{"title":"基于统计的端铣工件热变形监测测点选择","authors":"Meng-Hao Yang, Feng Zhang, K. Teramoto","doi":"10.20965/ijat.2022.p0562","DOIUrl":null,"url":null,"abstract":"The deformation of the thermal workpiece in the end-milling process has a significant effect on the accuracy of machining. In-process direct measurement of workpiece deformation is difficult because process disturbances occur during machining. On the other hand, local temperatures of the workpiece can be easily and accurately measured using common measuring methods. This study aims to develop a monitoring method for workpiece deformations. A sensor-configured thermal simulation is proposed by combining local temperature measurements with thermal simulations to estimate the thermal states of the workpiece in small-lot production. Furthermore, an empirical modeling method is introduced to estimate the workpiece deformation from measured temperatures, thereby accelerating process time. A reliable estimation requires the selection of appropriate measuring points. Using multiple linear regression (MLR), a statistics-based selection method is proposed to establish a relationship between thermal deformation and temperatures of measuring points in various machining situations. During the end-milling process, the predicted time-series of deformations at the machining point and temperatures of the measuring points are regarded as output variables and input variables, respectively, in the finite element method (FEM)-based thermal simulation. The number of measuring points is determined by evaluating Akaike information criterion (AIC), and effective measuring points are selected using the p-value index. The proposed systematic construction method is evaluated using simulation-based case studies. The constructed temperature-based model for measuring workpiece deformation corresponded well to the FEM simulation. Therefore, the constructed model can represent workpiece deformation with the minimum number of measuring points.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"110 1","pages":"562-571"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Statistics-Based Measuring Point Selection for Monitoring the Thermal Deformation of a Workpiece in End-Milling\",\"authors\":\"Meng-Hao Yang, Feng Zhang, K. Teramoto\",\"doi\":\"10.20965/ijat.2022.p0562\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The deformation of the thermal workpiece in the end-milling process has a significant effect on the accuracy of machining. In-process direct measurement of workpiece deformation is difficult because process disturbances occur during machining. On the other hand, local temperatures of the workpiece can be easily and accurately measured using common measuring methods. This study aims to develop a monitoring method for workpiece deformations. A sensor-configured thermal simulation is proposed by combining local temperature measurements with thermal simulations to estimate the thermal states of the workpiece in small-lot production. Furthermore, an empirical modeling method is introduced to estimate the workpiece deformation from measured temperatures, thereby accelerating process time. A reliable estimation requires the selection of appropriate measuring points. Using multiple linear regression (MLR), a statistics-based selection method is proposed to establish a relationship between thermal deformation and temperatures of measuring points in various machining situations. During the end-milling process, the predicted time-series of deformations at the machining point and temperatures of the measuring points are regarded as output variables and input variables, respectively, in the finite element method (FEM)-based thermal simulation. The number of measuring points is determined by evaluating Akaike information criterion (AIC), and effective measuring points are selected using the p-value index. The proposed systematic construction method is evaluated using simulation-based case studies. The constructed temperature-based model for measuring workpiece deformation corresponded well to the FEM simulation. Therefore, the constructed model can represent workpiece deformation with the minimum number of measuring points.\",\"PeriodicalId\":13583,\"journal\":{\"name\":\"Int. J. Autom. 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引用次数: 1
摘要
热工件在立铣削加工过程中的变形对加工精度有重要影响。由于加工过程中会出现加工干扰,因此在加工过程中直接测量工件变形是困难的。另一方面,使用常用的测量方法可以方便、准确地测量工件的局部温度。本研究旨在开发一种工件变形监测方法。提出了一种传感器组态热模拟方法,将局部温度测量与热模拟相结合,以估计小批量生产中工件的热状态。此外,还引入了一种经验建模方法,通过测量温度来估计工件变形,从而加快了加工时间。可靠的估计需要选择适当的测量点。利用多元线性回归(MLR),提出了一种基于统计的选择方法,建立了不同加工情况下测点的热变形与温度之间的关系。在基于有限元法的铣削过程热仿真中,将加工点变形预测时间序列和测量点温度预测时间序列分别作为输出变量和输入变量。通过评价赤池信息准则(Akaike information criterion, AIC)确定测点数量,利用p值指标选择有效测点。采用基于仿真的案例研究对提出的系统构建方法进行了评估。所建立的基于温度的工件变形测量模型与有限元模拟结果吻合较好。因此,所构建的模型可以用最少的测点来表示工件的变形。
Statistics-Based Measuring Point Selection for Monitoring the Thermal Deformation of a Workpiece in End-Milling
The deformation of the thermal workpiece in the end-milling process has a significant effect on the accuracy of machining. In-process direct measurement of workpiece deformation is difficult because process disturbances occur during machining. On the other hand, local temperatures of the workpiece can be easily and accurately measured using common measuring methods. This study aims to develop a monitoring method for workpiece deformations. A sensor-configured thermal simulation is proposed by combining local temperature measurements with thermal simulations to estimate the thermal states of the workpiece in small-lot production. Furthermore, an empirical modeling method is introduced to estimate the workpiece deformation from measured temperatures, thereby accelerating process time. A reliable estimation requires the selection of appropriate measuring points. Using multiple linear regression (MLR), a statistics-based selection method is proposed to establish a relationship between thermal deformation and temperatures of measuring points in various machining situations. During the end-milling process, the predicted time-series of deformations at the machining point and temperatures of the measuring points are regarded as output variables and input variables, respectively, in the finite element method (FEM)-based thermal simulation. The number of measuring points is determined by evaluating Akaike information criterion (AIC), and effective measuring points are selected using the p-value index. The proposed systematic construction method is evaluated using simulation-based case studies. The constructed temperature-based model for measuring workpiece deformation corresponded well to the FEM simulation. Therefore, the constructed model can represent workpiece deformation with the minimum number of measuring points.