Since the development of Taylor’s wear equation in the early 1900s, empirical approaches to tool wear have been extensively used by industries. However, as many types of work materials and cutting tools have emerged, developing the empirical equation for each combination of work material and cutting tool is too costly and time-consuming. In this paper, we present a promising new model-based approach, where the developed model can be directly extended to other work materials and cutting tools. However, when machining pearlitic steels at high cutting speed, phase transformation restricts the model’s applicability. With phase transformation, the associated flank wear does not follow the model’s predictions. Evidently, the abrasive action of cementite is suppressed as the cementite phase in pearlitic microstructure transforms into austenite. This paper summarizes and reevaluates our previous experimental results in order to develop a model-based approach to understanding and predicting tool wear.

There is a strong need in industry for monitoring techniques that are capable of tracking the health of cutting tools under varying conditions. Unfortunately, most monitoring techniques that are currently available are dependent on the cutting direction and/or the sensor orientation, limiting their effectiveness in the typical industrial environment. With this in mind, this research focuses on developing a monitoring technique that is independent of both of these factors. This is accomplished by using multivariate autoregressive models that are fit to the output from a tri-axial accelerometer. The work shows that the eigenvalues of multivariate spectral matrices, calculated at the machining frequencies, are sensitive to the condition of the tool. Furthermore, it is theoretically demonstrated that these eigenvalues are independent of the direction of cutting and the orientation of the sensor. This independence is verified experimentally through tests conducted under a variety of cutting directions and sensor orientations.

The main objective of the work reported here was to develop an intelligent condition monitoring system able to detect when a cutting tool was worn out. To accomplish this objective the use of a hybrid intelligent system, based on an expert system and two neural networks was investigated. The neural networks were employed to process data from sensors and the classifications made by the neural networks were combined with information from the knowledge base to obtain an estimate of the wear state of the tool by the expert system. The novelty of this work is mainly associated with the configuration of the developed system. The combination of sensor-based information and inference rules, results in an on-line system that can learn from experience and update the knowledge base pertaining to information associated with different cutting conditions. The neural networks resolved the problem of interpreting the complex sensor inputs while the expert system, by keeping track of previous success, estimated which of the two neural networks was more reliable. Mis-classifications were filtered out through the use of a rough but approximate estimator, Taylor’s tool life model. The system’s modular structure would make it easy to update as required for different machines and/or processes. The use of Taylor’s tool life model, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring proved to be slightly more reliable then the Adaptive Resonance Theory neural network although overall the system made reliable, accurate estimates of the tool wear.

There is a great deal interest in minimizing thermal and residual stresses at the interfaces of metals and ceramics. These stresses develop because of the large mismatch in thermally induced strains that exists between these two materials. The differences are significant enough to cause premature component failure in a variety of applications, including thermal barrier coatings (TBCs) for turbine blades and ceramic coatings for cutting tools. One approach to minimizing these stresses involves functionally grading the material distribution at the metal-ceramic interface. A significant amount of effort has been focused on the development of functionally graded nickel-alumina composites as a model system to determine the architectural features of the material gradient that will minimize these stresses. This effort has led to the development of an experimentally verified modeling approach based on thermomechanical, elastoplastic finite element analysis that can be used to predict the stress distributions in functionally graded metal-ceramic composite materials. This approach has been coupled with a mathematical optimization technique, known as a Genetic Algorithm (GA), to determine the optimal architecture for minimizing thermal and residual stresses at metal-ceramic interfaces.

Chatter vibrations result in reduced productivity, poor surface finish and decreased cutting tool life. Milling cutters with non-constant pitch angles can be very effective in improving the stability of the process against chatter. In this paper, an analytical stability model and a design method are presented for non-constant pitch cutters. An explicit relation is obtained between the stability limit and the pitch variation which leads to a simple equation for optimal pitch angles. A certain pitch variation is effective for limited frequency and speed ranges which are also predicted by the model. The improved stability, productivity and surface finish are demonstrated by several examples.

This study describes a technique for measuring the three-dimensional (3D) shape of cutting tool edge, based on a CCD image sensor along the optical axis and on subsequently analyzing the image data. The image of a cutting edge formed by an optical system, such as a lens, contains geometric information about the shape of cutting edge. The focus errors can be deduced from the observed image. This deduction requires digital image processing. The focus error computation and a sequence of images obtained by continuously varying the distance between the lens and the image detector provides the possibility of obtaining high-resolution 3D coordinate measurement of cutting tool edge. The proposed technique is supported by experiments and further applications of the technique are indicated.

Vibration suppression of the tool results in improved surface texture, dimensional accuracy and enhanced productivity in machining operations. The vibration of the machine tool structure, as well as the tool/workpiece interaction are the main contributors to the tool vibration. In light of this, abating the tool vibration can be approached by isolating the tool from the machine structure in the presence of a random process representing the tool/workpiece interaction. An active tool holder capable of isolating the cutting tool from the vibration of the machine tool structure is constructed. Vibration isolation is accomplished by means of a novel, Kalman estimator–based control strategy, a high bandwidth magnetostrictive actuator, and two accelerometers. The proposed control technique focuses on lowering the transmitted force to the tool that is subject to the machine structure vibration (base excitation) in presence of cutting process disturbance. The important aspect of this control strategy is that, while it is designed based on a full order model of the plant, its implementation is reduced to the realization of a second order estimator irrespective of the order of the plant model. Machining experiments showed that up to 30% improvement in surface roughness of the workpiece has been achieved using the proposed technique.

A numerical analysis, using a control volume approach, is conducted to study the transient cutting tool temperatures with temperature dependent thermal properties. With temperature dependent thermal properties, the governing conduction equation is non-linear and thus, the standard analytical solutions are no longer valid. In any cutting processes, the temperature distribution is intrinsically three-dimensional and very steep temperature gradient may be generated in the vicinity of the tool-chip interface. In this region, where the maximum temperature occurs, the effect of variable thermal properties may become important. The full three-dimensional non-linear transient heat conduction equation is solved numerically to study these non-linear effects on cutting tool temperatures. The extremely small size of the heat input zone (tool-chip interface), relative to the tool insert rake surface area, requires the mesh to be dense enough in order to obtain accurate solutions. This usually requires very intensive computational efforts. Due to the size of the discretized domain, an efficient algorithm is desirable in the solution of the problem. Four different iterative schemes are explored, and an optimized numerical scheme is chosen to significantly reduce the required computing time. This numerical model can be used for process development in an industrial setting. The effect of two different heat flux input profiles, a spatially uniform plane heat flux and a spatially non-uniform plane heat flux at the tool-chip interface, on the tool temperatures are also investigated in the present study. Some recommendations are given regarding the condition when these non-linear effects can not be ignored.

Finite element (FE) assisted numerical modeling approach is known as a popular approach to predict the machining performance of different machining operations. Tapping operation is a well-known manufacturing process that is used to cut threads efficiently. In the automotive and aerospace applications, precisely machined tapped holes are required in the small size deep holes. Tapping process creates thread in the hole and make it ready for fastening with other mating components. Tapping operation is considered as one of the most complex machining operations due to the presence of multi-flutes and multi-land involvement between the workpiece and cutter materials. The outcome of the tapping process results in the generation of threads and accepted as one of the most commonly employed in fastening methods for the joining of different machine components. Literature revealed that tapping process has been very rarely investigated using computational modeling approaches, as most of the available studies are experimental in nature. The experimental work for tapping operation can be very time and cost consuming because of the expensive fabrication of the cutting tools. It has also been observed experimentally that minor change in the threading profiles can generate significant difference in the cutting torque. A possible solution is to analyse the whole tapping operation using finite element (FE) assisted numerical simulation. Similarly, there will be limitation towards experiments if the workpiece material is expensive and difficult to cut. It is a common observation in metal cutting industry that most of the times cutting tap results in breakage when exposed to the higher magnitude of torque. The current study is aimed on the finite element based computational investigations on the tapping process using Ti6Al4V as a workpiece material. High hot hardness and low thermal conductivity of the Ti6Al4V also plays a significant role towards the poor machining performance of the threading tool. Ti6Al4V is most commonly employed in the engineering applications where high strength to weight ratio and ability of operate at higher temperatures is required. Ti6Al4V is mainly utilized in the automotive, aerospace, biomedical and petrochemical industries. It has been identified that tapping operation is very rarely studied machining operation in the metal cutting scientific community. Different tapping process conditions were investigated computationally using finite element (FE) approach and as a result cutting forces, torques and power consumed were observed. The study provides a useful understanding towards the tapping process mechanics with respect to different cutting parameters.

To assess cutting-tools degradation, mounted accelerometers and current clamp probes were used to acquire machining vibration for both the band-saw machine and the drilling machine, of which a structural-steel machining center is composed. Significant features were first extracted through spectral analysis, and tool degradation assessment was conducted through using a supervised learning scheme, self-organizing map. The results reveal that 10% remaining useful life can be predicted before the band-saws and drills wore out.

The use of cutting tool systems with a high slenderness ratio is encountered in the machining of deep cavities in the mechanical engineering industry, especially in the manufacturing of tools and dies. Cutting tool systems with a large slenderness ratio, owing to their dynamic compliance, are prone to vibrations during machining processes. These vibrations affect the quality of the machining process and the life of machine components. Integration of a vibration absorber in the cutting tool system helps in the reduction of machining vibrations. The reduction in vibrations is due to a shift in the resonance frequency of the modified system. This experimental study presents the identification of design possibilities of a vibration absorber for integration in the cutting tool system. The mass and geometry of the vibration absorber are varied and its integration in the milling chuck is explored. Firstly, experimental modal analysis is conducted to determine the effects of the dynamic vibration absorber on the frequency response function of the modified cutting tool system. Secondly, the effects of the dynamic vibration absorber on the machining process for a range of technology parameters are illustrated. During the machining process, the cutting forces are measured using a three-component dynamometer in time domain. Finally, the results are evaluated based on process quality, i.e. surface roughness and analysis of cutting force signal in the frequency domain. This study provides an understanding of the relationship between the mass and the geometry of the vibration absorber integrated in the cutting tool system and their influence on process stability.

Light weight honeycomb structures lend themselves to important applications in aerospace. These range from aerodynamic and structural components such as wing edges, flaps, rotor blades and engine cowlings, to aircraft interior structures such as overhead luggage bins, compartment liners, bulkheads and the monument structures found in galleys and lavatory areas. Often the honeycomb is formed into a composite ply sandwich with fiberglass face sheets bonded to the honeycomb core. These panels are cut to shape using CNC routers and specially designed cutting tools. However, the quality of the cuts generated even with these special tools leaves much to be desired. The low stiffness of the structure leads to imperfections such as fraying of the cut face sheet edges and the generation of flags along the cut honeycomb edge. These impact the ease of assembly and often require manually intensive reworking to mitigate. The cutting of honeycomb structures and sandwich panels is challenging due to low stiffness, anisotropic mechanical properties and a high proportion of interrupted cutting due to the air voids that are present. The cutting mechanics are not well understood at this time. This paper presents findings from the study of cutting of honeycomb sandwich panels using high speed videography and correlates these with results of geometric modeling of the engagement between the cutter and workpiece. The study includes the impact of the trajectory of the tool path through the cell structures on the generation of flagging. It also reports on the effects of two different cutting tool geometries and the introduction of a lead angle on the size and structure of the flags generated. These findings present the case for a research regime similar to the one completed for solid metals, into modeling the mechanics behind machining honeycomb structures. This will help manufacturers using these materials to make better choices in the tools, cutting parameters and machining strategies that they employ in their process planning.

In machining operation, cutting tool performs a central role towards the overall machining performance. A user from metal cutting community always look for better cutting tools that can enhance productivity by reducing tool wear and cost. Modification in the micro-geometry of cutting edge is termed as edge preparation, and it is performed to improve the machining performance by strengthening the cutting edge, reducing internal stresses of coating and lowering the edge chipping etc. Edge preparation has a controlling influence on the formation of deformation zones, cutting temperature, cutting forces and stresses at the cutting interface. Vibration assisted machining (VAM) concept is gaining fame in the metal cutting sector community for machining difficult-to-machine materials. In VAM, cutting tool moves with a small amplitude vibration instead of moving with a constant cutting velocity. This small amplitude vibrational movement provides better machining performance for difficult-to-cut brittle materials. The current numerical study utilized different edge prepared micro-geometries such as sharp edge, round edge and chamfer edge etc. cutting tools, and then these cutting tools were used in the numerical simulations of VAM. The study shows higher magnitude of cutting forces under VAM with tools with modified geometry. The study is beneficial for the metal cutting community and opens new areas of industrial applications.

In machining, tool life is the measure that gives manufacturers fundamental indications on useful tool utilization. In this work, investigated is the influence of turning parameters such as spindle speed, depth of cut, and tool feed on tool life. Literature-reported studies typically utilize Taguchi orthogonal array method for experimental design while tool-wear estimates utilized mathematical expressions by means ofregression analysis. In this work, the authors combine a tabu search (TS) algorithm with the traditional regression analysis (REG) to introduce a combined (TS-REG) scheme that minimizes a weighted sum of the outputs that represent different measures of turning quality. The aim in this paper is to show that the proposed novel combination TS-REG reaches better fitted models in minimal time as compared with other statistical methods. Tool life estimates are plotted versus different turning process parameters. The results that represent optimized tool life of the process are compared to literature reported experimental values. Good correlations confirm desirable efficiency and the ability of the TS-REG method in estimating tool life in turning. In order to determine statistical significance decisions, both p-values and mean absolute percentage error (MAPE: mean of the relative absolute error) were employed. The TS-REG method aids in determining the optimal set of parameters for any combination of the weighting factors resulting in improved tool life.

Polycarbonate glass is one of the most widely used materials in the optical industries for making impact resistance lenses. Besides optical applications, polycarbonate glass has found applications in automotive and biomedical industries. The objective of this study is to investigate the effect of tool coating on the reduction of tool wear and cutting forces during micro-milling of polycarbonate glass. Both numerical modeling and experimental investigation have been carried out to investigate the effectiveness of various tool coatings on the carbide tool in minimizing the cutting forces, and hence tool wear. A series of experiments were conducted using CNC micro-milling of polycarbonate glass by varying feed rate, depth of cut, and tool coating. The three types of cutting tools used in this study were uncoated, titanium nitride (TiN) coated, and titanium aluminum nitride (TiAlN) coated tungsten carbide tools. The cutting forces have been recorded using the Kistler force dynamometer and the tool wear were analyzed using scanning electron microscope (SEM). It was found that all tools had reduced instances of failure, chipping, and abrasion at a moderately higher feed rate and depth of cut. Both very low and high feed rate were found to result in comparatively higher tool wear. The cutting forces increased with an increase of depth of cut, except for the TiAlN coated tool in some instances. With the increase of feed rate, the cutting forces gradually increased or stayed relatively constant across all depths of cut. It was found that the TiAlN coated tool reduced the amount of tool wear and cutting force across all feed rates and depths of cut. There is also a critical depth of cut around 0.3–0.5 mm and feed rate around 576–768 mm/min that reduced the amount of tool wear for the micro-milling of polycarbonate glass. Finally, the numerical modeling and simulation results of cutting forces were found to be in good agreement with the experimental cutting forces and the validated FEM models were then used to predict the cutting forces for higher spindle speed.

A successful approach to the development of reinforced materials for enhanced cutting tool inserts requires the formulation and application of innovative concepts at each step of material design development. In this paper, reinforced ceramic-based cutting tools with enhanced thermal and structural properties are developed for high-speed machining applications using a computational approach. A mean-field homogenization, effective medium approximation and J-integral based fracture toughness evaluation using an in-house code are used for predicting the effective structural and thermal properties for tool inserts as a function of reinforcement type, volume fraction, particle size and interface between matrix and reinforcement. Initially, several potential reinforcements are selected at the material design phase. SiC, TiB 2 , cBN and TiC were all found to be suitable candidates when reinforced into an alumina matrix as both single and hybrid inclusions for the enhancement of thermal and structural properties. For validation purposes, alumina-cubic boron nitride-silicon carbide composites are developed using Spark Plasma Sintering as hybrid systems, which are in line with the designed range of volume fraction and reinforcement particle size. Structural and thermal properties such as elastic modulus, fracture toughness and thermal conductivity are measured to complement the computational material design model.

High-speed milling has often been applied in injection mold manufacturing processes, where surface roughness is a significant criterion in product quality demands. It is equally applicable to automotive or industrial engineering and to toy manufacturing, where plastic parts with a high-quality surface finish have been processed using the injection molding technique. High-speed milling involves a number of process parameters that may affect the 3D surface topography formation. Literature analysis reveals that dynamical behavior is a significant factor in the end milling process on surface roughness parameters. To improve the accuracy of predicted surface topography models, it is important to include the dynamical behavior of milling factor. This paper describes the surface prediction model of combined end-milling geometrical and dynamical interaction models. The natural frequency of machine assembly and forced vibrations during the cutting process were measured during the flat-end milling process. Unevenly distributed cutting marks were revealed by surface 3D topography images and microscopy images of the machined samples. A mathematical model to predict surface topography was developed, including dynamical behavior and cutting geometries. Machine accuracy also has to be addressed. 3D surface topography parameters from the experimental sample provided the results for the mathematical prediction model. This model offers a software tool for manufacturers to improve the quality of machined part surfaces, taking into account the behavioral properties of their machining equipment. Relevant conclusions about the manufacturing equipment accuracy have been drawn. Vibrations in the milling system affect the cutting process and contribute to the surface topography prediction model. Local cutting tool vibrations do not have any influence on surface parameter mean values.

Metal Matrix Composites (MMCs) based on Aluminum Alloys 2024, 6061 and 7075 reinforced with Graphene was fabricated using powder metallurgy process followed by hot extrusion process. The extruded samples were used for conducting the turning experiments to evaluate the machinability of the developed composites. Turning experiments were conducted in ACE Micromatic made CNC lathe as per the Design of Experiments (DOE) designed using L18 Taguchi’s mixed orthogonal array. Uncoated and DLC coated carbide inserts, along with three levels of cutting speeds, feed rates and depths of cut were considered for the turning experiments. During the experiments the cutting force generated was recorded “online” and subsequent to the experimentation the surface roughness generated on the work piece and the surface hardness for every trial were recorded. The influence of the cutting tool material and other cutting parameters on the machinability of composites was analyzed using ANOVA. The microstructural observation on the surface of the machined specimen reveals the detachment of reinforcement materials from the composite and their impact of the surface quality.

Particle-reinforced metal matrix composites (pMMC) such as silicon carbide particle reinforced aluminum alloys (SiCp/Al) require special cutting tools due to the high hardness and abrasive properties of the ceramic particles. Diamond coated cutting tools are ideal for machining this type of pMMC. Previous research studies focus on the machinability of pMMCs with low ceramic content. The aim of this research is to determine the optimal cutting parameters for machining SiCp/Al material containing high silicon carbide particle reinforcement (>25%). Material removal rate (MRR) was used to determine the optimal cutting parameters with the tool wear and surface roughness as constraints. Cutting speed, feed rate, and depth of cut were used as design parameters for the design of experiment. High burr formation and cutting forces were observed during the experiments. Experimental milling tests are conducted using CVD diamond coated end mills and non-diamond tungsten carbide end mills. It was found that low tool rotation speeds, feed rates and depths of cut are necessary to achieve smoother surface finishes of Ra < 1 μm. A high MRR to low tool wear and surface roughness ratio was obtainable at a tool rotation speed of 6500 r/min, feed rate of 762 mm/min, and depth of cut of 3 mm. Results showed that a smooth surface roughness of the workpiece material was achieved with non-diamond tungsten carbide end mills, however, this was at the expense of extreme tool wear and high burr formation. An endurance test was run to test for complete tool failure.

The inspiration for developing this atomic model comes from Merchant’s models for studying chip strain and shear angle. In this paper the 2D Merchant’s Diagram of Circles has been replaced by atoms of the workpiece and tool. This research reveals that atom losing electrons in workpiece is common in metal cutting. Also at the atomic level, cutting workpiece leads to an electric process to occur, which valence electrons leave atoms of the workpiece material as cutting tool pushing forward, forming a charged zone in the workpiece which weakens its strength and eventually causes them to be removed as cutting chip. In this paper, the charged zone was calculated for cutting 1040 steel with a tungsten carbide tool. Experimental results of electromotive force are presented to support the existence of an electrical charge in metal cutting.