Modelling the Effects of Friction on Tool-Chip Interface Temperature During Orthogonal Cutting of Al6061-T6 Aluminium Alloy

© IEOM Society International - IEOM 2019In this work, finite element simulations based on the analytical model derived with the MATLAB software were used to establish the temperature fields within the cutting tool and tool-chip interface. The average tool-chip interface temperature model was simulated and the simulation results were compared with experimental results for validation. At a maximum cutting speed of 90 m/min, the maximum temperature obtained from the experiment was 410 oC, at same rake angle of 0o. However, the developed model predicted 490 oC under the same conditions. The higher value obtained by the model can be attributed to the negligence of heat losses to the surrounding by both convection and radiation modes, as an assumption in the formulated model. A similar trend of these results was also recorded for the case of rake angle and feed rate of 30o and 0.0635 mm/rev, respectively. It was observed that the simulation results and experimental measurements for the average tool-chip interface temperature agreed significantly.Final Published versio

Effects of machining system parameters and dynamics on quality of high-speed milling

This dissertation outlines research on studying the effects of machining parameters such that cutting speed, feed rate, axial depth of cut, radial depth of cut and helix angle on system dynamic stability and the surface quality of high-speed milling. With the use of structural tool modal parameters, the material cutting force coefficients and the axial depth of cut, the system can avoid the chatter phenomenon of the tool at high cutting speeds. The surface roughness finish in the milling process is determined by the machining parameters and tool structure dynamics. To perform high-speed milling, the chance of tool vibration (chatter phenomenon) which affects the cutting tool, must be minimized or eliminated. In this research, the linear and nonlinear mathematical force models including the effect of the helix angle are presented for an end-milling process. The linear force model includes cutting-edge coefficients. The cutting force coefficients are determined for an end-milling process using two methods, the average force method and the optimization technique method. The second method is developed to identify the cutting force coefficients in the milling process by forming the objective functions using the optimization technique to minimize the error between the experimental and the analytical forces. Moreover, this method produced a good force model that approximates the experimental force results, which compared with the average force method. The stability lobe diagrams are created using the analytical method to determine whether the cut is stable or unstable. In addition, simulations are performed to predict stability of the milling process. By comparing simulated and experimental results, the dynamics and stability of the milling operation can be easily identified before performing any cutting operation. The slot milling experiments show that while the system in the chatter region close to the stability limits and the axial depth of cut increased, the system changes from stable chatter to chaotic chatter. Furthermore, the nature of bifurcation in milling is investigated by performing experiments and simulations. The linear and nonlinear mathematical force models are used for simulating end-milling process. Simulated bifurcation diagrams are generated using both models and compared to experimental results. In addition, the effect of the feed rate on the location of the bifurcation point (start and end of bifurcation) is studied. By comparing simulated and experimental results, the simulation using a nonlinear force model is found more accurate in predicting the dynamics and stability of the milling operation. The applications of Taguchi and response surface methodologies (RSM) are used to minimize the surface roughness in the end milling process. Taguchi’s method for optimum selection of the milling process parameters is applied based on the signal to noise ratio and ANOVA analysis of the surface finish. A second-order model contains quadratic terms that have been created between the cutting parameters and surface roughness using response surface methodology (RSM). Surface roughness of the machined surfaces are measured and used to identify the optimum levels of the milling parameters. Based on Taguchi, ANOVA, and RSM analyses, the end milling process can be optimized to improve surface finish quality and machining productivity

Evaluation of Workpiece Temperature during Drilling of GLARE Fiber Metal Laminates Using Infrared Techniques: Effect of Cutting Parameters, Fiber Orientation and Spray Mist Application

The rise in cutting temperatures during the machining process can influence the final quality of the machined part. The impact of cutting temperatures is more critical when machining composite-metal stacks and fiber metal laminates due to the stacking nature of those hybrids which subjects the composite to heat from direct contact with metallic part of the stack and the evacuated hot chips. In this paper, the workpiece surface temperature of two grades of fiber metal laminates commercially know as GLARE is investigated. An experimental study was carried out using thermocouples and infrared thermography to determine the emissivity of the upper, lower and side surfaces of GLARE laminates. In addition, infrared thermography was used to determine the maximum temperature of the bottom surface of machined holes during drilling GLARE under dry and minimum quantity lubrication (MQL) cooling conditions under different cutting parameters. The results showed that during the machining process, the workpiece surface temperature increased with the increase in feed rate and fiber orientation influenced the developed temperature in the laminate

Machining of Inconel 718 nickel-based superalloy using nano-lubricants and liquid nitrogen

Utilization of cutting fluid incorporating graphene nanoplatelets (GNP) as well as application of liquid nitrogen (LN2) to the cutting area were investigated in drilling and turning of Inconel 718 iron-nickel-based superalloy in order to improve their machinability and reduce the use of mineral-based cutting fluids. The methodology was developed to establish correlations between the tribological properties of the surfaces and the role of interfacial friction on machining properties and understanding the improvements in machining from a microstructural point of view. In addition, response surface methodology (RSM) was employed to systematically optimized the cutting parameters. The results are presented in two parts: In ‎Chapter 3, a cutting fluid (CF) consisting of 70% water and 30% vegetable oil blended with GNPs was used in order to improve drilling performance of Inconel 718 alloy. The results showed that sliding of Inconel 718 workpiece using WC-Co drills with CF containing 54×10-5 wt.% graphene (GCF) reduced the coefficient of friction (COF) between the tool and workpiece surfaces from 0.16 to 0.08 as a result of formation of tribolayers on the sliding surfaces. A Similar tribolayer was observed when drilling under GCF condition which resulted in lower cutting torque and temperature, leading to lower surface roughness and subsurface microstructural deformation compared to conventional flooded and dry conditions. In ‎Chapter 4, turning experiments were conducted on the Inconel 718 using a stream of liquid nitrogen, and the effects of different cutting speed, feed rate and depth of cut values on the response factors, namely flank wear, cutting force and Ra surface roughness were investigated. Cryogenic cutting reduced flank wear compared to dry cutting, to values comparable to wear during flooded cutting. The results revealed that there could be an optimum set of values in which cryogenic cutting can provide a performance equivalent to the flooded cutting. Thus, experiments were designed according to RSM under cryogenic condition. Statistical analyses showed that cutting speed was the most influential parameter on flank wear and cutting force during cryogenic turning and a cutting speed of 81 m/min, a feed rate of 0.06 mm/rev and a depth of cut of 0.63 mm constituted the optimum set of cutting parameters considered in this investigation. Higher cutting speed and feed rate values can be used during the machining process by using a GNP-blended vegetable-based oil to shorten the cutting time, and thus, reduce the usage of cutting fluid for production of each part. Moreover, it was shown that complete omission of cutting fluid during the machining process would be feasible by employing cryogenic cutting. Liquid nitrogen evaporates after contacting the tool and workpiece surfaces leaving no contamination which eliminates the cleaning, recycling, and deposal costs after the machining process

On the characterization of Johnson-Cook constants : numerical and experimantal study of high speed machining aerospace alloys

The aerospace industry would eventually replace chemical machining by mechanical machining which is more accurate, more predictable and more ecological. In fact, the discharges in the case of chemical machining contain especially carbon dioxide and solvents that are difficult to degrade in groundwater. The mechanical machining also avoids an important quantity of hazardous substances and provides better chips recycling. However, the control of mechanical machined parts quality goes through the prediction and the optimization of the metal cutting processes. The most attractive computational tool to predict and optimize metal cutting processes is the finite element modeling (FEM). The success and the reliability of any FEM depend strongly on the constitutive laws which describe the thermo-mechanical behavior of the machined materials. The most commonly used one is that of Johnson and Cook (JC) which combines the effect of strains, strain rates, and temperatures. The determination of the material constants of JC under high strains, strain rates, and temperatures during machining conditions has long been a major challenge but a necessity for those who apply finite element modeling techniques in machining processes at the chip formation scale. This study aims at treating this subject in order to better understand the effect of the JC constitutive law on the prediction of cutting parameters (cutting forces, residual stresses, etc.) for aluminum alloys. In addition, in order to meet the interests of aerospace industry, three aluminum alloys (Al2024-T3, Al6061-T6 and Al7075-T6) commonly used in aircraft applications have been selected. This research work is divided into three consecutive steps. Firstly, a new approach to identify the material constants of JC for metal cutting is proposed. The approach is based on the inverse method (orthogonal machining tests) and the response surface methodology which allows generating a large number of cutting conditions within fixed ranges of cutting speed, feed rate, and rake angle. Based on this approach, the sensitivity of the material constants of JC to the rake angle for the three alloys was analysed. It was found that, for these three alloys, one set of the material constants obtained from the proposed approach predicts more accurate values of flow stresses as compared to those reported in the literature. Moreover, a 2D FEM investigation of the orthogonal cutting also showed a good agreement between the predicted cutting parameters (cutting forces and chip thickness) and experimental ones when using the material constants obtained by the proposed approach. Secondly, a specific focus was put on the influence of the rake angle on the material constants of JC and hence on the predicted cutting parameters (cutting forces, chip morphology, and tool-chip contact length). To achieve this goal, different sets of JC constants obtained at different rake angles (-8°, -5°, 0°, +5°, and +8°) were used in conjunction with a 2D finite element model to simulate the machining behavior of Al2024-T3 alloy. It was found that the material constants set obtained with 0° rake angle gives overall more accurate predictions of the cutting parameters as compared to other studied sets. Finally, the last step of this study is devoted to the prediction of induced residual stresses within the machined workpiece (Al2024-T3) and the temperature of the cutting tool(uncoated carbide). Three sets of JC based on the results obtained from the previous step with rake angles of -8°, 0°, and +8° were considered. Two finite element models were used; a 2D thermo-mechanical simulation to simulate chip formation and a 3D pure thermal analysis to obtain the temperature distribution. The results show that a better prediction of the residual stresses is obtained with JC at 0° while the other sets of JC at -8° and +8° tend to overestimate or underestimate the measured residual stresses, respectively. As far as the temperature of the cutting tool is concerned, the average values of the predicted températures of the cutting tool for each studied set of JC was considered in order to evaluate the best prediction. Based on these average values, the effect of the three sets of JC was not significant since the difference between the measured temperatures and the predicted average ones are less than 5.5% with the three cutting conditions

Smart machining system platform for CNC milling with the integration of a power sensor and cutting model

Novel techniques and strategies are investigated for dynamically measuring the process capability of machine tools and using this information for Smart Machine System (SMS) research. Several aspects of the system are explored including system integration, data acquisition, force and power model calibration, feedrate scheduling and tool condition monitoring. A key aspect of a SMS is its ability to provide synchronization between process measurements and model estimates. It permits real time feedback regarding the current machine tool process. This information can be used to accurately determine and keep track of model coefficients for the actual tooling and materials in use, providing both a continued improvement in model accuracy as well as a way to monitor the health of the machine and the machining process. A cutting power model is applied based on a linear tangential force model with edge effect. The robustness of the model is verified through experiments with a wide variety of cutting conditions. Results show good agreement between measured and estimated power. A test platform has been implemented for performing research on Smart Machine Systems. It uses a commercially available OAC from MDSI, geometric modeling software from Predator along with a number of modules developed at UNH. Test cases illustrate how models and sensors can be combined to select machining conditions that will produce a good part on the first try. On-line calibration allows the SMS to fine tune model coefficients, which can then be used to improve production efficiency as the machine learns its own capabilities. With force measurements, the force model can be calibrated and resultant force predictions can be performed. A feedrate selection planner has been created to choose the fastest possible feedrates subject to constraints which are related to part quality, tool health and machine tool capabilities. Monitoring tangential model coefficients is shown to be more useful than monitoring power ratio for tool condition monitoring. As the model coefficients are independent of the cutting geometry, their changes are more promising, in that KTC will increase with edge chipping and breakage, while KTE will increase as the flank wearland expands

Inverse Identification of the Ductile Failure Law for Ti6Al4V Based on Orthogonal Cutting Experimental Outcomes

Despite the prevalence of machining, tools and cutting conditions are often chosen based on empirical databases, which are hard to be made, and they are only valid in the range of conditions tested to develop it. Predictive numerical models have thus emerged as a promising approach. To function correctly, they require accurate data related to appropriate material properties (e.g., constitutive models, ductile failure law). Nevertheless, material characterization is usually carried out through thermomechanical tests, under conditions far different from those encountered in machining. In addition, segmented chips observed when cutting titanium alloys make it a challenge to develop an accurate model. At low cutting speeds, chip segmentation is assumed to be due to lack of ductility of the material. In this work, orthogonal cutting tests of Ti6Al4V alloy were carried out, varying the uncut chip thickness from 0.2 to 0.4 mm and the cutting speed from 2.5 to 7.5 m/min. The temperature in the shear zone was measured through infrared measurements with high resolution. It was observed experimentally, and in the FEM, that chip segmentation causes oscillations in the workpiece temperature, chip thickness and cutting forces. Moreover, workpiece temperature and cutting force signals were observed to be in counterphase, which was predicted by the ductile failure model. Oscillation frequency was employed in order to improve the ductile failure law by using inverse simulation, reducing the prediction error of segmentation frequency from more than 100% to an average error lower than 10%

Milling cutter software architecture for force and surface finish modeling

The main goal of this dissertation is to pave the way towards a milling simulation software platform that enables academic modeling research to be easily incorporated into industrial practice. The most significant contribution is creation of numeric structures that enable utilization of any milling cutting force model with any type of milling cutting tool in a computationally efficient manner. Efficiency and accuracy of the force and surface finish modeling are the main focus of this study. Force modeling is an important part of milling research since it is directly connected to tool health and workpiece quality. A number of force models are investigated for feedrate selection, assuming that calibration is limited to a spindle motor power sensor. Although more restricted, motor power sensors are cheaper and more practical alternative to table dynamometers for force model calibration purposes. Calibration of the force models with a motor power sensor is derived and their feasibility and accuracy is evaluated by a number of experimental cuts. It is shown that each force model performs much better than the simple Material Removal Rate model that is predominantly used in industry. Also, advantages of the different models under different cutting conditions are discussed. Significant problems to industrial use of milling models include the excessive computational time required by the algorithms and the difficulty of easily incorporating a large number of cutting tool types. Various numeric structures, which can be used as a Software Development Kit (SDK), are developed to address these problems. These structures allow utilization of any force model with any cutting tool in a computationally efficient manner. Also, the structures make the milling related programming much simpler and flexible. A surface modeling program is created using the structures and evaluated through a number of experiments. This program calculates cutting forces, tool vibrations and the resulting peripheral surface. Despite the complexity of the concepts in this program, it is less than 140 lines, and performed well when tested with two force models and three different cutting tools. Force predictions, surface roughness, and surface tolerance were shown to be reasonably accurate under most cutting conditions

A Micro-milling cutting force and chip formation modeling approach for optimal process parameters selection

Las últimas décadas evidencian una demanda creciente por componentes miniaturizados con dimensiones reducidas y tolerancias estrechas, lo cual ha conllevado al desarrollo de la micro y nanotecnología. El micro-fresado, dentro de los procesos de micro-mecanizado, tiene el potencial de ser uno de los procesos de remoción de material más costo-efectivos y eficientes debido a su facilidad de aplicación, variedad de materiales de trabajo y flexibilidad geométrica. Se enfrenta a unos retos complejos debido al efecto de tamaño, vibraciones y otros factores incontrolables. Este estudio analiza dicho proceso orientado hacia desarrollar una mejor comprensión de la mecánica del micro-corte para ser aplicada en la optimización de parámetros de proceso. Se propone un acercamiento al modelado híbrido en forma novedosa, que permite una evaluación numérica a priori para evaluación de fuerzas y esfuerzos, combinado con experimentación para evaluar parámetros relevantes a la industria (formación de rebabas, desgaste de herramientas, entre otros).DoctoradoDoctor en Ingeniería Mecánic