CN116833824A - Blade breakage monitoring and residual service life prediction method for indexable gear milling cutter - Google Patents
Blade breakage monitoring and residual service life prediction method for indexable gear milling cutter Download PDFInfo
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- 238000003801 milling Methods 0.000 title claims abstract description 193
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 13
- 230000001678 irradiating effect Effects 0.000 claims abstract description 8
- 230000004083 survival effect Effects 0.000 claims description 24
- 238000003754 machining Methods 0.000 claims description 20
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000009434 installation Methods 0.000 claims description 4
- 230000008054 signal transmission Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 9
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
- B23Q17/0957—Detection of tool breakage
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/007—Arrangements for observing, indicating or measuring on machine tools for managing machine functions not concerning the tool
- B23Q17/008—Life management for parts of the machine
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Abstract
The invention discloses a method for monitoring blade breakage and predicting residual service life of an indexable gear milling cutter, which comprises the following steps: 1) Two groups of laser transmitters and photoelectric sensors are adopted and are respectively arranged at two sides of the indexable gear milling cutter, and fixed angle positions are formed for irradiating and receiving signals; 2) The photoelectric sensor is connected with an I/O circuit board, the I/O circuit board inputs a voltage signal into a singlechip of the numerical control machine tool, and the numerical control system monitors the damage condition of the blade according to the rule 0 and 1; 3) When the blade is damaged, a machine tool is reversely controlled by a macro instruction preset in the numerical control system; 4) Transmitting the collected power sensor signals and the voltage signals of the singlechip to an intelligent cutter management and control terminal; 5) The intelligent cutter management and control terminal records a data set of effective milling times and cutter blade states of the cutter through analyzing voltage signals and power sensing signals; 6) And predicting the residual service life of the cutter of the indexable gear milling cutter according to the data set. Compared with the prior art, the blade breakage monitoring method fully utilizes the computational power resources of the current numerical control machine tool to avoid the problem of blade body breakage caused by blade breakage, and is connected with the intelligent cutter management and control terminal to predict the residual service life of the indexable gear milling cutter, so that the actual processing and production requirements are met.
Description
Technical Field
The invention belongs to the field of cutter state monitoring, and particularly relates to a cutter blade breakage monitoring and residual service life prediction method of an indexable gear milling cutter.
Background
Gears have wide application in the field of mechanical equipment (e.g., automobiles, boats, airplanes, industrial machinery), whose quality and performance directly affect the efficiency, reliability, and life of the overall mechanical system. Gear machining is therefore one of the important processes in the manufacturing industry, and is critical to ensure gear quality and machining accuracy.
Indexable gear milling cutters are widely used in gear machining as an efficient milling tool. When the breakage of the insert of the indexable milling cutter cannot be monitored and replaced in time, further damage to the insert body may result. The breakage of the blade may cause uneven milling force, cause the cutter to vibrate more, reduce the quality of the machined surface, and even cause damage to the gear. In addition, breakage of the insert may also cause abnormal noise and vibration during milling, further affecting the working environment and the normal operation of the equipment. These problems can result in significant economic losses including costs in equipment maintenance and replacement, reworking products, production downtime, and the like.
During machining, tool breakage monitoring and life prediction are critical to machining quality and production efficiency. For indexable gear milling cutters, the traditional blade breakage monitoring method often depends on subjective judgment of operators, and has the problems of strong subjectivity and poor accuracy. In addition, the traditional life prediction method often depends on an empirical formula or rule, is low in accuracy and cannot meet the requirements in actual production.
Therefore, the cutter blade damage condition of the indexable gear milling cutter is accurately monitored, and measures are timely taken to replace or repair, so that the cutter blade damage is very important to avoiding cutter body damage and reducing production downtime. Meanwhile, the residual service life of the indexable milling cutter is accurately predicted, the cutter replacement plan can be reasonably arranged, and the production efficiency and the economic benefit are improved. In response to these problems, there is a need for an accurate and reliable method of blade breakage monitoring and life prediction to ensure stability, controllability and economy of the gear machining process.
Disclosure of Invention
Aiming at the technical problems existing in the prior art, the invention provides a method for monitoring the damage of the blade and predicting the residual service life of the indexable gear milling cutter, which can fully utilize the computational power resource of the current numerical control machine tool and is connected with an intelligent cutter management and control terminal.
The invention provides a method for monitoring blade breakage and predicting residual service life of an indexable gear milling cutter, which comprises the following steps:
1) And (3) equipment installation: two groups of laser transmitters and photoelectric sensors are adopted and are respectively arranged on guide rails at two sides of the indexable milling cutter, and fixed angle positions are formed for irradiating and receiving signals;
2) I/O transmission: the photoelectric sensor is connected with an I/O circuit board, the I/O circuit board inputs a voltage signal into a singlechip of the numerical control machine tool, and the numerical control system monitors the damage condition of the blade according to the rule 0 and 1;
3) Macro back control of the machine tool: the numerical control system monitors the damage condition of the blade in real time according to the rule table 0 and 1, and when the blade is damaged, the machine tool is reversely controlled by utilizing a macro instruction preset in the numerical control system;
4) And (3) signal transmission: the power sensor and the singlechip are connected to an intelligent cutter management and control terminal, the effective milling starting point and the effective milling ending point are monitored according to a power sensing signal, and the blade state of the indexable gear milling cutter is judged according to a voltage signal;
5) The processing time is as follows: when the intelligent cutter management and control terminal identifies an effective milling starting point, the counter starts to work, and when the intelligent cutter management and control terminal identifies an effective milling ending point, the counter stops working; calculating effective milling times of the indexable gear milling cutter according to the counter rule, reflecting actual processing time through the effective milling times, and correlating the effective milling times with the blade state of the indexable gear milling cutter;
6) Life prediction: the intelligent cutter management and control terminal gives identifiers of the indexable gear milling cutter related to effective milling times and blade states, files and stores the identifiers in a database to form a cutter identifier data set, and the survival estimation algorithm predicts the total milling times of the indexable gear milling cutter by calling the cutter identifier data set and predicts the residual service life of the indexable gear milling cutter according to the total milling times and the milled times of the cutter.
In the above technical scheme, in step 1, the two sets of laser transmitters and the photoelectric sensor are both installed on the guide rail, and keep the same feeding speed movement when the cutter processes the gear.
In the above technical scheme, in step 1, the laser diameter of the laser transmitter is 1mm, and the laser transmitter is respectively arranged at the top end of the blade and the top of the blade body, and two groups of light rays form a certain angle and a certain position.
In the above technical scheme, in step 1, the laser in step 1 irradiates the photoelectric sensor to form a photocurrent, and the photocurrent is converted into a voltage signal through the processing of the amplifier and the circuit.
In the above technical scheme, in step 3, the 0,1 rule is divided into 3 cases, which are respectively 0,1, 0;1 is that the photoelectric sensor receives the voltage signal sent by the laser transmitter, and 0 is that the photoelectric sensor does not receive the laser signal sent by the laser transmitter.
In the above technical scheme, in step 3, a voltage signal of 0,0 indicates that two laser beams are blocked by the blade and the blade body, and the photoelectric sensor cannot receive the laser signals; the 1,1 voltage signal indicates that two laser beams pass through a gap between the blades, and the photoelectric sensor receives the laser signals; both signals indicate that the blade is not damaged, and the machine tool does not need to be controlled reversely; 1,0 indicates that the laser irradiating the top of the blade is received by a matched photoelectric sensor, and the laser irradiating the top of the blade body is blocked by the blade body and cannot be received by the matched photoelectric sensor; the 1,0 voltage signal indicates that the blade is damaged, and the machine tool is reversely controlled by a preset macro instruction of the numerical control system.
In the above technical scheme, the preset macro instruction is to make the machine tool alarm and the indexable gear milling cutter stop feeding and retracting when the numerical control system monitors the 1,0 voltage signal, and the time for completing the instruction is about 20 milliseconds.
In the technical scheme, the SVM algorithm of the intelligent cutter management and control terminal establishes a judging model to classify and judge the state of the machine tool by the power signals acquired in real time, and the machine tool is in a milling state or an idle state.
In the above technical scheme, in step 6, the indexable gear milling cutter identifier includes information of effective milling times and blade states, and when a brand new cutter monitors an effective milling start point in processing, a counter is set to zero to start working; when the used tool monitors an effective milling starting point in the machining process, the counter inherits the effective milling times in the tool identifier and starts working; when an effective milling end point is detected in the machining, the counter stops working.
In the above technical solution, the effective milling start point and the effective milling end point are the first time point and the last time point of the milling state of the machine tool in the processing process.
In the above technical solution, the effective milling frequency is 1 added to the indexable gear milling frequency recorded by the counter every time the voltage signal is changed from 1,1 to 0,0 or 1,1 to 1,0 in the area between the effective milling start point and the end point, and the intelligent tool management and control terminal updates the power sensor signal curve and the effective milling frequency in real time.
In the technical scheme, the intelligent cutter management and control terminal gives identifiers of the indexable gear milling cutter related to effective milling times and blade states, and files the identifiers into a database to form an identifier data set of the indexable gear milling cutter.
In this embodiment, the creation of the identifier dataset of the indexable gear milling cutter is performed by continuous machining of the type of cutter, several sets of data are generated from the first milling of the cutter until the breakage of the insert has undergone several machining, and the steps are repeated continuously, eventually forming the cutter identifier dataset.
According to the technical scheme, the intelligent cutter management and control terminal survival analysis algorithm is firstly used for preparing an identifier data set of the indexable gear milling cutter, estimating survival probability of the indexable gear milling cutter blade under different milling times, calculating to obtain a survival function, predicting the total milling times of the indexable gear milling cutter according to the reliability threshold set by a user, and predicting the service life of the residual milling times according to the current milling times.
The blade breakage monitoring and residual service life predicting method for the indexable gear milling cutter has the following beneficial effects:
compared with the traditional visual monitoring method, the photoelectric sensor and the laser can provide more accurate and reliable signals through the photoelectric sensing technology, so that the possibility of misjudgment and missed judgment is reduced;
the invention adopts 0,1 rule to monitor the blade breakage, which can simplify the design of algorithm and logic, the simplicity of the rule makes the monitoring system easy to realize and maintain, the computing power resource of the current numerical control machine tool can be fully utilized, the state of the blade can be rapidly judged, when the blade breakage is monitored, the numerical control system can immediately take countercontrol measures, and further damage and production accident are avoided;
the invention can prevent the damage of the whole cutter body caused by the damage of the indexable gear milling cutter blade, reduce the frequency of equipment maintenance and replacement, save maintenance cost and avoid production interruption and economic loss caused by cutter failure;
the intelligent control terminal can also record the effective milling times and the blade state of the indexable gear milling cutter in real time through the intelligent cutter control terminal, further predict the residual service life of the cutter, give reasonable cutter replacement suggestions and realize intelligent control of the indexable gear milling cutter.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for blade breakage monitoring and remaining useful life prediction for indexable gear milling cutters according to an embodiment of the invention;
FIG. 2 is a schematic diagram of the installation of a laser transmitter and a photosensor provided by an embodiment of the present invention;
FIG. 3 is a schematic view of a laser irradiation tool according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a method for detecting blade breakage and predicting remaining service life of an indexable gear milling cutter according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an effective milling start point and an effective milling end point according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the method for monitoring the damage of the blade and predicting the residual service life of the indexable gear milling cutter according to the embodiment of the invention comprises the following steps:
1) And (3) equipment installation: two groups of laser transmitters and photoelectric sensors are adopted and are respectively arranged on guide rails at two sides of the indexable milling cutter, and fixed angle positions are formed for irradiating and receiving signals;
2) I/O transmission: the photoelectric sensor is connected with an I/O circuit board, the I/O circuit board inputs a voltage signal into a singlechip of the numerical control machine tool, and the numerical control system monitors the damage condition of the blade according to the rule 0 and 1;
3) Macro back control of the machine tool: the numerical control system monitors the damage condition of the blade in real time according to the rule table 0 and 1, and when the blade is damaged, the machine tool is reversely controlled by utilizing a macro instruction preset in the numerical control system;
4) And (3) signal transmission: the power sensor and the singlechip are connected to an intelligent cutter management and control terminal, the effective milling starting point and the effective milling ending point are monitored according to a power sensing signal, and the blade state of the indexable gear milling cutter is judged according to a voltage signal;
5) The processing time is as follows: when the intelligent cutter management and control terminal identifies an effective milling starting point, the counter starts to work, and when the intelligent cutter management and control terminal identifies an effective milling ending point, the counter stops working; calculating effective milling times of the indexable gear milling cutter according to the counter rule, reflecting actual processing time through the effective milling times, and correlating the effective milling times with the blade state of the indexable gear milling cutter;
6) Life prediction: the intelligent cutter management and control terminal gives identifiers of the indexable gear milling cutter related to effective milling times and blade states, files and stores the identifiers in a database to form a cutter identifier data set, and the survival estimation algorithm predicts the total milling times of the indexable gear milling cutter by calling the cutter identifier data set and predicts the residual service life of the indexable gear milling cutter according to the total milling times and the milled times of the cutter.
As shown in fig. 2, in the present embodiment, 1 is a first group of laser transmitters, 1 'is a first group of photosensors, 2 is a second group of laser transmitters, 2' is a second group of photosensors, and two groups of laser transmitters and photosensors are respectively mounted on two sides of the indexable gear milling cutter for transmitting and receiving laser signals at the same time.
In this embodiment, both sets of laser transmitters and photosensors are mounted on the guide rail, with which the blade breakage monitoring device remains moving at the same feed rate as the indexable milling cutter machines the gear. In this embodiment, as shown in fig. 3, the laser diameter of the laser transmitter is 1mm, and the laser transmitter is respectively arranged at the top end of the blade and the top of the blade body, and the two groups of light rays form a certain angle and a certain position.
In this embodiment, the laser light irradiates the photosensor to form a photocurrent, which is converted into a voltage signal by the processing of an amplifier and a circuit.
As shown in fig. 4, in the present embodiment, the tool breakage monitoring device includes a laser transmitter, an indexable gear milling cutter, and a photoelectric sensor.
In the embodiment, the photoelectric sensor is connected to the singlechip through a network cable, and the singlechip transmits a voltage signal to the numerical control system of the machine tool through the I/O circuit board.
In the embodiment, the numerical control system processes the voltage signal according to the 0,1 rule, and when the blade is damaged, the machine tool is reversely controlled by a macro instruction preset by the numerical control system.
In this embodiment, the 0,1 rule is divided into 3 cases, which are 0,1, 0;1 is that the photoelectric sensor receives the voltage signal sent by the laser transmitter, and 0 is that the photoelectric sensor does not receive the laser signal sent by the laser transmitter.
In the embodiment, a voltage signal of 0 and 0 indicates that two laser beams are blocked by the blade and the blade body, and the photoelectric sensor cannot receive the laser signals; the 1,1 voltage signal indicates that two laser beams pass through a gap between the blades, and the photoelectric sensor receives the laser signals; both signals indicate that the blade is unbroken; 1,0 indicates that the laser irradiating the top of the blade is received by a matched photoelectric sensor, and the laser irradiating the top of the blade body is blocked by the blade body and cannot be received by the matched photoelectric sensor; the 1,0 voltage signal indicates a breakage of the blade.
In this embodiment, the preset macro instruction of the numerical control machine is to give an alarm to the machine and stop feeding and retracting the indexable gear milling cutter when the numerical control system monitors the 1,0 voltage signal, and the instruction is completed for about 20 ms
In this embodiment, the power sensor and the single chip microcomputer are connected to the intelligent cutter management and control terminal.
In the real-time example, the intelligent cutter management and control terminal establishes a machine tool state judging model through a machine learning algorithm, and monitors an effective milling starting point and an effective milling ending point according to a power sensing signal.
In this embodiment, during the actual milling process, the indexable gear milling cutter needs to overcome the resistance of removing the material when machining the material, and the machine tool consumes a relatively large power; in the unloaded state, the machine consumes less power as no resistance to removal of material needs to be overcome.
In this embodiment, the machine tool power signal may be monitored and analyzed in real time, and the power feature data set in the milling and idle state collected in advance may be used to determine the machine tool power signal.
In this embodiment, the SVM algorithm builds a discriminant model that requires collection and preparation of machine power signal data for training and testing.
In this embodiment, the data used by the discriminant model should include power signals under known milling and idle conditions, and corresponding machine tool state labels, either in a milling or idle condition.
In this embodiment, the discriminant model extracts useful characteristic parameters, such as average power, power peaks, power spectra, etc., from the machine tool power signal data.
In this embodiment, the feature parameters of the discriminant model should have the ability to distinguish between milling and idle conditions.
In this embodiment, the discriminant model divides the data set into a training set and a test set, uses the training set data to perform training of the support vector machine model, and uses the test set data to evaluate the performance of the trained model.
In this embodiment, the discrimination model uses the trained model to classify and determine the real-time machine tool power signal, and determines whether the numerically-controlled machine tool belongs to a milling state or an idle state, so that an effective milling start point and an effective milling end point are monitored.
In this embodiment, the intelligent tool management and control terminal determines the blade status and the count of the number of effective milling times according to the voltage signal.
In this embodiment, the intelligent tool management and control terminal monitors 0,0 and 1,1 voltage signals to indicate that the blade is intact and unbroken, and 1,0 voltage signals to indicate that the blade is broken.
In this embodiment, the intelligent tool management and control terminal identifies an effective milling start point, and the counter starts to work; the counter stops working when a valid milling end point is identified. And calculating the effective milling times according to the counter rule.
In this embodiment, the effective milling frequency refers to the number of effective milling frequencies of the indexable gear milling cutter recorded by the counter plus 1 every time the voltage signal is changed from 1,1 to 0,0 or 1,1 to 1,0 in the area between the effective milling start point and the end point, and the intelligent cutter management terminal updates the power sensor signal curve and the effective milling frequency in real time.
In this embodiment, the intelligent cutter management terminal assigns identifiers to indexable gear cutters that correlate the number of effective milling times and the status of the blades, and documents the identifiers to a database to form an identifier dataset for the indexable gear cutters.
In the embodiment, the identifier of the indexable gear milling cutter contains information of effective milling times and blade states, and when a brand new cutter monitors an effective milling starting point in processing, a counter is set to zero to start working; when the used tool monitors an effective milling starting point in the machining process, the counter inherits the effective milling times in the tool identifier and starts working; when an effective milling end point is detected in the machining, the counter stops working.
In this embodiment, the effective milling start point and the effective milling end point are the first time point and the last time point of the milling state of the machine tool during the machining process.
In this embodiment, the intelligent tool management and control terminal predicts the total milling times of the indexable gear milling cutter through a survival analysis algorithm, and predicts the remaining service life of the tool according to the total milling times and the milled times of the tool.
In this embodiment, the intelligent tool management and control terminal analysis algorithm needs to call the identifier dataset of the indexable gear milling cutter, the creation of the dataset is through the continuous machining of the type of tool, several sets of data are generated from the first milling of the tool until the breakage of the blade has undergone several machining, and the steps are repeated continuously to form the tool identifier dataset.
In this embodiment, the intelligent tool management and control terminal estimates the survival probability by using a Kaplan-Meier algorithm, so as to obtain a survival function, and predicts the total milling times achievable by the type of tools according to a set survival probability threshold, and the data is used as the milling times of the total service life of the tools. And predicting the service life of the rest milling times according to the current milling times.
In this embodiment, kaplan-Meier estimates a survival function used to estimate event occurrence time. The Kaplan-Meier estimation can be used to estimate the lifetime of a new tool class, i.e. the total milling times.
In this embodiment, each sample should include milling times and scrapping states, and for non-scrapped tools, the scrapping states are considered to be inspected, the data sets are ordered according to actual milling times, the number of tools and the number of scrapped tools corresponding to each milling time are recorded, and the survival probability of each milling time is calculated according to a Kaplan-Meier estimation formula.
In this embodiment, the survival probability is the probability that the tool keeps operating normally before a given time point, and the survival probability at the initial time is 1, and when the tool is scrapped, the survival probability is reduced.
In this embodiment, a survival function curve is drawn according to the calculated survival probability, the horizontal axis represents the milling number, and the vertical axis represents the survival probability. The survival function curve may describe the service life of the indexable gear milling cutter, i.e., the total number of milling passes of the cutter.
In this embodiment, the intelligent tool control terminal may predict the milling frequency according to the survival function curve and the survival probability threshold set by the user independently, as the total milling frequency of the indexable gear milling cutter.
In this embodiment, the intelligent tool management and control terminal analysis algorithm predicts the total milling times of the indexable gear milling cutter, and predicts the remaining service life of the tool according to the total milling times and the milled times of the tool.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (10)
1. The method for monitoring the damage of the blade and predicting the residual service life of the indexable gear milling cutter is characterized by at least comprising the following steps:
step 1) equipment installation: two groups of laser transmitters and photoelectric sensors are adopted and are respectively arranged on guide rails at two sides of the indexable milling cutter, and fixed angle positions are formed for irradiating and receiving signals;
step 2) I/O transfer: the photoelectric sensor is connected with an I/O circuit board, the I/O circuit board inputs a voltage signal into a singlechip of the numerical control machine tool, and the numerical control system monitors the damage condition of the blade according to the rule 0 and 1;
step 3) machine tool macro reverse control: the numerical control system monitors the damage condition of the blade in real time according to the rule table 0 and 1, and when the blade is damaged, the machine tool is reversely controlled by utilizing a macro instruction preset in the numerical control system;
step 4) signal transmission: the power sensor and the singlechip are connected to an intelligent cutter management and control terminal, the effective milling starting point and the effective milling ending point are monitored according to a power sensing signal, and the blade state of the indexable gear milling cutter is judged according to a voltage signal;
step 5) processing time: when the intelligent cutter management and control terminal identifies an effective milling starting point, the counter starts to work, and when the intelligent cutter management and control terminal identifies an effective milling ending point, the counter stops working; calculating effective milling times of the indexable gear milling cutter according to the counter rule, reflecting actual processing time through the effective milling times, and correlating the effective milling times with the blade state of the indexable gear milling cutter;
step 6) life prediction: the intelligent cutter management and control terminal gives identifiers of the indexable gear milling cutter related to effective milling times and blade states, files and stores the identifiers in a database to form a cutter identifier data set, and the survival estimation algorithm predicts the total milling times of the indexable gear milling cutter by calling the cutter identifier data set and predicts the residual service life of the indexable gear milling cutter according to the total milling times and the milled times of the cutter.
2. The method for detecting breakage of a cutting insert and predicting remaining life of an indexable gear milling cutter according to claim 1, wherein in step 1, the two sets of laser transmitters and photosensors are mounted on guide rails on both sides of the indexable gear milling cutter, and the same feeding speed is maintained during gear machining by the cutter.
3. The method for detecting the breakage of a blade and predicting the remaining service life of an indexable gear milling cutter according to claim 1, wherein in the step 1, the laser diameter of the laser transmitter is 1mm, the laser transmitter is respectively arranged at the top end of the blade and the top of the cutter body, and two groups of light rays form a certain angle and a certain position.
4. The method for detecting breakage of a cutting insert and predicting remaining life of an indexable gear milling cutter according to claim 1, wherein in step 3, the 0,1 rule is divided into 3 cases, 0,1, 0 respectively; both signals 0,0 and 1,1 indicate that the blade is unbroken, and the machine tool does not need to be controlled reversely; the 1,0 voltage signal indicates that the blade is damaged, and the machine tool is immediately reversely controlled by a preset macro instruction of the numerical control system.
5. The method for predicting the breakage of a cutting insert and the remaining service life of an indexable gear milling cutter according to claim 3, wherein the preset macro instruction is to give an alarm to the machine tool and stop feeding and retracting the indexable gear milling cutter when the numerical control system monitors a 1,0 voltage signal, and the instruction is completed for about 20 milliseconds.
6. The method for detecting the breakage of the blade and predicting the remaining service life of the indexable gear milling cutter according to claim 1, wherein in the step 4, the SVM algorithm of the intelligent cutter management and control terminal establishes a discrimination model to classify the state of the machine tool by the power signal collected in real time, and judges whether the machine tool is in a milling state or an idle state.
7. The method for predicting the breakage of a cutting insert and the remaining service life of an indexable gear milling cutter according to claim 1, wherein in step 6, the identifier of the indexable gear milling cutter contains information of effective milling times and a cutting insert state, and when a brand new cutter monitors an effective milling starting point in processing, a counter is set to zero to start working; when the used tool monitors an effective milling starting point in the machining process, the counter inherits the effective milling times in the tool identifier and starts working; when an effective milling end point is detected in the machining, the counter stops working.
8. The method for predicting the breakage of a cutting insert and the remaining life of an indexable gear milling cutter according to claim 7, wherein the effective milling start point and the effective milling end point are a first time point and a last time point of a milling state of the machine tool during the machining process.
9. The method for predicting the breakage of a cutting insert and the remaining life of an indexable gear milling cutter according to claim 7, wherein the effective milling frequency is 1 in the area between the starting point and the ending point of the effective milling, and the intelligent cutter management terminal updates the power sensor signal curve and the effective milling frequency in real time every time the voltage signal is changed from 1,1 to 0,0 or 1,1 to 1, 0.
10. The method for detecting breakage of a cutting insert and predicting remaining life of an indexable gear milling cutter according to claim 1, wherein in step 6, the intelligent cutter control terminal survival analysis algorithm firstly prepares an identifier dataset of the indexable gear milling cutter, estimates survival probability of the indexable gear milling cutter cutting insert at different milling times, calculates a survival function, and predicts total milling times of the indexable gear milling cutter according to a set reliability threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310836058.9A CN116833824A (en) | 2023-07-08 | 2023-07-08 | Blade breakage monitoring and residual service life prediction method for indexable gear milling cutter |
Applications Claiming Priority (1)
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| CN202310836058.9A CN116833824A (en) | 2023-07-08 | 2023-07-08 | Blade breakage monitoring and residual service life prediction method for indexable gear milling cutter |
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