Experimental investigation on thermally enhanced machining of high-chrome white cast iron and to study its machinability characteristics using Taguchi method and artificial neural network

Machining of hard-to-wear materials such as high-chrome white cast iron (HCWCI) and high-manganese steels is an uphill task when conventional route followed. Alternatively, thermally enhanced machining (TEM) can be used to minimize the tooling cost very effectively. This paper presents the detailed study of TEM of HCWCI in which the effect of cutting parameters and surface temperature of the stock material on machinability characteristics (cutting forces and surface roughness) are analyzed using ANOVA and artificial neural network (ANN). The experimental work was conducted to follow Taguchi techniques. HCWCI is finding newer applications in mining; mineral processing industries were the workpiece in the machining studies using cobalt-based cubic boron nitride insert tool. Localized heat was added at the tool-work interface which softens the metal and eases the machining operation. The influences of the control factors on the process responses have been analyzed using analysis of variance (ANOVA), and the results are correlated using ANN. Linear regression was used to establish the relation between the control parameters and the process responses. The results show that TEM causes easy shearing of the material, leading to the reduction in cutting forces with expected improvement in tool life and surprisingly good surface finish. The confirmation tests suggest both second-order regression and ANN which are better predictive models for quantitative prediction of TEM of HCWCI, and ANN is more accurate of the two. Also, it was proved that oxy-LPG flame heating is an economical option compared to laser-heated machining in hard turning process.     

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.     

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.     

DEVELOPMENT OF A VARIABLE FLOW STRESS MACHINING THEORY FOR ALUMINIUM ALLOYS

This paper describes the development of a variable flow stress predictive machining theory for aluminium alloys. This theory is based on the Oxley's machining theory which allows for the high strain-rate/high temperature flow stress and thermal properties of the work materials and has so far been applied and tested for plain carbon steels.

The developed predictive theory for aluminium has been applied in predicting cutting forces, chip thicknesses, etc., for a wide range of cutting conditions (cutting speeds ranging from 100 to 1000 m/min) when machining two alloys: one with 97.64%Al and the other with 93.89%Al (a free machining aluminium alloy). The flow stress properties required in applying the predictive method were obtained from bar turning test results and by applying the machining theory in reverse. For the above mentioned aluminium alloys, a comparison between predicted and experimental cutting force results shows good agreement.     

基于变形控制的薄壁结构件高速铣削参数选择

首先对国内外有关研究薄壁件铣削加工变形的文献进行了回顾。然后,对不同切削参数下铣削力变化规律以及因铣削力引起的加工变形进行了理论分析与试验研究,并以此为基础提出了薄壁件高速铣削切削参数选择原则。试验结果表明,采用优化的切削参数不仅使薄壁件加工精度得到了保证,加工效率也大大提高。     

Development of an automatic arc welding system using SMAW process

In end milling of pockets, variable radial depth of cut is generally encountered as the end mill enters and exits the corner, which has a significant influence on the cutting forces and further affects the contour accuracy of the milled pockets. This paper proposes an approach for predicting the cutting forces in end milling of pockets. A mathematical model is presented to describe the geometric relationship between an end mill and the corner profile. The milling process of corners is discretized into a series of steady-state cutting processes, each with different radial depth of cut determined by the instantaneous position of the end mill relative to the workpiece. For the cutting force prediction, an analytical model of cutting forces for the steady-state machining conditions is introduced for each segmented process with given radial depth of cut. The predicted cutting forces can be calculated in terms of tool/workpiece geometry, cutting parameters and workpiece material properties, as well as the relative position of the tool to workpiece. Experiments of pocket milling are conducted for the verification of the proposed method.     

Evaluation of performance of nanofluid using multiwalled carbon nanotubes for machining of Ti–6AL–4V

Machining of titanium alloys generate very high temperature in the cutting zone. This results in rapid tool wear and poor surface properties. Therefore, improvement in cutting performance in machining of titanium alloys is very much dependent on effectiveness of the cooling strategies applied. In the present work, performance of nanofluid using multiwalled carbon nanotubes (MWCNTs) dispersed in distilled water and sodium dodecyl sulfate (SDS) as surfactant is evaluated for turning operation on Ti–6Al–4V workpieces. Turning operations were carried out under three different conditions – dry, with conventional cutting fluid and with nanofluid. Nanofluid application was limited to 1 L/h and it was applied at the tool tip through gravity feed. Various machining responses like cutting force, surface finish and tool wear were analyzed while turning at optimum cutting parameters as 150 m/min, 0.1 mm/rev and 1 mm depth of cut. Later on, machining performance of nanofluid is confirmed at low cutting speed of 90 m/min. Nanofluid outperformed conventional cutting fluid with 34% reduction in tool wear, average 28% drop in cutting forces and 7% decrease in surface roughness at cutting speed of 150 m/min.     

Determining cutting parameters in wire EDM based on workpiece surface temperature distribution

The material removal process in wire electrical discharge machining (WEDM) may result in work-piece surface damage due to the material thermal properties and the cutting parameters such as varying on-time pulses, open circuit voltage, machine cutting speed, and dielectric fluid pressure. A finite element method (FEM) program was developed to model temperature distribution in the workpiece under the conditions of different cutting parameters. The thermal parameters of low carbon steel (AISI4340) were selected to conduct this simulation. The thickness of the temperature affected layers for different cutting parameters was computed based on a critical temperature value. Through minimizing the thickness of the temperature affected layers and satisfying a certain cutting speed, a set of the cutting process parameters were determined for workpiece manufacture. On the other hand, the experimental investigation of the effects of cutting parameters on the thickness of the AISI4340 workpiece surface layers in WEDM was used to validate the simulation results. This study is helpful for developing advanced control strategies to enhance the complex contouring capabilities and machining rate while avoiding harmful surface damage.     

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

The Effect of Argon-Enriched Environment in High-Speed Machining of Titanium Alloy

A major factor hindering the machinability of titanium alloys is their tendency to react with most cutting tool materials, thereby encouraging solution wear during machining. Machining in an inert environment is envisaged to minimize chemical reaction at the tool-chip and tool-workpiece interfaces when machining commercially available titanium alloys at higher cutting conditions. This article presents the results of machining trials carried out with uncoated carbide (ISO K10 grade) tools in an argon-enriched environment at cutting conditions typical of finish turning operations. Comparative trials were carried out at the same cutting conditions under conventional coolant supply. Results of the machining trials show that machining in an argon-enriched environment gave lower tool life relative to conventional coolant supply. Nose wear was the dominant tool-failure mode in all the cutting conditions investigated. Argon is a poor conductor of heat; thus, heat generated during machining tends to concentrate in the cutting region and accelerate tool wear. Argon also has poor lubrication characteristics, leading to increasing friction at the cutting interfaces during machining and an increase in cutting forces required for efficient shearing of the workpiece.     

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.     

TiAlN涂层铣刀铣削9SiCr钢切削性能试验研究

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

多工序多工步制造系统切削用量的优化研究

通过对建立的多工序多工步制造系统生产时间和生产成本的数学模型的优化分析，并考虑了相应的生产条件约束限制，从而提出了基于最大生产率多工序多工步有约束制造模型切削用量优化求解思想——主目标生产效率最大，次目标生产费用最小，并给出了有效的优化算法。     

切削速度对精密干式车削淬硬工具钢切削力的影响

通过使用PCBN刀具精密干式车削淬硬Cr12MoV工具钢(62±1 HRC)的试验,分析了切削速度对三向切削力的影响,得出了最优切削速度。试验表明:随切削速度提高,三向切削力先急剧增大,后急剧减小,再又缓慢增大。若从最小车削合力与提高加工效率两个角度来优化切削速度,则226 n/min是最优切削速度。试验结果也对精密干式切削淬硬工具钢具有实际指导意义与参考价值。     

Development of multi-objective optimization models for electrochemical machining process

Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.     

Design and optimization of machining fixture layout using ANN and DOE

In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout.     

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

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.     

正交切削高强耐磨铝青铜的有限元分析

采用热力耦合、平面应变、连续带状切屑的切削模型模拟了高强耐磨铝青铜的正交切削加工过程。采用增量步移动刀具的方法,结合有限元分析软件Marc的网格重划分功能,模拟了刀具从初始切入到切削力和切削温度达到稳态的切削加工过程,获得了不同切削深度和切削速度下的切屑形态、温度、应力、应变和应变速率的分布。并将模拟计算得到的切削力和切削温度与试验结果进行了比较,两者具有较好的一致性。     

MACHINING RESIDUAL STRESSES IN AISI 316L STEEL AND THEIR CORRELATION WITH THE CUTTING PARAMETERS

It is well known that machining results in residual stresses in the workpiece. These stresses correlate very closely with the cutting tool geometrical parameters as well as with the machining regime. This paper studies the residual stress induced in turning of AISI 316L steel. Particular attention is paid to the influence of the cutting parameters, such as the cutting speed, feed and depth of cut. In the experiments, the residual stresses have been measured using the X-ray diffraction technique (at the surface of the workpiece and in depth). The effects of cutting conditions on residual stresses are analyzed in association with the experimentally determined cutting forces. The orthogonal components of the cutting force were measured using a piezoelectric dynamometer.     

Development of an innovative plate dynamometer for advanced milling and drilling applications

Tool geometry optimization, workpiece material characterization, process monitoring and optimization are based on the measurement of cutting forces by using machining dynamometers. Commercial dynamometers cover a wide range of machining applications, nevertheless there is a lack of measuring devices suitable for investigating milling and drilling applications with relatively small cutters and high spindle speeds. In this work, the development and testing of an innovative plate dynamometer designed for this purpose is discussed. The new measuring system was based on three high-sensitive triaxial piezoelectric force sensors arranged in a novel triangular configuration. Component design was optimized by using FE numerical approaches, according to the general guidelines derived from mathematical modeling of sensor dynamics. The prototype of the proposed device was manufactured and experimentally tested against two high-end commercial plate dynamometers by performing static calibration, modal analysis and cutting tests. Experimental results proved the excellent characteristics of the new device and its effectiveness for investigating advanced machining applications.