镍基高温合金高速切削加工性

镍基高温合金具有优良的高温强度以及良好的热稳定性和抗热疲劳性能,广泛应用于航空发动机、燃气涡轮机等领域。但其可加工性极差,切削过程中刀具迅速磨损,加工效率较低。本文介绍了镍基高温合金高速切削加工性的研究进展,重点阐述了适宜高速切削镍基高温合金的陶瓷和PCBN刀具及加工参数,并总结了切削过程中刀具的失效形式和磨损机理,以及利用辅助加工方法等实现陶瓷刀具对镍基高温合金精加工的可能性。为高速切削镍基高温合金加工刀具的快速选择及加工工艺的改进提供了参考。     

镍基高温合金切削加工锯齿形切屑研究

以镍基高温合金GH4169和GH3030为研究对象,采用未涂层和涂层刀具进行车削加工试验,通过对金相、切削力、切削温度及刀具磨损形态的检测,阐明锯齿形切屑形成的机理,并对两种材料的切削加工性进行比较。研究结果为该类材料切削工艺的优化提供了试验依据。     

高硬度镍基高温合金的切削加工

<正> 近年来,我公司接受了美国普拉特惠特尼公司(简称PWA公司)的零件加工,这些零件的共同特点是薄壁件多,高温合金的比重大,加工量大,切削困难,有很多是我厂从来没有加工过的高硬度镍基高温合金。粗车这些零件时,切削用量低,刀具磨损快,加工周期长。很多零件粗车的工序需15天     

K24镍基高温合金切削性能试验研究

针对K24铸造镍基高温合金的特点,设计了合理的正交切削试验方案,通过铣削和钻削试验研究了K24高温合金的切削力、表面加工质量和刀具磨损等方面的切削性能,并给出了铣削力、钻削力和转矩的经验公式.研究工作对该材料加工过程中的切削用量、冷却液和刀具几何参数等的选择具有指导和参考意义.     

GH4169高温合金切削仿真分析及工艺参数优化

以GH4169高温合金为切削仿真分析对象,采用田口法、信噪比分析、极差分析和交互作用分别得出切削深度对切削力影响最大,切削速度对切削温度影响最大,切削温度随着切削速度和进给量的增大而略有增大,切削力随着切削深度和进给量的增大而显著增大,切削力增大会使切削温度升高。运用多目标遗传优化算法,得出切削参数的帕累托最优解集,优化结果和仿真试验结果误差率小于5%。     

PCBN刀具高速切削镍基高温合金切削力及刀具磨损试验研究

高速切削加工过程中切削参数的选择对刀具切削性能具有较大的影响。镍基高温合金因在高温条件下仍具有较高的抗疲劳强度、屈服强度、抗拉强度等特点,被广泛应用于航空航天、船舶、核电等行业。但是镍基高温合金的切削加工性能差,主要表现在切削力大、切削温度高、刀具磨损现象严重等方面。本文从切削速度、进给量、切削深度等切削参数对切削力的影响进行研究,同时对PCBN刀具磨损形貌进行分析。采用PCBN刀具进行高温合金车削试验,得到PCBN刀具切削高温合金GH4169时切削参数对切削力的影响规律,并探讨不同刀具磨损形貌产生的原因,为高温合金高速切削加工参数制定及工艺优化提供一定参考。     

镍基高温合金切削热力学仿真

应用Deform-3D对镍基高温合金进行切削加工仿真研究。采用单一改变切削参数、切削速度和进给量的方法,进行单变量切削仿真试验。通过对多组数据进行对比分析,得到刀具切削力和切削温度随切削变量变化的图形,从而获得最适宜该合金切削加工的热力学条件,弥补镍合金在切削加工、切削力和切削热方面研究不足的缺陷,并可将其应用于生产实践中。     

低碳车削高温合金切削参数优化研究

为了提高车削加工过程中的能源利用率,降低车削钴基高温合金GH605的碳排放量,通过车削GH605合金的单因素试验分析车削碳排放随切削参数的变化趋势与原因,并基于建立的车削碳排放模型进行响应曲面参数优化,研究切削用量三要素对车削碳排放的影响。通过方差分析得出,切削用量三要素对车削碳排放的影响主次顺序为a_p>f_z>v_c;经过响应面优化得到低碳排放的最优切削参数为v_c=128.85m/min,a_p=0.18mm,f_z=0.19mm/r,车削碳排放量为35.47g-CO₂。     

预应力条件对镍基高温合金切削加工的影响

为研究预应力条件对镍基高温合金切削加工的影响,采用Abaqus/Explicit软件建立了预应力切削镍基高温合金的三维有限元模型,研究了不同预应力状态下的切削力和加工表面各向应力的分布规律,分析了预应力条件对切屑形状、切削力和表面残余应力的影响,并与实验结果进行对比。研究结果表明:预应力作用对切削力和切屑形状的影响不显著;预应力作用对与预应力加载方向相同的已加工表面应力影响最大,并随着预应力的增大此方向上应力的分布层深显著增大,而对其他方向上的应力分布影响不显著;在一定范围内,随着预应力的增大已加工表面残余压应力值大小和分布层深都显著增大。     

增压器中铸造镍基高温合金K418的切削加工研究

因高温合金材料的特殊性,它的切削加工是国际公认的难题.高温合金又称耐热合金或热强合金,它具有优良的高温强度、热稳定性及抗热疲劳性能,能够在高温氧化气氛或燃气条件下工作.随着国内外对高温合金研究的深入,目前其已广范应用到各个领域,特别是航天、航空、造船等部门.车用增压器中关键零件涡轮转轴,是由铸造镍基高温合金涡轮叶轮与中碳调质钢转子轴焊接而成.随着车用增压器市场需求量的增加,以涡轮转轴采用电子束焊接加工为主的高效生产为代表的模式,逐渐被企业界所接受.但铸造镍基高温合金的难切削性和涡轮叶轮焊前形状高精度的加工要求,是制约国内企业采用这一高效生产模式的瓶颈之一.     

Study of machining parameters optimization for different materials in WEDM

In process planning of wire electrical discharge machining (WEDM), determination of appropriate machining conditions is likely to face problems in many ways. In addition to the construction of the relationship between machining parameters and machining characteristics, optimization search technique, a large number of experiments must be conducted repeatedly to renew parameters for different workpiece materials. The concept of specific discharge energy (SDE) was employed in this paper to represent the WEDM property of workpiece materials as one of the machining parameters. Two kinds of materials with distinctive SDE values, i.e., higher and lower, respectively, were selected for our experiments. The experimental data obtained were used, and a neural network that can accurately predict the relationship between machining parameters and machining characteristics was constructed. It was found that the predicted error was less than 7 %. The optimization technique of genetic algorithms was employed, and the optimal combination of machining parameters that meet the required machining characteristics for different workpiece materials was obtained. The system proposed in this study is both user-friendly and practical. It can save considerable time and cost during the construction of the database for the expert system of process planning.     

车削加工中切削用量的多目标优化建模

针对金属切削加工中切削用量优化的特点,在分析车削用量传统优化模型缺点的基础上,建立了车削用量多目标模糊优化模型,并根据模糊集合原理,将其转化为一个传统的单目标约束优化问题,可用任一非线性优化方法求解。为车削用量的合理选择提供了理论依据。     

Kinematical performance prediction in multi-axis machining for process planning optimization

This paper deals with a predictive model of kinematical performance in 5-axis milling within the context of high-speed machining. Indeed, 5-axis high-speed milling makes it possible to improve quality and productivity thanks to the degrees of freedom brought by the tool axis orientation. The tool axis orientation can be set efficiently in terms of productivity by considering kinematical constraints resulting from the set machine tool/NC unit. The capacities of each axis as well as NC unit functions can be expressed as limiting constraints. The proposed model relies on each axis displacement in the joint space of the machine tool and predicts the most limiting axis for each trajectory segment. Thus, the calculation of the tool feedrate can be performed, highlighting zones for which the programmed feedrate is not reached. This constitutes as an indicator for trajectory optimization. The efficiency of the model is illustrated through examples. Finally, the model could be used for optimizing process planning.     

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.     

Multi-objective optimization of machining parameters considering energy consumption

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product.     

Single and multiobjective optimization of Inconel 718 nickel-based superalloy in the wire electrical discharge machining

Inconel 718 is widely used in high-temperature environments, high-performance aircraft, and hypersonic missile weapon systems; however, it is very difficult to machine using conventional techniques. This study employed an L9 Taguchi orthogonal array for the analysis of wire electrical discharge machining parameters when used for the machining of Inconel 718. Our aim was to determine the optimal combination of parameters to minimize surface roughness while maximizing the material removal rate. The Taguchi method is widely applied in mechanical engineering with the aim of identifying the optimal combination of processing parameters as they pertain to single quality characteristics. Unfortunately, Taguchi analysis often leads to contradictory results when seeking to rectify multiple objectives. To resolve this issue, this study implemented gray relational analysis in conjunction with Taguchi method to obtain the optimal combination of parameters to deal specifically with multiple quality objectives. For the dual objectives of surface roughness and material removal rate, the optimal combination of parameters derived using gray relational analysis resulted in a mean surface roughness of 2.75 μm. In L9 orthogonal array experiments, run 1 produced the best gray relational grade with mean surface roughness of 2.80 μm, representing an improvement of 1.8%. The material removal rate achieved after the application of gray relational analysis was 0.00190 g/s, whereas the L9 experiment achieved a material removal rate of 0.00123 g/s, representing an improvement of 54.5%.     

Investigation of machining parameters for the multiple-response optimization of micro electrodischarge milling

This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R _a ), maximum peak-to-valley roughness height (R _y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R _a , R _y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R _a , R _y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min^?1, respectively for 4.79 μm s^?1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.