3-Dimensional kinematics simulation of face milling

Face milling is currently the most effective and productive manufacturing method for roughing and finishing large surfaces of metallic parts. Milling data, such as surface topomorphy, surface roughness, non-deformed chip dimensions, cutting force components and dynamic cutting behavior, are very helpful, especially if they can be accurately produced by means of a simulation program. This paper presents a novel simulation model which has been developed and embedded in a commercial CAD environment. The model simulates the true tool kinematics using the exact geometry of the cutting tool thus accurately forecasting the resulting roughness. The accuracy of the simulation model has been thoroughly verified, with the aid of a wide variety of cutting experiments. The proposed model has proved to be suitable for determining optimal cutting conditions for face milling. The software can be easily integrated into various CAD–CAM systems.     

THE SURFACE INTEGRITY IN MACHINING HARDENED STEEL SKD11 FOR DIE AND MOLD

Milling cutters were evaluated by tool wear, cutting force and vibration. Surface integrity of grinding and milling were investigated by comparing residual stress distributions, metallurgical structure, hardened layer depth and surface roughness. And influence of cutting tool wear on surface integrity was investigated. Experimentations revealed that the preferable surface integrity would be obtained if the proper milling cutter as well as a small wear criterion were adopted to avoid the advent of tempered martensite. The research results pointed out the feasibility of taking milling as the finish machining process instead of grinding in machining hardened steel with high efficiency.     

THE SURFACE INTEGRITY IN MACHINING HARDENED STEEL SKD11 FOR DIE AND MOLD

Milling cutters were evaluated by tool wear, cutting force and vibration. Surface integrity of grinding and milling were investigated by comparing residual stress distributions, metallurgical structure, hardened layer depth and surface roughness. And influence of cutting tool wear on surface integrity was investigated. Experimentations revealed that the preferable surface integrity would be obtained if the proper milling cutter as well as a small wear criterion were adopted to avoid the advent of tempered martensite. The research results pointed out the feasibility of taking milling as the finish machining process instead of grinding in machining hardened steel with high efficiency.     

An experimental investigation on the process parameters influencing machining forces during milling of carbon and glass fiber laminates

Milling is the most feasible machining operation for producing slots and keyways with a well defined and high quality surface. Milling of composite materials is a complex task owing to its heterogeneity and the associated problems such as surface delamination, fiber pullout, burning, fuzzing and surface roughness. The machining process is dependent on the material characteristics and the cutting parameters. An attempt is made in this work to investigate the influencing cutting parameters affecting milling of composite laminates. Carbon and glass fibers were used to fabricate laminates for experimentations. The milling operation was performed with different feed rates, cutting velocity and speed. Numerically controlled vertical machining canter was used to mill slots on the laminates with different cutting speed and feed combinations. A milling tool dynamo meter was used to record the three orthogonal components of the machining force. From the experimental investigations, it was noticed that the machining force increases with increase in speed. For the same feed rate the machining force of GFRP laminates was observed to be very minimal, when compared to machining force of CFRP laminates. It is proposed to perform milling operation with lower feed rate at higher speeds for optimal milling operation.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Performance of coated and uncoated mixed ceramic tools in hard turning process

The present contribution deals with the study of the effects of cutting speed, feed rate and depth of cut on the performance of machining which traditionally named “machinability”. The focus is made on the effect of the pre-cited cutting parameters on the evolution of surface roughness and cutting force components during hard turning of AISI D3 cold work tool steel with CC6050 and CC650 ceramic inserts. Also, for both ceramics a comparison of their wear evolution with time and its impact on the surface equality was proposed. The planning of experiments was based on Taguchi’s L₁₆ orthogonal array. The analysis of variance (ANOVA), the signal-to-noise ratio and response surface methodology (RSM) were adopted. Consequently, the validity of proposed linear regression model was checked and the most important parameter affecting the surface roughness and cutting force components were determined. Furthermore, in order to determine the levels of the cutting regime that lead to minimum surface roughness and minimum machining force the relationship between cutting factors was analyzed. The results revealed that the surface quality obtained with the coated CC6050 ceramic insert is 1.6 times better than the one obtained with uncoated CC650 ceramic insert. However, the uncoated ceramic insert was useful in reducing the machining force.     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.     

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.     

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.     

Micro-defects on machined carbon steel surfaces

Contrary to the evidence of stylus instruments, the residual roughness of machined 0.35% carbon steel surfaces changes as a function of cutting speed. The origin of the micro-defects, observed by electron microscopy, and with one exception, unresolved by conventional roughness measurement techniques, is thought not to be any malfunction of the machining process, but inherent in the process of surface formation. This paper describes micro-defects considered relevant to contacting surfaces and indicates that cutting speed selection for finish machining of tribological components based on conventional roughness measurements is questionable     

超精密车削表面粗糙度的控制与优化

金刚石车削是利用高精度机床与锋利的单晶金刚石刀具加工出尺寸精度高、表面完整性好的零件的一种金属加工技术。用回归分析的方法，根据金刚石车削铝合金的实验结果可以建立表面粗糙度预测模型，这种方法能够以较少的实验次数获得大量的加工信息。在一定条件下，利用优化设计软件可以实现切削参数的优选，用优选得到的最优切削参数组合进行超精密加工，能够获得超光滑加工表面。     

Prediction of cutting forces and machining error in ball end milling of curved surfaces -I theoretical analysis

This paper presents a theoretical model by which cutting forces and machining error in ball end milling of curved surfaces can be predicted. The actual trochoidal paths of the cutting edges are considered in the evaluation of the chip geometry. The cutting forces are evaluated based on the theory of oblique cutting. The machining errors resulting from force induced tool deflections are calculated at various parts of the machined surface. The influences of various cutting conditions, cutting styles and cutting modes on cutting forces and machining error are investigated. The results of this study show that in contouring, the cutting force component which influences the machining error decreases with increase in milling position angle; while in ramping, the two force components which influence machining error are hardly affected by the milling position angle. It is further seen that in contouring, down cross-feed yields higher accuracy than up cross-feed, while in ramping, right cross-feed yields higher accuracy than left cross-feed. The machining error generally decreases with increase in milling position angle.     

SURFACE ROUGHNESS PREDICTION MODEL FOR ULTRAPRECISION TURNING ALUMINIUM ALLOY WITH A SINGLE CRYSTAL DIAMOND TOOL

A surface roughness model utilizing regression analysis method is developed for predicting roughness of ultra-precision machined surface with a single crystal diamond tool. The effects of the main variables, such as cutting speed, feed, and depth of cut on surface roughness are also analyzed in diamond turning aluminum alloy. In order to predict and control the surface roughness before ultraprecision machining, constrained variable metric method is used to select the optimum cutting conditions during process planning. A lot of experimental results show that the model can predict the surface roughness effectively under a certain cutting conditions .     

基于动力学约束的端面铣削工艺参数优化

工艺参数优化对提高切削过程的加工效率和加工成本具有重要意义。将铣削系统动力学作为主要约束条件,提出端面铣削工艺参数的多目标优化模型。基于铣削系统动力学分析,得到了综合切削稳定性、工件表面粗糙度、主轴转速、切削力、切削功率等约束的工艺参数多目标优化模型。通过调节权重系数实现优化方向的控制,并采用快速粒子群算法对工艺参数进行优化计算。工艺优化实例及试验表明,采用基于动力学约束的工艺参数优化方法可以获得较好的工艺参数优化结果。     

Micro-defects on machined carbon steel surfaces

Contrary to the evidence of stylus instruments, the residual roughness of machined 0.35% carbon steel surfaces changes as a function of cutting speed. The origin of the microdefects, observed by electron microscopy and, with one exception, unresolved by conventional roughness measurement techniques, is thought not to be any malfunction of the machining process, but inherent in the process of surface formation. This paper describes micro-defects considered relevant to contacting surfaces and indicates that cutting speed selection for finish machining of tribological components based on conventional roughness measurements is questionable.     

基于表面粗糙度预测的数控车削加工物理仿真模型的研究

通过分析数控加工中表面粗糙度的产生原因,提出了表面粗糙度仿真系统模型的结构,建立了切削力、振动和表面粗糙度的数学模型,为通过物理仿真预测加工表面粗糙度提供了理论依据。     

A dynamic surface topography model for the prediction of nano-surface generation in ultra-precision machining

Materials induced vibration has its origin in the variation of micro-cutting forces caused by the changing crystallographic orientation of the material being cut. It is a kind of self-excited vibration which is inherent in a cutting system for crystalline materials. The captioned vibration results in a local variation of surface roughness of a diamond turned surface. In this paper, a dynamic surface topography model is proposed to predict the materials induced vibration and its effect on the surface generation in ultra-precision machining. The model takes into account the effect of machining parameters, the tool geometry, the relative tool–work motion as well as the crystallographic orientation of the materials being cut. A series of cutting experiments was performed to verify the performance of the model and good correlation has been found between the experimental and simulation results.     

Micro-drilling of Ti–6Al–4V alloy: The effects of cooling/lubricating

This paper presents a series of experimental investigations of the effects of various machining conditions [dry, flooded, minimum quantity lubrication (MQL), and cryogenic] and cutting parameters (cutting speed and feed rate) on thrust force, torque, tool wear, burr formation, and surface roughness in micro-drilling of Ti–6Al–4V alloy. A set of uncoated carbide twist drills with a diameter of 700 μm were used for making holes in the workpiece material. Both machining conditions and cutting parameters were found to influence the thrust force and torque. The thrust force and torque are higher in cryogenic cooling. It was found that the MQL condition produced the highest engagement torque amplitude in comparison to the other coolant–lubrication conditions. The maximum average torque value was obtained in the dry drilling process. There was no substantial effect of various coolant–lubrication conditions on burr height. However, it was observed that the burr height was at a minimum level in cryogenic drilling. Increasing feed rate and decreasing spindle speed increased the entry and exit burr height. The minimum surface roughness values were obtained in the flood cooling condition. In the dry drilling process, increased cutting speed resulted in reduced hardness on the subsurface of the drilled hole. This indicates that the surface and subsurface of the drilled hole were subject to softening in the dry micro-drilling process. The softening at the subsurface of drilled holes under different cooling and lubrication conditions is much smaller compared to the dry micro-drilling process.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

Abstract

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

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.