CN114905334A - Intelligent real-time cleaning cutting monitoring system and method - Google Patents
Intelligent real-time cleaning cutting monitoring system and method Download PDFInfo
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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
- B23Q17/09—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
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
- B23Q17/0966—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 by measuring a force on parts of the machine other than a motor
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
- B23Q17/12—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring vibration
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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
- B23Q17/20—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring workpiece characteristics, e.g. contour, dimension, hardness
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- 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
- G05B19/406—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 monitoring or safety
- G05B19/4065—Monitoring tool breakage, life or condition
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses an intelligent real-time cleaning cutting monitoring system and method, which are used for system monitoring and comprise the following steps: the system comprises an acquisition module, a processing module, a control module, a display module, a communication module, remote terminal equipment and a modeling system; the control module controls the acquisition module to synchronously acquire the processing process information, integrate the processing process information with hardware and send the processing process information to the modeling system; the processing module processes the signals acquired by the acquisition module to obtain two-dimensional data and transmits the two-dimensional data to the modeling system; the modeling system establishes an offline network model based on the two-dimensional data and the processing process information; the acquisition module transmits the real-time processing process information to the offline network module to obtain real-time cutter information, and the real-time cutter information is displayed through the display module; and the communication module transmits the real-time cutter information and the machining process information to the remote terminal equipment. The invention carries out real-time on-line monitoring on the cutting processing state, can consider different materials, different cutting quantities and different working conditions, and has higher system universality.
Description
Technical Field
The invention relates to the technical field of monitoring, in particular to an intelligent real-time cleaning cutting monitoring system and method.
Background
In the cutting process, the surface quality of a workpiece is reduced due to the abrasion of the cutter, and particularly in the severe abrasion stage of the cutter, the abrasion value of the cutter is greatly changed in a short time, so that the dimensional precision of a processed part cannot meet the target requirement. Monitoring the wear state of the cutter is one of important means for reducing the manufacturing cost, reducing the manufacturing environment hazard and ensuring the normal and efficient operation of a production and manufacturing system and the product quality. And the machining state can be more visually displayed based on the online monitoring of the roughness of the machined surface under the abrasion state of the cutter, so that the machining process is ensured to be smoothly carried out. The existing cutting intelligent monitoring system is not high in integration and real-time performance and covers an environment sensor related to clean cutting.
Therefore, a new cutting intelligent monitoring system and method are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an intelligent real-time clean cutting monitoring system and method, which solve the problem of real-time online monitoring of tool abrasion and surface roughness in a machining process under the background of clean cutting and intelligent manufacturing.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent real-time cleaning cutting monitoring system, comprising: the system comprises an acquisition module, a processing module, a control module, a display module, a communication module, remote terminal equipment and a modeling system;
the control module controls the acquisition module to synchronously acquire the processing process information, integrate the hardware and send the processing process information to the modeling system;
the processing module processes the signals acquired by the acquisition module to obtain two-dimensional data, and transmits the two-dimensional data to the modeling system;
the modeling system establishes an off-line network model based on the two-dimensional data and the processing procedure information;
the acquisition module transmits the real-time machining process information to the offline network module to obtain real-time cutter information, and the real-time cutter information is displayed through the display module;
and the communication module transmits the real-time cutter information and the machining process information to the remote terminal equipment.
Optionally, the collecting module includes: three-dimensional piezoelectric type cutting force sensor, three-dimensional vibration acceleration sensor, sound sensor, dust particle sensor, kirschner microscope and surface roughness appearance.
Optionally, the machining process information includes work information, tool information, and workpiece material information in the machining process of the machine tool.
Optionally, the processing module processes the acquired signal by using LabVIEW software and combining with a deep learning neural network program of python.
An intelligent real-time cleaning cutting monitoring method comprises the following steps:
collecting working information, cutter information and workpiece material information in the machining process of a machine tool;
analyzing and processing the working information through a PCA technology and a radar map, and establishing an offline network model by combining the cutter;
inputting the real-time work information and the workpiece material information into the offline network model to obtain a real-time offline network model;
and inputting the real-time working information into the real-time off-line network model to obtain the real-time cutter information.
Optionally, the work information includes: cutting force, cutting force variation, cutting position vibration amount, noise variation, and dust amount.
Optionally, the tool information includes: the abrasion loss of the cutter and the surface roughness of the workpiece.
Optionally, the analysis processing method includes: quantitative characterization, data dimension reduction and feature fusion.
Optionally, the method further includes: and displaying the cutting force, the vibration quantity of the cutting position, the noise, the dust quantity and the cutter information in real time.
Optionally, the specific content of the real-time cutter information obtained by inputting the real-time working information into the real-time offline network model is as follows:
the data characteristics obtained after the processing by the characteristic fusion method are extracted according to a characteristic formula, and time domain characteristics and frequency domain characteristics which reflect the cutter wear information and the machined surface roughness information are extracted;
and taking the data characteristics n seconds before the real-time working information and the workpiece material information as the input of the real-time off-line network model to obtain the real-time cutter information.
Compared with the prior art, the beneficial effects of the invention are as follows:
the monitoring system with higher software and hardware integration is provided, the cutting machining state is monitored on line in real time, different materials, different cutting quantities and different working conditions can be considered, and the system universality is higher; an environment sensor related to clean cutting is added, and meanwhile, environmental parameters such as sound, dust and the like are monitored on line in real time; and the multisource heterogeneous data collected by the multiple sensors are subjected to fusion and dimension reduction processing and are used for monitoring tool wear and surface roughness, so that the accuracy and the model training efficiency are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of the hardware system construction of the system of the present invention;
FIG. 2 is a system display front panel of the present invention;
FIG. 3 is a block diagram of a system implementation of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The embodiment discloses a clean cutting monitored control system in real time of intelligence, includes: the system comprises an acquisition module, a processing module, a control module, a display module, a communication module, remote terminal equipment and a modeling system;
the control module controls the acquisition module to synchronously acquire the processing process information, integrate the processing process information with hardware and send the processing process information to the modeling system;
the processing module processes the signals acquired by the acquisition module to obtain two-dimensional data and transmits the two-dimensional data to the modeling system;
the modeling system establishes an offline network model based on the two-dimensional data and the processing process information;
the acquisition module transmits the real-time processing process information to the offline network module to obtain real-time cutter information, and the real-time cutter information is displayed through the display module;
and the communication module transmits the real-time cutter information and the machining process information to the remote terminal equipment.
Specifically, the method comprises the following steps:
the collection module includes: three-dimensional piezoelectric type cutting force sensor, three-dimensional vibration acceleration sensor, sound sensor, dust particle sensor, kirschner microscope and surface roughness appearance. The three-way piezoelectric type cutting force sensor is used for measuring the cutting force and the variation of an X, Y, Z shaft in the machining process of a machine tool in real time; the three-way vibration acceleration sensor is used for measuring the vibration quantity of a cutting position in the machining process of the machine tool in real time; the sound sensor is used for measuring sound change and noise in the machining process of the machine tool in real time; the dust particle sensor is used for measuring the values of dust harmful to human bodies, such as PM2.5, PM10 and the like in the machining process of the machine tool in real time; the Ginshi microscope and the surface roughness instrument are used for observing tool wear and surface roughness data; in the embodiment, the cutting force sensor is arranged between the machine tool workbench and a workpiece material, the vibration sensor is arranged on the workpiece material, the sound sensors are respectively arranged in the machine tool box body, the dust particle sensor is arranged outside the machine tool box body, various sensor signals are collected by the collecting card, and the machine box is connected with the display screen to display data in real time and is connected with the remote transmission equipment through a network cable.
The processing process information comprises working information, cutter information and workpiece material information in the processing process of the machine tool.
And the processing module adopts LabVIEW software and combines a deep learning neural network program of python to process the acquired signals.
The display module is used for displaying cutting force, vibration acceleration, noise condition, PM2.5, PM10, cutter abrasion condition, machining surface roughness and the like on an interface in real time by building a LabVIEW front panel; the communication module remotely transmits the LabVIEW front panel by adopting a wireless network or a server to realize the purpose of remote real-time monitoring. Fig. 2 shows a software display front panel in this embodiment, in which the left side displays the time domain and frequency domain value changes of the directly measured data, and displays the values in real time, wherein the value display is updated every second, and the value is equal to the average value of the second data. And a setting panel for processing parameters is arranged above the right side, and different sensor measurement modes and indirect measurement network models are determined according to different parameter settings. And a real-time display interface for tool abrasion and machining surface roughness is arranged below the right side, the machining state is determined through real-time displayed data, and whether tool changing is needed or not is determined.
Example 2
The embodiment discloses an intelligent real-time cleaning cutting monitoring method, which comprises the following steps:
an online data acquisition stage: collecting working information, cutter information and workpiece material information in the machining process of a machine tool;
an off-line modeling stage: analyzing and processing the working information through a PCA technology and a radar map, and establishing an offline network model by combining a cutter;
inputting the real-time working information and the workpiece material information into an offline network model to obtain a real-time offline network model; specifically, the method comprises the following steps: aiming at different materials, different cutting amounts and different working conditions, a large amount of processing data features measured by a sensor in a monitoring system are combined with a principal component analysis PCA and radar chart method for dimensionality reduction processing to become two-dimensional data only containing the perimeter and the area of the radar chart as input, and a neural network is trained offline together with tool wear and surface roughness data observed by a Gihness microscope and a surface roughness meter to obtain an offline network model, and the trained network model is put into a cutting processing database.
A processing information real-time monitoring stage: and inputting the real-time working information into the real-time off-line network model to obtain real-time cutter information.
The work information includes: cutting force, cutting force variation, cutting position vibration amount, noise variation, and dust amount. The tool information includes: the abrasion loss of the cutter and the surface roughness of the workpiece.
The analysis processing method comprises the following steps: quantitative characterization, data dimension reduction and feature fusion.
Further comprising: and displaying the cutting force, the vibration quantity of the cutting position, the noise, the dust quantity and the cutter information in real time.
Inputting the real-time working information into a real-time off-line network model, and obtaining the specific content of the real-time cutter information as follows:
the data characteristics obtained after the processing by the characteristic fusion method are extracted according to a characteristic formula to obtain time domain characteristics and frequency domain characteristics which reflect the cutter abrasion information and the processing surface roughness information; specifically, the method comprises the following steps: the three-direction cutting force, the three-direction vibration acceleration and the sound signal are dynamic high-frequency signals, the dust particle signal is a dynamic low-frequency signal, and different signal sources and data structures are different. In the on-line data acquisition stage, multi-source heterogeneous signals of three-way cutting force, three-way vibration acceleration, sound signals and dust particle signals are dynamically acquired, quantitative representation, data dimensionality reduction and feature fusion are carried out, and physical quantities which can be directly monitored are obtained; cutting force, vibration acceleration, sound, PM2.5, PM10 are displayed in real time. Wherein, the time domain characteristic and the frequency domain characteristic which can objectively reflect the tool wear information and the processing surface roughness information are extracted from the data characteristic according to the expressions (1) to (10).
Time domain characteristics:
Max＝max(|x i |)&Min＝min(|x i |)(2、3)；
wherein x is i Represents the signal collected by the ith sensor in a certain time period in the cutting process, wherein i is 1, 2, 3, … … N; n is the number of sensors; m represents the mean value of the monitoring signals collected in a certain time period in the cutting process, namely the length of a power spectrum;
frequency domain characteristics: max represents the maximum value of the absolute value of the monitoring signal acquired in a certain time period in the cutting process; min is the minimum value of the absolute value of the monitoring signal acquired within a certain time period in the cutting process; RMS denotes the intensity of the monitoring signal at a certain time period during the cutting process; var represents the degree of fluctuation of the monitoring signal around the mean value in a certain period of time during the cutting process; cov (X, Y) represents the total error condition of two variables of the monitoring signal over a certain period of time during the cutting process; skew (x) represents the asymmetry of the monitoring signal with the mean as the symmetry line in a certain time period in the cutting process; kurt represents the kurtosis of the monitoring signal collected in a certain time period in the cutting process, namely the transient phenomenon and the stationarity of the monitoring signal;
wherein f is i The spectrum of the monitoring signal acquired in a given certain time period is converted from a time domain signal, namely an original signal, through Fast Fourier Transform (FFT); p (f) i ) Representing a power spectral density of the monitored signal; FCG is the frequency center of gravity of the monitoring signal and is the static part of the frequency spectrum; FV is the frequency variance of the monitoring signal, the dynamic part of the spectrum, which reflects the degree of fluctuation of the spectrum of the monitoring signal near the center of gravity of the frequency.
It should be noted that M is an average value of the monitoring signals acquired within a certain time period in the cutting process, is a static part of the monitoring signals, and reflects a variation trend of the monitoring signals; max and Min are respectively the maximum value and the minimum value of the absolute value of the monitoring signal acquired in a certain time period in the cutting process, and reflect the variation range of the monitoring signal;
and taking the data characteristics n seconds before the real-time working information and the workpiece material information as the input of the real-time off-line network model to obtain the real-time cutter information. Real-time processing data, cutter abrasion and surface roughness information are displayed on a constructed LabVIEW front panel in real time, and data in the LabVIEW front panel are displayed on a remote mobile phone end or a remote computer end through networking service.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The utility model provides a real-time clean cutting monitored control system of intelligence which characterized in that includes: the system comprises an acquisition module, a processing module, a control module, a display module, a communication module, remote terminal equipment and a modeling system;
the control module controls the acquisition module to synchronously acquire the processing process information, integrate the hardware and send the processing process information to the modeling system;
the processing module processes the signals acquired by the acquisition module to obtain two-dimensional data, and transmits the two-dimensional data to the modeling system;
the modeling system establishes an off-line network model based on the two-dimensional data and the processing procedure information;
the acquisition module transmits the real-time machining process information to the offline network module to obtain real-time cutter information, and the real-time cutter information is displayed through the display module;
and the communication module transmits the real-time cutter information and the machining process information to the remote terminal equipment.
2. The intelligent real-time cleaning cutting monitoring system of claim 1, wherein the acquisition module comprises: three-dimensional piezoelectric type cutting force sensor, three-dimensional vibration acceleration sensor, sound sensor, dust particle sensor, kirschner microscope and surface roughness appearance.
3. The system for intelligently monitoring the real-time cleaning cutting of the machine tool as claimed in claim 1, wherein the processing information comprises working information, tool information and workpiece material information in the processing process of the machine tool.
4. The intelligent real-time cleaning cutting monitoring system as claimed in claim 1, wherein the processing module processes the collected signals by using LabVIEW software in combination with python's deep learning neural network program.
5. An intelligent real-time cleaning cutting monitoring method is characterized by comprising the following steps:
collecting working information, cutter information and workpiece material information in the machining process of a machine tool;
analyzing and processing the working information through a PCA technology and a radar map, and establishing an offline network model by combining the cutter;
inputting the real-time work information and the workpiece material information into the offline network model to obtain a real-time offline network model;
and inputting the real-time working information into the real-time off-line network model to obtain the real-time cutter information.
6. The intelligent real-time cleaning cutting monitoring method according to claim 5, wherein the working information comprises: cutting force, cutting force variation, cutting position vibration amount, noise variation, and dust amount.
7. The intelligent real-time cleaning cutting monitoring method according to claim 5, wherein the cutter information comprises: the abrasion loss of the cutter and the surface roughness of the workpiece.
8. The intelligent real-time cleaning cutting monitoring method according to claim 5, wherein the analysis processing method comprises the following steps: quantitative characterization, data dimension reduction and feature fusion.
9. The intelligent real-time cleaning cutting monitoring method according to claim 5, further comprising: and displaying the cutting force, the vibration quantity of the cutting position, the noise, the dust quantity and the cutter information in real time.
10. The intelligent real-time cleaning cutting monitoring method according to claim 5, wherein the real-time working information is input into the real-time off-line network model, and the specific content of the real-time cutter information is as follows:
the data characteristics obtained after the processing by the characteristic fusion method are extracted according to a characteristic formula, and time domain characteristics and frequency domain characteristics which reflect the cutter wear information and the machined surface roughness information are extracted;
and taking the data characteristics n seconds before the real-time working information and the workpiece material information as the input of the real-time off-line network model to obtain the real-time cutter information.
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| CN108846581A (en) * | 2018-06-21 | 2018-11-20 | 武汉科技大学 | A kind of machine tool reliability evaluation system and method |
| CN109822399B (en) * | 2019-04-08 | 2020-07-14 | 浙江大学 | Numerical control machine tool cutter wear state prediction method based on parallel deep neural network |
| CN110561193B (en) * | 2019-09-18 | 2020-09-29 | 杭州友机技术有限公司 | Cutter wear assessment and monitoring method and system based on feature fusion |
| CN110900307B (en) * | 2019-11-22 | 2020-12-15 | 北京航空航天大学 | Numerical control machine tool cutter monitoring system driven by digital twin |
| CN111069976B (en) * | 2020-01-19 | 2021-08-17 | 南京理工大学 | Intelligent mobile monitoring system and method for damage of cutter for workshop or production line |
| CN111783544B (en) * | 2020-06-02 | 2023-09-01 | 华侨大学 | Method for building diamond milling grinding head state monitoring system for processing ceramic mobile phone backboard |
| CN111890127B (en) * | 2020-08-06 | 2022-09-20 | 南京航空航天大学 | Cutting state edge intelligent monitoring method based on online incremental wear evolution model |
| CN112192319A (en) * | 2020-09-28 | 2021-01-08 | 上海交通大学 | Tool wear monitoring method and system of unsupervised model |
| CN112578732A (en) * | 2020-12-15 | 2021-03-30 | 航天科工深圳（集团）有限公司 | Intelligent cutting process monitoring system and monitoring method thereof |
| CN113741377A (en) * | 2021-09-29 | 2021-12-03 | 上海理工大学 | Machining process intelligent monitoring system and method based on cutting characteristic selection |
| CN113927371A (en) * | 2021-11-05 | 2022-01-14 | 太原科技大学 | Cutter wear prediction method based on multi-sensor feature fusion |
| CN114273977A (en) * | 2021-12-15 | 2022-04-05 | 重庆文高科技有限公司 | MES-based cutter wear detection method and system |
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| CN115139155A (en) * | 2022-08-22 | 2022-10-04 | 北京理工大学 | Performance evaluation method for intelligent monitoring system in machining process |
| CN115139155B (en) * | 2022-08-22 | 2023-10-20 | 北京理工大学 | Performance evaluation method of intelligent monitoring system for machining process |
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