超精度加工的实验分析

目前，超精度金刚石切削技术被认为是生产高级光学产品和机械产品关键部件的最重要的加工技术之一。本文评述了超精度切削在实验研究中的最新发展，包括薄片形成、表面特性、切削力和切削温度的基础研究。文中也描述了超精度切削技术应用的新领域：例如将其用于易脆材料和难加工材料。     

中国微米纳米-三维集成车削力传感器数据采集与分析系统设计

切削力与切屑形成、切削热、刀具磨损和切削振动等现象有着密切联系,是影响加工精度、刀具寿命和切削效率的重要因素。通过实时测量切削力,及时调整切削参数、优化切削工艺,对于保证加工质量、延长刀具寿命、提高切削效率等有着重要意义。切削力的准确测量和处理离不开优良的数据采集与分析系统,本文针对基于MEMS压阻式芯片的三维集成车削力传感器,以微处理器STM32为控制核心研制了一种三维集成车削力传感器数据采集与分析系统,实现了三维车削力的标定、实时采集和数据分析功能。     

三维集成车削力传感器数据采集与分析系统设计

切削力与切屑形成、切削热、刀具磨损和切削振动等现象有着密切联系,是影响加工精度、刀具寿命和切削效率的重要因素.通过实时测量切削力,及时调整切削参数、优化切削工艺,对于保证加工质量、延长刀具寿命、提高切削效率等有着重要意义.切削力的准确测量和处理离不开优良的数据采集与分析系统,针对基于MEMS压阻式芯片的三维集成车削力传感器,以微处理器STM32为控制核心研制了一种三维集成车削力传感器数据采集与分析系统,实现了三维车削力的标定、实时采集和数据分析功能.     

南京航空学院研制成功“计算机辅助切削试验数据采集与处理系统”

南京航空学院切削加工研究室研制的“计算机辅助切削试验数据采集与处理系统”于1987年5月16日通过了航空工业部组织的技术签定。该系统包括刀具磨损测量系统、切削力测量系统、切削温度测量系统、车床主轴转速自动检测控制系统以及刀具振动测量系统,功能齐全,软件丰富,可以在各种试验条件下进行刀具耐用度、切削力、切削温度及快速刀具     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

增益模糊自整定的增量式神经元非模型控制及其应用

车削系统的切削力控制是机械加工自动化的关键环节,目前仍未能有效地解决切削过程中切削深度变化时控制系统的稳定性等问题.本文提出了增益模糊自整定的增量式神经元非模型控制方法,通过在线学习来调节控制系统的增益,使切削力在切削深度大范围变化时保持恒定,达到改善切削过程的动态品质、增强控制系统稳定性的目的.使用该方法对某车削系统进行了仿真实验研究,结果表明这种新颖的控制方法非常简单,具有很强的鲁棒性、抗干扰性和满意的控制品质.     

一种基于锰铜薄膜的小型化切削力传感器

切削状态监测是高档数控机床实现智能加工的关键技术之一。而切削力是反映切削状态最直接、有效的技术指标,针对目前商业化的切削力传感器存在的体积庞大、兼容性差等问题。设计了一种基于锰铜薄膜的小型化切削力传感器,该传感器以整体代替刀垫的形式嵌入到刀具当中,该切削力传感器为典型的三明治结构:基底、薄膜传感层和盖板。该切削力传感器以基底为弹性敏感元件,附着于基底上的锰铜薄膜电阻为转换元件,通过仿真分析,依据基底上应力分布规律设计了关于基底长对称轴对称分布的栅条状锰铜薄膜电阻结构,并利用微纳加工技术实现了薄膜电阻的制备,最后利用盖板封装以形成传感器原理样机。所研制的切削力传感器结构紧凑、兼容性好。实验结果显示该切削力传感器的静态总精度为8.09%、线性度为2.60%,具有基本具有切削力测量的能力,为后续研究提供了可借鉴的经验。     

正交切削加工过程仿真

为优化正交切削加工参数,采用弹-塑热耦合有限元方法,建立正交切削加工有限元模型.应用DEFORM软件,模拟出45号钢件切削过程中变形区内温度、应力、应变以及切削力的分布,采用自适应网格重划技术避免大塑性变形引起的网格畸变.该仿真结果能对切削加工参数的选择及实际的切削加工提供指导.     

刀尖圆弧半径对主偏角影响的分析与实验研究

刀尖圆弧半径对加工精度、切削力等切削参数有重要影响,而主偏角直接影响切 削变形和切削力的变化。为了研究车刀刀尖圆弧半径对主偏角的影响,建立了刀具要素间的几 何关系。根据切削深度和刀尖圆弧半径大小,将切削条件划分为 4 种：①刀尖圆弧半径小于切 削深度,且主偏角为 90°;②刀尖圆弧半径小于切削深度,且主偏角小于 90°;③刀尖圆弧半径 小于切削深度,且主偏角大于 90°;④刀尖圆弧半径大于切削深度。根据刀尖圆弧半径和切削 深度之间的几何关系,分别计算了 4 种切削条件下刀尖圆弧半径导致的实际主偏角的变化。为 了验证分析结果,进行了切削实验,通过分析背向力和进给力的夹角计算实验主偏角。实验结 果证明,刀尖圆弧半径导致主偏角变小。     

钛合金常规加工的有限元模拟

为验证不同Johnson-Cook本构参数对钛合金切削仿真的可靠性,基于AdvantEdge建立钛合金的二维正交切削仿真模型。然后将查找文献得到的不同钛合金本构参数与实验进行对比,分析切削过程中切削力和最高切削温度的误差。发现AdvantEdge软件的自定义Johnson-Cook本构在钛合金的切削仿真过程中,不同本构参数存在相对误差过大的问题,需要后续调节参数减小与实验的误差。     

立铣加工过程中的微元铣削力建模分析

针对单纯通过高速切削技术制造某些大型飞机零件过程中,存在难以控制的加工变形和较强表面残余拉应力分布等突出问题,以直齿和螺旋齿立铣加工过程为研究对象,基于微元切削机理,通过对刀齿铣削过程的分析,建立动态铣削加工仿真模型,导出铣削面积、铣削力、主轴扭矩、铣削功率与切削用量的关系.数值模拟结果与试验值较吻合,表明该模型可以实现动态铣削力预测,优化切削用量.     

高速铣削颤振识别和稳定性预测

颤振是刀具与工件之间剧烈的自激振动,是影响工件表面质量与刀具磨损的重要因素。通过高速铣削试验,对加工过程中铣削力与振动信号进行分析,给出了一种通过监测加工过程中信号功率谱能量比变化来识别颤振的方法。试验结果表明:颤振发生时信号功率谱最主要的特性是在主轴转动频率、切削频率及其谐波两边等间距处会出现相应的颤振频率,当主颤振频率处的能量超过一定的阈值时,加工系统颤振,否则,无颤振。建立了颤振动力学模型,通过试验获得了铣削系统频响函数和铣削力系数,绘制了铣削加工稳定性曲线。结合提出的颤振识别方法,验证了动力学模型的准确性,可为实际加工中合理选择加工参数和颤振监测提供参考。     

基于多传感器的刀具状态模糊识别

建立了以切削力、主电机功率和声发射为基础的多传感器检测系统，提出了聚合度方法对多传感器特征信息进行筛选，并采用模糊模式识别方法对多传感器信号进行融合。从而实现对加工过程中的刀具状态的实时、可靠识别。     

A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming

Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analyzing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.     

Position regulator for spindle of milling machine by embedded magnetic bearings

The goal of this work is to concurrently counter-balance the dynamic cutting force and regulate the spindle position deviation by integrating active magnetic bearing (AMB) technique, fuzzy logic algorithm and an adaptive self-tuning feedback loop. The mathematic model for cutting dynamics is constructed by experiments so that the system parameters can be on-line estimated by employing the proposed fuzzy logic algorithm. Once the cutting force can be real-time estimated, the corresponding compensation force can be exerted by the equipped AMB to counter-balance the cutting force (i.e., via inner-loop), in addition to the spindle position regulation by the feedback of spindle position (i.e., via outer-loop). At the end, the experimental simulations on realistic milling are presented to verify the efficacy of the fuzzy controller for spindle position regulation and the capability of the dynamic cutting force counterbalance.     

Simulation of cutting force using nonstationary Gaussian process

Cutting force signals exhibit a set of stochastic elements that repeat also in a stochastic manner. In this study, it is shown that nonstationary Gaussian processes (i.e., processes wherein the mean and the standard deviation of a normally distributed variable change with time) are able to model and simulate the stochastic elements of cutting force signals. The effectiveness of the proposed approach is demonstrated by comparing the simulated cutting force signal with real cutting force signal in terms of both frequency spectrum and correlation dimension. As realistic and user-friendly simulation of cutting force signals is needed for better process planning and monitoring of material removal processes, the use of the presented approach will help in this regard.     

ForceNet: An offline cutting force prediction model based on neuro-physical learning approach

Accurate cutting force prediction serves as an important reference to the optimization of numerically controlled machining process. Traditional cutting force modeling via theoretical cutting mechanism hampers accurate prediction for actual machining process due to its highly suppressed modeling flexibility. On the other hand, machine learning based modeling approaches demand large amount of diversified labeled samples to achieve comparable prediction results, while collecting these samples can be tedious and costly because the cutter workpiece engagement (CWE) keeps changing during actual process. This paper presents a cutting force prediction model, named ForceNet, which incorporates elementary physical priori into structured neural networks to predict cutting force for end-milling process of complex CWE. The main idea is to use grayscale images to represent CWE geometry, providing a universal input to the ForceNet. Unlike traditional deep neural networks served as an unexplainable black box, the core of the ForceNet is constructed by the vector summation of directional primitive cutting force elements, which are approximated using elementary neural networks. Preliminary results indicate that ForceNet outperformed existing methods not only with greater prediction accuracy in unseen cutting situations, but also with less training data needed thanks to its inherent neuro-physical structure.     

Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio

The aim of this research is to propose the practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the dynamic cutting force ratio. The proposed model is developed based on the experimentally obtained results by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the dynamic cutting force ratio. The experimentally obtained results showed that the frequency of the dynamic cutting force corresponds with the frequency of the surface roughness profile in the frequency domain. Hence, the dimensionless dynamic cutting force ratio is proposed regardless of the cutting conditions to predict the in-process surface roughness by taking the ratio of the area of the dynamic cutting force in X axis to that in Z axis. The multiple regression analysis is adopted to calculate the regression coefficients at 95 % confident level. The experimentally obtained model has been verified by using the new cutting conditions. It is understood that the developed surface roughness model can be used to predict the in-process surface roughness with the high accuracy of 92.82 % for the average surface roughness and 91.54 % for the surface roughness.