Tool Wear Progression Research Articles

COMBINED EFFECTS OF FLANK AND CRATER WEAR ON CUTTING FORCE MODELING IN ORTHOGONAL MACHINING—PART I: MODEL DEVELOPMENT

Progressive tool wear, mainly in the forms of flank and crater wear, occurs on the tool flank and rake faces, respectively. Cutting forces may increase or decrease depending on the combined contribution from the flank and/or crater wear. Quantitative understanding and prediction of cutting forces under both tool flank and crater wear conditions is of importance to cutting process thermal modeling, tool life estimation, chatter prediction, and tool condition monitoring purposes. Unfortunately, there is no adequately documented work to analytically study the combined effects of both flank and crater wear on cutting forces and investigate the associated underlying machining physics. In this study, a slip-line field based force modeling approach is proposed to capture the worn tool cutting mechanism under the combined effects of both flank and crater wear in orthogonal cutting. The proposed slip-line field and the associated hodograph are introduced, and the slip-line models for the primary shear zone, the flank wear land and each part of the secondary shear zone are discussed in detail. The model implementation procedure is also proposed in terms of the hodograph geometric relationship and the force and moment equilibrium in order to predict cutting forces of interest.

On the removal of critical cutting regions by trochoidal grooving

Particularly in high-speed roughing by a straight end mill, contour-parallel paths often cause an excessive tool load in a critical cutting region such as a sharp corner and a narrow slot. Trochoidal grooving can be used to remove such regions safely, prior to high-speed contour-parallel cutting. This paper present a systematic tool path generation strategy for high-speed 2.5-dimensional end milling with the removal of critical cutting regions by trochoidal grooving. We first present a scheme to extract critical cutting regions for an arbitrary two-dimensional pocket contour by using its medial axis. A trochoidal tool path to remove an arbitrary contour can be generated also by using the medial axis. Two experimental case studies are presented to show the efficiency and tool wear progress in the roughing by a straight end mill with trochoidal grooving to remove critical cutting regions, in comparison with conventional contour-parallel roughing by a ball end mill.

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.

Machinability study of tungsten carbide using PCD tools under ultrasonic elliptical vibration cutting

Sintered tungsten carbide (WC) is an extremely hard and brittle material extensively used in tool manufacturing industries. However, the current cutting technologies for shaping this typical hard-to-machine material are still cost ineffective. In this study, polycrystalline diamond (PCD) tools are used to study the machinability of sintered WC (~15% Co) by applying the ultrasonic elliptical vibration cutting (UEVC) technique. Firstly, it presents the UEVC principle and the effects of speed ratio (i.e. the ratio of the nominal cutting speed to the maximum tool vibration speed in the cutting direction) on the tool–workpiece relative motion as the cutting speed greatly influences the UEVC performance. Then UEVC experiments are carried out to analyze the cutting force, tool-wear progression, chip formation and surface quality against the cutting time at different speed ratios. The results show that when the speed ratio decreases, the resultant cutting force and the tool flank wear decrease while the surface finish improves. Average surface roughness, R a , in a range between 0.030 and 0.050 μm is achieved at speed ratios less than 0.107. The experimental findings suggest that the commercial PCD tools can be used to machine sintered WC to achieve ultraprecision surface by applying the UEVC technique, which will be cost effective for miniature cutting technologies in future.

프레스 금형 마모에 대한 음-초음파 신호 특성 분석

This paper reports on the research which investigates acoustic signals acquired in progressive compressing, hole blanking, shearing and burr compacting process. The work piece is the head pin of the electric connector, whose raw material is the preformed steel bar. An acoustic sensor was set on the bed of hydraulic press. Because the acquired signals include the dynamic characteristics generated for all the processes, it is required to investigate signal characteristics corresponding to unit process. The corresponding dynamic characteristics to the respective process were first studied by analyzing the signals respectively acquired from compressing, blanking and compacting process. The combined signals were then periodically analyzed from the grinding to the grinding in the sound frequency domain and in the ultrasonic wave. The frequency of around 9 kHz in the sound frequency domain was much correlated to the tool wear. The characteristic frequency in the acoustic emission domain between 100 kHz and 500 kHz was not only clearly observed right after tool grinding but its amplitude was also related to the wear. The frequency amplitudes of 160 kHz and 320 kHz were big enough to be classified by the noise. The noise amplitudes are getting bigger, and their energy was much bigger as coming to the next regrinding. The signal analysis was based on the real time data and its frequency spectrum by Fourier Transform. As a result, the acousto-ultrasonic signals were much related to the tool wear progression.

Surface topography assessment techniques based on an in-process monitoring approach of tool wear and cutting force signature

One of the most influential sources in machining affecting surface roughness is the instantaneous state of tool edge. The main objective of the current work is to relate the in-process immeasurable cutting edge deformation and surface roughness to a more reliable easy-to-measure force signals using a robust non-linear time-dependent modeling regression techniques. Correlation between wear propagation and roughness variation is developed throughout the different edge lifetimes. The surface roughness is further evaluated in the light of the variation in both the static and the dynamic force signals. Consistent correlation is found between surface roughness variation and tool wear progress within its initial and constant regions. At the first few seconds of cutting, expected and well known trend of the effect of the cutting parameters is observed. Surface roughness is positively influenced by the level of the feed rate and negatively by the cutting speed. As cutting continues, roughness is affected, to different extents, by the rather localized wear modes either on the tool nose or on its flank areas. Moreover, it seems that roughness varies as wear attitude transfers from one mode to another and, in general, it is shown that it is improved as wear increases but with possible corresponding workpart dimensional inaccuracy. The dynamic force signals are found reasonably sensitive to simulate either the progressive or the random modes of tool edge deformation. While the frictional force components, feeding and radial, are found informative regarding progressive wear modes, the vertical (power) components is found more representative carrier to system instability resulting from the edge's random deformation.

Identification of Wear Characteristics in Tool Wear Model of Cutting Process

The tool wear progress is simulated with the cutting force and the cutting temperature. The cutting force is predicted with piling up the orthogonal cuttings in the force model based on the minimum cutting energy. The cutting temperature is predicted in the finite volume method with assuming the stress distribution. The abrasive wear model is applied to the flank wear prediction. The wear characteristic constants in the wear model are identified to minimize the model error between the prediction and the measured wear rates. Because the initial flank wear generally occurs in the flank wear progress, the prediction starts at the initial wear offset. The initial wear offsets are also identified to minimize the error between the predicted and the measured flank wear lands. Some of flank wear progresses are measured to identify the wear characteristics constants and the initial wear offsets in the turning operation. The tool wear prediction is performed to verify the identification of the wear characteristics.

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.

Effect of tool wear progression on cutting forces and surface quality during cutting metal matrix composites

Metal matrix composites (MMCs) have joined the group of difficult-to-cut materials because of the inherent abrasiveness of ceramic reinforcements. Flank wear is the dominant form of tool failure due to severe abrasion of the tool material by the reinforcements during cutting MMCs. The progression of tool wear is so rapid that sometimes the cutting tool lasts only for few seconds at high cutting speeds. The main objective of this paper is to investigate the changes in the magnitude of machining forces generated and the alterations in the quality of machined surface as a consequence of rapid tool wear progression during cutting aluminium MMCs. Dry cutting experiments were carried out on A356/SiC and 6061/Alumina aluminium MMCs. Different process parameters and cutting tool geometries were tested using coated and uncoated tungsten carbide inserts on MMCs reinforced with varying volume fractions and average particulate sizes. The experimental results showed a substantial increase in the cutting forces. The effect of wear progression on the obtained surface quality was also quantified. The results provide valuable information on the relationship between the generated machining forces, progression of tool wear and the resulted surface quality during machining MMCs.

Manufacturing of cylindrical gears by generating cutting processes: A critical synthesis of analysis methods

The eternal goal achieving optimum gear manufacturing results in a quick and flexible way can be obtained by efficient machine tools and thorough processes know how. Gear manufacturing is associated with complicated generating kinematics, chip formation and tool wear mechanisms. To capture quantitatively the tool wear progress and the cutting loads, empirical, analytical, numerical as well as FEM-based methods describing the chip geometry and predicting the tool life and cutting forces have been developed. The application of innovative tool materials and coatings, optimized tool geometries and appropriate conduct of reconditioning procedures contribute to the significant reducing of the manufacturing cost.

Modelling and experimental analysis of the effects of tool wear, minimum chip thickness and micro tool geometry on the surface roughness in micro-end-milling

Tool wear, minimum chip thickness and micro tool geometry are found to have a significant influence on the surface roughness through experimental analysis of the micro-end-milling process. To address these issues, a surface roughness model is developed and validated in this present work. Firstly, experimental analysis for the tool wear and surface roughness was performed based on the micro-end-milling experiments of OFHC Copper by using 0.1 mm diameter micro endmills with a miniaturized machine tool. The cutting velocity and material removal volume are found to have a great effect on the tool wear, which will in turn affect the surface roughness significantly. Then, a trajectory-based surface roughness model for micro-end-milling is proposed and proven capable of capturing the minimum chip thickness, micro tool geometry and process parameters. Finally, based on this model, a surface roughness model with tool wear effect is developed by taking the material removal volume and cutting velocity into account and is experimentally validated. This model accurately predicts the surface roughness variation with tool wear progress and provides the means for further process design and optimization studies of the micro-end-milling process.

Modeling tool wear progression by using mixed effects modeling technique when end-milling AISI 4340 steel

Semi-dry and dry machining is being adopted by the metal cutting industry worldwide to reduce the harmful effects of traditional metal cutting fluids and the cost associated with procurement, use, and disposal of these fluids. This research focuses on end-milling of AISI 4340 steel with multi-layer physical vapor deposition (PVD) coated carbide inserts under semi-dry and dry cutting conditions and proposes a mixed effects model for the analysis of the longitudinal data obtained from a designed experiment. This modeling approach considers unobserved heterogeneity during machining and proposes a tool wear progression model that has a higher power of detecting effects of significant factors than traditional regression models. One such source of variation is work piece hardness that was observed within and across test blocks. From the wear progression model developed, the lowest initial flank wear values are obtained at a cutting speed of 183 m/min, a feed rate of 0.10 mm/rev under semi-dry cutting conditions. A higher rate of wear progression and lower tool life was observed at the higher cutting speed level of 229 m/min. Cutting speed has the most significant effect on flank wear progression in this study. Depth of cut on the other hand did not show any significant effect on tool wear when compared to cutting speed, feed and cutting conditions. The wear mechanism of the insert was analyzed using electron dispersive X-ray (EDX) and environmental scanning electron microscope (ESEM) images. From this analysis, diffusion wear was confirmed under both semi-dry and dry machining conditions. It is expected that the proposed model can reduce the number of repetitions in tool wear modeling experiment for tool manufacturers leading to substantial cost savings.

Hard turning of an alloy steel on a machine tool with a polymer concrete bed

Finish turning of 39NiCrMo3 alloy steel in the hardened state has been widely investigated under dry, minimum quantity of lubricant and wet cutting conditions, using inserts in ceramic and PCBN materials, on turning centers equipped with polymer concrete and cast iron beds. The progress of workpart surface roughness and tool wear with cutting time has been measured and the results analysed and discussed in detail. It has been observed that dry cutting leads to the lowest values of tool wear and surface roughness, whilst the minimal quantity of lubricant technique does not provide advantages regarding to dry turning. Furthermore, the PCBN inserts are characterised by a longer tool life than the one exhibited by the ceramic inserts. Finally, the outstanding damping and high rigidity of the polymer concrete bed has a beneficial effect on both tool wear and workpiece surface finish.

Machinability study on 3%Co–12%Cr stainless steel in milling

12%Cr stainless steels are among the most difficult to cut materials because of their high work-hardening trend and high resistance to heat. 3%Co–12%Cr heat-resistant stainless steel is one of the 12%Cr steels used for critical parts in ultra super critical power plant. The main objectives of this study are to investigate wear and failure mechanism of cemented carbide tool of TiCN/TiN multilayer coating during milling process of 3%Co–12%Cr stainless steel and to explore the machinability of the material. The machined surfaces were analyzed by scanning electron microscopy (SEM). The tool life experiments were based on single-tooth-milling using two kinds of inserts with different geometry. It was found with the experiments that cutting tools with 17° rake angle inserts have longer life than that of with rake angle of 28°. SEM and EDX analysis were used to analyze wear and failure mechanism of the tool. It is found with the experiment that tool wear progression follows a three-stage wear pattern and abrasive, attrition, adhesive and diffusive wear appear in different stages. The correlations among cutting conditions, surface integrity and tool wear were also explored.

A strategy for improvement of the C<SUB align=right>pk of DRAM modules in the depanel process of PCBA

This paper is to improve the dimensional accuracy of Small Outline Dual Inline Memory Modules (SO-DIMMs), which is a kind of Dynamic Random Access Memory (DRAM) modules for notebook computer, in the depanel process. This study proposes the implementation of combining grey relation analysis with Statistical Process Control (SPC) analysis, so as to quickly search the result that progressive tool wear is the dominant parameters adversely influencing the accuracy in the depanel process. To resolve tool wear issue, this paper implements the pretreated milling for a certain working distance before practically proceeding the depanel process. The accuracy of products, therefore, appears to be feasibly controllable. Furthermore, this study adopts SPC analysis to evaluate the improvement effect, revealing that Cpk value dramatically increases from 0.788 to 2.175, and the tool life of mill can also be extended from milling of 25 to 45 panels. It has also been successfully implemented in an international factory, resulting in a yield of more than 1 million DRAM modules.

Process Optimization of Finish Turning of Hardened Steels

Process planning and optimization is crucial to help establish the economic and quality viability of hard turning processes with the presence of a wide spectrum of tooling and process parameters. A systematic methodology is discussed in this paper to design the optimal tool geometry and cutting conditions for hard turning, incorporating the consideration of part finish, tool wear, and material removal rate. Experimental demonstration of the optimization scheme is presented at two levels: the first level is to validate the process prediction results and the second is to validate the optimization results. Hardened AISI 1053 steel was selected as the workpiece material in this study and its material property related parameters, including the Johnson–Cook constants and wear coefficients, were determined based on the machining tests. It is seen that the cutting force and tool wear progression agrees well with the predictions from 3-D oblique cutting model, and the machined surface roughness can be predicted with a surface kinematic model incorporating the plowing effect. The experimental results also showed that the process configuration as derived from the analytical optimization procedure lends itself to superior results in comparison to other experimental results under non-optimal configurations.

White layer formation and tool wear in high speed milling of 57HRc tool steel using coated and uncoated tools

Advances in process technology have opened new possibilities for rapid manufacturing. High Speed Machining (HSM) is one of these innovative areas. One demanding application is the HSM of hardened steels for die and mould tooling applications. A significant impediment in the wide-spread use of HSM in hard machining is a lack of understanding and subsequent control of possible micro-structural changes to the surface of machined components. These changes can occur in the form of surface and sub-surface layers induced by grain refinement, rapid heating and quenching and or reactions with the environment. Some surface layers are known for decreasing the material fatigue life due to their brittleness. Generally, these affected surface layers are referred to as the white layer. This paper focuses on formation of white layers during high speed milling of hardened tool steels. The machining was carried out using uncoated and TiAlCrN coated micro-grain carbide end mills. The cutting tools were also analysed for tool wear. The paper explores the correlation of white layer formation to tool wear progression and how this is affected by the PVD coating. Surface hardening, sub-surface tempering, surface finish and compositional changes are also presented. The results show that in milling, tool wear is a significant driver for white layer thickness progression. Moreover, increased oxygen content suggests that oxidation could also play a role in white layer formation.

Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 3—Optimum Cutting Conditions and Selection of Cutting Tools

This paper presents a summary of recent developments in developing performance-based machining optimization methodologies for turning operations. Four major machining performance measures (cutting force, tool wear/tool life, chip form/chip breakability, and surface roughness) are considered in the present work, which involves the development and integration of hybrid models for single and multi-pass turning operations with and without the effects of progressive tool wear. Nonlinear programming techniques were used for single-pass operations, while a genetic algorithms approach was adopted for multi-pass operations. This methodology offers the selection of optimum cutting conditions and cutting tools for turning with complex grooved tools.

Measurement of TiAlN Coating Effects on Machining Characteristics of High Hardened Steel

Effects of TiAlN coating conditions on the cutting characteristics during machining high hardened tool steel using coated ball end mill were studied in this research. KP4 steel and STD 11 steel were used as workpieces. Minimum quantity lubrication (MQL) was utilized in the cutting. Scanning electron microscope, optical tool, and a tool dynamometer were used to measure coating thickness and progress of tool wear. Results showed that the cutting distance using TiAlN coated ball end mills was longer than that using WC-Co ball end mill by 2.3~5.7 times for KP4 steels and about 2.5~4.3 times for STD11 heat treated steels. The multi layer TiAlN coated ball end mill showed better performance of machining of hard steels than single layered coated tools.

Milling of wood and wood-based materials with a computerized numerically controlled router V: development of adaptive control grooving system corresponding to progression of tool wear

A laser measuring instrument was installed in a computerized numerically controlled (CNC) router, and an automatic measurement system, which was developed in a previous study, was used to automatically measure the cutting edge profile of throw-away type straight bits without stopping the CNC router. In addition to the above-mentioned system, in this study, an adaptive control program based on experimental data was installed, and an adaptive control grooving system that improved machining accuracy and controlled the burr formation corresponding to the progression of tool wear under processing was developed. Verification experiments of this system were carried out. The main results obtained are summarized as follows: (1) the between-process method was adopted for this system, and three types of processing methods (type 1, 2, 3), which consisted of a combination of an up-milling surface and a down-milling surface after processing, were investigated; (2) from the results of verification experiments, type 2 and type 3 methods showed remarkable ability to improve the machining accuracy and control the burr formation; and (3) it was found that the system employing adaptive control processing corresponding to the progression of tool wear in grooving is very effective.