Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6

Surface roughness has an important role in the performance of finished components. End ball milling is used for achieving high surface quality, especially in complex geometries. Depending on the cutting conditions selected for ball end milling, different milling strategies can be applied. The produced surface quality is greatly affected from the selected milling strategy. The present paper examines the influence of the milling strategy selection on the surface roughness of an Al7075-T6 alloy. A number of cutting parameters are tested (axial and radial depth of cut, feed rate, inclination angles φ and ω) in order to perform 96 experiments and their results are processed using regression analysis and analysis of variance. All possible milling strategies are considered (vertical, push, pull, oblique, oblique push and oblique pull) and for each one of them, a mathematical model of the surface roughness is established, considering both the down and up milling. All models are statistically validated and experimentally verified, and can be used within the limits of the investigating cutting conditions. The polynomials produced are of the third order and the statistically most significant parameters are presented.     

薄壁零件高速铣削实验

通过高速铣削单因素实验，研究高速铣削参数对工件表面质量和铣削力变化的影响规律，优化薄壁零件高速铣削参数。在此基础上进行了薄壁零件的高速铣削实验，结果表明，采用优化后的高速铣削参数加工薄壁零件，能够有效地提高薄壁零件的加工精度和加工效率。     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.     

TiAlN涂层铣刀铣削9SiCr钢切削性能试验研究

采用TiAlN涂层刀具,对合金工具钢9SiCr的高速铣削加工性能进行试验研究,分析铣削速度对铣削力、表面粗糙 度、表面形貌、切屑变形和刀具的磨损的影响。并获得能够保证对其进行高效高精度加工的合理工艺参数。     

Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

A manufacturing system is oriented towards higher production rate, quality, and reduced cost and time to make a product. Surface roughness is an index for determining the quality of machined products and is influenced by the cutting parameters. Surface roughness prediction in machining is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of two different hybrid intelligent techniques, adaptive neuro fuzzy inference system (ANFIS) and radial basis function neural network- fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. An experimental data set is obtained with speed, feed, depth of cut and vibration as input parameters and surface roughness as output parameter. The input-output data set is used for training and validation of the proposed techniques. After validation they are forwarded for the prediction of surface roughness. Both the hybrid techniques are found to be superior over their respective individual intelligent techniques in terms of computational speed and accuracy for the prediction of surface roughness.     

Surface-texture measurement and characterisation with applications to machine-tool monitoring

In this paper, a comprehensive review is given of surface-texture measurement methods, characterisation of surfaces, surface finish and machine-condition monitoring using surface-texture information. A comparative evaluation of surface-texture modelling is provided. Directions in the areas of surface-texture research are also identified.     

The machinability and tribological characteristics of aluminum alloys with improved elevated temperature properties using rapidly solidified powder

In this investigation the tribological characteristics of rapidly solidified Al–8Fe–4Ce with improved elevated temperature properties were studied. Such characteristics were compared with cast aluminum–silicon alloy and cast zinc–aluminum alloy. These materials included Al–13Si, Zn–35Al, Zn–35Al–Si, Zn–35Al–3.75Si and Zn–35Al–5.8Si. The wear rates of all materials were tested on a crossed-cylinders wear machine against 440C stainless steel counterface lapped by random abrasion using diamond paste to the desired average surface roughness. The effects of sliding distance on both the worn volume and the coefficient of friction were examined. The aluminum–iron–cerium alloy (Al–8Fe–4Ce) showed the lowest wear rate. The experiments were then extended on this material to examine the effect of varying the applied load and sliding speed on its wear rate. It was found that increasing the applied load increased the wear rate while it was slightly sensitive to the change in sliding speed. As the wear results showed that the Al–8Fe–4Ce alloy has the lowest wear rates, its machinability during turning operation was studied. Statistically-based experimental design (response surface methodology) using central composite second-order rotatable design technique was used to improve the experimentation design without loss of accuracy of the results. The interaction of cutting parameters (cutting speed, feed rate and depth of cut) was examined and their effect on the average surface roughness was reported. It was found that employing a combination of high cutting speed and small depth of cut with small feed rate causes a significant reduction in R_a. The data were represented in three-dimensional and contour graphs for selecting the appropriate machining conditions required to achieve desired values of surface roughness.     

Cr2N/CrN multilayer coatings for wood machining tools

The present paper describes results from a recent research project aimed at forming a wear resistant coating based on chromium on tools to wood machining. Cr₂N/CrN multilayer coatings deposited on HS6-5-2 steel substrates using cathodic arc evaporation were tested. These coatings were formed from 7 bilayers being ca. 340 nm thick and equally thick Cr₂N and CrN layers. For comparison, Cr₂N and CrN monolayer coatings were also prepared. Hardness measurements, indentation and scratch tests, friction and wear were performed to characterize the mechanical properties. The wear tracks and Rockwell indentations enable to assess wear mechanisms of the coatings. The results of the Cr₂N/CrN coatings investigated show high hardness: ca. about 22 GPa and a critical force being higher than 95 N and a low wear rate.The industrial tests of planer knives with Cr₂N/CrN multilayer coatings were carried out on a down-spindle milling machine to determine the durability of tools with wear resistant coatings for woodworking. These tools show increase of “life time” two times. Another positive feature of the use of such tools is the increase of the quality of wood surface machined when compared with uncoated tools.     

Influence of machining parameters on surface roughness in finish cut of WEDM

Surface roughness is significant to the finish cut of wire electrical discharge machining (WEDM). This paper describes the influence of the machining parameters (including pulse duration, discharge current, sustained pulse time, pulse interval time, polarity effect, material and dielectric) on surface roughness in the finish cut of WEDM. Experiments proved that the surface roughness can be improved by decreasing both pulse duration and discharge current. When the pulse energy per discharge is constant, short pulses and long pulses will result in the same surface roughness but dissimilar surface morphology and different material removal rates. The removal rate when a short pulse duration is used is much higher than when the pulse duration is long. Moreover, from the single discharge experiments, we found that a long pulse duration combined with a low peak value could not produce craters on the workpiece surface any more when the pulse energy was reduced to a certain value. However, the condition of short pulse duration with high peak value still could produce clear craters on the workpiece surface. This indicates that a short pulse duration combined with a high peak value can generate better surface roughness, which cannot be achieved with long pulses. In the study, it was also found that reversed polarity machining with the appropriate pulse energy can improve the machined surface roughness somewhat better compared with normal polarity in finish machining, but some copper from the wire electrode is accreted on the machined surface.     

Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations

A fuzzy-nets-based in-process adaptive surface roughness control (FN-ASRC) system was developed to be able to adapt cutting parameters in-process and in a real time fashion to improve the surface roughness of machined parts when the surface roughness quality was not meeting customer requirements in the end-milling operations. The FN-ASRC system was comprised of two sub-systems: (1) fuzzy-nets in-process surface roughness recognition (FN-IPSRR); and (2) fuzzy-nets adaptive feed rate control (FN-AFRC) sub-system. To test the system, while the machining process was taking place, the FN-IPSRR system predicted the surface roughness, which was then compared to the desired surface roughness. If the desired surface roughness was not met, then, the FN-AFRC system proposed a new feed rate for the machining process. Once the feed rate was changed, and the cutting continued, the output of the surface roughness of the new feed rate was compared with the desired surface roughness. This proposed FN-ASRC system has been demonstrated to be successful using 25 experimental tests with 100% success rate.     

Impact of parameters on the process response: A Taguchi orthogonal analysis for laser engraving

Compared to conventional methods, laser engraving is the most effective technique in the machining of hard materials that have a complex geometry. Therefore, laser based machining is widely used in many industries like mold making, and the manufacture of automotive, electronics and biomedical parts. The present study investigates the machinability of hard metal produced with powder metallurgy and puts forward a new approach relating to the laser engraving of P/M metals. The main objective of this study is to determine the impact of laser engraving process on Vanadis 10. For this purpose, three process parameters – namely effective scan speed, frequency, and laser effective power – were correlated with the surface roughness (R_a) and engraving depth (D). The Taguchi and linear regression were used in the analysis. The experiments were performed in accordance with an L₉ orthogonal array. Based on the S/N ratio for R_a and D, the optimal condition was found as SS₃F₂P₁ for R_a and SS₁F₂P₃ for D. It was found that scan speed has a statistically significant effect on both R_a and D. Furthermore, a mathematical model for both R_a and D was established and estimated using linear regression. The model was also tested using different experimental conditions than existing ones. The results obtained from the new experimental conditions show that the predicted models could explain the process.     

A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations

This paper describes a fuzzy-nets approach for a multilevel in-process surface roughness recognition (FN-M-ISRR) system, the goal of which is to predict surface roughness (Ra ) under multiple cutting conditions determined by tool material, workpiece material, tool size, etc. Surface roughness was measured indirectly by extrapolation from vibration signal and cutting condition data, which were collected in real-time by an accelerometer sensor. These data were analysed and a model was constructed using a neural fuzzy system. Experimental results showed that parameters of spindle speed, feedrate, depth of cut, and vibration variables could predict surface roughness (Ra) under eight different combinations of tool and workpiece characteristics. This neural fuzzy system is shown to predict surface roughness (Ra ) with 90% prediction accuracy during a milling operation.     

In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations

This paper describes a new approach for surface roughness recognition (ISRR) systems to predict surface roughness (Ra) in-process using an accelerometer to measure vibration signals and cutting conditions while end-milling is taking place. The analysis of the data and the model building is carried out using a neural fuzzy system. Experimental results show that the parameters of spindle speed, feedrate, depth of cut, and vibration variables can predict the surface roughness (Ra) effectively. Surface roughness can also be predicted with a 96% accuracy rate by ISRR using the neural fuzzy system.     

Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron

This paper focused on optimizing the cutting conditions for the average surface roughness (R_a) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials. Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L₁₈ orthogonal array was used to design of experiment. Optimal cutting conditions was determined using the signal-to-noise (S/N) ratio which was calculated for R_a according to the “the-smaller-the-better” approach. The effects of the cutting parameters and tool materials on surface roughness were evaluated by the analysis of variance. The statistical analysis indicated that the parameters that have the biggest effect on R_a for Ni-Hard materials with 50 HRC and 62 HRC are the cutting speed and feed rate, respectively. Additionally, the optimum cutting conditions for the materials with 50 HRC and 62 HRC was found at different levels.     

超薄套筒类零件内孔磨削工艺

薄壁件产品是机械制造行业常见的零件,该类零件在航空飞机起落架主要承力件及附件上经常使用,且随着航空科技的发展和新型材料的出现,在保证性能的前提下,为了减轻质量等原因,产品设计中出现大量的超薄件产品。此类零件加工变形大、精度要求高,工艺设计相对难度很大。本文通过某飞机起落架附件产品超薄套筒成功加工典型范例,介绍了超薄套筒类零件的机械加工工艺设计,尤其是内孔磨削工艺,以供参考。     

基于铣削力/力矩模型的铣削表面几何误差模型

在端铣加工过程中 ,影响铣削表面的因素包括铣削力 /铣削扭矩、机床和工件的性质等等。通过研究这些因素 ,基于铣削力 /铣削扭矩和瞬时未变形切屑厚度的关系 ,建立了一个考虑了铣削力 /铣削扭矩的解析模型 ,用来预报在端铣情况下工件的表面几何误差。与数值模型相比 ,解析模型能够更好地来研究工艺参数和工件质量、产品设计、工艺规划和控制之间的关系。并且可以对铣削工艺的设计和优化提供帮助。一系列的试验验证了模型的有效性 ,并且通过仿真结果得到一些有用的结论     

干式切削技术在铣削加工中的应用

干式切削是一种绿色制造工艺技术,它已成为金属切削加工发展的趋势之一。文中分析了干式切削加工对刀具的要求,讨论了刀具材料的选择,并结合实例分析了干式切削技术在铣削中的应用。     

β21s高强度钛合金的铣削试验研究

针对一种新型的难加工钛合金材料β21s,进行了刀具磨损试验和铣削力的测量试验,对材料的切削加工性进行评价,并且给出优选结果,为实际生产提供参考。