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.     

SIMULATED ANNEALING TO ESTIMATE THE OPTIMAL CUTTING CONDITIONS FOR MINIMIZING SURFACE ROUGHNESS IN END MILLING Ti-6Al-4V

This study presents the estimation of the optimal effect of the radial rake angle of the tool, combined with cutting speed and feed in influencing the surface roughness result. Studies on optimization of cutting conditions for surface roughness in end milling involving radial rake angle are still lacking. Therefore, considering the radial rake angle, this study applied simulated annealing in determining the solution of the cutting conditions to obtain the minimum surface roughness when end milling Ti-6Al-4V. Considering a set of experimental machining data, the regression model is developed. The best regression model was considered to formulate the fitness function of the simulated annealing. It was recommended that the cutting conditions should be set at highest cutting speed, lowest feed and highest radial rake angle in order to achieve the minimum surface roughness of 0.1385 µm. Subsequently, it was found that by using simulated annealing, the minimum surface roughness was much lower than the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.     

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.     

A MACHINING PERFORMANCE STUDY IN DRY CONTOUR TURNING OF ALUMINUM ALLOYS WITH FLAT-FACED AND GROOVED DIAMOND TOOLS

This paper presents the results from an experimental study of dry contour turning operations on aluminum alloys (6061B and 2011-T3) using PCD flat-faced and diamond coated grooved tools. The machining performance is assessed on the basis of cutting forces, chip flow, chip-form and surface roughness observed during contour turning operations. The constantly varying cutting conditions (especially effective depth of cut due to varying geometry of the contour surface) and effective tool geometry cause a wide fluctuation in cutting forces and the ensuing chip flow. The chip flow angle is measured along the contour geometry using high-speed filming techniques and these results are compared with predicted chip flow values from the measured experimental cutting forces (which are measured along the entire contour geometry). The resultant surface roughness at different locations along the contour profile is measured and correlated with the chip flow and chip-form variations. Machining performance issues specifically relevant to dry contour turning of aluminum (such as problems due to poor chip flow and the resultant poor surface roughness) are studied and the effectiveness of selective work-tool (both tool material and tool geometry) pairs is illustrated.     

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.     

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.     

An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling Operations

Surface roughness is influenced by the machining parameters and other uncontrollable factors resulting from the cutting tool in end milling operations. To perform the in-process surface roughness prediction (ISRP) system accurately, the uncontrollable factors must be monitored. In this paper, an empirical approach using a statistical analysis was employed to discover the proper cutting force to represent the uncontrollable factors in end milling operations. Furthermore, an in-process neural network-based surface roughness prediction (INN-SRP) system was developed. A neural network associated with sensing technology was applied as a decision-making system to predict the surface roughness for a wide range of machining parameters. The good accuracy of the results for a wide range of machining parameters indicates that the system is suitable for application in industry. ID="A1"Correspondance and offprint requests to: Dr J. C. Chen, Department of Industrial Education and Technology, Iowa State University, 221 I. ED II, Ames, IA 50011–3130, USA. E-mail: cschen@iastate.edu     

Built-up edge effects on process outputs of titanium alloy micro milling

Built-up edge (BUE) is generally known to cause surface finish problems in the micro milling process. The loose particles from the BUE may be deposited on the machined surface, causing surface roughness to increase. On the other hand, a stable BUE formation may protect the tool from rapid tool wear, which hinders the productivity of the micro milling process. Despite its common presence in practice, the influence of BUE on the process outputs of micro milling has not been studied in detail. This paper investigates the relationship between BUE formation and process outputs in micro milling of titanium alloy Ti6Al4V using an experimental approach. Micro end mills used in this study are fabricated to have a single straight edge using wire electrical discharge machining. An initial experimental effort was conducted to study the relationship between micro cutting tool geometry, surface roughness, and micro milling process forces and hence conditions to form stable BUE on the tool tip have been identified. The influence of micro milling process conditions on BUE size, and their combined effect on forces, surface roughness, and burr formation is investigated. Long-term micro milling experiment was performed to observe the protective effect of BUE on tool life. The results show that tailored micro cutting tools having stable BUE can be designed to machine titanium alloys with long tool life with acceptable surface quality.     

Kinematics prediction and experimental validation of machined surface roughness

Online monitoring of surface roughness is a desirable capability for machining processes; however, 100 % inspection of all parts is not feasible unless it can be integrated into the machining process itself through real-time monitoring of cutting conditions. One strategy is to feed these conditions into a predictive modeling kernel which would in turn give the properties of the finished part. In the case of roughness, the surface resulting from turning can be largely represented as the trace of the passing tool geometry. The question addressed herein is whether computationally intensive modeling of the surface accounting for tool nose radius is necessary for online monitoring of surface roughness. This paper presents a predictive modeling methodology wherein the tool-workpiece contact position varies under a simple cutting model, and the resulting surface roughness is estimated. It presents the concept of calculating a “pseudo-roughness” value based only on tool tip locations and to compare this value to that determined by full predictive modeling of the tool geometry. Cutting experimental data has been presented and compared to predictions for model validation. It is found that the root mean square roughness calculation is dominated by tool geometry, rather than tool position deviations and surface roughness estimation could be implemented without a computationally intensive modeling component, thereby enabling online monitoring and potentially real-time control of the part finish.     

<Emphasis Type="Bold">Prediction of Surface Topomorphy and Roughness in Ball-End Milling</Emphasis>

Milling is today the most effective, productive and flexible-manufacturing method for machining complicated or sculptured surfaces. Ball-end tools are used for machining 3D freeform surfaces for dies, moulds, and various parts, such as aerospace components, etc. Milling data, such as surface topomorphy, surface roughness, non-deformed chip dimensions, cutting force components and dynamic cutting behaviour, are very helpful, especially if they can be accurately produced by means of a simulation program. This paper presents a novel simulation model, the so-called MSN-Milling Software Needle program, which is able to determine the surface produced and the resulting surface roughness, for ball-end milling. The model simulates precisely the tool kinematics and considers the effect of the cutting geometry on the resulting roughness. The accuracy of the simulation model has been thoroughly verified, with the aid of a wide variety of cutting experiments. Many roughness measurements were carried out on workpieces, which were cut using a 5-axis machining centre. The calculated roughness levels were found to be in agreement with the experimental ones. The proposed model has proved to be suitable for determining optimal cutting conditions, when finishing complex surfaces. The software can be easily integrated into various CAD-CAM systems.     

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.     

An experimental investigation on micro machining of fine-grained graphite

Industrial applications of the micro milling process require sufficient experimental data from various micro tools. Research has been carried out on micro milling of various engineering materials in the past two decades. However, there is no report in the literature on micro milling of graphite. This paper presents an experimental investigation on micro machinability of micro milling of moulded fine-grained graphite. Full immersion slot milling was conducted using diamond-coated, TiAlN-coated and uncoated tungsten carbide micro end mills with a uniform tool diameter of 0.5 mm. The experiments were carried out on a standard industrial precision machining centre with a high-speed micro machining spindle. Design of experiments (DoE) techniques were applied to design and analysis of the machining process. Surface roughness, surface topography and burrs formation under varying machining conditions were characterized using white light interferometry, SEM and a precision surface profiler. Influence of variation of cutting parameters including cutting speeds, feedrate and axial depth of cut on surface roughness and surface damage was analysed using ANOVA method. The experimental results show that feedrate has the most significant influence on surface roughness for all types of tools, and diamond tools are not sensitive to cutting speed and depth of cut. Surface damage and burrs analysis show that the primary material removal mode is still brittle fracture or partial ductile in the experimental cutting conditions. 3D intricate micro EDM electrodes were fabricated with good dimensional accuracy and surface finishes using optimized machining conditions to demonstrate that micro milling is an ideal process for graphite machining.     

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.     

Prediction of specific force coefficients from a FEM cutting model

This paper presents a method to obtain the specific cutting coefficients needed to predict the milling forces using a mechanistic model of the process. The specific coefficients depend on the tool–material couple and the geometry of the tool, usually being calculated from a series of experimental tests. In this case, the experimental work is substituted for virtual experiments, carried out using a finite element method model of the cutting process. Through this approach, the main drawbacks of both types of models are solved; it is possible to simulate end milling operations with complex tool geometries using fast mechanistic models and replacing the experimental work by virtual machining, a more general and cheap way to do it. This methodology has been validated for end milling operations in AISI 4340 steel.     

高速铣削高体积分数SiCp/Al复合材料表面形貌及切屑机制的研究

为了研究高体积分数SiCp/Al复合材料的切削机理及工件表面形貌,采用PCD刀具对干式切削和水溶性冷却液浇注冷却的湿式切削两种切削条件下的高速铣削进行了研究。结果表明,在对颗粒尺寸大、体积分数高的SiCp/Al复合材料进行高速铣削时,干式切削无论是在工件已加工表面形貌和微观结构,还是在切屑形成及形貌上,都好于湿式切削。两种切削条件下均可获得较理想的表面粗糙度。     

An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy

Nimonic C-263 alloy is extensively used in the fields of aerospace, gas turbine blades, power generators and heat exchangers because of its unique properties. However, the machining of this alloy is difficult due to low thermal conductivity and work hardening characteristics. This paper presents the experimental investigation and analysis of the machining parameters while turning the nimonic C-263 alloy, using whisker reinforced ceramic inserts. The experiments were designed using Taguchi’s experimental design. The parameters considered for the experiments are cutting speed, feed rate and depth of cut. Process performance indicators, viz., the cutting force, tool wear and surface finish were measured. An empirical model has been created for predicting the cutting force, flank wear and surface roughness through response surface methodology (RSM). The desirability function approach has been used for multi response optimization. The influence of the different parameters and their interactions on the cutting force, flank wear and surface roughness are also studied in detail and presented in this study. Based on the cutting force, flank wear and surface roughness, optimized machining conditions were observed in the region of 210 m/min cutting speed and 0.05 mm/rev feed rate and 0.50 mm depth of cut. The results were confirmed by conducting further confirmation tests.     

The performance Of DLC-coated and uncoated ultra-fine carbide tools in micromilling of Inconel 718

Coating is an important factor that affects cutting-tool performance. In particular, it directly affects surface quality and burr formation in the micro milling process. After the micromechanical machining process, surface quality is very hard to increase by a second process (grinding, etc.). In addition, in micromechanical machining, the cutting tool needs to have a good resistance to wear, owing to the fact that the cutting process is carried out at high speed. In this study, the machinability of Inconel 718 superalloy was investigated, using a Diamond Like Carbon (DLC) coated tool. The experimental tests were carried out in dry cutting conditions for different feed rates and depth of cuts. It was found that the dominant wear mechanism for all cutting parameters was identified to be abrasive and diffusive wear. Besides, a significantly Built Up Edge (BUE) formation was observed in uncoated tool. The results clearly show that DLC coating significantly decreased BUE. In addition, a smaller cutting force and better surface roughness were obtained with a DLC-coated tool. In conclusion, DLC coating can be used in micro milling of Inconel 718. It reduces the BUE and burr formation, improves surface roughness.     

Tool wear and hole quality investigation in dry helical milling of Ti-6Al-4V alloy

This paper focused on combined study on the evolution of tool wear and its influence on borehole quality in dry helical milling of Ti-6Al-4V. The carbide tools with TiAlN coating were used in this experimental investigation. The tool wear characteristics both at front and periphery cutting edges were investigated using an optical microscope and SEM-EDS techniques. The experimental results demonstrate that the combined effects of chipping/fracture, diffusion, and oxidation have significant bearings on front cutting edge failure, while the flank wear was predominant at the periphery cutting edges. The cutting speed was correlated with tool failure mechanizes, and the different wear rates at front and periphery cutting edge caused different variation trends of cutting force in thrust and horizontal direction during hole-making process. The quality of machined holes was evaluated in terms of geometry accuracy, burr formation, and surface roughness. The exit-burrs of machined hole were closely correlated with front cutting edge wear. However, high hole quality was observed even the near end of tool life from the point of view of diameters, roundness error, and surface finish due to the smooth wear pattern at periphery cutting edges. Severe tool wear at front cutting edges will cause excessive exit-burrs, but it has no obvious effect on geometry and surface roughness in helical milling of Ti-6Al-4V.     

Specific cutting force, tool wear and chip morphology characteristics during dry drilling of austempered ductile iron (ADI)

Dry machining is being recognized as ecological machining due to its less environmental impact and manufacturing cost. However, the choice of dry machining is mainly influenced by the workpiece material properties, machining operation and cutting conditions. The recent emergence of austempered ductile iron (ADI) can be considered a significant economic advantage to the increasing industrial demand for cost- and weight-efficient materials. However, due to its microstructure-induced inherent properties, ADI is considered hard-to-machine material. Thus, the dry drilling of ADI is investigated in this paper. The ADI material used in the present study is produced using an innovative process route for near net shape casting production. Drilling experiments are conducted on a DMU80P Deckel Maho five-axis machining centre using PVD-coated carbide tools under dry cutting environment. The dry drilling of ADI under different cutting conditions is evaluated in terms of specific cutting force and tool wear analysis. The influence of cutting conditions on chip morphology and surface roughness is also investigated. The experimental results revealed that the combination of the low feed rate and higher cutting speed leads to the higher mechanical and thermal loads on the tool's cutting edge, resulting in higher specific cutting force values. This behaviour is further supported by the chip morphology analysis, which revealed the formation of segmented chips at higher cutting speed with segment spacing increase with an increase in feed rate. Depending upon the cutting parameters, different modes of tool failures including crater wear, flank wear, chipping, breakage and built-up edge were observed. Surface roughness analysis revealed the influence of tool wear and chip morphology on the machined surface finish.     

Fabricating micromilling tool using wire electrodischarge grinding and focused ion beam sputtering

In the present research, wire electrical discharge machining (WEDM) of γ titanium aluminide is studied. Selection of optimum machining parameter combinations for obtaining higher cutting efficiency and accuracy is a challenging task in WEDM due to the presence of a large number of process variables and complicated stochastic process mechanisms. In general, no perfect combination exists that can simultaneously result in both the best cutting speed and the best surface finish quality. This paper presents an attempt to develop an appropriate machining strategy for a maximum process criteria yield. A feed-forward back-propagation neural network is developed to model the machining process. The three most important parameters – cutting speed, surface roughness and wire offset – have been considered as measures of the process performance. The model is capable of predicting the response parameters as a function of six different control parameters, i.e. pulse on time, pulse off time, peak current, wire tension, dielectric flow rate and servo reference voltage. Experimental results demonstrate that the machining model is suitable and the optimisation strategy satisfies practical requirements.