CN114905334A - An intelligent real-time cleaning and cutting monitoring system and method - Google Patents
An intelligent real-time cleaning and cutting monitoring system and method Download PDFInfo
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Abstract
Description
技术领域technical field
本发明涉及监控技术领域,特别涉及一种智能实时清洁切削监控系统和方法。The invention relates to the technical field of monitoring, in particular to an intelligent real-time cleaning and cutting monitoring system and method.
背景技术Background technique
在切削加工过程中,刀具磨损会降低工件的表面质量,特别是在刀具剧烈磨损阶段,在较短的时间内刀具磨损值会发生较大变化,导致所加工零件的尺寸精度不能满足目标要求。监测刀具磨损状态是降低制造成本、减少制造环境危害,保证生产制造系统正常高效运行和产品质量的重要手段之一。而基于刀具磨损状态下对加工表面粗糙度的在线监测可以更加直观的展示加工状态,保证加工过程顺利进行。目前已有的切削智能监测系统集成性、实时性不高且涵盖清洁切削相关的环境传感器。In the cutting process, tool wear will reduce the surface quality of the workpiece, especially in the stage of severe tool wear, the tool wear value will change greatly in a short period of time, resulting in the dimensional accuracy of the machined parts cannot meet the target requirements. Monitoring tool wear status is one of the important means to reduce manufacturing costs, reduce manufacturing environmental hazards, and ensure the normal and efficient operation of manufacturing systems and product quality. The online monitoring of the machined surface roughness based on the tool wear state can display the machining state more intuitively and ensure the smooth progress of the machining process. The existing cutting intelligence monitoring systems are not highly integrated and real-time, and cover environmental sensors related to clean cutting.
因此,亟需一种新的切削智能监测系统和方法以解决上述问题。Therefore, there is an urgent need for a new cutting intelligent monitoring system and method to solve the above problems.
发明内容SUMMARY OF THE INVENTION
本发明的目的在于,提供一种智能实时清洁切削监控系统和方法,解决在清洁切削和智能制造背景下对加工过程刀具磨损和表面粗糙度的实时在线监测的问题。The purpose of the present invention is to provide an intelligent real-time cleaning and cutting monitoring system and method, which solves the problem of real-time online monitoring of tool wear and surface roughness during processing under the background of clean cutting and intelligent manufacturing.
为了实现上述目的,本发明提供了如下技术方案:In order to achieve the above object, the present invention provides the following technical solutions:
一种智能实时清洁切削监控系统,包括:采集模块、处理模块、控制模块、显示模块、通信模块、远程终端设备、建模系统;An intelligent real-time cleaning and cutting monitoring system, comprising: an acquisition module, a processing module, a control module, a display module, a communication module, a remote terminal device, and a modeling system;
所述控制模块控制所述采集模块对加工过程信息进行同步采集和硬件集成,并发送至所述建模系统;The control module controls the acquisition module to perform synchronous acquisition and hardware integration of processing process information, and send it to the modeling system;
所述处理模块对所述采集模块采集的信号进行处理,得到两维数据,并传输至所述建模系统;The processing module processes the signals collected by the acquisition module to obtain two-dimensional data, which are transmitted to the modeling system;
所述建模系统基于所述两维数据和所述加工过程信息建立离线网络模型;The modeling system establishes an offline network model based on the two-dimensional data and the processing process information;
所述采集模块将实时所述加工过程信息传输至所述离线网络模块,获得实时刀具信息,并通过所述显示模块进行显示;The acquisition module transmits the real-time processing process information to the offline network module, obtains real-time tool information, and displays it through the display module;
所述通信模块将所述实时刀具信息、所述加工过程信息传输至所述远程终端设备。The communication module transmits the real-time tool information and the machining process information to the remote terminal device.
可选的,所述采集模块包括:三向压电式切削力传感器、三向振动加速度传感器、声音传感器、尘埃粒子传感器、基恩士显微镜及表面粗糙度仪。Optionally, the acquisition module includes: a three-way piezoelectric cutting force sensor, a three-way vibration acceleration sensor, a sound sensor, a dust particle sensor, a Keyence microscope and a surface roughness meter.
可选的,所述加工过程信息包括:机床加工过程中工作信息、刀具信息、工件材料信息。Optionally, the processing process information includes: work information, tool information, and workpiece material information in the machining process of the machine tool.
可选的,所述处理模块采用LabVIEW软件并结合python的深度学习神经网络程序对采集到的信号进行处理。Optionally, the processing module uses LabVIEW software combined with a python deep learning neural network program to process the collected signals.
一种智能实时清洁切削监控方法,包括以下内容:An intelligent real-time cleaning and cutting monitoring method, comprising the following contents:
采集机床加工过程中工作信息、刀具信息、工件材料信息;Collect work information, tool information and workpiece material information during machine tool processing;
通过PCA技术和雷达图对所述工作信息进行分析处理,并结合所述刀具建立离线网络模型;Analyze and process the work information through PCA technology and radar charts, and establish an offline network model in combination with the tool;
将实时所述工作信息和所述工件材料信息输入所述离线网络模型,获得实时离线网络模型;Inputting the real-time work information and the workpiece material information into the offline network model to obtain a real-time offline network model;
将实时所述工作信息输入所述实时离线网络模型,得到实时所述刀具信息。The real-time working information is input into the real-time offline network model to obtain the real-time tool information.
可选的,所述工作信息包括:切削力、切削力变化量、切削位置振动量、噪声、噪声变化量、粉尘量。Optionally, the work information includes: cutting force, amount of change in cutting force, amount of vibration at cutting position, noise, amount of noise change, and amount of dust.
可选的,所述刀具信息包括:刀具磨损量、工件表面粗糙度。Optionally, the tool information includes: tool wear amount and workpiece surface roughness.
可选的,所述分析处理方法包括:量化表征、数据降维、特征融合。Optionally, the analysis and processing method includes: quantitative representation, data dimensionality reduction, and feature fusion.
可选的,还包括:实时将切削力、切削位置振动量、噪声、粉尘量、刀具信息进行显示。Optionally, it also includes: real-time display of cutting force, vibration amount of cutting position, noise, dust amount, and tool information.
可选的,将实时所述工作信息输入所述实时离线网络模型,得到实时所述刀具信息的具体内容为:Optionally, the real-time work information is input into the real-time offline network model, and the specific content that obtains the real-time tool information is:
将经所述特征融合方法处理后获得数据特征,按特征公式提取出反映刀具磨损信息和加工表面粗糙度信息的时域特征、频域特征;The data features are obtained after being processed by the feature fusion method, and the time domain features and frequency domain features that reflect the tool wear information and the machining surface roughness information are extracted according to the feature formula;
将实时所述工作信息和所述工件材料信息前n秒的数据特征作为所述实时离线网络模型的输入,获得实时所述刀具信息。The real-time tool information is obtained by using the real-time data features of the work information and the workpiece material information in the first n seconds as the input of the real-time offline network model.
经由上述的内容可知与现有技术相比,本发明的有益效果在于:Compared with the prior art, the beneficial effects of the present invention are as follows:
提出了一种软件硬件集成性较高的监测系统,对切削加工状态进行实时的在线监测,并能考虑到不同材料、不同切削用量和不同工况,系统普适性较高;加入了与清洁切削相关的环境传感器,同时对声音、粉尘等环境参数实时在线监测;对多传感器采集到的多源异构数据融合降维处理用于刀具磨损和表面粗糙度的监测提高准确性和模型训练效率。A monitoring system with high software and hardware integration is proposed, which can conduct real-time online monitoring of cutting processing status, and can take into account different materials, different cutting quantities and different working conditions, and the system has high universality; Cutting-related environmental sensors, and real-time online monitoring of environmental parameters such as sound and dust; fusion and dimension reduction processing of multi-source heterogeneous data collected by multi-sensors to monitor tool wear and surface roughness to improve accuracy and model training efficiency .
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据提供的附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative work.
图1为本发明系统硬件系统搭建示意图;1 is a schematic diagram of the construction of the hardware system of the system of the present invention;
图2为本发明系统显示前面板;Fig. 2 is the display front panel of the system of the present invention;
图3为本发明系统实现框图。FIG. 3 is a block diagram of the implementation of the system of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
实施例1Example 1
本实施例公开了一种智能实时清洁切削监控系统,包括:采集模块、处理模块、控制模块、显示模块、通信模块、远程终端设备、建模系统;The present embodiment discloses an intelligent real-time cleaning and cutting monitoring system, including: a collection module, a processing module, a control module, a display module, a communication module, a remote terminal device, and a modeling system;
控制模块控制采集模块对加工过程信息进行同步采集和硬件集成,并发送至建模系统;The control module controls the acquisition module to synchronously acquire and integrate the processing process information, and send it to the modeling system;
处理模块对采集模块采集的信号进行处理,得到两维数据,并传输至建模系统;The processing module processes the signals collected by the acquisition module, obtains two-dimensional data, and transmits it to the modeling system;
建模系统基于两维数据和加工过程信息建立离线网络模型;The modeling system establishes an offline network model based on two-dimensional data and processing process information;
采集模块将实时加工过程信息传输至离线网络模块,获得实时刀具信息,并通过显示模块进行显示;The acquisition module transmits real-time machining process information to the offline network module, obtains real-time tool information, and displays it through the display module;
通信模块将实时刀具信息、加工过程信息传输至远程终端设备。The communication module transmits real-time tool information and machining process information to remote terminal equipment.
具体的:specific:
采集模块包括:三向压电式切削力传感器、三向振动加速度传感器、声音传感器、尘埃粒子传感器、基恩士显微镜及表面粗糙度仪。其中,三向压电式切削力传感器用于实时测量机床加工过程中的X、Y、Z轴的切削力大小和变化量;三向振动加速度传感器用于实时测量机床加工过程中切削位置的振动量;声音传感器用于实时测量机床加工过程中的声音变化和噪声大小;尘埃粒子传感器用于实时测量机床加工过程中的PM2.5和PM10等对人体有危害的粉尘数值;基恩士显微镜及表面粗糙度仪用于观测的刀具磨损与表面粗糙度数据;在本实施例中将切削力传感器安装在机床工作台和工件材料中间,振动传感器安装在工件材料上,声音传感器分别放在机床箱体之中,尘埃粒子传感器放在机床箱体之外,用采集卡采集各类传感器信号,将机箱连接显示屏实时显示数据并通过网线连接远程传输设备。The acquisition module includes: three-way piezoelectric cutting force sensor, three-way vibration acceleration sensor, sound sensor, dust particle sensor, KEYENCE microscope and surface roughness meter. Among them, the three-way piezoelectric cutting force sensor is used for real-time measurement of the cutting force and variation of X, Y, and Z axes during the machining process of the machine tool; the three-way vibration acceleration sensor is used for real-time measurement of the vibration of the cutting position during the machining process of the machine tool. The sound sensor is used to measure the sound change and noise level in the machining process of the machine tool in real time; the dust particle sensor is used to measure the PM2. The surface roughness meter is used to observe the tool wear and surface roughness data; in this embodiment, the cutting force sensor is installed between the machine tool table and the workpiece material, the vibration sensor is installed on the workpiece material, and the sound sensor is placed in the machine box. In the machine body, the dust particle sensor is placed outside the machine tool box. The acquisition card is used to collect various sensor signals, and the box is connected to the display screen to display the data in real time and connect to the remote transmission equipment through the network cable.
加工过程信息包括:机床加工过程中工作信息、刀具信息、工件材料信息。The machining process information includes: work information, tool information, and workpiece material information in the machining process of the machine tool.
处理模块采用LabVIEW软件并结合python的深度学习神经网络程序对采集到的信号进行处理。The processing module uses LabVIEW software combined with python's deep learning neural network program to process the collected signals.
显示模块通过对LabVIEW前面板搭建将切削力、振动加速度、噪声情况、PM2.5、PM10、刀具磨损情况、加工表面粗糙度等在一个界面上实时显示;通信模块采用无线网络或服务器将LabVIEW前面板远程传输实现远程实时监测的目的。如图2所示为本实施例中软件显示前面板,其左侧显示直接测量数据的时域、频域数值变化,并把数值实时显示出来,其中数值显示每秒更新一次,数值等于这一秒钟数据的平均值。右侧上方为对加工参数的设置面板,根据不同的参数设置确定不同的传感器测量方式和间接测量的网络模型。右侧下方为刀具磨损及加工表面粗糙度的实时显示界面,通过实时显示的数据确定加工状态,并决策是否需要换刀。The display module displays cutting force, vibration acceleration, noise, PM2.5, PM10, tool wear, surface roughness, etc. on one interface in real time by building the LabVIEW front panel; the communication module uses a wireless network or a server to display the LabVIEW front panel. Panel remote transmission realizes the purpose of remote real-time monitoring. Figure 2 shows the software display front panel in this embodiment, the left side of which displays the time domain and frequency domain numerical changes of the directly measured data, and displays the numerical value in real time, wherein the numerical value display is updated once per second, and the numerical value is equal to this The average of the second data. The upper right side is the setting panel for processing parameters, and different sensor measurement methods and indirect measurement network models are determined according to different parameter settings. The bottom right is the real-time display interface of tool wear and surface roughness. The processing status is determined through the real-time displayed data, and the decision whether to change the tool is needed.
实施例2Example 2
本实施例公开了一种智能实时清洁切削监控方法,包括以下内容:The present embodiment discloses an intelligent real-time cleaning and cutting monitoring method, which includes the following contents:
在线数据采集阶段:采集机床加工过程中工作信息、刀具信息、工件材料信息;Online data collection stage: collect work information, tool information, workpiece material information during machine tool processing;
离线建模阶段:通过PCA技术和雷达图对工作信息进行分析处理,并结合刀具建立离线网络模型;Offline modeling stage: analyze and process the work information through PCA technology and radar chart, and establish an offline network model combined with the tool;
将实时工作信息和工件材料信息输入离线网络模型,获得实时离线网络模型;具体的:针对不同材料、不同切削用量以及不同工况条件下,把大量用监测系统中传感器实测的加工数据特征结合主成分分析PCA和雷达图的方法降维处理变成只包含有雷达图周长和面积的两维数据作为输入,和基恩士显微镜及表面粗糙度仪观测的刀具磨损与表面粗糙度数据共同离线训练神经网络,得到离线网络模型,并把训练好的网络模型放入切削加工数据库。Input the real-time work information and workpiece material information into the offline network model to obtain the real-time offline network model; specifically: for different materials, different cutting amounts and different working conditions, combine a large number of processing data features measured by sensors in the monitoring system with the main Component analysis PCA and radar map method dimensionality reduction processing becomes only two-dimensional data containing radar map perimeter and area as input, and offline tool wear and surface roughness data observed by KEYENCE microscope and surface roughness meter Train the neural network to obtain an offline network model, and put the trained network model into the machining database.
加工信息实时监测阶段:将实时工作信息输入实时离线网络模型,得到实时刀具信息。Real-time monitoring stage of processing information: input real-time work information into real-time offline network model to obtain real-time tool information.
工作信息包括:切削力、切削力变化量、切削位置振动量、噪声、噪声变化量、粉尘量。刀具信息包括:刀具磨损量、工件表面粗糙度。The working information includes: cutting force, cutting force change, vibration at cutting position, noise, noise change, and dust. Tool information includes: tool wear amount, workpiece surface roughness.
分析处理方法包括:量化表征、数据降维、特征融合。Analysis and processing methods include: quantitative representation, data dimensionality reduction, and feature fusion.
还包括:实时将切削力、切削位置振动量、噪声、粉尘量、刀具信息进行显示。Also includes: real-time display of cutting force, vibration of cutting position, noise, dust, and tool information.
将实时工作信息输入实时离线网络模型,得到实时刀具信息的具体内容为:The real-time work information is input into the real-time offline network model, and the specific content of the real-time tool information is obtained as follows:
将经特征融合方法处理后获得数据特征,按特征公式提取出反映刀具磨损信息和加工表面粗糙度信息的时域特征、频域特征;具体的:三向切削力、三向振动加速度、声音信号均为动态高频信号,尘埃粒子信号为动态低频信号,且不同信号来源不同、数据结构不同。在在线数据采集阶段对三向切削力、三向振动加速度、声音信号和尘埃粒子信号的多源异构信号动态采集并进行量化表征、数据降维和特征融合,并将能够直接监测的物理量;切削力、振动加速度、声音、PM2.5、PM10实时显示出来。其中,数据特征按式(1)~(10)提取出能客观反映刀具磨损信息和加工表面粗糙度信息的时域特征、频域特征。The data features are obtained after being processed by the feature fusion method, and the time domain features and frequency domain features that reflect the tool wear information and the machined surface roughness information are extracted according to the feature formula; specific: three-way cutting force, three-way vibration acceleration, sound signal All are dynamic high-frequency signals, and dust particle signals are dynamic low-frequency signals, and different signal sources and data structures are different. In the online data acquisition stage, the multi-source heterogeneous signals of three-dimensional cutting force, three-dimensional vibration acceleration, sound signal and dust particle signal are dynamically collected and quantitatively characterized, data dimensionality reduction and feature fusion, and the physical quantities that can be directly monitored; cutting; Force, vibration acceleration, sound, PM2.5, PM10 are displayed in real time. Among them, the data features are extracted according to formulas (1) to (10) to extract the time domain features and frequency domain features that can objectively reflect the tool wear information and the machined surface roughness information.
时域特征:Time Domain Features:
Max=max(|xi|)&Min=min(|xi|)(2、3);Max=max(|x i |)&Min=min(|x i |)(2, 3);
其中,xi表示第i个传感器在切削过程中的某一时间段内采集到的信号,i=1,2,3,……N;N为传感个数;M表示在切削过程中的某一时间段内采集到的监测信号的均值,即功率谱的长度;Among them, x i represents the signal collected by the ith sensor in a certain period of time during the cutting process, i=1, 2, 3,...N; N is the number of sensors; M represents the number of sensors in the cutting process The average value of the monitoring signals collected in a certain period of time, that is, the length of the power spectrum;
频域特征:Max表示在切削过程中的某一时间段内采集到的监测信号绝对值的最大值;Min为在切削过程中的某一时间段内采集到的监测信号的绝对值的最小值;RMS表示在切削过程中的某一时间段监测信号的强度;Var表示在切削过程中的某一时间段内监测信号在均值附近波动的程度;Cov(X,Y)表示在切削过程中的某一时间段内监测信号的两个变量的总体误差情况;Skew(X)表示在切削过程中的某一时间段内监测信号以均值为对称线的不对称度;Kurt表示在切削过程中的某一时间段内采集到的监测信号的峭度,即监测信号的瞬态现象和平稳性;Frequency domain feature: Max represents the maximum value of the absolute value of the monitoring signal collected in a certain period of time during the cutting process; Min is the minimum value of the absolute value of the monitoring signal collected in a certain period of time during the cutting process ;RMS indicates the intensity of the monitoring signal in a certain period of time during the cutting process; Var indicates the degree of fluctuation of the monitoring signal near the mean value in a certain period of time during the cutting process; Cov(X, Y) indicates the degree of fluctuation of the monitoring signal during the cutting process The overall error of the two variables of the monitoring signal in a certain period of time; Skew(X) represents the asymmetry of the monitoring signal in a certain period of time in the cutting process with the mean value of the symmetrical line; Kurt represents the asymmetry in the cutting process. The kurtosis of the monitoring signal collected in a certain period of time, that is, the transient phenomenon and stationarity of the monitoring signal;
其中,fi表示给定的某一时间段内采集到的监测信号通过快速傅里叶变换FFT从时域信号,即原始信号转换而来的频谱;P(fi)表示监测信号的功率谱密度;FCG为监测信号的频率重心,是频谱的静态部分;FV为监测信号的频率方差,频谱的分动态部,反映了监测信号的频谱在频率重心附近的波动的程度。Among them, f i represents the spectrum converted from the time domain signal, that is, the original signal, by the fast Fourier transform of the monitoring signal collected in a given period of time; P(fi ) represents the power spectrum of the monitoring signal Density; FCG is the frequency center of gravity of the monitoring signal, which is the static part of the spectrum; FV is the frequency variance of the monitoring signal, and the dynamic part of the spectrum reflects the degree of fluctuation of the spectrum of the monitoring signal near the frequency center of gravity.
需要说明的是,M为在切削过程中的某一时间段内采集到的监测信号的均值,是监测信号的静态部分,反映了监测信号的变化趋势;Max和Min分别为在切削过程中的某一时间段内采集到的监测信号的绝对值的最大和最小值,反映了监测信号的变化范围;It should be noted that M is the average value of the monitoring signals collected in a certain period of time during the cutting process, which is the static part of the monitoring signals and reflects the changing trend of the monitoring signals; Max and Min are the average value of the monitoring signals during the cutting process. The maximum and minimum absolute values of the monitoring signals collected in a certain period of time reflect the variation range of the monitoring signals;
将实时工作信息和工件材料信息前n秒的数据特征作为实时离线网络模型的输入,获得实时刀具信息。并把实时的加工数据和刀具磨损与表面粗糙度信息实时显示在已搭建好的LabVIEW前面板,并通过网络化服务将LabVIEW前面板中的数据显示在远程的手机端或电脑端。The real-time working information and the data features of the workpiece material information in the first n seconds are used as the input of the real-time offline network model to obtain real-time tool information. And the real-time machining data and tool wear and surface roughness information are displayed on the built LabVIEW front panel in real time, and the data in the LabVIEW front panel is displayed on the remote mobile phone or computer through the network service.
对所公开的实施例的上述说明,使本领域技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说是显而易见的,本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下,在其它实施例中实现。因此,本发明将不会限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。The above description of the disclosed embodiments enables 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 implemented in 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.
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