process parameters on line, screening the unqualified area again, and analogizing in turn to obtain the qualified processing process parameters of the whole area.
(3) And (4) accumulating and updating the process parameters off line. And completing single process parameter optimization through online process parameter regulation and control and unqualified screening, and performing offline learning and accumulation on the optimized process parameters, wherein the process parameters accumulated through learning are used for the processing process of the next part. Through the continuous iterative learning cycle process, the technological parameters are continuously accumulated and updated, so that the optimization of the milling technological parameters is realized.
The following describes a specific embodiment of the present invention, taking an aircraft engine blade milling process as an example, using constraint conditions that the power of a main shaft is constant in a machining process, and an optimized process parameter is a feeding speed. The examples are illustrative and are intended to be illustrative of the invention and should not be construed as limiting the invention.
Refer to fig. 1 to 5. The specific implementation mode of the free-form surface processing technological parameter optimization method is as follows:
the method comprises the following steps: and fuzzy control is carried out to regulate and control the process parameters on line. First, a target cutting power P is set obj Collecting the main shaft power of the machine tool in the actual processing process, and comparing the actual processing power with a target power value P obj Making a comparison to obtain a power error E p And rate of change of power error C p Error in power E p And rate of change of power error C p As input variables for fuzzy control. And taking the feeding speed of the numerical control machine tool as an output quantity. In the actual milling process, the on-line regulation and control of the process parameters are realized by regulating the feed multiplying power of the numerical control machine. The specific process of fuzzy control on-line regulation and control of process parameters is as follows:
(1) and collecting the power of the main shaft. And acquiring the power value of the spindle motor in the actual processing process by using a built-in sensor of the numerical control machine tool.
(2) Given target power P obj And calculating the error e between the actually collected power value and the given power value.
(3) The error rate of change ec, i.e. (de/dt), is calculated. The error is differentiated and the amount of change in the power error is determined over a sampling period.
(4) And fuzzifying the input quantity. Obtained by the above processError of (2) and error rate of change are accurate numerical calculations that must be converted to fuzzy linguistic values E p And C p ：
The universe of discourse for the error is set to [ -x, + x]The fuzzy set domain of the error is { -n, -n +1, …, 0, …, n-1, n +1}, so the quantization factor K of the error e It can be derived that:
Figure BDA0002454870210000061
quantization factor K of the same error variation c And the scale factor of the output control quantity can also be obtained by the method;
and then converting the accurate numerical calculation result into a fuzzy language value according to the quantization factor.
(5) Fuzzy reasoning. Fuzzified input quantity E p And C p And the fuzzy control quantity U is used as an input part of the fuzzy inference and is obtained by carrying out the fuzzy inference according to the established fuzzy control rule.
(6) Defuzzification. And converting the obtained fuzzy control quantity U into an accurate quantity, changing the feed speed value through the feed multiplying power delta U, and continuously ensuring that the main shaft power is kept near a given target power through feedback.
Step two: and screening the unqualified optimized area through space-time mapping. Establishing a space-time mapping relation between the spindle power and a part machining position, simultaneously reading a machining position coordinate and spindle power data from a numerical control system, mapping the spindle power data into a function of the machining position, drawing a spindle power cloud chart, and setting a target power value P according to the set power value obj And setting power deviation threshold values eta and sigma to screen the unqualified areas, optimizing the unqualified optimized areas again, and the like to obtain the qualified processing technological parameters of the whole area.
Screening unqualified optimized areas through space-time mapping, which comprises the following specific steps:
(1) and (5) grid division of the curved surface of the blade. Meshing the blades according to the u and v directions of the curved surface parameters, and according to the actual processingThe collected machining position data determines the number of parameter lines. In the blade milling process, the v parameter direction is the milling direction, the number of the parameter lines is recorded as num1, the number of the u parameter lines is smaller than the number of the cutting lines, data are guaranteed to exist in each grid, and the number is recorded as num 2. After multiple tests, the number of the parameter lines is selected to be 20-30, which are moderate, the number of the grids is determined by the number sequence of u and v directions, which is recorded as C ij 。
(2) The power signal is discrete. Dividing the acquired spindle power into data blocks, wherein the power data corresponding to each grid is a short time domain data, and the short time domain average amplitude represents the short time domain data. Numbering the power short time domain average amplitude values according to the parameter plane grid numbering sequence, and recording the data average amplitude value in each grid as M ij And forming a power average amplitude table.
(3) The number of the curved surface mesh corresponds to the number of the power average amplitude. And mapping the main shaft power data into a function of the processing position by utilizing time synchronization through the simultaneity of the processing position and the main shaft power data acquisition, so as to realize the one-to-one correspondence of the curved surface mesh number and the power average amplitude number.
(4) And drawing a power cloud picture, and screening unqualified optimized areas. Ensuring that the power of each grid main shaft fluctuates in a range, and converting the actual processing power P ij And target power P obj The deviation of (2) is divided into three parts, namely qualified, transitional and unqualified areas, the area range is divided according to the values of eta and sigma determined by the processing working condition, and the following relations exist:
and (3) power constraint:
Figure BDA0002454870210000071
qualified area Q 1 ：(1-η)P obj ≤P ij ≤(1+η)P obj
Transition region Q 2 ：
Figure BDA0002454870210000072
Out of tolerance region Q 3 :
Figure BDA0002454870210000073
Step three: and (5) learning and updating the process parameters off line. And completing single process parameter optimization through online process parameter regulation and control and unqualified screening, and performing offline learning and accumulation on the optimized process parameters, wherein the process parameters accumulated through learning are used for the processing process of the next part. Through the continuous iterative learning cycle process, the technological parameters are continuously accumulated and updated, so that the optimization of the machining technological parameters of the free-form surface type parts is realized.
The milling process parameter offline learning method comprises the following specific steps:
(1) and (6) optimizing and matching. And synchronously matching the result of the online regulation and control of the milling feed speed of the blade, the NC code for processing the blade and the structural characteristics of the blade.
(2) And (6) parameter correction. And recalculating the feeding speed and correcting the feeding speed in the NC codes according to the feeding speed adjustment amount of each point on the NC codes obtained by online regulation.
(3) And smoothing the parameters. And analyzing the corrected NC codes of the milling of the blade, and smoothing the technological parameters in the NC codes to avoid the phenomenon that the acceleration and deceleration of the feeding speed of the machine tool are too violent in the processing process so as to influence the processing quality and even damage the cutter or the machine tool.
The whole process realizes off-line accumulation and updating of the technological parameters and provides a better initial NC code for the processing of the next part.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.

Claims (3)

1. A free-form surface processing technological parameter optimization method is characterized by comprising the following steps: the method comprises the following steps:
step 1: fuzzy control on-line regulation and control process parameters:
step 1.1: extracting processing process data required by optimizing process parameters; the processing process data is obtained by calculation according to the acquired processing process constraint parameters;
step 1.2: converting the processing process data extracted in the step 1.1 into a fuzzy language value;
step 1.3: reasoning the processing process data converted into the fuzzy language value through a set fuzzy rule to obtain corresponding fuzzy control quantity;
step 1.4: defuzzifying the fuzzy control quantity obtained by the fuzzy reasoning to obtain the accurate quantity of the process parameter to be executed finally;
step 2: screening unqualified optimized areas through space-time mapping:
step 2.1: mesh division is carried out on the curved surface according to the u and v directions of the curved surface parameters, and the number of parameter lines is determined according to processing position data actually acquired by processing: in the curved surface machining process, the v parameter direction is the milling direction, the number of parameter lines is recorded as num1, the number of u parameter lines is smaller than the number of cutting lines, data are guaranteed to exist in each grid, and the number is recorded as num 2;
step 2.2: dividing the acquired processing constraint parameters into data blocks, wherein the processing constraint parameter data corresponding to each grid is data of a short time domain, and the average amplitude value in the short time domain is adopted to represent the data in the short time domain; numbering the short time domain average amplitude of the constraint parameter in the processing process according to the parameter plane grid numbering sequence, and recording the average amplitude in each grid as M ij Forming a constraint parameter average amplitude table in the machining process;
step 2.3: according to the simultaneity of the acquisition of the constraint parameters of the processing position and the processing process, mapping the data of the constraint parameters of the processing process into a function of the processing position by utilizing time synchronization, and realizing the one-to-one correspondence of the number of the curved surface mesh and the number of the average amplitude of the constraint parameters of the processing process;
step 2.4: constraining the actual value P of the parameter for the process in each grid ij And a target value P obj Calculating difference value, and screening out unsatisfied products
Figure FDA0003291724640000011
Wherein η is a set deviation threshold;
step 2.5: the screened unqualified area is subjected to fuzzy control on-line regulation and control of process parameters again according to the step 1, then unqualified area screening is carried out again, and the like, until the qualified processing process parameters of the whole area are obtained;
and step 3: off-line learning and updating of process parameters:
completing single process parameter optimization through the step 1 and the step 2; off-line learning and accumulation are carried out on the optimized technological parameters, and the technological parameters after learning and accumulation are used for the processing process of the next part; through the continuous iterative learning cycle process, the technological parameters are continuously accumulated and updated, so that the optimization of the machining technological parameters of the free-form surface type parts is realized.
2. The method for optimizing the parameters of the free-form surface machining process according to claim 1, wherein the method comprises the following steps: in step 1.1, the process data is the error between the actual value and the target value of the process constraint parameter and the error change rate.
3. The method for optimizing the parameters of the free-form surface machining process according to claim 2, wherein the method comprises the following steps: in step 1.1, the constraint parameter of the machining process is the power of the main shaft.
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