Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.     

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.     

System identification and admittance model-based nanodynamic control of ultra-precision cutting process

The control of diamond turning is usually achieved through a laser-interferometer feedback of slide position. The limitation of this control scheme is that the feedback signal does not account for additional dynamics of the tool post and the material removal process. If the tool post is rigid and the material removal process is relatively statie, then such a non-collocated position feedback control scheme may suffice. However, as the accuracy requirement gets tighter and desired surface contours become more complex, the need for direct tool-tip sensing becomes inevitable. The physical constraints of the machining process prohibit any reasonable implementation of a tool-tip motion measurement. It is proposed that the measured force normal to the face of the workpiece can be filtered through an appropriate admittance transfer function, providing an estimated depth of cut. This can be compared to the desired depth of cut to generate the adjustment control action in addition to position feedback control. In this work, the design methodology on admittance model-based control with a conventional controller is presented. The recursive least-squares algorithm with forgetting factor is proposed to identify the parameters and update the cutting process in real time. The normal cutting forces are measured to identify the cutting dynamics in the real diamond turning process using a precision dynamometer. Based on the parameter estimation of cutting dynamies and the admittance model-based nanodynamic control scheme, simulation results are shown.     

Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach

In present work performance of coated carbide tool was investigated considering the effect of work material hardness and cutting parameters during turning of hardened AISI 4340 steel at different levels of hardness. The correlations between the cutting parameters and performance measures like cutting forces, surface roughness and tool life, were established by multiple linear regression models. The correlation coefficients found close to 0.9, showed that the developed models are reliable and could be used effectively for predicting the responses within the domain of the cutting parameters. Highly significant parameters were determined by performing an Analysis of Variance (ANOVA). Experimental observations show that higher cutting forces are required for machining harder work material. These cutting forces get affected mostly by depth of cut followed by feed. Cutting speed, feed and depth of cut having an interaction effect on surface roughness. Cutting speed followed by depth of cut become the most influencing factors on tool life; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower feed value, lower depth of cut and by limiting the cutting speed to 235 and 144 m/min; while turning 35 and 45 HRC work material, respectively, ensures minimum cutting forces, surface roughness and better tool life.     

Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms

In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.     

Experimental investigations of surface roughness in orthogonal turning of unidirectional glass-fiber reinforced plastic composites

Cutting parameters and the resulting cutting forces have a great effect on the machinability of materials during the turning process. The effects of cutting parameters on machinability have been examined by many researchers and studies on determination of suitable cutting conditions for various materials are still under investigation. In this study, surface roughness of unidirectional glass-fiber reinforced plastic composite was examined on the basis of cutting parameters such as depth of cut, feed rate, tool geometry, and cutting speed. The surface quality was found to relate closely to the feed rate, cutting speed, and cutting tool.     

Cutting force in stone lapping

The present work shows empirical models to foresee the cutting forces developed during stone lapping. The developed empirical model puts into relationship the cutting force and energy with the relevant cutting parameters, such as the depth of cut and the feed rate, for the stone known as Coreno Perlato Royal. The cutting force and energy have been modelled as a function of material removal rate by a simple linear relationship. The developed models may be used to guide the selection of cutting conditions. The chip generation and removal process has been quantified in order to assist both the toolmaker and the stonemason in optimising the tool composition and cutting process parameters, respectively.     

Cutting force model considering tool edge geometry for micro end milling process

The analysis of the cutting force in micro end milling plays an important role in characterizing the cutting process, as the tool wear and surface texture depend on the cutting forces. Because the depth of cut is larger than the tool edge radius in conventional cutting, the effect of the tool edge radius can be ignored. However, in micro cutting, this radius has an influence on the cutting mechanism. In this study, an analytical cutting force model for micro end milling is proposed for predicting the cutting forces. The cutting force model, which considers the edge radius of the micro end mill, is simulated. The validity is investigated through the newly developed tool dynamometer for the micro end milling process. The predicted cutting forces were consistent with the experimental results.     

Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology

The hard turning process has been attracting interest in different industrial sectors for finishing operations of hard materials. In this paper, the effects of cutting speed, feed rate, and depth of cut on surface roughness, cutting force, specific cutting force, and power in the hard turning were experimentally investigated. An experimental investigation was carried out using ceramic cutting tools, composed approximately with (70 %) of Al₂O₃ and (30 %) of TiC, in surface finish operations on cold work tool steel AISI D3 heat-treated to a hardness of 60 HRC. Based on 3³ full factorial designs, a total of 27 tests were carried out. The range of each parameter is set at three different levels, namely, low, medium, and high. Analysis of variance is used to check the validity of the model. Experimental observations show that higher cutting forces are required for machining harder work material. This cutting force gets affected mostly by feed rate followed by depth of cut. Feed rate is the most influencing factor on surface roughness. Feed rate followed by depth of cut become the most influencing factors on power; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower depth of cut value, higher cutting speed, and by limiting the feed rate to 0.12 and 0.13 mm/rev, while hard turning of AISI D3 hardened steel, respectively, ensures minimum cutting forces and better surface roughness. Higher values of depth of cut are necessary to minimize the specific cutting force.     

HIGH SPEED MILLING OF GRAPHITE ELECTRODE WITH ENDMILL OF SMALL DIAMETER

Graphite becomes the prevailing electrode material in electrical discharging machining (EDM)currently.Orthogonal cutting experiments are carried out to study the characteristics of graph- ite chip formation process.High speed milling experiments are conducted to study tool wear and cutting forces.The results show that depth of cut has great influence on graphite chip formation.The removal process of graphite in high speed milling is the mutual result of cutting and grinding process. Graphite is prone to cause severe abrasion wear to coated carbide endmills due to its high abrasive- ness nature.The major patterns of tool wear are flank wear,rake wear,micro-chipping and breakage. Cutting forces can be reduced by adoption of higher cutting speed,moderate feed per tooth,smaller radial and axial depths of cut,and up cutting.     

Micro cutting of tungsten carbides with sem direct observation method

This paper describes the micro cutting of wear resistant tungsten carbides using PCD (Poly-Crystalline Diamond) cutting tools in performance with SEM (Scanning Electron Microscope) direct observation method. Turning experiments were also carried out on this alloy (V50) using a PCD cutting tool. One of the purposes of this study is to describe clearly the cutting mechanism of tungsten carbides and the behavior of WC particles in the deformation zone in orthogonal micro cutting. Other purposes are to achieve a systematic understanding of machining characteristics and the effects of machining parameters on cutting force, machined surface and tool wear rates by the outer turning of this alloy carried out using the PCD cutting tool during these various cutting conditions. A summary of the results are as follows : (1) From the SEM direct observation in cutting the tungsten carbide, WC particles are broken and come into contact with the tool edge directly. This causes tool wear in which portions scrape the tool in a strong manner. (2) There are two chip formation types. One is where the shear angle is comparatively small and the crack of the shear plane becomes wide. The other is a type where the shear angle is above 45 degrees and the crack of the shear plane does not widen. These differences are caused by the stress condition which gives rise to the friction at the shear plane. (3) The thrust cutting forces tend to increase more rapidly than the principal forces, as the depth of cut and the cutting speed are increased preferably in the orthogonal micro cutting. (4) The tool wear on the flank face was larger than that on the rake face in the orthogonal micro cutting. (5) Three components of cutting force in the conventional turning experiments were different in balance from ordinary cutting such as the cutting of steel or cast iron. Those expressed a large value of thrust force, principal force, and feed force. (6) From the viewpoint of high efficient cutting found within this research, a proper cutting speed was 15 m/min and a proper feed rate was 0.1 mm/rev. In this case, it was found that the tool life of a PCD tool was limited to a distance of approximately 230 m. (7) When the depth of cut was 0.1 mm, there was no influence of the feed rate on the feed force. The feed force tended to decrease, as the cutting distance was long, because the tool was worn and the tool edge retreated. (8) The main tool wear of a PCD tool in this research was due to the flank wear within the maximum value of V_max being about 260 μ.     

超精密车削表面微观形貌的几何建模与仿真研究

在综合考虑刀具几何参数，刀具振动和最小切削厚度等因素对已加工表面形貌影响的前提下编写了表面微观形貌的仿真程序，在仿真时把一个随机振动信号成功地叠加在理论表面粗糙度中。提出一种建立圆弧刃金刚石车刀超精密车削表面粗糙度模型的新方法。结果表明，仿真得到的表面微观形貌，能够比较正确地反映出超精密加工将要获得的表面轮廓。     

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.     

Modeling and simulation of cutting forces generated during vertical micro-grinding

Micro-grinding using micro-tools has become very prevalent due to the miniaturization of products with increased process requirements. Moreover, this process provides an edge over other competitive processes, especially as a final process step. The quality of the part produced by the micro-scale grinding process can be influenced by various factors, particularly by the induced mechanical forces. Therefore, predictive model of cutting force can provide guidance for further development and optimization of the process. Although there has been a lot of a research conducted on conventional grinding, little knowledge has been accumulated on micro-scale grinding due to the fact that it is an emerging field of research. The early grinding models developed are mostly based on parameters such as wheel and workpiece velocity, depth of cut and grit size of the grinding wheel. Those early models narrated that the grits penetrate and cut the material from the workpiece surface with the generated grinding forces proportional to the removed material. However, those models may not be appropriate for micro-scale grinding due to the mode of material removal and the method of contact between surfaces which is different from the macro-scale method. In addition to that, due to the small feed rate used in brittle material machining, ploughing force needs to be considered intensively in addition to the chip formation force. Therefore, a new analytical model has been proposed to evaluate cutting forces of micro-grinding process based on the process configuration, workpiece material properties and micro-grinding tool topography. The size effect of micro-machining has been carefully considered in this proposed model. Therefore, this approach allows the derivation of cutting force comprising of both the chip formation force and ploughing force. Experimental investigation in a micro-grinding configuration has been pursued to validate the proposed predictive model. The estimated cutting force showed a good correlation with the experimental values except for higher depth of cut and lower feed rate. Additionally, paired T test has been performed to quantify the difference between the predicted and experimental results.     

Multiple-response optimization of turning machining by the taguchi method and the utility concept using uni-directional glass fiber-reinforced plastic composite and carbide (k10) cutting tool

This paper presents a utility concept for multi-response optimization in turning uni-directional glass fiber-reinforced plastics composite using Carbide (K10) cutting tool. The single response optimization resulted in the non-optimization of other responses. The Taguchi method (Orthogonal L₁₈ array) was employed in the experimental work. The process parameters selected for this study were tool nose radius, tool rake angle, feed rate, cutting speed, depth of cut, and cutting environment. Statistically significant parameters were found to simultaneously minimize surface roughness and maximize the material removal rate by ANOVA. The results were further verified by confirmation experiments.     

Force modelling in shallow cuts with large negative rake angle and large nose radius tools—application to hard turning

In finish turning, the applied feedrate and depth of cut are generally very small. In some particular cases, such as the finishing of hardened steels, the feedrate and depth of cut are much smaller than tool nose radius. If a tool with a large tool nose radius and large negative rake angle is used in finish turning, the ploughing effect is pronounced and needs to be carefully addressed. Unfortunately, the ploughing effect has not yet been systematically considered in force modelling in shallow cuts with large negative rake angle and large nose radius tools in 3-D oblique cutting. In this study, in order to model the forces in such shallow cuts, first the chip formation forces are predicted by transforming the 3-D cutting geometry into an equivalent 2-D cutting geometry, then the ploughing effect mechanistic model is proposed to calculate the total 2-D cutting forces. Finally, the 3-D cutting forces are estimated by a geometric transformation. The proposed approach is verified in the turning of hardened 52100 steel, in which cutting conditions are typified as shallow cuts with negative rake angle and large nose radius tools. The workpiece material property of hardened 52100 steel is represented by the Johnson-Cook equation, which is determined from machining tests. The comparison between the experimental results and the model predictions is presented.     

Distribution of unit forces on the tool edge rounding in the case of finishing turning

An experimental investigation was conducted to determine the effects of tool cutting edge geometry on the cutting forces in finish turning, where the applied feed and depth of cut are small and often comparable with the tool edge radius. If a tool with large tool edge radius is used in finish turning, the ploughing effect begins to determine the machined surface. This paper presents the results of analytical considerations concerning the unit forces on a cutting edge. The aim of this paper is to indicate possibilities of modelling the unit forces and stress distribution based on cutting resistance. The forces calculated in the feed and cutting speed directions were projected onto the tangential and normal directions of the rounded cutting edge surface. An important assumption in all the considerations was that the thermo-mechanical properties of the materials used remained constant. The minimum thickness of cut was defined, and some characteristic points were identified dividing the cutting zone into three subregions: where a chip is formed, where the machined surface is formed and an unstable region.     

HSM-ADAPTED TOOL PATH CALCULATION FOR POCKETING

High-speed milling imposes a precise choice of cutting conditions, because the feed rate and the radial depth of cut influence the maximum forces on cutting edges. But the control of these cutting conditions for pocket machining is very difficult due to the complex tool path shape. Our work is focused on the improvement of the geometrical definition of the tool path, in order to ensure a better respect of the cutting conditions required for HSM. Initially, we study variations in the radial depth of cut and the real feed rate, when the tool follows usual tool paths for pocketing. Numerical simulations and experimental measurements are used. Next, a new tool path computation method that increases the real feed rate and respects radial depth of cut requirements is proposed. The computation takes into account both the geometrical requirements and the HSM dynamic requirements. Such tool paths reduce machining time and respect initial cutting parameters which are favorable for process reliability and tool life.     

HSM-ADAPTED TOOL PATH CALCULATION FOR POCKETING

High-speed milling imposes a precise choice of cutting conditions, because the feed rate and the radial depth of cut influence the maximum forces on cutting edges. But the control of these cutting conditions for pocket machining is very difficult due to the complex tool path shape. Our work is focused on the improvement of the geometrical definition of the tool path, in order to ensure a better respect of the cutting conditions required for HSM. Initially, we study variations in the radial depth of cut and the real feed rate, when the tool follows usual tool paths for pocketing. Numerical simulations and experimental measurements are used. Next, a new tool path computation method that increases the real feed rate and respects radial depth of cut requirements is proposed. The computation takes into account both the geometrical requirements and the HSM dynamic requirements. Such tool paths reduce machining time and respect initial cutting parameters which are favorable for process reliability and tool life.     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.