MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input?Coutput patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.     

Experimental and finite element analysis of parameters in manufacturing of metal bellows

The current article presents an investigation into predicting tool wear in hard machining D2 AISI steel using neural networks. An experimental investigation was carried out using ceramic cutting tools, composed approximately of Al2O3 (70%) and TiC (30%), on cold work tool steel D2 (AISI) heat treated to a hardness of 60 HRC. Two models were adjusted to predict tool wear for different values of cutting speed, feed and time, one of them based on statistical regression, and the other based on a multilayer perceptron neural network. Parameters of the design and the training process, for the neural network, have been optimised using the Taguchi method. Outcomes from the two models were analysed and compared. The neural network model has shown better capability to make accurate predictions of tool wear under the conditions studied.     

Thermodynamical matching of alumina-based composite ceramic tools with typical workpiece materials

Cutting temperature always highly reaches to over 1,000°C during high speed. Diffusion of tool material element may have important influence on tool wear at such high temperature. The advanced ceramic cutting tools have very good wear resistance, high refractoriness, good mechanical strength, and hot hardness. In this paper, the rules of diffusion wear for alumina-based ceramic cutting tools are proposed and analyzed based on thermodynamics theory. Dissolution concentrations in typical normal workpiece materials of ceramic tool materials at different temperatures are then calculated. Diffusion reaction rules in high temperature are developed and analyzed using Gibbs free energy criterion. The machining tests were conducted using the alumina-based composite ceramic tools at different cutting speeds of 10, 150, and 250 m/min, feed of 0.2 and 0.3, and depth of cut of 1, 2, 2.5, and 5 mm, respectively, on PUMA300LM numerically controlled lathe. It was found that the theoretical results were uniform with the experimental data; the results will provide useful references for tool material design and selection.     

Precision Machining of Glass Fibre Reinforced Plastics with respect to Tool Characteristics

When using glass fibre reinforced plastics (GFRP) it is often necessary to cut the material, but the cutting of GFRP is often made difficult by the delamination of the composite and the short tool life. In this paper, the machinability of GFRP by means of tools made of various materials and geometries was investigated experimentally. By proper selection of cutting tool material and geometry, excellent machining of the workpiece is achieved. The surface quality relates closely to the feedrate and cutting tool.     

基于进化神经网络的刀具寿命预测

为预测道具寿命,引入人工神经网络技术,建立了刀具寿命预测神经网络模型,同时对切削参数进行优化选择.在刀具寿命预测中,针对反向传播算法存在收敛速度慢、容易陷入局部极小值及全局搜索能力弱等缺陷,采用遗传算法训练反向传播神经网络,设计了进化神经网络的学习算法.实验和仿真结果表明:基于进化计算的反向传播神经网络可以克服单纯使用反向传播网络易陷入局部极小值等难题,刀具寿命的预测精度较高,从而为刀具需求计划制定、刀具成本核算,以及切削参数制定提供理论依据,节约了制造执行系统中的生产成本.     

An electron microprobe and cathodoluminescence study of chemical reactions between tool and workpiece when turning steel with alumina-based ceramics

Crater wear of alumina-based ceramic cutting tools when machining steel is predominantly dependent upon superficial plastic deformation. Such tool surface deformation may be greatly affected by chemical reactions with workpiece material. Crater surfaces of worn alumina-based ceramic tools in Coromant grade CC 620 (a pure ceramic, containing Al₂O₃ and ZrO₂) have been analysed by electron microprobe and cathodoluminescence after turning steel SS 2541 (similar to AISI 4337). It was found that the deformed surface layer had increased concentrations of iron and magnesium. Both these elements were probably present as spinel phases FeO. Al₂O₃ and MgO. nAl₂O₃ in solid solution. The spinel phases have higher yield strengths and probably also higher ductility than alumina itself, which may explain why wear rates are reduced when such compounds are found at the alumina tool-chip interface.     

Optimisation of machining parameters of glass-fibre-reinforced plastic (GFRP) pipes by desirability function analysis using Taguchi technique

This paper presents a new approach for optimizing the machining parameters on turning glass-fibre-reinforced plastic (GFRP) pipes. Optimisation of machining parameters was done by an analysis called desirability function analysis, which is a useful tool for optimizing multi-response problems. In this work, based on Taguchi’s L₁₈ orthogonal array, turning experiments were conducted for filament wound and hand layup GFRP pipes using K₂₀ grade cemented carbide cutting tool. The machining parameters such as cutting velocity, feed rate and depth of cut are optimized by multi-response considerations namely surface roughness, flank wear, crater wear and machining force. A composite desirability value is obtained for the multi-responses using individual desirability values from the desirability function analysis. Based on composite desirability value, the optimum levels of parameters have been identified, and significant contribution of parameters is determined by analysis of variance. Confirmation test is also conducted to validate the test result. It is clearly shown that the multi-responses in the machining process are improved through this approach. Thus, the application of desirability function analysis in Taguchi technique proves to be an effective tool for optimizing the machining parameters of GFRP pipes.     

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.     

Numerical simulation of orthogonal machining process using multilayer and single-layer coated tools

The tool edge radius significantly affects material deformation and flow, tool?Cchip friction, and a variety of machining performance measures (such as the cutting forces and tool wear) in mechanical micro/meso-scale machining. The tool edge-related research, either theoretically or experimentally, has been only focused in machining cases in which no built-up edge (BUE) is generated. To close this research gap, a comparative study of sharp and round-edge tools in orthogonal machining with BUE formation is conducted, including both experimental investigations and theoretical modeling. The experimental results show that the variations of the cutting forces are more stable in machining with a sharp tool than those in machining with a round-edge tool. A round-edge tool produces higher vibration magnitudes than does a sharp tool. The cutting vibrations do not necessarily have the same varying pattern as that of the cutting forces in machining with either a sharp tool or a round-edge tool. A neural network-based theoretical model is developed to predict three distinct regions of BUE formation (namely BUE Initiation Region, Steady BUE Region, and Unsteady BUE Region) in machining with a round-edge tool. The developed neural network model has been proven valid using a separate set of cutting experiments under different cutting conditions from those used for network training and testing.     

Performance of ceramic tools in high-speed cutting iron-based superalloys

A series of turning tests were conducted to investigate the cutting performance of ceramic tools in high-speed turning iron-based superalloys GH2132 (A286). Three kinds of ceramic tools, KY1540, CC650, and CC670 were used and their materials are Sialon, Al₂O₃–Ti(C,N), and Al₂O₃–SiC_w, respectively. The cutting forces, cutting temperatures, tool wear morphologies, and tool failure mechanisms are discussed. The experimental results show that with the increase in cutting speed, the resultant cutting forces with KY1540 and CC670 tools show a tendency to increase first and then decrease while those for CC650 increase gradually. The cutting temperature increases monotonically with the increase in cutting speed. The optimum cutting speeds for KY1540 and CC650 when turning GH2132 are less than 100 m/min, while those for CC670 are between 100 and 200 m/min. Flank wear is the main reason that leads to tool failure of KY1540 and CC670 while notch wear is the main factor that leads to tool failure of CC650. Tool failure mechanisms of ceramic tools when machining GH2132 include adhesion, chipping, abrasion, and notching. Better surface roughness can be got using CC670 ceramic tools.     

工况信息融合的神经网络刀具监控方法研究

基于加工过程中刀具产生的动态信号,利用BP神经网络多输入、多输出和非线性映射的特性,通过融合多种加工特征信号,建立了切削参数与加工动态过程之间的关系模型,实现了刀具在线加工状况的检测与预报。仿真结果表明,基于工况信息融合的神经网络刀具监控方法不但可以减少加工参数变化对刀具状态检测的影响,而且提高了在线检测刀具磨损量的精确度,验证了该方法的有效性。     

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.     

A method to predict possibility of arcing in EDM of TiB2p reinforced ferrous matrix composite

Electrical Discharge Machining (EDM) is very popular for machining conductive metal matrix composites (MMCs) because the hardness rendered by the ceramic reinforcements to these composites causes very high tool wear and cutting forces in conventional machining processes. EDM requires selection of a number of parameters for desirable results. Inappropriate parameter selection can lead to high overcuts, tool wear, excessive roughness, and arcing during machining and adversely affect machining quality. Arcing leads to short circuit gap conditions resulting in large energy discharges and uncontrolled machining. Arcing is a detrimental phenomenon in EDM which causes spoiling of workpiece and tool electrode and tends to damage the power supply of EDM machine. Parameter combinations that lead to arcing during machining have to be identified and avoided for every tool, work material, and dielectric combination. Proper selection of parameter combinations to avoid arcing is essential in EDM. In the work, experiments were conducted using L27 design of experiment to determine the parameter settings which cause arcing in EDM machining of TiB₂p reinforced ferrous matrix composite. Important EDM process parameters were selected in roughing, intermediate, and finishing range so as to study the occurrence of arcing. Using the experimental data, an artificial neural network (ANN) model was developed as a tool to predict the possibility of arcing for selected parameter combinations. This model can help avoid the parameter combinations which can lead to arcing during actual machining using EDM. The ANN model was validated by conducting validation experiments to ensure that it can work accurately as a predicting tool to know beforehand whether the selected parameters will lead to arcing during actual machining using EDM. Validation results show that the ANN model developed can predict arcing possibility accurately when the depth of machining is included as input variable for the model.     

An accuracy design approach for a multi-axis NC machine tool based on reliability theory

Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool.     

车削过程切削力的计算机数值仿真

切削力是表征切削过程最重要特征的物理量,其动态变化将直接影响加工过程中刀具与工件的相对位移、刀具磨损和表面加工质量等,所以对切削力建模是进行加工过程物理仿真研究的基础。因此在基于实时工况的切削实验研究基础上,考虑切削参数的因素,利用BP（back pmpagation）神经网络建立车削过程中的切削力的仿真模型。通过大量的样本训练,使神经网络能够对切削力进行较准确地数值仿真。     

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.     

Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN

Fibre reinforced plastics (FRP) contain two phases of materials with drastically distinguished mechanical and thermal properties, which brings in complicated interactions between the matrix and the reinforcement during machining. Surface quality and dimensional precision will greatly affect parts during their useful life especially in cases where the components will be in contact with other elements or materials during their useful life. Therefore, their study and characterisation is extremely important and, above all, those cases subjected to adverse environmental conditions and in contact with other elements or materials. Thus, measuring and characterising surface properties represent one of the most important aspects in manufacturing processes. In this paper, orthogonal cutting tests were carried out on unidirectional glassfibre reinforced plastics (GFRP), using cermet tools. During the tests, the depth of cut (a), feedrate (f), cutting speed (Vc) were varied, whereas the cutting direction was held parallel to the fibre orientation. Turning experiments were designed based on statistical three level full factorial experimental design technique. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness on the turned part surface. In the development of predictive models, cutting parameters of cutting speed, depth of cut and feed rate were considered as model variables. The required data for predictive models are obtained by conducting a series of turning test and measuring the surface roughness data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for GFRPs turned part surfaces are compared with each other for accuracy and computational cost.     

Multi-objective optimization of machining parameters considering energy consumption

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product.