Anticorrosion property enhancement of Al–Li alloy 2A97 by lowering machined surface roughness

In this study, after face-milling Al–Li alloy 2A97 under dry machining condition, the machined surface roughness S_a and S_z are dealt with statistical analyses. Then, the supplementary face-milling trials are conducted to verify the analyses and gain the effect of the feed rate per tooth (f_z) on S_a and S_z. Lastly, the electrochemical impedance spectroscopy tests are conducted on the machined surfaces to determine their corrosion resistance. The variance analyses show that although f_z and the depth of cut are more important than the width of cut and the cutting speed, each of the four parameters is individually insignificant to S_a and S_z. Therefore, the quadratic regression models that take the interaction effects of the parameters into consideration are established. The validity of the models is proved with the comparisons between the measured and calculated S_a and S_z. The equivalent circuit models are proposed on the basis of the shape characteristics of the impedance spectra for machined surfaces. The estimations from the circuit models reveal the superiority in the corrosion resistances of face-milled surfaces with the optimized surface roughness.     

An element-free Galerkin method approach for estimating sensitivity of machined surface parameters

The main objective of this study is to implement a parameter sensitivity analysis method to be used in the search of optimal machining conditions with respect to surface quality. Presently, the element-free Galerkin (EFGM) approximating functions are used to evaluate the properties of machined surfaces with cutting parameters when turning AISI 4140 steel using arbitrary sets of experimental values and the EFGM approximation functions, based on the moving least-squares method, in order to obtain the sensitivities through proper local derivations. This method shows the sensitivity of each surface parameter for each input variable. The variables investigated were cutting speed (v_c), depth of cut (a_p), feed rate (f) and the surface roughness (R_a). The sensitivity results showed that the feed rate has the highest influence on surface roughness when turning AISI 4140 steel followed by cutting speed and depth of cut.     

Surface integrity of finished turned Ti–6Al–4V alloy with PCD tools using conventional and high pressure coolant supplies

 Surfaces generated when machining Ti–6Al–4V alloy with PCD tools using conventional and high pressure coolant supplies was investigated. Longer tool life was recorded when machining Ti–6Al–4V with high-pressure coolant supplies and the recorded surface roughness R_a values were well below the tool rejection criterion (1.6 μm) for all cutting conditions investigated. The micro-structure of the machined surfaces were examined on a scanning electron microscope. Micrographs of the machined surfaces show that micro-pits and re-deposited work material were the main damages to the surfaces. Micro-hardness analysis showed hardening of the top machined surfaces when machining with conventional coolant while softening of the subsurface layer was observed when machining under high-pressure coolant supplies. The later is probably due to lower heat generated, with the consequent tempering action when machining with PCD tools with high-pressure coolant supplies. The microstructure below the machined surfaces had minimal or no plastic deformation when machining with conventional and high-pressure coolant supplies.     

Analysis and estimation of state variables in CNC face milling of AL6061

In this study, the effect of cutting parameters and machining forces on surface roughness and material removal rate of AL6061 in CNC face milling operation is investigated. Based on the experimental data, two different modeling techniques, namely regression analysis and multilayer perceptron, MLP, neural network, have been used to estimate the state variables (i.e. surface roughness, R _a , and material removal rate, MRR). Simulation results presented using machining data demonstrate that the MLP neural network possesses more powerful capacity than the regression analysis and performs the estimation of the R _a and MRR, simultaneously.     

Experimental investigation into cutting forces and active grain density during abrasive flow machining

It is important to know cutting force components and active grain density during abrasive flow machining (AFM) as this information could be used to evaluate the mechanism involved in AFM. The results show that cutting force components and active grain density govern the surface roughness produced during AFM process. In this paper, an attempt has been made to study the influence of these two parameters, namely cutting force and active grain density, on the surface roughness. This study will help in developing a more realistic theoretical model.The present paper highlights a suitable two-component disc dynamometer for measuring axial and radial force components during AFM. The influence of three controllable variables (extrusion pressure, abrasive concentration and grain size) on the responses (material removal, reduction in surface roughness (R_a value), cutting forces and active grain density) are studied. The preliminary experiments are conducted to select the ranges of variables by using single-factor experimental technique. Five levels for abrasive concentration and six levels for extrusion pressure and abrasive grain size were used. A statistical 2³ full factorial experimental technique is used to find out the main effect, interaction effect and contribution of each variable to the machined workpiece surface roughness. The machined surface textures are studied using a scanning electron microscope.     

An in-process surface recognition system based on neural networks in end milling cutting operations

An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness R_a values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness R_a values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.     

Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network

An artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between cutting and process parameters during high-speed turning of nickel-based, Inconel 718, alloy. The input parameters of the ANN model are the cutting parameters: speed, feed rate, depth of cut, cutting time, and coolant pressure. The output parameters of the model are seven process parameters measured during the machining trials, namely tangential force (cutting force, F_z), axial force (feed force, F_x), spindle motor power consumption, machined surface roughness, average flank wear (VB), maximum flank wear (VB_max) and nose wear (VC). The model consists of a three-layered feedforward backpropagation neural network. The network is trained with pairs of inputs/outputs datasets generated when machining Inconel 718 alloy with triple (TiCN/Al₂O₃/TiN) PVD-coated carbide (K 10) inserts with ISO designation CNMG 120412. A very good performance of the neural network, in terms of agreement with experimental data, was achieved. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the process parameters in metal-cutting operations and for the optimisation of the cutting process for efficient and economic production.     

A Taguchi and experimental investigation into the optimal processing conditions for the abrasive jet polishing of SKD61 mold steel

This study introduces an abrasive jet polishing (AJP) technique in which the pneumatic air stream carries not only abrasive particles, but also an additive of either pure water or pure water with a specified quantity of machining oil. Taguchi design experiments are performed to identify the optimal AJP parameters when applied to the polishing of electrical discharge machined SKD61 mold steel specimens. A series of experimental trials are then conducted using the optimal AJP parameters to investigate the respective effects of the additive type and the abrasive particle material and diameter in achieving a mirror-like finish of the polished surface. The Taguchi trials indicate that when polishing is performed using pure water as an additive, the optimal processing parameters are as follows: an abrasive material to additive ratio of 1:2, an impact angle of 30°, a gas pressure of 4 kg/cm², a nozzle-to-workpiece height of 10 mm, a platform rotational velocity of 200 rpm, and a platform travel speed of 150 mm/s. Applying these processing parameters, it is found that the optimal polishing effect is attained using #8000SiC abrasive particles and a 1:1 mixture of water-solvent machining oil and pure water. The experimental results show that under these conditions, the average roughness of the electrical discharge machined SKD61 surface is reduced from an original value of R_a=1.03 μm (R_max: 7.74 μm) to a final value of R_a=0.13 μm (R_max: 0.90 μm), corresponding to a surface roughness improvement of approximately 87%.     

Prediction and control of surface roughness in CNC lathe using artificial neural network

In this study, a neural network approach is presented for the prediction and control of surface roughness in a computer numerically controlled (CNC) lathe. Experiments have been performed on the CNC lathe to obtain the data used for the training and testing of a neural network. The parameters used in the experiment were reduced to three cutting parameters which consisted of depth of cutting, cutting speed, and feed rate. Each of the other parameters such as tool nose radius, tool overhang, approach angle, workpiece length, workpiece diameter and workpiece material was taken as constant. A feed forward multi-layered neural network was developed and the network model was trained using the scaled conjugate gradient algorithm (SCGA), which is a type of back-propagation. The adaptive learning rate was used. Therefore, the learning rate was not selected before training and it was adjusted during training to minimize training time. The number of iterations was 8000 and no smoothing factor was used. R_a, R_z and R_max were modeled and were evaluated individually. One hidden layer was used for all models while the numbers of neurons in the hidden layer of the R_a model were five and the numbers of neurons in the hidden layers of the R_z and R_max models were ten. The results of the neural network approach were compared with actual values. In addition, inasmuch as the control of surface roughness is proposed, a control algorithm was developed in the present investigation. The desired surface roughness was entered into the control system as a reference value and the controller determined the cutting parameters for these surface roughness values. A new surface roughness value was determined by sending the cutting parameters to the observer (ANN block). The obtained surface roughness was fed back to the comparison unit and was compared with the reference value and the difference surface roughness was then sent to the controller. The iteration was continued until the difference was reduced to a certain value of surface roughness which could be permitted for machining accuracy. When the surface roughness reached the permitted value, these cutting parameters were sent to the CNC turning system as input values. In conclusion, both the surface roughness values corresponding to the cutting parameters and suitable cutting parameters for a certain surface roughness can be determined prior to a machining operation using the ANN and control algorithm.     

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

Hard machining of hardened bearing steel using cubic boron nitride tool

In many cases, hard machining remains an economic alternative for bearing parts fabrication using hardened steels. The aim of this experimental investigation is to establish the behaviour of a CBN tool during hard turning of 100Cr6-tempered steel. Initially, a series of long-duration wear tests is planned to elucidate the cutting speed effects on the various tool wear forms. Then, a second set of experiments is devoted to the study of surface roughness, cutting forces and temperature changes in both the chip and the workpiece. The results show that CBN tool offers a good wear resistance despite the aggressiveness of the 100Cr6 at 60HRC. The major part of the heat generated during machining is mainly dissipated through the chip. Beyond 280 m/min, the machining system becomes unstable and produces significant sparks and vibrations after only a few minutes of work. The optimal productivity of machined chip was recorded at a speed of 120 m/min for an acceptable tool flank wear below 0.4 mm. Beyond this limiting speed, roughness (R_a) is stabilized because of a reduction in the cutting forces at high speeds leading to a stability of the machining system. The controlling parameter over roughness, in such hard turning cases, remains tool advance although ideal models do not describe this effect rationally. Surface quality obtained with CBN tool significantly compared with that of grinding despite an increase in the advance by a factor of 2.5. A relationship between flank wear (VB) and roughness (R_a) is deduced from parametric analysis based on extensive experimental data.     

Identification of common sensory features for the control of CNC milling operations under varying cutting conditions

The main focus of this study is to identify the most influential and common sensory features for the process quality characteristics in CNC milling operations—dimensional accuracy (bore size tolerance) and surface roughness—using three different material types (6061-T6 aluminum, 7075-T6 aluminum, and ANSI-4140 steel). The materials were machined on a vertical CNC mill, retrofitted with multiple sensors and data acquisition systems, to investigate the effects of variations in material types and machining parameters. The sensor data include cutting force measurements, spindle quill vibration, and acoustic emission, each of which further divided into measurable components, such as x, y, and z components in cutting force, x and y spindle quill vibration, DC, AC, and Count Rate for acoustic emission signals. Those components were filtered and analyzed to determine the sensory features that best correlate with process quality characteristics. Tool wear rate and machining characteristics appeared differently, depending on the material types, yet some components of the sensory data were found to be significant with relation to the variations in bore size and surface roughness for all three types of materials. This suggests that even under the varying cutting conditions involving different materials, the identified sensory features can be used for the reliable and accurate control of milling operations.     

Experimental investigation of effects of cutting parameters on surface roughness in the WEDM process

The experimental study presented in this paper aims to select the most suitable cutting and offset parameter combination for the wire electrical discharge machining process in order to get the desired surface roughness value for the machined workpieces. A series of experiments have been performed on 1040 steel material of thicknesses 30, 60 and 80 mm, and on 2379 and 2738 steel materials of thicknesses 30 and 60 mm. The test specimens have been cut by using different cutting and offset parameter combinations of the “Sodick Mark XI A500 EDW” wire electrical discharge machine in the Middle East Technical University CAD/CAM/Robotics Center. The surface roughness of the testpieces has been measured by using a surface roughness measuring device. The related tables and charts have been prepared for 1040, 2379, 2738 steel materials. The tables and charts can be practically used for WEDM parameter selection for the desired workpiece surface roughness.     

Performance of single Si3N4 and mixed Si3N4+PCBN wiper cutting tools applied to high speed face milling of cast iron

In this work two face milling cutter systems were used in high speed cutting of gray cast iron under cutting condition encountered in the shop floor. The first system, called ‘A’, has 24 Si₃N₄ ceramic inserts all with square wiper edges. The second system, called ‘B’, is a mixed tool material system, having 24 wiper inserts, 20 of them are Si₃N₄ intercalated by four PCBN inserts. Cutting speed (v_c), depth of cut (doc) and feed rate per tooth (f_z) were kept constant. Surface roughness (R_a and R_t) and waviness (W_t), tool life (based on flank wear, VB_Bmax) and burr formation (length of the burr, h) were the parameters considered to compare the two systems. System ‘B’ presented better performance according to all parameters, although only end of life criterion based on R_t parameter has been reached.     

Examination of wire electrical discharge machining of Al2O3p/6061Al composites

Alumina particle reinforced 6061 aluminum matrix composites (Al₂O₃p/6061Al) have excellent physical and chemical properties than those of a traditional metal; however, their poor machinability lead to worse surface quality and serious cutting tool wear. In this study, wire electrical discharge machining (WEDM) is adopted in machining Al₂O₃p/6061Al composite. In the experiments, machining parameters of pulse-on time were changed to explore their effects on machining performance, including the cutting speed, the width of slit and surface roughness. Moreover, the wire electrode is easily broken during the machining Al₂O₃p/6061Al composite, so this work comprehensively investigates into the locations of the broken wire and the reason of wire breaking.The experimental results indicate that the cutting speed (material removal rate), the surface roughness and the width of the slit of cutting test material significantly depend on volume fraction of reinforcement (Al₂O₃ particles). Furthermore, bands on the machined surface for cutting 20 vol.% Al₂O₃p/6061Al composite are easily formed, basically due to some embedded reinforcing Al₂O₃ particles on the surface of 6061 aluminum matrix, interrupt the machining process. Test results reveal that in machining Al₂O₃p/6061Al composites a very low wire tension, a high flushing rate and a high wire speed are required to prevent wire breakage; an appropriate servo voltage, a short pulse-on time, and a short pulse-off time, which are normally associated with a high cutting speed, have little effect on the surface roughness.     

预测数控加工表面粗糙度的解析模型研究

解析模型是基于刀具切削刃包络面形成的原理来研究零件表面形貌的形成.在解析模型的基础上研究球头刀铣削过程的零件表面生成机理、分析影响加工表面粗糙度大小的因素以及表面粗糙度的趋势,进而预测表面粗糙度,有助于数控加工条件的最优化.本文利用计算机图形学算法进行建模,该模型能够仿真已加工表面轮廓的形成和表面形貌的可视化、预测表面粗糙度和评估加工过程参数的合理性.     

The effect of machined topography and integrity on fatigue life

The paper reviews published data which address the effect of machining (conventional and non-conventional processes) and the resulting workpiece surface topography/integrity on fatigue performance, for a variety of workpiece materials. The effect of post-machining surface treatments, such as shot peening, are also detailed. The influence of amplitude height parameters (Ra, Rt), amplitude distribution (Rsk) and shape (Rku) parameters, as well as spatial (Std, Sal) and hybrid (Ssc) measures, are considered.There is some disagreement in the literature about the correlation between workpiece surface roughness and fatigue life. In most cases, it has been reported that lower roughness results in longer fatigue life, but that for roughness values in the range 2.5–5 μm Ra it is primarily dependent on workpiece residual stress and surface microstructure, rather than roughness. In the absence of residual stress, machined surface roughness in excess of 0.1 μm Ra has a strong influence on fatigue life. Temperatures above 400 °C reduce the effects of both residual stress and surface roughness on fatigue, due to stress relieving and the change in crack initiation from the surfaces to internal sites. The presence of inclusions an order of magnitude larger than the machined surface roughness generally overrides the effect of surface topography.     

铝含量对AS系列铸造镁合金机械加工性能的影响(英文)

研究铝含量对AS系列铸造镁合金机械加工性能的影响。通过测量切削力和表面粗糙度对镁合金的机械加工性能进行评估。研究合金的微观结构和拉伸性能。结果表明,切削力随着铝含量的增加而增大;AS91镁合金的表面粗糙度和力学性能最高;对力学性能有影响的主要机制是存在金属间相Mg2Si和Mg17Al12。在机械加工镁合金中,切削力随着切割速度的增大而增大。所测得的数据与机械加工合金的力学性能一致。     

Modelling the machining dynamics of peripheral milling

The machining dynamics involves the dynamic cutting forces, the structural modal analysis of a cutting system, the vibrations of the cutter and workpiece, and their correlation. This paper presents a new approach modelling and predicting the machining dynamics for peripheral milling. First, a machining dynamics model is developed based on the regenerative vibrations of the cutter and workpiece excited by the dynamic cutting forces, which are mathematically modelled and experimentally verified by the authors [Liu, X., Cheng, K., Webb, D., Luo, X.-C. Improved dynamic cutting force model in peripheral milling—Part 1: Theoretical model and simulation. Int. J. Adv Manufact Tech, 2002, 20, 631–638; Liu, X., Cheng, K., Webb, D., Longstaff, A. P., Widiyarto, H. M., Jiang, X.-Q., Blunt, L., Ford, D. Improved dynamic cutting force model in peripheral milling—Part 2: Experimental verification and prediction. Int. J. Adv Manufact Tech, 2004, 24, 794–805]. Then, the mechanism of surface generation is analysed and formulated based on the geometry and kinematics of the cutter. Thereafter a simulation model of the machining dynamics is implemented using Simulink. In order to verify the effectiveness of the approach, the transfer functions of a typical cutting system in a vertical CNC machine centre were measured in both normal and feed directions by an instrumented hammer and accelerometers. Then a set of well-designed cutting trials was carried out to record and analyse the dynamic cutting forces, the vibrations of the spindle head and workpiece, and the surface roughness and waviness. Corresponding simulations of the machining processes of these cutting trials based on the machining dynamics model are investigated and the simulation results are analysed and compared to the measurements. It is shown that the proposed machining dynamics model can well predict the dynamic cutting forces, the vibrations of the cutter and workpiece. There is a reasonable agreement between the measured and predicted roughness/waviness of the machined surface. Therefore the proposed approach is proven to be a feasible and practical approach analysing machining dynamics and surface roughness/waviness for shop floor applications.     

Ultra-precision surface finish of the hardened stainless mold steel using vibration-assisted ball polishing process

A vibration-assisted spherical polishing system driven by a piezoelectric actuator has been newly developed on a machining center to improve the burnished surface roughness of hardened STAVAX plastic mold stainless steel and to reduce the volumetric wear of the polishing ball. The optimal plane surface ball burnishing and vibration-assisted spherical polishing parameters of the specimens have been determined after conducting the Taguchi's L₉ and L₁₈ matrix experiments, respectively. The surface roughness R_a=0.10 μm, on average, of the burnished specimens can be improved to R_a=0.036 μm (R_max=0.380 μm) using the optimal plane surface vibration-assisted spherical polishing process. The improvement of volumetric wear of the polishing ball was about 72% using the vibration-assisted polishing process compared with the non-vibrated polishing process. A simplified kinetic model of the vibration-assisted spherical polishing system for the burnished surface profile was also derived in this study. Applying the optimal plane surface ball burnishing and vibrated spherical polishing parameters sequentially to a fine-milled freeform surface carrier of an F-theta scan lens, the surface roughness of R_a=0.045 μm (R_y=0.65 μm), on average, within the measuring range of 149 μm×112 μm on the freeform surface, was obtainable.