This paper proposed a modified Oxley’s model using the nonequipartition parallel shear-zone theory. By introducing a strain and strain rate distribution model in the first shear band, the assumption that primary deformation zone was divided equally by the main shear plane was eliminated. Then the expressions of shear-zone thickness and resultant angle were derived according to Oxley’s cutting theory. Finally, a force prediction model for orthogonal cutting was established by analyzing the mechanical-thermal characteristics in the deformation zone. The thin-walled cylinder made of AISI 304 was used to carry out the orthogonal turning experiment. A variety of cutting force prediction models were used to compare with the experimental results. The calculation results from the proposed model show that the prediction accuracy of cutting force in both cutting and feed directions is improved.

In the milling process is often accompanied by a lot of vibration, these vibrations will lead to the instability of the processing process resulting in the occurrence of chatter, chatter in the process will seriously affect the processing efficiency, reduce the quality of processing, so the mechanism of chatter and influencing factors to study, and through the analysis of stable milling processing parameters, to achieve efficient precision machining is very important. In this paper, based on the traditional milling model, the regeneration effect, process damping effect and modal coupling effect are taken into account, and the friction effect of the front cutter face is also taken into account for frictional chatter to make the prediction range of the model more accurate. Then, a milling dynamic model was established combining various effects, and the stability lobe diagram was solved by the fully discrete method. The influence of each effect on milling stability was analyzed and the influence of friction effect was emphatically discussed. Finally, the accuracy of the model was verified by the milling stability experiment.

CNC machining center linear axis thermal positioning errors, seen as the synthetic consequences of geometric and thermal errors, respectively generated due to the manufacturing and assembling inaccuracies and the asymmetric thermal deformation of the machining center structure, are sig-nificantly affected by varying position of the cutting point and shifting state of temperature field. Hence, developing a practical approach to reduce or even eliminate thermal positioning errors is crucial. This paper proposes a novel approach to decouple and separate machining center linear axis thermal positioning errors, based on which a highly accurate prediction model of the thermal positioning error is formulated. Firstly, a new concept on thermal positioning error sensitivity is presented where grey correlation analysis is borrowed to characterize the mapping between varying temperature fields and thermal positioning errors, according to which the sensor sensitivities and distributions are derived and optimized, respectively. Then, the thermal positioning errors are decoupled and separated into geometric and thermal errors by adopting multiple linear regression and GM (1, n) algorithms, respectively. Finally, the corresponding embedded compensation module is also developed within the SIEMENS 840D CNC system to realize the online compensation strategy providing the engineering applications. Experimental validations are performed on a commercial machining center, where the thermal positioning errors of the Z-axis are measured with the help of a laser interferometer testing kit and a thermal inspection instrument. The data comparisons indicate that the maximum thermal positioning errors of the Z-axis in the cold and warm state are respectively decreased for 86.5 % and 71.6 % after activating the compensation module, which also suggests that the proposed approach is adequate and accurate to decouple and separate the thermal positioning errors.

CNC machining center linear axis thermal positioning errors, seen as the synthetic consequences of geometric and thermal errors, respectively generated due to the manufacturing and assembling inaccuracies and the asymmetric thermal deformation of the machining center structure, are significantly affected by varying position of the cutting point and shifting state of temperature field. Hence, developing a practical approach to reduce or even to eliminate thermal positioning errors is crucial. This paper proposes an approach to decouple and separate machining center linear axis thermal positioning errors, based on which a highly accurate prediction model of the thermal positioning error is formulated. A sensitivity analysis-based thermal critical point optimization method is presented where grey theory is borrowed to characterize the mapping between thermal positioning error and varying temperature fields, according to which the highly related temperature sensors are derived. The thermal positioning errors are then decoupled and separated into geometric and thermal errors by adopting multiple regression algorithm and linear fitting approach, respectively. Accordingly, the comprehensive thermal positioning error prediction model is constrcuted, based on which the compensation approach is also proposed. Next, the corresponding compensation module is developed within the SIEMENS 840D CNC system to realize the online compensation strategy, providing the engineering applications. Experimental validations are performed on a commercial machining center, where the thermal positioning errors of the Z-axis are measured with the help of a laser interferometer testing kit and a thermal inspection instrument. The data comparisons indicate that the maximum thermal positioning errors of the Z-axis in the cold and warm state are respectively decreased for 87.09 % and 49.87 % after activating the compensation module, which also suggests that the proposed approach is adequate and accurate to decouple and separate the thermal positioning errors.

In view of the strong nonlinearity of the signals in the gestation period of milling chatter, and the problem that the traditional time-frequency analysis methods cannot reveal the weak characteristics of the gestation period of chatter well, a chaotic characteristic analysis method of milling vibration information is proposed. The milling force signals of stable milling, chatter gestation and chatter outbreak states are collected through variable working condition milling force measurement experiments, and the chaotic phase space reconstruction method is used to obtain the attractor images of milling force signals in different vibration states. The experiments show that the attractor features in the chatter gestation period are more significant than the traditional time-frequency features, and the chaotic attractor images can better reveal the weak features in the chatter gestation period.

针对数控机床多热源所致的温升与主轴热误差之间复杂的非线性关系问题,提出一种鸡群优化(chicken swarm optimization,CSO)算法与支持向量机(support vector machines,SVM)相结合的主轴热误差预测模型(以下简称热误差模型).以某精密数控机床的主轴单元为研究对象,采用五点法对其在空转状态下的轴向热变形进行测量,并借助热电偶传感器对机床的4个关键温度测点的温度进行采集.以SVM为理论基础,随机选取75％的数据样本进行训练,进而构建主轴热误差模型.其中,利用CSO算法优化SVM模型的惩罚参数c和核参数g,以提升热误差模型的预测能力及鲁棒性.以余下的25％的样本作为测试数据集,对所得热误差模型进行验证.利用CSO-SVM模型对不同工况下主轴的热误差进行预测,并将预测结果与测量结果进行对比.结果表明:当主轴转速为3000 r/min时,CSO-SVM模型的平均预测精度高达97.32％,相较于多元线性回归模型和基于粒子群优化的SVM模型分别提升了6.53％和4.68％;当主轴转速为2000,4000 r/min时,CSO-SVM模型的平均预测精度分别为92.53％、91.82％,表明该模型具有较高的预测能力和良好的鲁棒性.CSO-SVM模型具有较强的实用性和工程应用价值.

作为切削系统工作特性的输出端,刀尖点动力学行为具有强非线性及分异特征,直接影响着零件加工质量与生产效率.现阶段,基于理论或实验方法的刀尖点动力学特性研究存在建模复杂或测试成本过高等局限性.为此,以基于逆稳定性求解的半理论方法为理论基础,充分考虑切削系统中刀尖点及工件的双柔性,提出一种刀尖点动力学行为分异特征辨识方法.该方法将刀尖点在不同运行状态下动力学行为分异特征的辨识转化为一类优化设计问题,即以不同运行条件下刀尖点动力学参数为变量,以理论预测与实验标定所得极限切深及颤振频率的综合偏差最小为目标构建优化模型并求解,从而揭示刀尖点动力学行为的分异特征及演化规律.通过实施变切深铣削实验,对所得结果进行验证.对比分析表明,所提方法能较准确地获取刀尖点动力学行为的分异特征,有利于实现运行状态下切削稳定性的精准预测.

切削颤振孕育期介于稳定切削与颤振爆发之间,该阶段切削力信号中颤振特征具有典型微弱信息特性.采用基于总体经验模态分解(ensemble empirical mode decomposition,简称EEMD)与奇异值分解(singular value decomposition,简称SVD)相结合的方法对颤振孕育期信号进行降噪时,大多存在噪声剔除不充分或微弱目标特征信息失真等问题.首先,通过引入功率谱密度(power spectral density,简称PSD)与常相干函数(common coherency function,简称CCF)对EEMD降噪机制进行改进,使微弱目标特征所在本征模态函数(intrinsic mode function,简称IMF)分量得到有效提取;其次,借助池化原理(pooling principle,简称PP)降低IMF分量复杂度,并联合SVD对其实施分块降噪,以实现对微弱目标特征中所含噪声进行有效消减;最后,耦合上述改进并重构信号,可面向微弱目标特征信号形成基于改进EEMD-SVD(improved EEMD-SVD,简称IES)的降噪方法.分别利用IES与EEMD-SVD对Rossler混沌信号进行降噪处理,并通过比较信噪比、均方误差及平滑度等降噪评价指标,对所提方法在降噪有效性及信息保真度方面的优势进行量化验证.在此基础上,再次借助所提IES方法对变轴向切深铣削实验中颤振孕育期铣削力信号进行降噪分析.结果表明,该方法能显著抑制颤振孕育期信号噪声,并能有效避免微弱颤振特征信号失真问题.

针对机床几何误差元素多、误差测量与辨识过程繁琐等问题,利用Sobol'全局灵敏度分析方法对空间误差模型中的几何误差元素进行灵敏度分析,筛选出影响较大的几何误差元素,从而降低误差测量与辨识过程的复杂度,简化空间误差模型.以螺旋理论为建模基础,建立机床空间误差模型;对所有几何误差元素进行Sobol序列抽样并通过蒙特卡洛估计法求解灵敏度,计算各误差元素的一阶灵敏度值及全局灵敏度值,从21个误差项中筛选出对机床空间误差影响较大的12项;将简化模型与完备模型进行对比,空间误差元素简化率为48％,其预测精度大于80％,说明了误差元素筛选的有效性,为机床空间误差建模、误差元素辨识以及空间误差补偿工作的简化提供参考.

构建五轴加工中心空间误差模型的关键环节在于准确辨识旋转轴位置相关几何误差元素(PDGE)和位置无关几何误差元素(PIGE).以某五轴加工中心为研究对象,提出了一种面向旋转轴PDGE和PIGE的区别建模辨识方法.以多体系统理论和齐次坐标变换为基础,以两运动链末端所构空间向量欧氏范数的演变规律为依据,推导建立旋转轴PDGE与PIGE辨识基本方程,并借助球杆仪获取辨识基本方程求解所需参数;结合所建辨识基本方程揭示旋转轴PDGE与PIGE的耦合机制,提出了一种迭代方法以实现旋转轴PDGE和PIGE的准确分离与解耦.为验证上述辨识方法的有效性与准确性,提出一种基于虚拟样机的数值验证策略.仿真结果表明,所提辨识方法较好地解决了五轴加工中心旋转轴两类几何误差元素之间的耦合问题,可为建立加工中心空间模型提供准确的数据支撑.

Since the high-speed effects comprising centrifugal forces, gyroscopic moments, and rolling bearing thermal preload have considerable impacts on the dynamic behaviors of spindle units, it is of significance to study speed-varying characteristics of tool center point (TCP) dynamic behaviors. A semi-theoretical methodology is presented in this paper to identify the TCP speed-varying dynamic parameters, i.e., modal frequency, modal stiffness, and modal damping ratio. Cutting stability under an operational state is theoretically predicted and experimentally calibrated, in terms of axial limit stable cutting depth and chatter frequency. All the derived outcomes are subscribed into an optimization model whose objective is to minimize the deviations between predicted and calibrated cutting stability by searching the actual dynamic parameters of TCP. An integrated algorithm is designed to obtain the global optimized solutions so that the speed-varying dynamic parameters of TCP can be identified. A vertical machining center is selected as the platform to apply and validate the proposed methodology. The results indicate that speed dependent dynamic parameters of TCP can be accurately identified by adopting the proposed methodology, leading to accurate predictions of cutting stability with respect to the corresponding spindle speeds. Experimental validations presented in this paper also suggest that the identified speed-varying dynamic parameters can be used for predicting cutting stability at the adjacent speeds, where the relative errors are acceptable for engineering applications, achieving much more accurate cutting stability predictions than adopting TCP dynamic parameters under idle state.

It is crucial to perform volumetric error forward analyses of horizontal machining centers, including modeling, validation, and compensation, so that workpiece machining accuracies can be improved. Aiming at solving three major problems so far emerged in the machine tool volumetric error forward analyses, an integrated methodology is proposed in this paper. Firstly, in order to formulate a kinematic model without uncertainties and inconsistencies, a principle to determine the correct product relations of nominal motions to motion errors is systematically developed. Secondly, a novel validation approach based on the concept of volumetric vector Euclidean norm is demonstrated in this paper, assuring that the constructed volumetric error model can be quantitatively assessed without engaging any compensation processes. Thirdly, an optimal compensation technology is also presented to achieve the objective that the compensation residual errors, which are likely generated from the existing inverse superposition compensation strategies, can be diminished or even eliminated. A commercial horizontal machining center is selected to apply and verify the proposed integrated methodology.

刀尖点动力学特性直接影响切削稳定性和加工表面的质量.现阶段对主轴运行状态下刀尖点动力学特性的理论研究和实验研究分别存在着建模复杂和设备昂贵等局限性.为此,提出一种基于半理论法的主轴运行状态下刀尖点动力学行为分异特征辨识方法.该方法将分异特征辨识转化为一类优化设计问题,即以不同转速下刀尖点动力学特性参数为变量、以极限切深和颤振频率的实验标定值与理论预测值的偏差之和最小为目标构建优化模型,并借助粒子群退火优化算法进行求解,从而获得在不同转速下刀尖点动力学行为的分异特征及规律.以某型立式加工中心为平台,通过变切深铣削实验,对所提出的辨识方法进行验证,结果显示极限切深预测值与标定值吻合度较高.在不需复杂建模和昂贵实验设备的条件下,利用所提出的方法能够准确预测运行状态下刀尖点动力学行为分异特征,实现切削稳定性的精准预测,为进一步提高铣削加工质量和效率提供理论基础和数据支撑.

由于新冠肺炎疫情的影响,绝大多数留学生无法返校学习,且不同国家留学生的学习基础、文化背景、逻辑思维等差异较大,给"PLC控制与编程"课程的在线教学带来了很大挑战.对"PLC控制与编程"课程教学内容进行分析,并结合留学生在线学习的特点进行教学设计,采用图示引入、视频引入、现场习题等多种教学方法,取得了较好的教学效果,为今后留学生的在线教学提供了参考,同时有效提升了留学生理论联系实际的能力.

The traditional milling stability prediction approaches has mainly focused on the fixed cutting conditions where the radial cutting widths are constant. The derived outputs are then not appropriate for choosing the chatter free milling parameters on varying cutting conditions where the radial cutting widths are changeable versus time. In this paper, according to the mechanisms of regenerative chatter, a two degrees of freedom milling dynamic model is established by considering both process damping and asymmetric dynamic behaviors of tool center point, where the instantaneous engaging and exit angles caused by the time-varying radial cutting width is also introduced. Time-domain full discrete method is adopted to determine the stabilities of the milling dynamic model within the three dimensional space created by taking account of varying cutting conditions including rotary velocities, axial cutting depths, and radial cutting widths. Following the 3D stability lobe surface being plotted, the entire 3D milling stability prediction methodology can be finally organized. A vertical machining center is selected as the experimental platform on which the milling tests are performed on varying cutting conditions. According to the comparisons between calculated and measured results, the proposed methodology is verified to be capable of predicting the limit stable milling parameters accurately on varying cutting conditions, where the maximum relative error is found 5.9%. It is then beneficial to realize the maximum metal removal rate by optimizing the milling parameters subjecting to the predicted stability.

面向加工精度指标提出机床关键几何误差元素辨识及其公差设计方法.以某型立式加工中心为对象,基于螺旋理论对机床空间误差进行建模;结合所得机床空间误差模型及加工精度指标定义,推导建立加工精度评估模型,同时提出间接试验验证策略及实施技术;通过正交实验、统计学对影响加工精度的关键几何误差元素进行辨识,并利用数值试验对辨识结果进行验证,并基于响应面法构建加工精度与关键几何误差元素的映射关系,由此将后者的公差设计转换为一类优化问题;利用遗传算法获取各关键几何误差元素最优公差,在加工精度指标满足要求的同时,机床产品成本达到最低,表明所提方法有利于实现机床精度稳健设计.

为了提高学生的综合能力,在机械制造工艺学课程教学中引入项目教学法,根据课程的教学目标与核心能力,设计了与课程核心能力密切相关的课程项目,探讨了项目教学法在课程中的实施过程与方法,研究了适应项目教学法的课程考核体系.

针对现阶段机床空间误差建模过程中存在的繁琐性与非统一性问题,以及现有模型验证策略难以实现量化评价的局限性,以某卧式加工中心为研究对象,提出一套较完善的空间误差建模方法及其验证技术体系。以螺旋理论与串联机构运动学为理论基础,结合机床运动链拓扑分析,推导建立加工中心运动学模型。所提运动学建模方法可以有效避免传统方法中矩阵变换时潜在的奇异性问题,并且有利于简化机构运动学分析。通过系统讨论在不同参考系下定义运动误差对运动学模型的影响,提出名义运动矩阵与运动误差矩阵乘积关系的确定方法及原则,进而在此基础上构建卧式加工中心空间误差模型。为量化验证所建空间误差模型的准确性,提出基于空间矢量欧氏范数的量化验证策略与实施技术。数据对比显示,空间矢量欧氏范数偏差的预测值与实测值较吻合,最大相对误差为15.79%,表明所提空间误差建模方法可行且准确性较高,所提模型量化验证策略具有较好的直观性与有效性。

There exist various kinds of spatial frequency errors on the ultra-precision machined surfaces, which seriously influence their performances. According to different performances of workpieces, it is necessary to use an effective decomposition method to extract the topography containing the spatial frequency errors at specific frequency bands. The traditional spatial frequency error decomposition method has the serious problem of modal aliasing. In order to solve this problem, an adaptive bidimensional variational mode decomposition (BVMD) algorithm is proposed to decompose a three-dimensional surface topography. First, image continuation and self-convolution Hanning window arc introduced to preprocess the truncation errors when collecting 31) topographic data. Then, the particle swarm annealing optimization algorithm is used to optimize the penalty coefficient and the number of decomposition layers in the BVMD algorithm. Among them, the fitness function of the optimization algorithm is constructed by taking KI, divergence among modal components as aliasing indicators, introducing the minimum risk Bayesian decision theory, and combining KI, divergence with reconstruction errors. Finally, the measured topography of the ultra-precision machined surface is analyzed and compared with those by the discrete wavelet decomposition method and the bidimensional empirical mode decomposition methods. The results show that the KI, divergence by the proposed method is several hundred, much higher than those by the other two methods. The proposed method has a good inhibition ability for frequency error modal aliasing, and can effectively decompose the spatial frequency errors of an ultra-precision machined surface.

以某卧式加工中心为研究对象,通过定义机床各部件局部坐标系间初始位置特征矩阵和初始位置误差特征矩阵,构建机床空间误差完备模型,解决传统建模方法中若干项几何误差元素缺失的问题.借助体对角线定位精度测量实验,对所建完备模型准确性进行验证,进而在此基础上提出几何误差元素实际参预度的概念及其计算方法,并由此形成基于空间误差完备模型和实际参预度的关键几何误差元素辨识新方法.分别根据计算所得实际参预度和灵敏度,对给定加工中心关键几何误差元素进行甄别.对比分析显示,相较于传统灵敏度分析,所提基于实际参预度的甄别方法具有更高的准确性.甄别结果表明,该加工中心关键几何误差元素有7项,且均与位置相关,与X轴进给相关的关键几何误差元素有4项,说明机床X轴运动组件制造精度可能存在较大缺陷.