Monitoring the tool wear,surface roughness and chip formation occurrences using multiple sensors in turning

Tool wear, chip formation and surface roughness of workpiece under different cutting conditions have been investigated in machining using acoustic emission (AE) and vibration signature in turning. The investigation has shown that the AE and vibration components can effectively respond to the different occurrences in turning including tool wear and surface roughness. The AE has shown a very significant response to the tool wear progression whereas the resultant vibration (V) represented the surface roughness in turning. The vibration components V_x, V_y and V_z described the chip formation type and are found to have the most significant response to the change of feed, depth of cut and cutting speed respectively. The amplitude of vibration components, V_x, V_y and V_z increased with the increase of feed rate, depth of cut and cutting speed respectively. Even though the frequency of different signal components fluctuated at the different stages of tool wear and at different cutting conditions, the frequency of vibration components was always within a band of 98–40 kHz, and the AE has varied between 51 kHz and 620 kHz.     

Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel

This paper presents the application of Taguchi method with logical fuzzy reasoning for multiple output optimization of high speed CNC turning of AISI P-20 tool steel using TiN coated tungsten carbide coatings. The machining parameters (cutting speed, feed rate, depth of cut, nose radius and cutting environment) are optimized with considerations of the multiple performance measures (surface roughness, tool life, cutting force and power consumption). Taguchi’s concepts of orthogonal arrays, signal to noise (S/N) ratio, ANOVA have been fuzzified to optimize the high speed CNC turning process parameters through a single comprehensive output measure (COM). The result analysis shows that cutting speed of 160 m/min, nose radius of 0.8 mm, feed of 0.1 mm/rev, depth of cut of 0.2 mm and the cryogenic environment are the most favorable cutting parameters for high speed CNC turning of AISI P-20 tool steel.     

Numerical analysis of the tool wear effect in the machining induced residual stresses

Machining is a dynamic process involving coupled phenomena: high strain and strain rate and high temperature. Prediction of machining induced residual stresses is an interesting objective at the manufacturing processes modelling field. Tool wear results in a change of tool geometry affecting thermo-mechanical phenomena and thus has a significant effect on residual stresses. The experimental study of the tool wear influence in residual stresses is difficult due to the need of controlling wear evolution during cutting. Also the involved phenomena make the analysis extremely difficult. On the other hand, Finite Element Analysis (FEA) is a powerful tool used to simulate cutting processes, allowing the analysis of different parameters influent on machining induced residual stresses.The aim of this work is to develop and to validate a numerical model to analyse the tool wear effect in machining induced residual stresses. Main advantages of the model presented in this work are, reduced mesh distortion, the possibility to simulate long length machined surface and time-efficiency. The model was validated with experimental tests carried out with controlled worn geometry generated by electro-discharge machining (EDM). The model was applied to predict machining induced residual stresses in AISI 316 L and reasonable agreement with experimental results were found.     

Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

In a modern machining system, tool condition monitoring systems are needed to get higher quality production and to prevent the downtime of machine tools due to catastrophic tool failures. Also, in precision machining processes surface quality of the manufactured part can be related to the conditions of the cutting tools. This increases industrial interest for in-process tool condition monitoring (TCM) systems. TCM supported modern unmanned manufacturing process is an integrated system composed of sensors, signal processing interface and intelligent decision making strategies. This study includes key considerations for development of an online TCM system for milling of Inconel 718 superalloy. An effective and efficient strategy based on artificial neural networks (ANN) is presented to estimate tool flank wear. ANN based decision making model was trained by using real time acquired three axis (F_x, F_y, F_z) cutting force and torque (M_z) signals and also with cutting conditions and time. The presented ANN model demonstrated a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of flank wear.     

Theoretical predictions and experimental validations on machining the Nimonic C-263 super alloy

The current paper presents the simulated 3D Finite Element Model (FEM) and experimental validation while turning the Nimonic C-263 super alloy using a cemented carbide cutting tool. FEM machining simulations was carried out using a Lagrangian finite element based machining model to predict the tangential cutting force, temperature distribution at tool tip and the effective stress and strain. All simulations were performed according to the cutting conditions designed, using the orthogonal array. The work piece was considered as perfectly plastic and its shape was taken as a curved model. An experimental validation of the cutting process was conducted in order to verify the simulated results of tangential cutting force and temperature at tool tip and the comparison shows that the percentage error 6% was observed and the shear friction factor 0.6 indicates good agreement between the simulated results and the experiment results. As the cutting speed is increased from 22 m/min to 54 m/min at higher feed rate, a larger strain to an extent of up to 6.55 mm/mm, a maximum value of 810 MPa stress and higher temperature localization to an extent of 620 °C at tool tip were observed.     

A microgripper using piezoelectric actuation for micro-object manipulation

Design, fabrication and tests of a monolithic compliant-flexure-based microgripper were performed. The geometry design and the material stresses were considered through the finite element analysis. The simulation model was used to study in detail profiles of von Mises stresses and deformation. The maximum stress in the microgripper is much smaller than the critical stress values for fatigue. The microgripper prototype was manufactured using micro-wire electrode discharge machining. A displacement amplification of 3.0 and a maximum stroke of 170 μm were achieved. The use of piezoelectric actuation allowed fine positioning. Micromanipulation tests were conducted to confirm potential applications of the microgripper with piezoelectric actuation in handling micro-objects. The simulation and experimental results have proven the good performance of the microgripper.     

Shear localization sensitivity analysis for Johnson–Cook constitutive parameters on serrated chips in high speed machining of Ti6Al4V

This research aims to investigate the influence of material constitutive parameters on the serrated chip formation during high speed machining (HSM) of Ti6Al4V alloys with finite element simulations and cutting experiments. The Johnson–Cook (JC) constitutive model and JC fracture model with an energy-based ductile failure criterion are adopted to simulate the HSM process. Five JC constitutive model parameters such as initial yield stress, hardening modulus, strain hardening coefficient, strain rate dependency coefficient, and thermal softening coefficient are included in this research. Shear localization sensitivity is novelly proposed to describe variations of serrated chips under different JC constitutive model parameters. Shear localization sensitivity is subdivided into chip serration sensitivity and chip bending sensitivity. The research finds that the influences of initial yield stress and thermal softening coefficient parameters on the chip serration and bending are much more prominent than those of the rest three JC constitutive model parameters. With initial yield stress or hardening modulus in JC constitutive model increasing, the chip serration sensitivity increases and the chip bending sensitivity decreases. However, the influences of the rest three parameters on chip serration sensitivity are opposite. High speed orthogonal cutting experiments of Ti6Al4V are carried out to validate the simulation results under different cutting speeds ranging from 50 m/min to 3000 m/min and fixed uncut chip thickness with 0.1 mm. The results show that the serrated degree of chips increases with the cutting speed increasing until the chips become completely fragmented. The cutting speed break point of chip morphology from serrated to fragmented ones for Ti6Al4V is about 2500 m/min. The average cutting force decreases with the cutting speed increasing, which is a prominent advantage for HSM. This paper can help to get deeper insights into the serrated chip formation mechanism in HSM.     

Sensitivity analysis of material input data influence on machining induced residual stress prediction in Inconel 718

Inconel 718 is commonly used in structural critical components of aircraft engines due to its properties at high temperatures. In order manufacture the final part, these components have to be machined, so the final surface integrity obtained after machining becomes a key issue. Residual stresses, which are included in surface integrity, are an important issue. Although much of the research carried out on machining induced residual stresses has been empirical, finite element modelling appears to be a complementary solution to gain understanding of it. However, some of the major drawbacks still need to be solved before it can become a reliable tool for industry, such us the identification of input parameters and computational cost. This paper deals with the study of machining induced residual stresses. An orthogonal cutting 2D finite element model was used and a sensitivity analysis was conducted to determine the influence of model input data on the predicted residual stresses. The results obtained from the sensitivity analysis showed that material constitutive law was the most relevant input data when predicting residual stress fields. Importantly the material behaviour at a high heating rate in adition to high strain rate must be considered.     

Modelling of residual stresses in the shot peened material C-1020 by artificial neural network

This study consists of two cases: (i) The experimental analysis: Shot peening is a method to improve the resistance of metal pieces to fatigue by creating regions of residual stress. In this study, the residual stresses induced in steel specimen type C-1020 by applying various strengths of shot peening, are investigated using the electrochemical layer removal method. The best result is obtained using 0.26 mm A peening strength and the stress encountered in the shot peened material is ?276 MPa, while the maximum residual stress obtained is ?363 MPa at a peening strength of 0.43 mm A. (ii) The mathematical modelling analysis: The use of ANN has been proposed to determine the residual stresses based on various strengths of shot peening using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R² values are 0.996112 and 0.99896 for annealed before peening and shot peened only, respectively. Similarly, these values for testing data are 0.995858 and 0.999143, respectively. As seen from the results of mathematical modelling, the calculated residual stresses are obviously within acceptable uncertainties.     

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al₂O₃ + TiN and Al₂O₃) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R², RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.     

A particle swarm approach for multi-objective optimization of electrical discharge machining process

This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle swarm (MOPSO) algorithm. A Box–Behnken design of response surface methodology has been adopted to estimate the effect of machining parameters on the responses. The responses used in the analysis are material removal rate, electrode wear ratio, surface roughness and radial overcut. The machining parameters considered in the study are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Fifty four experimental runs are conducted using Inconel 718 super alloy as work piece material and the influence of parameters on each response is analysed. It is observed that tool material, discharge current and pulse-on-time have significant effect on machinability characteristics of Inconel 718. Finally, a novel MOPSO algorithm has been proposed for simultaneous optimization of multiple responses. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence. The Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory to avoid subjectiveness and impreciseness in the decision making. The analysis offers useful information for controlling the machining parameters to improve the accuracy of the EDMed components.     

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R_a and R_t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.     

A simulation approach for estimating flank wear and material removal rate in turning of Inconel 718

The present work attempts to study the effect of important machining variables on performance characteristics such as material removal rate and tool wear in turning of Inconel 718 using chemical vapour deposition (CVD) coated tungsten carbide (WC) tool. A three dimensional machining model using Lagrangian approach has been developed using DEFORM 3D. The machining simulation is carried out to predict the flank wear and material removal rate (MRR). Flank wear is calculated using Usui’s wear model in the simulation model. The results from simulation model are compared with experimental data generated by the use of Taguchi’s L₁₆ orthogonal array for reducing the experimental runs. Analysis of variance (ANOVA) is performed to identify the most influencing variables for both the performance characteristics. It is found that simulation results are in good agreement with experimental results. A valid simulation models helps the tool engineers to gather relevant process related information without resorting to costly and time consuming experimentation.     

Optimizing functionally graded nickel–zirconia coating profiles for thermal stress relaxation

The investigations on optimization of composite composition of nickel–zirconia for the functionally graded layered thermal barrier coating for the lowest but uniform stress field under thermal loading is presented. The procedure for obtaining temperature- and composition-dependent thermal and mechanical properties of various coating compositions is discussed. These material parameters were used in thermo-mechanical finite element stress analyses of a nickel substrate with the coating. The results showed that the Von-Mises stresses in the substrate and the interfaces were the lowest with the coating profile that followed a concave power law relationship with the index n ≈ 2.65.     

Development of wireless compressive/relaxation-stress measurement system integrated with pressure-sensitive carbon black-filled silicone rubber-based sensors

In order to detect the installation compressive stress and monitor the stress relaxation between two bending surfaces on a defensive furnishment, a wireless compressive-stress/relaxation-stress measurement system based on pressure-sensitive sensors is developed. The flexible pressure-sensitive stress sensor array is fabricated by using carbon black-filled silicone rubber-based composite. The wireless stress measurement system integrated with this sensor array is tested with compressive stress in the range from 0 MPa to 3 MPa for performance evaluation. Experimental results indicate that the fractional change in electrical resistance of the pressure-sensitive stress sensor changes linearly and reversibly with the compressive stress, and its fractional change goes up to 355% under uniaxial compression; the change rate of the electrical resistance can track the relaxation stress and give out a credible measurement in the process of stress relaxation. The relationship between input (compressive stress) and output (the fractional change in electrical resistance) of the pressure-sensitive sensor is ΔR/R₀ = σ × 1.2 MPa^?1. The wireless compressive stress measurement system can be used to achieve sensitivity of 1.33 V/MPa to the stress at stress resolution of 920.3 Pa. The newly developed wireless stress measurement system integrated with pressure-sensitive carbon black-filled silicone rubber-based sensors has advantages such as high sensitivity to stress, high stress resolution, simple circuit and low energy consumption.     

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8 mm and 2.5 mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data.     

Identification of new material model for machining simulation of Inconel 718 alloy and the effect of tool edge geometry on microstructure changes

Johnson–Cook (J-C) material model is often used for Finite Element (FE) modeling of cutting processes and it affects significantly the results of simulation. Since experimentally determination of J-C equation parameters is an expensive and time-consuming task, in this paper, a new and efficient methodology was implemented based on the evolutionary optimization algorithm to identify new J-C material constants for Inconel 718 superalloy. Then, orthogonal cutting process of Inconel 718 was simulated using the new material models. The results of simulation were compared extensively with experimental results of cutting forces, chip geometry, and maximum temperature to select and then validate the most suitable material model. After that, a user subroutine was implemented in FE code to model microstructure changes using the dynamic recrystallization mechanism in cutting process of Inconel 718. The Zener–Hollomon and Hall–Petch equations were used to respectively predict the grain size and hardness. The results of simulation were verified successfully with corresponding experiments in terms of near-surface profile. At the end, the effect of tool edge geometry including honed and chamfered tool edge was investigated on microstructure changes.     

Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming

This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand–mica mixtures based on experimental results. The experimental database used for GP modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R² = 0.95 for damping ratio and R² = 0.98 for shear modulus).     

Simulation of media behaviour in vibration assisted abrasive flow machining

Abrasive flow machining (AFM) is an abrasive-based precision finishing process used for achieving surface finish in micro and nano-level. The AFM polishes surfaces by extruding a visco-elastic media in contact with the workpiece. The media, also called a ‘flexible tool’, plays a key role in the performance of the process. Ultrasonic assisted abrasive flow machining (UAAFM) is a new variant of the AFM process in which the workpiece is subjected to mechanical vibration orthogonal to the media flow direction. In this process a high frequency, in the range of about 5–20 kHz, is given to the workpiece with the help of a piezo actuator and a specially designed fixture. The present work highlights on the possible behaviour of the tool (media) during UAAFM and its effect on the machining process through a computation based approach. Commercially available simulation tool was used to study the effect of the media in response to different set of machining conditions. The responses were evaluated in terms of changes in the fluid pressure, velocity profile of the fluid, temperature distribution in the working fluid and the possible wall shear on the work surface. A three-dimensional model was constructed for simulating the UAAFM process. The simulation shows that the abrasive particles tend to hit the target surface at an angle ‘θ’ which significantly affects the basic mechanisms involved and enhances the effectiveness of the process. The computed wall shear explains that the process will have higher finishing rate and hence the performance. The enhanced interaction of abrasive media in UAAFM while compared to simple AFM could be explained by the resultant pressure–velocity phenomena. Results show that while changes in the amplitude of applied vibration (10 μm and 50 μm) significantly affect the wall shear, the media velocity and pressure profiles are only marginally sensitive to this parameter. The simulation results also confirm that the rise in temperature during the process will not affect the media stability. Results have been discussed vis-a-vis the basic mechanism of the process through suitable illustrations.     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.