基于响应曲面法的钛合金干式车削工艺参数优化

为了提高钛合金干式车削加工质量,采用响应曲面法对主要车削工艺参数进行了优化,以工件表面粗糙度Ra和刀具磨损量VC作为评价指标,设计了切削速度、背吃刀量和进给量三因素的Box-Behnken实验模型。利用方差和拟合残差概率分布分析三因素的显著性及交互作用,并结合实验检验所建表面粗糙度和刀具磨损二阶响应预测模型的有效性。响应曲面法优化后的最佳工艺参数为:切削速度20 m/min、背吃刀量0.1788 mm、进给量0.1 mm/r,此时得到的表面粗糙度和刀具磨损量为1.031μm和155.6μm,与预测值的误差分别为:9.93%和1.58%。结果表明:基于响应曲面法的钛合金干式车削表面粗糙度和刀具磨损量预测模型准确有效。     

Statistical analysis of the effects of machining parameters and workpiece hardness on the surface finish of machined medium carbon steel

The objective of this study is to ascertain the effect of machining parameters and workpiece hardness on surface roughness of machined components and to develop a better understanding of the effect of process parameters on the machined surface. Such an understanding can provide insight into the problems of controlling the finish of machined surfaces when the process parameters are adjusted to obtain a certain surface finish. The collected data were analyzed using parametric analyses of variance (ANOVA) with surface finish as the dependent variable and hardness of the workpiece material, cutting tool position from the surface of the clamping device (chuck), depth of cut, cutting velocity, and cutting feed as independent variables. The results showed that surface roughness is significantly affected by the workpiece hardness, cutting feed, cutting speed, depth of cut, cutting tool position from the chuck, and their interactions with each other. The results suggest that feed rate and cutting speed can be adjusted to produce a certain surface finish when the position of the cutting tool from the surface of a clamping device or the hardness of the workpiece is changed.     

Experimental Investigations to Study the Effects of Microwave Treatment Strategy on Tool Performance in Turning Operation

In the present study, microwave treatment has been used to enhance the tribological properties of single-point tungsten carbide (WC) cutting tool inserts such as wear resistance and hardness. The tool hardness and cutting parameters were considered to evaluate the performance of microwave-treated WC inserts in turning operation. The optimum cutting parameters were identified using response surface method (RSM)-based desirability approach. The relationship between cutting parameters and output responses, viz. flank wear, cutting force and surface roughness, was developed using the RSM. The investigations revealed that the increase in tool hardness due to complex carbide formation results in a significant reduction in tool wear, cutting forces and improvement in the surface finish of workpiece. Further, the statistical models results were validated with the experimental results. Metallurgical properties of treated and untreated tool inserts were analyzed using scanning electron microscope, x-ray diffraction method and Vickers microhardness tests. The results highlighted the importance of microwave treatment in enhancing the machining performance in turning operation.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

Cutting performance of diamond tools during ultra-precision turning of electroless-nickel plated die materials

This study is an attempt (a) to observe the wear characteristic of diamond tool with 200 km cutting distance and to study the effects of wear on the surface roughness and cutting forces and (b) to optimize various cutting parameters such as depth of cut, feed rate, spindle speed and phosphorus content. The experimental results showed that tool wear was not so significant although some defects on rake face were observed after cutting 15.6 km. Further cutting showed that the surface roughness increases with cutting distance, and that the cutting forces were larger than thrust force at the beginning of cutting, but after cutting 130 km, thrust force became larger and increased rapidly. It was also observed that forces increase with the increase of depth of cut, spindle speed and feed rate, and decrease with the increase of phosphorus content of the plating. Depth of cut has no significant effect on surface roughness, while it increases with increase of feed rate and decreases with the increase of percentage of phosphorus content in the workpieces. In case of spindle speed, surface roughness decreases with the increase of spindle speed up to a certain value and then starts to increase with the increase of spindle speed.     

Machinability analysis in turning tungsten–copper composite for application in EDM electrodes

Electro-discharge machining (EDM) is widely used in tooling industry, where it is applied on materials, which are too hard to be machined with conventional techniques. The tungsten–copper is broadly used as an EDM electrode for machining of die steel and tungsten carbide workpieces. As, tungsten–copper electrode is more costly than conventional electrodes, there is a need to understand the machinability aspects in turning of this material. Hence, an attempt has been made in this paper to study the effects of cutting conditions on machinability characteristics such as cutting force, feed force, depth force, machining force, power, specific cutting force, arithmetic average surface roughness and maximum peak to valley height during tungsten–copper turning with K10 carbide cutting tool. The response surface methodology (RSM) based second order mathematical models of machinability aspects are developed using the data obtained through full factorial design (FFD). The adequacy of the machinability models is tested through the analysis of variance (ANOVA). The response surface analysis reveals that a combination of higher cutting speed with low-to-medium feed rate is advantageous in reducing the forces, power and surface roughness, which in turn increases the specific cutting force.     

Mechanisms involved in the improvement of Inconel 718 machinability by laser assisted machining (LAM)

The aim of the present research work has been to gain a broader understanding of how or why laser assisted machining (LAM) improves machinability of Inconel 718, a hard-to-machine material of interest in the aeronautic industry. This has been accomplished by, first, running short run tests to determine the laser parameters and configuration for which highest force reductions are obtained and also to determine the effect of cutting parameters (feed, cutting speed and depth of cut) on force reduction. Secondly, long run tests have been performed in order to analyze process variables such as cutting forces, tool wear and surface roughness. Temperatures and hardness have been also measured in order to gain a broader perspective of the process.Experimental results have demonstrated that LAM improves machinability of Inconel 718 since machining forces and final surface roughness are reduced. The novelty reached with the present research work is the identification of three mechanisms associated to the laser heating as the responsible of this machinability improvement: material yield strength reduction, material base hardness reduction (only in precipitation hardened Inconel 718) and elimination of the work hardening generated in previous machining passes. The reduction of the work hardening leads also to a lower notch wear that limits the risk of sudden failure of the cutting tool and thus the wear mode is changed to flank wear, which leads to a controllable tool life and better surface roughness.     

微粉金刚石钎焊砂轮磨削氧化铝陶瓷的磨削力和表面粗糙度特征

在不同磨削深度、砂轮转速和进给速度组合下,研究微粉金刚石钎焊砂轮磨削氧化铝陶瓷过程的磨削力及工件的表面粗糙度的变化规律,并筛选出低磨削力和低工件表面粗糙度的加工工艺参数。试验结果表明:在微粉金刚石钎焊砂轮的磨削过程中,氧化铝陶瓷主要通过脆性断裂的方式去除;随着磨削深度、进给速度的增加,砂轮在进给方向和切深方向的力以及工件表面粗糙度都上升;随着砂轮转速的增加,进给方向和切深方向的力以及工件表面粗糙度都下降。试验获得的低磨削力和低工件表面粗糙度精密加工工艺参数分别为:磨削深度为1.0 μm,进给速度为12 mm/min,砂轮转速为24 000 r/min和磨削深度为1.0 μm,进给速度为1 mm/min,砂轮转速为20 000 r/min。低磨削力磨削时,微粉金刚石钎焊砂轮受到的X方向和Z方向的磨削力分别为0.15 N和0.72 N;精密加工后的氧化铝陶瓷的表面粗糙度值可达0.438 μm。      

Development of flank wear prediction model of Zirconia Toughened Alumina (ZTA) cutting tool using response surface methodology

Flank wear is an important criterion for machinability assessment of a material. The present study is an attempt to evaluate the influence of factors such as cutting speed, feed rate and depth of cut on flank wear during hard turning of EN 24 steel with newly developed transformed toughened nano-composite Zirconia Toughened Alumina (ZTA) ceramic inserts. ZTA provides a cost effective materials solution to the most demanding applications which require wear resistance, corrosion resistance, high temperature stability and superior mechanical strength. Several machining experiments were performed and mathematical models for flank wear have been postulated by using Response Surface Methodology (RSM). The analysis was based on a first order model in which the flank wear (V_b) is expressed as a function of three independent variables i.e. cutting speed (V), feed rate (F) and depth of cut (T). Analysis of Variance (ANOVA) was applied to check the adequacy of the mathematical model and their respective parameters. Key parameters and their interactive effect on flank wears have also been presented in graphical contours which may help for choosing the process parameters and predict the cutting condition for maximum tool life.     

面向绿色高表面质量制造的硬态车削工艺多目标参数优化

目的 为了进行硬态车削绿色制造与工艺性能协同优化研究,提出一种同时考虑碳排放量和表面粗糙度的多目标优化方法。方法 首先,通过分析硬态车削过程中切削参数、工件材料、刀具材料等因素对切削功率的影响建立碳排放目标函数,针对工件的表面粗糙度受到切削条件、工件材料、刀具材料等诸多因素的影响,利用正交试验和广义回归神经网络建立轴承硬态车削表面粗糙度目标函数。然后,考虑加工过程中机床特性和硬车实际工况等约束条件,建立以切削参数为优化变量,以碳排放量和表面粗糙度为优化目标的多目标优化模型,引入权重系数将其转化为单目标优化模型。最后,利用遗传算法对优化模型进行优化求解,深入分析切削参数对优化目标的影响。结果 在工厂实际轴承产品硬车试验中验证了优化模型的有效性,结果表明,切削速度为225 m/min、进给量为0.08 mm/r、背吃刀量为0.10 mm时,碳排放量和表面粗糙度的综合优化指标最低。相比优化前,虽然碳排放量上升了13.05%,但表面质量提升了34.44%。结论 研究结果对面向绿色制造的轴承硬车工艺参数优化提供理论方法有重要意义。     

基于人工智能算法的最优加工表面粗糙度预测研究

以切削速度、进给量、切削深度、刀尖圆弧半径为设计变量,采用正交试验法进行了立方氮化硼(CBN)刀具干式车削冷作模具钢Cr12MoV的试验研究。利用神经网络的非线性拟合能力和遗传算法的全局寻优能力,建立了加工表面粗糙度预测模型并获得了使表面粗糙度达到最优的切削用量与刀尖圆弧半径组合。利用遗传算法获得的最优表面粗糙度值比田口方法和切削试验所获得的最佳表面粗糙度值分别降低了7.1%和17.2%。文中所采用的方法也为切削加工中刀具磨损、切削力和残余应力等问题的建模与参数优化提供理论参考。     

高速铣削模具钢3Cr2NiMo表面粗糙度试验研究

采用单因素试验法和正交试验法,在高速加工中心上对模具钢3Cr2NiMo进行切削试验,通过改变影响加工过程的切削参数:主轴转速、进给速度、轴向切削深度和径向切削深度,研究了影响工件加工表面粗糙度值程度的因素。结果表明:增大机床的主轴转速,粗糙度值显著降低,而增大进给速度、轴向铣削深度,粗糙度值增大,但增大的幅度不同,径向铣削深度的影响不明显。     

Experimental investigation on hard milling of high strength steel using PVD-AlTiN coated cemented carbide tool

High strength steel 30Cr3SiNiMoVA (30Cr3) is usually used to manufacture the key parts in aviation industry owing to its outstanding mechanical properties. However, 30Cr3 has poor machinability due to its high strength and high hardness. Hard milling is an efficient way in machining high strength steels. This paper investigated hard milling of 30Cr3 using a PVD-AlTiN coated cemented carbide tool with regard to cutting forces, surface roughness, chip formation and tool wear, respectively. The experimental results indicated that the increase of cutting speed from 70 to 110 m/min leads to direct reduction of cutting forces and improvement of surface finish, while both feed rate and depth of cut have negative effect on surface finish. The occurrence of oxidation on chip surfaces under high cutting temperature makes the chips show different colors which are strongly influenced by cutting speed. Saw-toothed chips were observed with the occurrence of the thermo-plastic instability within the primary shear zone. Micro-chipping and coating peeling were confirmed to be the primary tool failure modes. Serious abrasion wear and adhesive wear with some oxidative wear were confimed to be the main wear mode in hard milling of 30Cr3.     

300 M超高强钢车削加工表面质量

目的研究切削参数对300M超高强度钢加工表面质量的影响。方法选用硬质合金刀具车削加工300M超高强度钢,研究切削参数对表面加工硬化、残余应力及表面粗糙度的影响。通过HXD-1000显微硬度检测仪、X-350A型X射线应力测试系统、TR240表面粗糙度测量仪对实验过程进行检测分析。通过单因素试验研究影响表面粗糙度的主次因素,并通过正交试验,以进给量f、切削速度v、刀尖圆弧半径rε、背吃刀量a_p为变量建立表面粗糙度的预测模型。结果背吃刀量a_p=0.2 mm,切削速度v为60~120 m/min,进给量f为0.1~0.25 mm/r时,300M钢经切削加工后,维氏硬度在467~550HV范围内变化。切削速度从60 m/min增大至200 m/min时,表面残余应力从压应力-59.13 MPa变为拉应力257.33 MPa,次表层残余应力的最大残余压应力从-147.46 MPa增大到-422.65 MPa,并且层深至50μm左右处,工件材料的加工变质层结束。结论表面硬度随着进给量和切削速度的增大而减小,并且越往里层,硬度越低,直至达到基体的硬度。影响表面粗糙度的最主要因素为进给量,其次是刀尖圆弧半径,再次为切削速度,背吃刀量对表面粗糙度的影响最小。建立的表面粗糙度预测模型通过了试验验证,具有很高的加工精度。     

Tool wear and machining performance of cBN–TiN coated carbide inserts and PCBN compact inserts in turning AISI 4340 hardened steel

PCBN is the dominant tool material for hard turning applications due to its high hardness, high wear resistance, and high thermal stability. However, the inflexibility of fabricating PCBN inserts with complex tool geometries and the prohibitive cost of PCBN inserts are some of the concerns in furthering the implementation of CBN based materials for hard turning. In this paper, we present the results of a thorough investigation of cBN plus TiN (cBN–TiN) composite-coated, commercial grade, carbide inserts (CNMA 432, WC–Co (6% Co)) for hard turning applications in an effort to address these concerns. The effect of cutting speed and feed rate on tool wear (tool life), surface roughness, and cutting forces of the cBN–TiN coated carbide inserts was experimented and analyzed using analysis of variance (ANOVA) technique, and the cutting conditions for their maximum tool life were evaluated. The tool wear, surface roughness, and cutting forces of the cBN–TiN coated and commercially available PCBN tipped inserts were compared under similar cutting conditions. Both flank wear and crater wear were observed. The flank wear is mainly due to abrasive actions of the martensite present in the hardened AISI 4340 alloy. The crater wear of the cBN–TiN coated inserts is less than that of the PCBN inserts because of the lubricity of TiN capping layer on the cBN–TiN coating. The coated CNMA 432 inserts produce a good surface finish (<1.6 μm) and yield a tool life of about 18 min per cutting edge. In addition, cost analysis based on total machining cost per part was performed for the comparison of the economic viability between the cBN–TiN coated and PCBN inserts.     

Predictive machinability models for a selected hard material in turning operations

In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. Process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machinability models. Two important data mining techniques are used; they are response surface methodology and neural networks. Data of 28 experiments when turning austenitic AISI 302 have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the considered material.     

基于灰色关联理论的空冷辅助车削钛合金端面的工艺参数优化

以TC4钛合金棒料为试验对象,以数控车床作为试验平台,对空冷辅助车削钛合金端面的切削速度、背吃刀量、进给量等主要工艺参数进行正交试验研究。采用灰色关联分析法,对工件表面粗糙度、刀具磨损量以及工件表面硬度等工艺目标进行综合评价,使多工艺目标转化为单一考察指标,得到了最佳的工艺优化组合方案,并提高了试验效率。研究结果证明:用灰色关联分析法优化后的工艺参数能够有效降低工件表面粗糙度,减少刀具磨损,使工件硬度更符合切削要求。     

Experimental study on ultrasonic elliptical vibration cutting of hardened steel using PCD tools

Diamond tools cannot usually be applied for machining hardened steels while applying conventional cutting technique. As an alternative, ultrasonic elliptical vibration cutting (UEVC) technique was successfully applied for obtaining mirror surface on such steels using single crystal diamond (SCD) tools. In order to reduce production cost without compromising mirror surface quality, polycrystalline diamond (PCD) tools may be tested against highly expensive SCD tools. However, study on machining of hardened steel using PCD tools applying the UEVC technique has not yet been reported. The current research presents an experimental study on UEVC of hardened stainless steel (a typical Stavax, hardness 49 HRC) using the PCD tools. Face turning experiments were carried out to investigate the effects of three machining parameters: nominal depth of cut, feed rate, and nominal cutting speed on output performances such as cutting force, tool flank wear, surface roughness, and chip formation. Experimental results show that nominal cutting speed has very strong influence on the output performances, compared to the other two parameters. The surface roughness improves with a decrease in cutting speed. A mirror-like surface of approximately 804 mm² with a roughness value R_a of 11 nm was achieved at a lower cutting speed. Theoretical explanations have been given to support the results drawn from the UEVC experiments. It can be concluded that, while applying the UEVC technique, the inexpensive PCD tools instead of the SCD tools can be effectively applied to obtain optical surface for producing precise molds from the hardened steel.     

微沟槽织构对切削力和表面粗糙度的影响

为研究微织构对切削过程中产生的切削力和已加工表面粗糙度的影响,在聚晶立方氮化硼(PCBN)刀片前刀面制备与主切削刃平行的宽度为32.6μm的微沟槽织构。分别用微沟槽刀具和无织构刀具在主轴转速为450、500、600 r/min的条件下切削淬硬钢GCr15,分析切削力和已加工表面粗糙度。试验结果表明:微沟槽改善了刀具的切削性能,主切削力、进给力和切深力均小于无织构刀具;进给力、切深力随着主轴转速的增加均变大,主切削力表现为先减小再增大;用微沟槽织构刀具切削的已加工表面粗糙度大于无织构刀具,表明微沟槽不利于获得表面质量较好的工件;随着主轴转速增加,微沟槽刀具和无织构刀具切削的表面粗糙度均减小。