涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。     

RuT400切削力预测模型及参数优化

针对蠕墨铸铁RuT400难加工的问题,通过使用硬质合金涂层刀具对RuT400进行高速铣削试验,以实验参数为基础构建了切削力预测模型,结合其切削性能并使用响应面法对切削参数进行优化。实验结果表明:使用硬质合金涂层刀具切削RuT400是可行的,而且刀具价格便宜,加工经济性更好。切削速度、进给速度、切削深度与切削力之间存在显著的线性关系,根据实际加工参数使用切削力预测模型可以对切削力作出精确预测;切削力随着切削深度的增加以严格的线性方式递增;切削用量对切削力影响的显著性顺序为:切削深度进给速度切削速度。一般情况下,较小的切削深度,适当的进给速度和较大的切削速度能获得较低的切削力和良好的加工效率。     

高温镍基合金Inconel718的切削特性研究

高温镍基合金Inconel718是一种较难切削的材料,相应刀具的寿命受到极大制约。为获取该材料的切削加工特性,以Ti CN涂层刀作为刀具进行切削试验,并进行数理分析。以刀具的磨损率评定刀具寿命,以刀具的进给率、切削深度及切削速度三个工艺参数作为控制因子,利用田口试验法获取各影响因子的信噪比,得到了最佳的工艺参数组合,并进行试验结果的再现性进行了验证。结果表明,切削速度是影响寿命的关键因子,且宜取中等值、而切削深度及进给率则应取较小值,试验方法可行,试验结果正确。     

CVD涂层刀具高速铣削大理石试验

为了探索高速铣削大理石的切削特性,改善大理石加工表面质量,使用CVD氮化钛涂层刀具进行高速铣削大理石试验。通过显微镜观测刀具磨损表面,并采用网格法计算出刀具磨损面积,探讨切削参数的改变与刀具磨损情况的关系;利用粗糙度测试仪检测大理石加工表面粗糙度,研究切削参数对大理石加工表面质量的影响。最终得到刀具磨损量和大理石表面粗糙度均与切削速度负相关,与进给速度和切削深度正相关。试验结果表明:所建立的数学模型显著性很高,能够较准确地揭示刀具磨损量和大理石表面粗糙度与切削参数间的关系,为合理选择切削参数以提高大理石加工表面质量提供了一定理论基础。     

基于高斯过程回归方法的钛合金铣削刀具磨损预测

通过钛合金材料切削试验,对铣削加工中切削参数对高进给铣刀刀齿后刀面最大磨损量的影响规律进行分析。采用高斯过程回归法建立了刀齿后刀面最大磨损宽度的预测模型,并进行试验验证。预测结果与试验结果吻合程度较高,验证了预测模型的有效性,为钛合金铣削刀具的磨损预测提供了理论方法和试验依据。     

AISI304不锈钢铣削加工性能实验分析

利用硬质合金铣刀,对奥氏体不锈钢AISI 304进行铣削加工实验研究。讨论切削速度和进给率的变化对刀具寿命的影响。实验结果表明,刀具寿命随切削速度的增加而降低,随进给率的增加而增加。以生产率最高和刀具寿命最长为目标,给出单刃铣刀铣削加工的最佳切削参数。     

CVD涂层刀具高速铣削大理石切削力研究

选定切削参数(切削速度、进给速度、切削深度)设计正交试验,进行CVD氮化钛涂层刀具高速铣削大理石切削力试验,利用测力仪测出各组试验的切削力信号图,分析信号图特征得出切削力值,并记录各组切削力试验结果。分析单一切削参数(切削速度、进给速度,切削深度)对切削力变化的影响规律。采用最小二乘法原理对切削力经验公式回归系数进行参数估计,并对经验公式进行相关性检验,检验结果表明显著性很高。最终通过试验结果得出:切削力随着进给速度和切削深度的增加而增大,随着切削速度的增加而减小;并且切削深度对切削力影响最大,切削速度次之,进给速度对切削力的影响最小。     

高强度钢高进给铣削时切削参数对切削力的影响

高进给铣削是一种高效铣削方式,不同于传统的加工方法,其切削深度较浅,每齿进给量较大,而高强度钢是一种典型的难加工材料。通过正交试验法对高进给铣削高强度钢的切削力进行仿真,应用极差和方差分析研究切削速度、切削深度和每齿进给量对切削力的影响。结果表明,通过提高切削速度来增加高进给铣削高强度钢的切削效率,有利于减小切削力。以切削力最小为目标的最优切削参数为v_c=100m/min,a_p=0.8mm,f_z=1.0mm/z。     

主轴滚子轴承铣削加工中切削深度优化量算

为了提高铣削加工的工艺水准,需要进行切削深度优化量算模型设计。提出一种基于多轴联动加工切削深度误差修正的主轴滚子轴承铣削加工中切削深度优化量算方法,采用响应面分析法构建铣削参数与主轴滚子轴承加工质量之间的数学模型,以顶隙系数、切齿深度和切削深度为目标优化参量,建立多目标优化模型,在切削速度和进给速度多轴联动约束下进行切削深度误差修正,实现主轴滚子轴承铣削加工参量的优化解算。仿真结果表明,采用该方法进行主轴滚子轴承铣削加工参量量算,加工工艺参数的全局优化性能较好,切削精度较高,加工误差收敛性较好。     

PVD涂层刀具高速铣削砂岩表面粗糙度研究

砂岩装饰品已逐渐走进人们的生活,提高砂岩加工表面质量已备受关注。利用正交试验,研究PVD涂层刀具高速铣削砂岩的切削速度、进给速度及切削深度对加工表面粗糙度的影响。分析了表面粗糙度随不同切削参数的变化规律:随着进给速度与切削深度的增加,表面粗糙度值变大,随着切削速度的增加,表面粗糙度值减小,当切削速度由3000r/min提高到12000r/min时,表面粗糙度值由4.98μm减小到3.64μm。建立了表面粗糙度的回归预测模型,经F检验具有较高的显著性,为实际生产加工砂岩制品提供了一定理论依据与参考价值。     

Prediction of tool wear using regression and ANN models in end-milling operation

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.     

PCD刀具高速铣削GH4169合金的刀具磨损规律研究

为研究PCD刀具高速铣削GH4169合金时刀具的磨损规律,采用单因素试验法分别对不同铣削参数下后刀面磨损程度随切削路程的变化进行对比。结果显示主轴转速对高速铣削GH4169合金时刀具磨损的影响不大,采用顺铣、切削液冷却的方式,并适当降低每齿进给量有助于减小刀具磨损。使用BP神经网络对试验数据进行训练,建立了刀具磨损预测的模型,预测结果与实际结果误差在5%以内。     

TC17钛合金低温铣削表面粗糙度预测

进行了TC17钛合金低温铣削试验,研究了不同切削条件下的已加工表面粗糙度.采用回归分析方法建立了表面粗糙度经验模型,研究了射流温度、每齿进给量、铣削速度和径向切削深度对表面粗糙度的影响规律.基于BP神经网络建立了表面粗糙度预测模型,并与经验模型进行了对比分析.研究结果表明,基于经验模型表面粗糙度值与参数间存在强相关性(...     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Tool wear rate prediction in ultrasonic vibration-assisted milling

Abstract

In the current study, a predictive model on tool flank wear rate during ultrasonic vibration-assisted milling is proposed. One benefit of ultrasonic vibration is the frequent separation between tool and workpiece as the cutting time is reduced. In order to account for this effect, three types of tool–workpiece separation criteria are checked based on the tool center instantaneous position and velocity. Type I criterion examines the instantaneous velocity of tool center under feed movement and vibration. If the tool is moving away from workpiece, there is no contact. Type II criterion examines the position of tool center. If the tool center is far from the uncut workpiece surface, there is no contact even though the tool is getting closer. Type III criterion describes the smaller chip size due to the overlaps between current and previous tool paths as a result of vibration. If any criterion is satisfied, the tool flank wear rate is zero. Otherwise, the flank wear rate is predicted considering abrasion, adhesion and diffusion. The proposed predictive tool flank wear rate model is validated through comparison to experimental measurements on SKD 61 steel with uncoated carbide tool. The proposed predictive model is able to match the measured tool flank wear rate with high accuracy of 10.9% average percentage error. In addition, based on the sensitivity analysis, smaller axial depth of milling, larger feed per tooth or higher cutting speed will result in higher flank wear rate. And the effect of vibration parameters is less significant.     

Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V

The present study focuses on the development of predictive models of average surface roughness, chip-tool interface temperature, chip reduction coefficient, and average tool flank wear in turning of Ti-6Al-4V alloy. The cutting speed, feed rate, cutting conditions (dry and high-pressure coolant), and turning forces (cutting force and feed force) were the input variables in modeling the first three quality parameters, while in modeling tool wear, the machining time was the only variable. Notably, the machining environment influences the machining performance; yet, very few models exist wherein this variable was considered as input. Herein, soft computing-based modeling techniques such as artificial neural network (ANN) and support vector machines (SVM) were explored for roughness, temperature, and chip coefficient. The prediction capability of the formulated models was compared based on the lowest mean absolute percentage error. For surface roughness and cutting temperature, the ANN and, for chip reduction coefficient, the SVM revealed the lowest error, hence recommended. In addition, empirical models were constructed by using the experimental data of tool wear. The adequacy and good fit of tool wear models were justified by a coefficient of determination value greater than 0.99.     

铣刀磨损对铣削稳定性及表面位置误差的影响

为了分析刀具正常磨损后铣削颤振稳定域和表面位置误差,对刀具不同磨损状态下的切削力系数进行辨识,基于全离散法研究刀具正常磨损后铣削颤振稳定域和表面位置误差特性。发现当刀具正常磨损后,铣削系统的稳态临界切深呈现上升的趋势;随着工件表面洛氏硬度的提高,铣削系统稳态临界切深逐步下降,刀具正常磨损后临界切深与后刀面无磨损临界切深的差别逐步变小;在稳定域的局部会出现表面位置误差增加的情况。试验表明,该理论模型可以有效优化刀具正常磨损后的加工参数。     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

铍铜合金断续切削过程切削参数对后刀面温度的影响

为了分析切削参数对刀具温度的影响,以期在加工过程中改善刀具磨损和提高加工质量。采用以断续车削代替铣削加工的仿铣削试验平台,选取热电偶法对断续切削过程中不同切削参数下的后刀面温度进行测量,通过正交试验和单因素试验研究了切削参数对刀具温度的影响。结果表明,在v=200m/min,f=0.15mm/r,a p=0.75mm时,刀具温度最低,切削速度v和进给速度f对刀具温度的影响高度显著,背吃刀量对刀具温度的影响并不显著。在铍铜合金断续切削过程中,刀具温度在v=500m/min出现峰值,随着进给量的增大,刀具温度呈减小趋势,在f=0.11mm/r出现突变的趋势,与后刀面上的热量生成、热源移动和分配等因素的影响密不可分。