Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel

In the present research, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting forces and surface roughness in hard milling of AISI H13 steel with coated carbide tools. Based on Taguchi’s method, four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut) four-level orthogonal experiments were employed. Three cutting force components and roughness of machined surface were measured, and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces. The optimal cutting parameters for minimal cutting forces and surface roughness in the range of this experiment under these experimental conditions are searched. Two empirical models for cutting forces and surface roughness are established, and ANOVA indicates that a linear model best fits the variation of cutting forces while a quadratic model best describes the variation of surface roughness. Surface roughness under some cutting parameters is less than 0.25 μm, which shows that finish hard milling is an alternative to grinding process in die and mold industry.     

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.     

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.     

金刚石磨头刀具磨铣加工对高体份SiCp/Al复合材料加工表面质量的影响

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

Process investigation on meso-scale hard milling of ZrO2 by diamond coated tools

This paper investigates the feasible machining of zirconium oxide (ZrO₂) ceramics, in the hard state, via milling by diamond coated miniature tools (from here on briefly indicated as meso-scale hard milling). The workpiece material is a fully sintered yttria stabilized tetragonal zirconia polycrystalline ceramic (Y-TZP). Diamond coated WC mills, 2 mm in diameter, four flutes and large corner radius (0.5 mm) are chosen as cutting tools, and experiments are conducted on a state-of-the-art micro milling machine centre. The influence of cutting parameters, including axial depth of cut (a_p) and feed per tooth (f_z), on the achievable surface quality is studied by means of a one-factor variation experimental design. Further tests are also conducted to monitor the process performance, including surface roughness, tool wear and machining accuracy, over the machining time. Mirror quality surfaces, with average surface roughness Ra below 80 nm, are obtained on the machined samples; the SEM observations of the surface topography reveal a prevailing ductile cutting appearance. Tool wear initiates with delamination of the diamond coating and progresses with the wear of the WC substrate, with significant effect on the cutting process and its performance. Main applications of this research include three dimensional surface micro structuring and superior surface finishing.     

Machining Characteristics of Micro-Flow Channels in Micro-Milling Process

□ This article presents a method for machining micro-flow channels on dies for precise and effective mass production of metallic bipolar plates of proton exchange membrane fuel cells (PEMFCs). To find an effective method for machining micro-flow channels on dies of metallic bipolar plates, machining experiments are conducted on micro-flow channels using a micro-scale milling process under various machining conditions. Machining variables are axial depth of cut, feed per tooth, supply/non-supply of cold wind, and single-tool/multi-tool cutting processes. The machining process is monitored by acquiring cutting force signals and acoustic emission signals while conducting the experiments. Surface conditions of machined micro-flow channels are analyzed. In this study, a cold-wind process is better than other processes in terms of burr generation and surface roughness. A cold-wind process with multi-tool process has a particularly better surface quality than the other processes.     

A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations

This paper describes a fuzzy-nets approach for a multilevel in-process surface roughness recognition (FN-M-ISRR) system, the goal of which is to predict surface roughness (Ra ) under multiple cutting conditions determined by tool material, workpiece material, tool size, etc. Surface roughness was measured indirectly by extrapolation from vibration signal and cutting condition data, which were collected in real-time by an accelerometer sensor. These data were analysed and a model was constructed using a neural fuzzy system. Experimental results showed that parameters of spindle speed, feedrate, depth of cut, and vibration variables could predict surface roughness (Ra) under eight different combinations of tool and workpiece characteristics. This neural fuzzy system is shown to predict surface roughness (Ra ) with 90% prediction accuracy during a milling operation.     

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

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

The role of stepover ratio in prediction of surface roughness in flat end milling

Surface roughness prediction studies in end milling operations are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The stepover ratio is usually neglected without investigating it. The aim of this study is to discover the role of the stepover ratio in surface roughness prediction studies in flat end milling operations. In realising this, machining experiments are performed under various cutting conditions by using sample specimens. The surface roughnesses of these specimens are measured. Two ANN structures were constructed. First of them was arranged with considering, and the second without considering the stepover ratio. ANN structures were trained and tested by using the measured data for predicting the surface roughness. Average RMS error of the ANN model considering stepover ratio is 0.04 and without considering stepover ratio is 0.26. The first model proved capable of prediction of average surface roughness (Ra) with a good accuracy and the second model revealed remarkable deviations from the experimental values.     

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.     

微铣金属表面微沟槽结构的粗糙度及形貌分析

为了提高和改善微沟槽表面质量,设计了高速微铣削实验,研究了微沟槽底面表面粗糙度和侧壁残留毛刺的变化规律。从理论角度引入了已加工表面的形成机理,建立了微观表面粗糙度理论模型,提出了刀具跳动对侧壁形貌变化影响的规律。利用三轴联动精密微细铣削机床加工微细直沟槽,并选取主轴转速、轴向切深、进给速度、刀具跳动量和材料组织结构为研究因素。采用多因素正交实验和极差分析法,对表面粗糙度值进行数值分析。铝合金,钢和钛合金三类微沟槽底面对应的最佳表面粗糙度值变化范围分别为1.073~1.481 μm,0.485~0.883 μm,0.235~0.267 μm;无刀具跳动钛合金微沟槽壁毛刺的最大高度为7.637 μm,而当刀具存在0.3 μm的径向综合跳动量时对应的微槽壁毛刺的最大高度为21.79 μm。铣削参数对表面粗糙度值的影响按从大到小依次为进给速度、主轴转速、轴向切深,且随着进给速度和轴向切深的增大,表面粗糙度值增大;随着主轴转速的增大,表面粗糙度值先减小后增大;在相同加工条件下,若微圆弧刀刃无磨损,微刀具的跳动量对微直沟槽侧壁表面质量有较大影响。同时,不同金属材料特性也是影响微沟槽表面质量的潜在因素。     

Investigation of cutting forces,surface integrity,and tool wear when high-speed milling of high-volume fraction SiC<Subscript>p</Subscript>/Al6063 composites in PCD tooling

This paper focused on high-speed milling of Al6063 matrix composites reinforced with high-volume fraction of small-sized SiC particulates and provided systematic experimental study about cutting forces, thin-walled part deformation, surface integrity, and tool wear during high-speed end milling of 65% volume fraction SiC_p/Al6063 (Al6063/SiC_p/65p) composites in polycrystalline diamond (PCD) tooling. The machined surface morphologies reveal that the cutting mechanism of SiC particulates plays an important role in defect formation mechanisms on the machined surface. In high-speed end milling of Al6063/SiC_p/65p composites, the cutting forces are influenced most considerably by axial depth of cut, and thus the axial depth of cut plays a dominant role in the thin-walled parts deformation. Increased milling speed within a certain range contributes to reducing surface roughness. The surface and sub-surface machined using high-speed milling suffered from less damage compared to low-speed milling. The milling speed influence on surface residual stress is associated with milling-induced heat and deformation. Micro-chipping, abrasive wear, graphitization, grain breaking off, and built-up edge are the dominated wear mechanism of PCD tools. Finally, a series of comparative experiments were performed to study the influence of tool nose radius, average diamond grain size, and machining parameters on PCD tool life.     

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.     

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning

Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R _a value has been obtained as 1.1999???m and the corresponding predicted surface roughness value is 1.1859???m, which implies greater accuracy.     

Effect of machining parameters on the milling process of 2.5D C/SiC ceramic matrix composites

Abstract

The C/SiC ceramic matrix composites are widely used for high-value components in the nuclear, aerospace and aircraft industries. The cutting mechanism of machining C/SiC ceramic matrix composites is one of the most challenging problems in composites application. Therefore, the effects of machining parameters on the machinability of milling 2.5D C/SiC ceramic matrix composites is are investigated in this article. The related milling experiments has been carried out based on the C/SiC ceramic matrix composites fixed in two different machining directions. For two different machining directions, the influences of spindle speed, feed rate and depth of cut on cutting forces and surface roughness are studied, and the chip formation mechanism is discussed further. It can be seen from the experiment results that the measured cutting forces of the machining direction B are greater than those of the in machining direction A under the same machining conditions. The machining parameters, which include spindle speed, feed rate, depth of cut and machining direction, have an important influence on the cutting force and surface roughness. This research provides an important guidance for improving the machining efficiency, controlling and optimizing the machined surface quality of C/SiC ceramic matrix composites in the milling process.     

Application of GONNS to predict constrained optimum surface roughness in face milling of high-silicon austenitic stainless steel

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In the present research, a genetically optimized neural network system (GONNS) is proposed for prediction of constrained optimal cutting conditions in face milling of a high-silicon austenitic stainless steel (UNS J93900) in order to minimize surface roughness. In order to attain minimum operation numbers and decrease the cost of machining, an experimental scheme was arranged by using Taguchi method. The considered parameters were cutting speed, feed, depth of cut, and engagement. Cutting force components and surface roughness were measured, and then analysis of variance is performed. The results show that the feed is the dominant factor affecting the surface roughness. Backpropagation artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting the experimental data, and the genetic algorithm was employed to find the constrained optimum of surface roughness. Finally, in order to validate the method, an experiment with the obtained optimal cutting condition was carried out, and the results were compared with the predicted value of surface roughness. The corresponding results show the capability of GONNS to predict constrained surface roughness.     

Feedrate optimisation/scheduling on sculptured surface machining: a comprehensive review, applications and future directions

Free-form or sculptured surface milling is one of the continually used manufacturing processes for die/mould, aerospace (especially turbine blades), precision machine design, bio-medical devices and automotive industries. Developments of machining technologies for quality enhancement of machining results have become a very important fact in current real industry. Therefore, reducing milling time, tool wear, cutter deflection and improving surface texture quality and machining operations through adaptation and optimisation of tool feedrates based on changing surface geometry in sculptured surface machining is a great step in this direction. Various feedrate optimisation strategies have different feedrate rescheduling control parameters such as chip thickness, material removal rate (MRR), min(mrr,chip,force), max(expo.Acc/dec) and resultant forces. Some commercial CAM softwares come with MRR-based feedrate optimisation algorithms which have a very short calculation time. However, commercial feedrate scheduling systems have some limitations in generating the scheduled feedrates because they use the MRR or the cutting force model which is dependent on milling conditions. However, for the processes in which machining precision/accuracy is very important, it is inevitable that mechanistic force-based feedrate optimisation approaches, for which the calculation time is improved, will be integrated into commercial CAM software packages. Here, developing only the mechanistic cutting force-based algorithm is not enough. In this paper, improvement and optimisation of machining feedrate value, which is one of the cutting parameters which has a tremendous effect on the precise machining of free-form surfaces, was discussed by using the virtual machining framework. For this purpose, the boundary representation solid modelling technique-based free-form milling simulation and feedrate optimisation system integrated with commercial CAD/CAM software is developed for three-axis ball-end milling. This review study includes the information regarding the following topics: The algorithms developed for the feedrate value optimisation, MRR calculation approaches, cutting force computation methods, details of algorithms, the effects on the surface accuracy, the effects on the machining time, the capabilities of the present commercial CAM software packages, the encountered difficulties and overcoming those difficulties, recent developments and future research directions.     

In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations

This paper describes a new approach for surface roughness recognition (ISRR) systems to predict surface roughness (Ra) in-process using an accelerometer to measure vibration signals and cutting conditions while end-milling is taking place. The analysis of the data and the model building is carried out using a neural fuzzy system. Experimental results show that the parameters of spindle speed, feedrate, depth of cut, and vibration variables can predict the surface roughness (Ra) effectively. Surface roughness can also be predicted with a 96% accuracy rate by ISRR using the neural fuzzy system.     

Modeling and Analytical Solution of Chatter Stability for T-slot Milling

T-slot milling is one of the most common milling processes in industry. Despite recent advances in machining technology, productivity of T-slot milling is usually limited due to the process limitations such as high cutting forces and stability. If cutting conditions are not selected properly the process may result in the poor surface finish of the workpiece and the potential damage to the machine tool. Currently, the predication of chatter stability and determination of optimal cutting conditions based on the modeling of T-slot milling process is an effective way to improve the material removal rate(MRR) of a T-slot milling operation. Based on the geometrical model of the T-slot cutter, the dynamic cutting force model was presented in which the average directional cutting force coefficients were obtained by means of numerical approach, and leads to an analytical determination of stability lobes diagram(SLD) on the axial depth of cut. A new kind of SLD on the radial depth of cut was also created to satisfy the special requirement of T-slot milling. Thereafter, a dynamic simulation model of T-slot milling was implemented using Matlab software. In order to verify the effectiveness of the approach, the transfer functions of a typical cutting system in a vertical CNC machining center were measured in both feed and normal directions by an instrumented hammer and accelerators. Dynamic simulations were conducted to obtain the predicated SLD under specified cutting conditions with both the proposed model and CutPro?. Meanwhile, a set of cutting trials were conducted to reveal whether the cutting process under specified cutting conditions is stable or not. Both the simulation comparison and experimental verification demonstrated that the satisfactory coincidence between the simulated, the predicted and the experimental results. The chatter-free T-slot milling with higher MRR can be achieved under the cutting conditions determined according to the SLD simulation.     

Study on burr occurrence and surface integrity during slot milling of multidirectional and plain woven CFRPs

Machining carbon fiber reinforced composite (CFRP) is often accompanied with cutting edge defects and surface damage including interlayer delamination, cavities and debonding of fiber-matrix. A detailed understanding of the effect of fiber configuration and cutting parameters on cutting force, burr occurrence/formation and surface integrity is necessary. In this paper, experimental data is presented relating to fiber burrs on entry surface, cutting force, surface roughness and workpiece integrity when slot milling CFRP laminates with varying fiber configurations (0°/90°, 45°/135° and plain woven) at different cutting speed (60 and 120 m/min) and feed rate (0.05 and 0.1 mm/rev). Lateral cutting force is recorded down to 56 N and highly dependent on fiber orientation. The length (up to ~?5.6 mm) and amount of fiber burrs are highly related to fiber orientation and fiber cutting angle. Surface roughness Ra down to ~?1.4 μm was recorded when milling type 2 (45°/135°) and type 3 (plain woven) laminates. Various surface defects predominantly occurred due to different cutting conditions and fiber configurations, which are mainly located in the layers with fibers orientated at 45°/135°. The occurrence and propagation of fiber burrs and surface cavities were also investigated based on different fiber fracture mechanisms.