Statistical model to determine surface roughness when milling hastelloy C-22HS

The aim of this study is to develop the surface roughness prediction models, with the aid of statistical methods, for hastelloy C-22HS when machined by PVD and CVD coated carbide cutting tools under various cutting conditions. These prediction models were then compared with the results obtained experimentally. By using response surface method (RSM), first order models were developed with 95 % confidence level. The surface roughness models were developed in terms of cutting speed, feed rate and axial depth using RSM as a tool of design of experiment. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. It was found that the feed rate, cutting speed and axial depth played a major role in determining the surface roughness. On the other hand, the surface roughness increases with a reduction in cutting speed. PVD coated cutting tool performs better than CVD when machining hastelloy C-22HS. It was observed that most of the chips from the PVD cutting tool were in the form of discontinuous chip while CVD cutting tool produced continuous chips.     

Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm

Modeling and optimization of cutting parameters are one of the most important elements in machining processes. The present study focused on the influence machining parameters on the surface roughness obtained in drilling of AISI 1045. The matrices of test conditions consisted of cutting speed, feed rate, and cutting environment. A mathematical prediction model of the surface roughness was developed using response surface methodology (RSM). The effects of drilling parameters on the surface roughness were evaluated and optimum machining conditions for minimizing the surface roughness were determined using RSM and genetic algorithm. As a result, the predicted and measured values were quite close, which indicates that the developed model can be effectively used to predict the surface roughness. The given model could be utilized to select the level of drilling parameters. A noticeable saving in machining time and product cost can be obtained by using this model.     

涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design

Many previous researches on high-speed machining have been conducted to pursue high machining efficiency and accuracy. In the present study, the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra high-speed face milling of AISI H13 steel (46–47 HRC) are experimentally investigated. It is found that the ultra high cutting speed of 1,400?m/min can be considered as a critical value, at which relatively low mechanical load, good surface finish, and high machining efficiency are expected to arise at the same time. When the cutting speed adopted is below 1,400?m/min, the contribution order of the cutting parameters for surface roughness Ra is axial depth of cut, cutting speed, and feed rate. As the cutting speed surpasses 1,400?m/min, the order is cutting speed, feed rate, and axial depth of cut. The developing trend of the surface roughness obtained at different cutting speeds can be estimated by means of observing the variation of the chip shape and chip color. It is concluded that when low feed rate, low axial depth of cut, and cutting speed below 1,400?m/min are adopted, surface roughness Ra of the whole machined surface remains below 0.3?μm, while cutting speed above 1,400?m/min should be avoided even if the feed rate and axial depth of cut are low.     

Machining and simulation studies of bimetallic pistons

Light aluminium alloy piston is suitably reinforced at high load-bearing region with cast iron insert, and machining of such bimetallic material is more difficult with a single cutting tool material. Present study focuses on the orthogonal cutting of bimetallic material machining using cubic boron nitride as a cutting tool through finite element analysis. The effects of cutting parameters such as cutting velocity, feed rate and depth of cut on resultant cutting forces and the surface roughness were analysed. Those parameters yielding minimum cutting forces were identified as minimal cutting force parameters, so numerical simulation and experiments were carried out on these parameters. After machining, the intermediate bonding between metallic regions was studied using ultrasonic testing. Bimetallic machining is successfully simulated, and its potential is readily applied to an industrially important component.     

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

高速铣削铝合金叶片表面质量的试验研究

使用硬质合金球头铣刀对铝合金叶轮叶片进行了高速铣削试验。研究了切削速度和进给量对加工表面粗糙度的影响。试验结果表明:在高速加工中,每齿进给量比铣削行距对加工表面质量的影响更大;提高切削速度和减少每齿进给量有利于降低加工表面粗糙度。但当切削速度超过某一范围后,进一步提高速度对降低表面粗糙度的作用并不明显;每齿进给量减小到一定范围后,表面粗糙度反而会有所增加;对于铝合金叶片曲面的加工,合理选择切削速度、进给量和行距可获得较低的表面粗糙度值和较高的加工效率。     

A study of surface roughness in drilling using mathematical analysis and neural networks

Drilling is one of the most common and fundamental machining processes. It is most frequently performed in material removal and is used as a preliminary step for many operations, such as reaming, tapping and boring. Because of their importance in nearly all production operations, twist drills have been the subject of numerous investigations. The aim of this study is to identify suitable parameters for the prediction of surface roughness. Back propagation neural networks are used for the detection of surface roughness. Drill diameter, cutting speed, feed and machining time are given as inputs to the neural network structure and surface roughness was estimated. Drilling experiments with 12 mm drills are performed at three cutting speeds and feeds. The number of neurons are selected from 1,2,3, ..., 20. The learning rate was selected as 0.01, and no smoothing factor was used. The best structure of neural network was selected based on a criteria including the minimum of sum of squares with the actual value of surface roughness. For mathematical analysis, an inverse coefficient matrix method was used for calculating the estimated values of surface roughness. Comparative analysis was performed between actual values and estimated values obtained by mathematical analysis and neural network structures.     

PCD刀具车削超硬铝合金的切削性能研究

以Al7075-T6为加工对象,通过车削试验对PCD刀具车削超硬铝合金的三向动态切削力和表面粗糙度展开研究,建立基于BP神经网络的切削力和表面粗糙度预测模型。结果表明:随着切削用量三要素的变化,切削力变化显著;对于表面粗糙度而言,背吃刀量、进给量和切削速度之间无交互作用;基于L-M优化算法的BP神经网络对样本的拟合度高,且对切削力和表面粗糙度的预测精度高。     

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.     

Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V

The present study focuses on the development of predictive models of average surface roughness, chip-tool interface temperature, chip reduction coefficient, and average tool flank wear in turning of Ti-6Al-4V alloy. The cutting speed, feed rate, cutting conditions (dry and high-pressure coolant), and turning forces (cutting force and feed force) were the input variables in modeling the first three quality parameters, while in modeling tool wear, the machining time was the only variable. Notably, the machining environment influences the machining performance; yet, very few models exist wherein this variable was considered as input. Herein, soft computing-based modeling techniques such as artificial neural network (ANN) and support vector machines (SVM) were explored for roughness, temperature, and chip coefficient. The prediction capability of the formulated models was compared based on the lowest mean absolute percentage error. For surface roughness and cutting temperature, the ANN and, for chip reduction coefficient, the SVM revealed the lowest error, hence recommended. In addition, empirical models were constructed by using the experimental data of tool wear. The adequacy and good fit of tool wear models were justified by a coefficient of determination value greater than 0.99.     

Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In order to avoid the costly trial-and-error process in machining parameters determination, the Gaussian process regression (GPR) was proposed for modeling and predicting the surface roughness in end face milling. Cutting experiments on C45E4 steel were conducted and the results were used for training and verifying the GPR model. Three parameters, spindle speed, feed rate, and depth of cut were considered; the experiment results showed that depth of cut is the main factor affecting the surface roughness and regression results showed that the GPR model has a good precision in predicting the surface roughness in different cutting conditions. The prediction accuracy was nearly about 84.3 %. Based on the GPR prediction model, 3D-maps of surface roughness under various cutting parameters could be obtained. It is very concise and useful to select the appropriate cutting parameters according to the maps. As experimental results did not conform to the empirical knowledge, frequency spectrums of the tool were analyzed according to the 3D-maps, it was found that tool vibration is also a crucial factor affecting the machined surface quality.     

Dynamic surface generation modeling of two-dimensional vibration-assisted micro-end-milling

An integrated model is proposed to simulate the surface generation in two-dimensional vibration-assisted micro-end-milling (2-D VAMEM). The model includes the developed submodels as dynamic cutting force model, machining system response model, and machined surface generation algorithm. The effects of feed rate on cutting force and surface roughness are investigated through simulations. It is found that the cutting force increases while the surface roughness decreases with the increment of the feed rate when the feed per tooth is smaller than the tool edge radius. The trials have been carried out to evaluate and validate the proposed model and the simulation results. The integrated model contributes to the comprehensive understanding of the process of machined surface generation in 2-D VAMEM and will assist the machining operators to select optimal machining parameters.     

Analysis model of parameters affecting cutting performance in high-speed machining

Further progress in green cutting applications depends on the innovativeness of machine tools, advances in tool development, and, especially, more complex tool and cutting technologies. Therefore, this study analyzes the factors influencing high-speed cutting performance. Grey relational analysis and the Taguchi method are then incorporated in the experimental plan with high-speed milling of AISI H13 tool steel. Experimental results indicate that the contributions of tool grinding precision, geometric angle, and cutting conditions to the multiple quality characteristics of high-speed milling for AISI H13 tool steel are 11.75, 9.80, and 73.11 %, respectively. For rough machining, tool life and metal removal volume are the primary evaluation indicators and cutting parameters should be prioritized, especially cutting speed and feed per tooth. In finish machining, workpiece surface roughness is the primary evaluation indicator. Besides the selection of cutting parameters, the design and grinding of endmill are critical factors, especially the design and grinding of relief angles.     

金刚石磨头刀具磨铣加工对高体份SiCp/Al复合材料加工表面质量的影响

针对高体份SiCp／Al复合材料,采用佥刚石磨头刀具磨铣切削的加工方法,研究了高速磨铣加工中机床主轴转速、工件进给速度及背吃刀量对材料加工表面形貌损伤以及表面粗糙度的影响规律。研究表明,机床主轴转速的提高、工件进给速度的减小都能够减小材料表面形貌的损伤情况,改善加工表面粗糙度质量：背吃刀量的改变对材料表面形貌损伤以及表面粗糙度的影响不大。     

基于响应曲面法的表面粗糙度预测模型研究

通过正交试验,研究了高速端铣加工中切削参数对表面粗糙度的影响。采用田口设计方法和响应曲面法构建了表面粗糙度预测模型,分析了主轴转速、进给量、切深对表面粗糙度的影响。结果显示,进给量对表面粗糙度的影响最显著,主轴转速次之,切深的影响不大。模型预测精度为99．84％,达到了较高的预测水平。