CN111538290A - Precise numerical control machining method based on Kalman filtering algorithm - Google Patents

Precise numerical control machining method based on Kalman filtering algorithm Download PDF

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CN111538290A
CN111538290A CN202010421532.8A CN202010421532A CN111538290A CN 111538290 A CN111538290 A CN 111538290A CN 202010421532 A CN202010421532 A CN 202010421532A CN 111538290 A CN111538290 A CN 111538290A
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error
detection
kalman filtering
filtering algorithm
machining
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赵铭
贾志纲
卫青珍
张宇
吉宏斌
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Taiyuan University of Science and Technology
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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
    • G05B19/408—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 data handling or data format, e.g. reading, buffering or conversion of data
    • G05B19/4086—Coordinate conversions; Other special calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
    • G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
    • 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
    • G05B2219/35—Nc in input of data, input till input file format
    • G05B2219/35356—Data handling

Abstract

The invention provides a precise numerical control machining method based on a Kalman filtering algorithm. The precision numerical control machining method based on the Kalman filtering algorithm comprises the following steps: s1: establishing a process system equation and a detection equation; s2: denoising a detection vector of the online detection system by using a Kalman filtering algorithm; establishing a machining state model by using a time sequence analysis method, and regarding a machining process as a random dynamic process; because the error signal measured by the error detection system necessarily contains noise interference signals with certain frequencies, the noise of the online detection system is removed by using a Kalman filtering algorithm to obtain the optimal estimation value of the processing error; the numerical control machine tool performs compensation motion according to the optimal estimated value of the machining error, avoids the machining error caused by the fluctuation of the detection vector of the online detection system, and improves the machining precision.

Description

Precise numerical control machining method based on Kalman filtering algorithm
Technical Field
The invention relates to the technical field of machine manufacturing, in particular to a precise numerical control machining method based on a Kalman filtering algorithm.
Background
Precision and ultra-precision machining are important fields in machine manufacturing, and have important influence on the development of advanced technologies and defense industry. The precise machine tool is provided with an on-line detection system for detecting the displacement position of a moving part of the machine tool and a precise feedback closed-loop control system for ensuring the machining size precision. However, the precision of the detection system is affected by thermal deformation in the machining process, vibration caused by cutting fluid and shavings, arrangement of the sensor, performance of the sensor and the like, and the detection vector is deviated, so that the machining precision is affected.
Therefore, it is necessary to provide a new precision numerical control machining method based on the kalman filter algorithm to solve the above technical problems.
Disclosure of Invention
The invention aims to provide a precise numerical control machining method based on a Kalman filtering algorithm, which has high precision and strong practicability.
In order to solve the technical problem, the precision numerical control machining method based on the Kalman filtering algorithm comprises the following steps:
s1: establishing a process system equation and a detection equation;
s2: denoising a detection vector of the online detection system by using a Kalman filtering algorithm;
s3: and the closed-loop system performs compensation motion according to the de-noised detection vector.
Preferably, the establishing of the process system equation and the detection equation in the step S1 includes the following steps:
s101: analyzing the error rule of the process system, and establishing a process system equation by combining a tool path; analyzing an error rule of an online detection system, and establishing a detection equation;
s102: inputting a machining program, clamping a blank, setting a tool, setting an initial value k to be 1, and starting machining;
s103: and at the kth moment, the online detection device detects the machining state to obtain a machining state detection vector.
Preferably, the denoising processing of the detection vector of the online detection system by the kalman filter algorithm in step S2 includes the following steps:
s201: and (3) applying a Kalman filtering algorithm to perform denoising processing on the processing state detection vector.
Preferably, after the motion compensation in step S3, the process proceeds to step S4:
s4: whether the processing is finished or not;
when the judgment result in the step S4 is No, and the preset value k is k +1, returning to the step S103;
when the determination result in the step S4 is Yes, the flow proceeds to a step S5:
s5: and finishing the processing.
Preferably, the process system error rule is analyzed in the step S101, and a process system equation is established in combination with the tool path; the method for analyzing the error rule of the online detection system and establishing the detection equation comprises the following steps:
repeatedly detecting the occurrence condition of the error, analyzing the value and the direction of the error, searching the rule of the error, finding out main factors influencing the error, determining an error item, analyzing and obtaining the expected value and the variance of the error, wherein the processing error generally comprises the following steps: principle errors, clamping errors, process system precision, cutter abrasion and the like;
establishing a process system equation according to the path and error rule of the tool in the machining process, MkIs the state vector of the process system at the kth moment:
Figure BDA0002497116710000031
the system noise follows a gaussian distribution:
wk～N(0，R)；
analyzing the error of the online detection system, and finding out the rule, the expected value and the variance of the online detection system; causes of online detection errors include: thermal deformation in the machining process, vibration caused by cutting fluid and chips, arrangement of a sensor and performance of the sensor; establishing a detection equation, AkDetect the vector for the kth time:
Figure BDA0002497116710000032
the detection noise follows a normal distribution:
vk～N(0，Q)。
preferably, the step S201 of applying a kalman filter algorithm to perform denoising processing on the processing state detection vector includes the following steps:
denoising the error signal by applying a Kalman filtering algorithm to obtain the optimal estimation of the error value; five steps are needed for eliminating the noise of the detection vector through a Kalman filtering algorithm;
calculating the predicted value of the process state vector at the k moment through the processing state vector at the k-1 moment and a system equation:
Figure BDA0002497116710000033
covariance prediction:
Figure BDA0002497116710000034
kalman gain:
Figure BDA0002497116710000041
and (3) the optimal estimation value of the machining state vector after noise removal of the k moment detection system is as follows:
Figure BDA0002497116710000042
and the optimal estimated value of the covariance at the k moment is used for calculating the next moment:
Figure BDA0002497116710000043
compared with the related technology, the precision numerical control machining method based on the Kalman filtering algorithm has the following beneficial effects:
the invention provides a precise numerical control machining method based on a Kalman filtering algorithm, which improves the traditional closed-loop numerical control machining system; establishing a machining state model by using a time sequence analysis method, and regarding a machining process as a random dynamic process; because the error signal measured by the error detection system necessarily contains noise interference signals with certain frequencies, noise is removed through a Kalman filtering algorithm, machining errors caused by fluctuation of detection vectors of the detection system are reduced, an optimal estimation value of the machining errors is obtained, and the machine tool performs compensation motion according to the optimal estimation value of the machining errors, so that the machining precision is improved.
Drawings
FIG. 1 is a basic flow chart of a precision numerical control machining method based on a Kalman filtering algorithm provided by the invention;
FIG. 2 is a detailed flow chart of a precision numerical control machining method based on a Kalman filtering algorithm provided by the invention;
FIG. 3 is a model diagram of a process system equation and a detection equation;
FIG. 4 is a model diagram of a detection vector denoising process of an online detection system by a Kalman filtering algorithm.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
Please refer to fig. 1, fig. 2, fig. 3, and fig. 4 in combination, wherein fig. 1 is a basic flowchart of a precision numerical control processing method based on a kalman filter algorithm according to the present invention; FIG. 2 is a detailed flow chart of a precision numerical control machining method based on a Kalman filtering algorithm provided by the invention; FIG. 3 is a model diagram of a process system equation and a detection equation; FIG. 4 is a model diagram of a detection vector denoising process of an online detection system by a Kalman filtering algorithm. The precise numerical control machining method based on the Kalman filtering algorithm comprises the following steps:
s1: establishing a process system equation and a detection equation;
s2: denoising a detection vector of the online detection system by using a Kalman filtering algorithm;
s3: and the closed-loop system performs compensation motion according to the de-noised detection vector.
The process system equation and the detection equation established in the step S1 include the following steps:
s101: analyzing the error rule of the process system, and establishing a process system equation by combining a tool path; analyzing an error rule of an online detection system, and establishing a detection equation;
s102: inputting a machining program, clamping a blank, setting a tool, setting an initial value k to be 1, and starting machining;
s103: and at the kth moment, the online detection device detects the machining state to obtain a machining state detection vector.
The detection vector denoising processing of the online detection system by the Kalman filtering algorithm in the step S2 comprises the following steps:
s201: and (3) applying a Kalman filtering algorithm to perform denoising processing on the processing state detection vector.
After the step S3 compensates for the motion, the process proceeds to step S4:
s4: whether the processing is finished or not;
when the judgment result in the step S4 is No, and the preset value k is k +1, returning to the step S103;
when the determination result in the step S4 is Yes, the flow proceeds to a step S5:
s5: and finishing the processing.
Analyzing the error rule of the process system in the step S101, and establishing a process system equation by combining a cutter path; the method for analyzing the error rule of the online detection system and establishing the detection equation comprises the following steps:
repeatedly detecting the occurrence condition of the error, analyzing the value and the direction of the error, searching the rule of the error, finding out main factors influencing the error, determining an error item, analyzing and obtaining the expected value and the variance of the error, wherein the processing error generally comprises the following steps: principle errors, clamping errors, process system precision, cutter abrasion and the like;
establishing a process system equation according to the path and error rule of the tool in the machining process, MkState vector of the process system at the kth moment:
Figure BDA0002497116710000061
the system noise follows a gaussian distribution:
wk～N(0，R)；
analyzing the error of the online detection system, and finding out the rule, the expected value and the variance of the online detection system; causes of online detection errors include: thermal deformation in the machining process, vibration caused by cutting fluid and chips, arrangement of a sensor and performance of the sensor; establishing a detection equation, AkDetect the vector for the kth time:
Figure BDA0002497116710000071
the detection noise follows a normal distribution:
vk～N(0，Q)。
in step S201, applying a kalman filter algorithm to perform denoising processing on the processing state detection vector includes the following steps:
denoising the error signal by applying a Kalman filtering algorithm to obtain the optimal estimation of the error value; five steps are needed for eliminating the noise of the detection vector through a Kalman filtering algorithm;
calculating the predicted value of the process state vector at the k moment through the processing state vector at the k-1 moment and a system equation:
Figure BDA0002497116710000072
covariance prediction:
Figure BDA0002497116710000073
kalman gain:
Figure BDA0002497116710000074
the optimal estimation value of the machining state vector after the noise is removed by the detection system at the kth moment is as follows:
Figure BDA0002497116710000075
and (3) the optimal estimation value of the covariance at the k moment is used for calculating the k +1 moment:
Figure BDA0002497116710000076
compared with the related technology, the precision numerical control machining method based on the Kalman filtering algorithm has the following beneficial effects:
the invention provides a precise numerical control machining method based on a Kalman filtering algorithm, which improves the traditional closed-loop numerical control machining system; establishing a machining state model by using a time sequence analysis method, and regarding a machining process as a random dynamic process; because the error signal measured by the error detection system necessarily contains noise interference signals with certain frequencies, noise is removed through a Kalman filtering algorithm to obtain the optimal estimation value of the machining error, and the machine tool performs compensation motion according to the optimal estimation value of the machining error, so that machining precision fluctuation caused by the error of a sensor of the detection system can be reduced, and the machining precision is improved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (6)

1. A precise numerical control machining method based on a Kalman filtering algorithm is characterized by comprising the following steps:
s1: establishing a process system equation and a detection equation;
s2: denoising a detection vector of the online detection system by using a Kalman filtering algorithm;
s3: and the closed-loop system performs compensation motion according to the de-noised detection vector.
2. The precise numerical control machining method based on the Kalman filtering algorithm of claim 1, wherein the establishing of the process system equation and the detection equation in the step S1 comprises the following steps:
s101: analyzing the error rule of the process system, and establishing a process system equation by combining a tool path; analyzing an error rule of an online detection system, and establishing a detection equation;
s102: inputting a machining program, clamping a blank, setting a tool, setting an initial value k to be 1, and starting machining;
s103: and at the kth moment, the online detection device detects the machining state to obtain a machining state detection vector.
3. The precision numerical control machining method based on the Kalman filtering algorithm as claimed in claim 1, wherein the denoising processing of the detection vector of the online detection system by the Kalman filtering algorithm in the step S2 comprises the following steps:
s201: and (3) applying a Kalman filtering algorithm to de-noize the processing state detection vector obtained by the online detection system.
4. The precise numerical control machining method based on the Kalman filtering algorithm of claim 1, wherein the step S3 is executed after compensating motion and the step S4 is executed:
s4: whether the processing is finished or not;
when the judgment result in the step S4 is No, and the preset value k is k +1, returning to the step S103;
when the determination result in the step S4 is Yes, the flow proceeds to a step S5:
s5: and finishing the processing.
5. The precise numerical control machining method based on the Kalman filtering algorithm of claim 2, characterized in that the process system error rule is analyzed in the step S101, and a process system equation is established by combining a tool path; analyzing an error rule of an online detection system, and establishing a detection equation; the method comprises the following steps:
repeatedly detecting the occurrence condition of the error, analyzing the value and the direction of the error, searching the rule of the error, finding out main factors influencing the error, determining an error item, analyzing and obtaining the expected value and the variance of the error, wherein the processing error generally comprises the following steps: principle errors, clamping errors, process system precision, cutter abrasion and the like;
establishing a process system equation according to the path and error rule of the tool in the machining process, MkState vector of the process system at the kth moment:
Figure FDA0002497116700000021
the system noise follows a gaussian distribution:
wk～N(0，R)；
analyzing the error of the online detection system, and finding out the rule, the expected value and the variance of the online detection system; causes of online detection errors include: thermal deformation in the machining process, vibration caused by cutting fluid and chips, arrangement of a sensor and performance of the sensor; establishing a detection equation, AkDetect the vector for the kth time:
Figure FDA0002497116700000022
the detection noise follows a gaussian distribution:
vk～N(0，Q)。
6. the precise numerical control machining method based on the kalman filter algorithm according to claim 3, wherein the applying the kalman filter algorithm in the step S201, and the denoising processing of the machining state detection vector includes the following steps:
denoising the error signal by applying a Kalman filtering algorithm to obtain the optimal estimation of the processing state vector; five steps are needed for eliminating the noise of the detection vector through a Kalman filtering algorithm;
calculating the predicted value of the process state vector at the k moment through the processing state vector at the k-1 moment and a system equation:
Figure FDA0002497116700000031
covariance prediction:
Figure FDA0002497116700000032
kalman gain:
Figure FDA0002497116700000033
and (3) the optimal estimation value of the machining state vector after noise removal of the k moment detection system is as follows:
Figure FDA0002497116700000034
and the optimal estimated value of the covariance at the k moment is used for calculating the next moment:
Figure FDA0002497116700000035
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CN114528760A (en) * 2022-02-11 2022-05-24 长春工业大学 Aero-engine gas circuit health state prediction method considering monitoring error

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Publication number Priority date Publication date Assignee Title
CN113199304A (en) * 2021-04-27 2021-08-03 中国科学院合肥物质科学研究院 Tool changing monitoring method based on extended Kalman filtering and cutting force model
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CN114528760A (en) * 2022-02-11 2022-05-24 长春工业大学 Aero-engine gas circuit health state prediction method considering monitoring error

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Application publication date: 20200814