2250轧机传动轴头高精度圆弧面的高效精加工

某公司重点产品2250轧机传动轴轴头材质特殊,加工长度长,且圆弧面上有孔,使加工时刀具处于断续切削状态,传统的镗削工艺方案加工困难。通过改进工艺方案,采用先进的数控螺旋插补铣削再镗的工艺使产品加工效率和质量均得到提高。     

HOLE MAKING USING BALL HELICAL MILLING ON TITANIUM ALLOYS

In this article, results of helical ball milling for hole making on Ti-6Al-4V alloy are presented and compared with drilling. Two different machining strategies were tested with a ball end mill. In the first strategy only a helical milling path was used to achieve the nominal diameter. The second strategy has two stages; first, helical milling considering a diameter 50 µm below the nominal, and second, the tool flank of the ball end mill were used to remove the stock left with a single contouring operation. Experimental tests were performed taking into account the process time, final quality of holes, hole diameter, roughness and burr formation at tool entrance and exit. With helical milling two advantages were concluded: the process is versatile because one tool is suitable for a range of diameters and negligible burrs are produced. However hardness in the zones close to hole internal surfaces machined with the ball end mill tool decreases with respect to twist drilling. The information obtained from this research work defines suitable cutting parameters for the helical milling process in the titanium alloy Ti-6Al-4V with ball end mills.     

螺旋铣孔工艺的切削稳定性及工艺参数规划

以螺旋铣孔工艺时域解析切削力建模、时域与频域切削过程动力学建模、切削颤振及切削稳定性建模为基础,研究了螺旋铣孔的切削参数工艺规划模型和方法。切削力模型同时考虑了刀具周向进给和轴向进给,沿刀具螺旋进给方向综合了侧刃和底刃的瞬时受力特性;动力学模型中同时包含了主轴自转和螺旋进给两种周期对系统动力学特性的影响,并分别建立了轴向切削稳定域和径向切削稳定域的预测模型,求解了相关工艺条件下的切削稳定域叶瓣图。在切削力和动力学模型基础之上,研究了包括轴向切削深度、径向切削深度、主轴转速、周向进给率、轴向进给率等切削工艺参数的多目标工艺参数规划方法。最后通过试验对所规划的工艺参数进行了验证,试验过程中未出现颤振现象,表面粗糙度、圆度、圆柱度可以达到镗孔工艺的加工精度。     

钛合金大直径孔螺旋铣削工艺优化研究

本文基于螺旋铣孔技术,采用正交试验和极差值分析方法,在钛合金上进行了19.05mm直径孔的螺旋铣削试验。分析了不同切削参数对轴向切削力、钛合金孔径、粗糙度等的影响,以此为指标优化出最佳工艺参数。在此基础上研究了最佳参数下切削力、加工质量和刀具磨损随加工孔数的变化,发现在大直径孔加工中,螺旋铣孔技术可有效改善加工质量、提高加工效率。     

基于Pareto遗传算法的螺旋铣加工参数优化

螺旋铣是主要针对航空领域中难加工材料的先进制孔工艺技术。在螺旋铣孔过程中,主轴转速、每齿进给量和每转轴向切削深度是3个最主要的加工参数。以材料去除量和刀具耐用度为优化目标,基于Pareto多目标遗传算法,针对螺旋铣削钛合金材料在稳定性切削条件下的切削参数进行了优化,主要考虑铣削参数对孔表面质量的影响。最终通过切削实验进行了验证。     

Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques

This study focuses on Ti–6Al–4V ELI titanium alloy machining by means of plain peripheral down milling process and subsequent modeling of this process, in order to predict surface quality of the workpiece and identify optimal cutting parameters, that lead to minimum surface roughness. For the purpose of accomplishing this task a set of experiments were performed on a CNC milling centre and design of experiments based on Box Behnken Design (BBD) for a three factor and three level central composite design concept was conducted. Depth of cut, cutting speed and feed rate were selected as input parameters and surface roughness was measured after each experiment performed. At first, Response Surface Methodology (RSM) was employed for establishing a quadratic relationship between input and output parameters. Analysis of variance (ANOVA) was then conducted for the evaluation of the proposed formula. RSM was also used for the optimization analysis that followed for the determination of milling cutting parameters for minimum surface roughness. The analysis indicates that the use of BBD can reduce the number of experiments needed for modeling and optimizing the milling operation of Titanium alloys. Furthermore, this method is able to provide models that can reliably be used for any cutting conditions within the limits of the input data. Finally, Artificial Neural Networks (ANN) models were developed to allow for a more robust simulation model to be built and comparison between ANN and RSM models to be performed. From the presented results, for RSM, the mean square error and the correlation coefficient were determined to be 8.633 × 10⁻³ and 0.9713, respectively; for ANN models, the corresponding values were 2 × 10⁻³ and 0.9824, for the test group of the optimum model. Simulations indicated that, although input data were too few, a considerably reliable ANN model was able to be built and despite of its complexity compared to RSM model, it was proven to be superior in terms of prediction accuracy.     

飞机壁板柔性装配螺旋铣孔单元的研制

针对现代飞机壁板高效精密制孔的需求,研制一种面向大型航空组件柔性装配的新型螺旋铣孔单元。首先,通过对国内外现有机身自动制孔系统的技术分析,并结合壁板装配制孔的作业特点,引入一种新兴的制孔技术——螺旋铣孔。其次,对螺旋铣孔运动进行功能分解,展开了螺旋铣孔单元的模块化设计,确定了偏心调节模块的传动方案,设计了铣刀结构与装夹方式。最后,基于螺旋铣孔单元样机,对航空铝合金和碳纤维复合材料进行了切削试验,结果表明铣孔质量基本满足飞机装配的精度要求。     

螺旋铣孔技术的原理及其应用

螺旋铣孔技术相对于传统钻孔技术具有很大的优势,在航空制造业中得到了应用。本文在对比了传统钻孔与螺旋铣孔特点的基础上,分析了螺旋铣孔的切削过程与切屑形成机理,并在钛合金的孔加工中得到验证。试验结果表明,螺旋铣孔加工钛合金时,切向力与法向力随着每齿轴向进给量的增加而增大,随着每齿切向进给量的增加而减小;由于不同的切屑流向而形成2种不同的切屑,并与切屑形成的机理相吻合。     

Enhancement and verification of a machined surface quality for glass milling operation using CBN grinding tool—Taguchi approach

Nowadays, the demand for high product quality focuses extensive attention to the quality of machined surface. The (CNC) milling machine facilities provides a wide variety of parameters set-up, making the machining process on the glass excellent in manufacturing complicated special products compared with other machining processes. However, the application of grinding process on the CNC milling machine could be an ideal solution to improve the product quality, but adopting the right machining parameters is required. Taguchi optimization method was used to estimate optimum machining parameters with standard orthogonal array L₁₆ (4⁴) to replace the conventional trial and error method as it is time-consuming. Moreover, analyses on surface roughness and cutting force are applied which are partial determinant of the quality of surface and cutting process. These analyses are conducted using signal to noise (S/N) response analysis and the analysis of variance (Pareto ANOVA) to determine which process parameters are statistically significant. In glass milling operation, several machining parameters are considered to be significant in affecting surface roughness and cutting forces. These parameters include the lubrication pressure, spindle speed, feed rate, and depth of cut as control factors. While, the lubrication direction is considered as a noise factor in the experiments. Finally, verification tests are carried out to investigate the improvement of the optimization. The results showed an improvement of 49.02% and 26.28% in the surface roughness and cutting force performance, respectively.     

Prediction of cutting forces in helical milling process

The prediction of cutting forces is important for the planning and optimization of machining process in order to reduce machining damage. Helical milling is a kind of hole-machining technique with a milling tool feeding on a helical path into the workpiece, and thus, both the periphery cutting edges and the bottom cutting edges all participated in the machining process. In order to investigate the characteristics of discontinuous milling resulting in the time varying undeformed chip thickness and cutting forces direction, this paper establishes a novel analytic cutting force model of the helical milling based on the helical milling principle. Dynamic cutting forces are measured and analyzed under different cutting parameters for the titanium alloy (Ti–6Al–4V). Cutting force coefficients are identified and discussed based on the experimental test. Analytical model prediction is compared with experiment testing. It is noted that the analytical results are in good agreement with the experimental data; thus, the established cutting force model can be utilized as an effective tool to predict the change of cutting forces in helical milling process under different cutting conditions.     

球头铣刀骨铣削力有限元建模与试验验证

在骨科手术中,铣削力对骨裂纹和加工表面质量影响较大。由于临床球形骨铣刀结构复杂,目前尚无有效的理论模型预测切削力。通过引入三维有限元模型模拟球形铣刀加工骨材料过程,评估不同加工参数下的铣削力值。搭建骨铣削试验平台模拟临床操作中的铣削过程,并利用采集到的加工信号分析铣削力。通过试验结果与仿真结果的对比,验证了有限元仿真模型的合理性。该骨铣削有限元模型能够满足不同加工参数下铣削力预测精度的要求,方便指导医生根据不同要求选择合适的加工参数。     

Surface topography and roughness in hole-making by helical milling

Helical milling is used to generate holes with a cutting tool traveling on a helical path into the workpiece in which the diameter of the hole can be adjusted through that of the helical path. Based on an improved Z-map model, a 3D surface topography simulation model is established to simulate the surface finish profile generated after a helical milling operation using a cylindrical end mill. The surface topography simulation model incorporates the effects of the relative motion between the cutting tool and the workpiece, in which the effect of the insert runout error of the cutting tool is considered. Furthermore, the roughness parameters are deduced from simulations of the 3D surface topography. The experimental result shows that the proposed simulation algorithm can predict well the surface roughness in a helical milling operation. The surface topography simulation model is used to study the effects of cutting conditions such as the tangential feedrate, the diameter of the cutting tool and the hole, the insert runout error of the cutting tool, as well as the revolution of the cutting tool around the axis of the hole on the surface finish profile. It is found that the surface quality can be improved by optimization of the cutting conditions. As a result, the proposed model will be helpful in determining the cutting conditions to meet surface finish requirements in helical milling operation.     

The role of stepover ratio in prediction of surface roughness in flat end milling

Surface roughness prediction studies in end milling operations are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The stepover ratio is usually neglected without investigating it. The aim of this study is to discover the role of the stepover ratio in surface roughness prediction studies in flat end milling operations. In realising this, machining experiments are performed under various cutting conditions by using sample specimens. The surface roughnesses of these specimens are measured. Two ANN structures were constructed. First of them was arranged with considering, and the second without considering the stepover ratio. ANN structures were trained and tested by using the measured data for predicting the surface roughness. Average RMS error of the ANN model considering stepover ratio is 0.04 and without considering stepover ratio is 0.26. The first model proved capable of prediction of average surface roughness (Ra) with a good accuracy and the second model revealed remarkable deviations from the experimental values.     

基于主轴电流的铣削力间接监测方法

实时准确地监测铣削状态对于提高加工质量与加工效率具有重要意义,切削力作为重要的加工状态监测对象,因其监测设备昂贵且安装不便而受到限制,为此提出一种考虑刀具磨损的基于主轴电流的铣削力监测方法.首先基于切削微元理论建立了考虑后刀面磨损的铣削力模型,并通过铣削实验进行铣削力模型系数标定;然后对主轴电流与铣削力的关系进行理论建...     

Prediction of vibration amplitude from machining parameters by response surface methodology in end milling

Decreasing vibration amplitude during end milling process reduces tool wear and improves surface finish. Mathematical model has been developed to predict the acceleration amplitude of vibration in terms of machining parameters such as helix angle of cutting tool, spindle speed, feed rate, and axial and radial depth of cut. Central composite rotatable second-order response surface methodology was employed to create a mathematical model, and the adequacy of the model was verified using analysis of variance. The experiments were conducted on aluminum Al 6063 by high-speed steel end mill cutter, and acceleration amplitude was measured using FFT analyzer. The direct and interaction effect of the machining parameter with vibration amplitude were analyzed, which helped to select process parameter in order to reduce vibration, which ensures quality of milling.     

A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces

The objective of this paper is to develop a Taguchi optimization method for low surface roughness in terms of process parameters when milling the mold surfaces of 7075-T6 aluminum material. Considering the process parameters of feed, cutting speed, axial-radial depth of cut, and machining tolerance, a series of milling experiments were performed to measure the roughness data. A regression analysis was applied to determine the fitness of data used in the Taguchi optimization method using milling experiments based on a full factorial design. Taguchi orthogonal arrays, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are used to find the optimal levels and the effect of the process parameters on surface roughness. A confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method. It can be concluded that Taguchi method is very suitable in solving the surface quality problem of mold surfaces.     

A method to predict possibility of arcing in EDM of TiB2p reinforced ferrous matrix composite

Electrical Discharge Machining (EDM) is very popular for machining conductive metal matrix composites (MMCs) because the hardness rendered by the ceramic reinforcements to these composites causes very high tool wear and cutting forces in conventional machining processes. EDM requires selection of a number of parameters for desirable results. Inappropriate parameter selection can lead to high overcuts, tool wear, excessive roughness, and arcing during machining and adversely affect machining quality. Arcing leads to short circuit gap conditions resulting in large energy discharges and uncontrolled machining. Arcing is a detrimental phenomenon in EDM which causes spoiling of workpiece and tool electrode and tends to damage the power supply of EDM machine. Parameter combinations that lead to arcing during machining have to be identified and avoided for every tool, work material, and dielectric combination. Proper selection of parameter combinations to avoid arcing is essential in EDM. In the work, experiments were conducted using L27 design of experiment to determine the parameter settings which cause arcing in EDM machining of TiB₂p reinforced ferrous matrix composite. Important EDM process parameters were selected in roughing, intermediate, and finishing range so as to study the occurrence of arcing. Using the experimental data, an artificial neural network (ANN) model was developed as a tool to predict the possibility of arcing for selected parameter combinations. This model can help avoid the parameter combinations which can lead to arcing during actual machining using EDM. The ANN model was validated by conducting validation experiments to ensure that it can work accurately as a predicting tool to know beforehand whether the selected parameters will lead to arcing during actual machining using EDM. Validation results show that the ANN model developed can predict arcing possibility accurately when the depth of machining is included as input variable for the model.     

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.     

A new tool wear estimation method based on shape mapping in the milling process

In the milling process, tool wear has a great influence on product machining quality, especially for a difficult-to-cut material. In this paper, a new approach based on shape mapping is proposed to acquire tool wear in order to establish an off-line tool wear predicting model for assessing the degree of wear and remaining useful life. The new approach maps tool wear shape into a metal material by milling holes mode after finishing each of the machining experiments. The metal material has low influence on tool wear compared to the experimental material. Thus, a series of mapped holes, which can represent the worn tool information, are formed on the metal material when finishing all milling experiments. These mapped holes on the metal material are analyzed according to all types of milling cutters in order to establish the relationship between the characteristic parameters of these mapped holes and tool wear. According to the established relationship, the characteristic parameters of these mapped holes are measured on the coordinate measure machine. The tool wear of each machining experiment can be obtained from the measured characteristic parameters of these mapped holes. The new tool wear estimation method does not require the stoppage of the machine tool and the removal of the cutter to measure tool wear in the process of conducting tool wear experiments. The new method can increase the machine tool efficiency of tool wear machining experiments and provide an efficient way to acquire tool wear in the process of establishing an off-line tool wear predicting model. In order to verify the new tool wear estimation method, a series of machining experiments were conducted on the five-axis machining center for cemented carbide cutting tool milling stainless steel. Experiments show that the shape mapping strategy of tool wear can allow for an effective assessment of tool wear and indicate good correlation with the expected wear characteristics and easily conduct tool wear experiments.     

Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.