End-milling Operation Research Articles

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Sensor Fusion for Condition Monitoring System of End Milling Operations

This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.

Design Studies on Hybrid Excitation Motor for Main Spindle Drive in Machine Tools

This paper presents a hybrid excitation motor for a main spindle drive in end- and face-milling tools, which is operated with a high speed of 50 000 r/min. The proposed motor has not only a permanent magnet but also the field coil which makes field-weakening and field-strengthening controls possible with taking no notice of the permanent magnet demagnetization. In addition, the sum of iron and copper losses of the motor at the end-milling operation is significantly reduced by the field-weakening control. The 3-D finite-element-method-based design studies on the proposed motor for the target application are demonstrated.

Surface Roughness Generation and Material Removal Rate in Ball End Milling Operations

Surface roughness plays an important role in the performance of a finished part. Surface roughness generated in machining operations is influenced by an enormous set of factors such as cutting parameters, cutting tool characteristics, workpiece properties, or cutting phenomena. Cutting geometric characteristics, when ball end mill is used, clearly affect surface crests generated. In this paper, we study the influence of the geometric characteristics of ball end mill cut on the theoretical surface roughness obtained. The crests height on the surface (h) and roughness average parameter (Ra) are calculated as function of cutting tool radius (R) and radial depth of cut (Ae). Surface angularity is also considered. This work also analyzes cutting parameters implication on material removal rate (MRR) of ball end milling operations. The equations and especially the figures presented in this paper can be easily applied in workshops to improve quality and productivity of ball end milling operations. Finally, experimentation carried out permits to observe and quantify the deviations of the theoretical approach.

The development of an end-milling process depth of cut monitoring system

Efforts to manage end-milling cutter-tool life depend upon the accuracy of the information used in the assessment of the work done. This presents a problem in many instances because the measurement of cutting-related parameters is difficult, especially when the operations being undertaken do not form part of a repeated manufacturing cycle. This paper presents a methodology utilising ultrasonic sensors for the real-time monitoring of the depth of cut arising during end-milling operations. The paper outlines the architecture of a tool condition monitoring system based upon state of the art microcontrollers. It then considers how depth of cut information can be integrated into such a system in order to support effective end-mill tool-life management functions.

Trial Surface-Property Prediction in End-Mill Operation

Because surface precision is an important factor in machined surfaces, we propose a model for predicting surface properties in end-mill operation. Prediction considers cutting edges including a cutting tool movement and rotation and cutter runout and vibration. Experiments confirmed the model’s feasibility.

Optimization Of End-Milling Operation Using Response Surface Methodology in Combination With Simulated Annealing

Optimization Of End-Milling Operation Using Response Surface Methodology in Combination With Simulated Annealing

Influence of Cutting and Geometrical Parameters on the Cutting Force in Milling

This paper presents a numerical modelling of the dynamic behaviour of the Machine-Tool-Part system (MOP) in milling. The numerical study of such complex structure requires the use of sophisticated method like finite elements one. Simulation is employed to predict cutting forces and dynamic response of Machine-Tool-Part system (MOP) during end-milling operation. Finally, spectral analysis is presented to see the influence of feed direction in the vibration.

Influence of Cutting and Geometrical Parameters on the Cutting Force in Milling

This paper presents a numerical modelling of the dynamic behaviour of the Machine-Tool-Part system (MOP) in milling. The numerical study of such complex structure requires the use of sophisticated method like finite elements one. Simulation is employed to predict cutting forces and dynamic response of Machine-Tool-Part system (MOP) during end-milling operation. Finally, spectral analysis is presented to see the influence of feed direction in the vibration.

Stability Predictions for End Milling Operations With a Nonlinear Cutting Force Model

The aim of this paper is to obtain the stability lobes for milling operations with a nonlinear cutting force model. The work is focused on the generation of stability lobes based on a formulation with Chebyshev polynomials and the semidiscretization method, considering a nonlinear cutting force model. Comparisons were conducted between experimental data at 5% radial immersion with aluminum workpiece and predictions based on Chebyshev and semidiscretization. In all cases, the use of nonlinear cutting force model provides better prediction of process stability conditions.

Optimization of surface roughness in an end-milling operation using nested experimental design

This paper presents an experimental study to optimize the surface quality of an end-milled surface on a Vertical Machining Centre using Taguchi’s nested experimental design. The effect of various machining parameters on surface roughness was investigated on two different work piece materials, Aluminium alloy and Plain Carbon Steel. Other control factors, namely, feed rate and spindle speed, depth of cut and radial engagement of tool were varied in the experiment to measure surface roughness at four different positions on the work piece. Position was taken as an uncontrollable noise factor. Depth of cut was observed to be the most significant factor that affecting the surface roughness. Also, better surface finish was obtained while machining aluminum alloy as compared to plain carbon steel. Spindle speed and feed rate were the other two significant factors while machining aluminum alloy parts, although these factors did not significantly affect the finish for steel. Radial engagement of tool had no impact on the surface finish for aluminium alloy, while it had a significant impact for plain carbon steel. Further, the analysis of results shows that position P2 (middle of the milled surface) had the best surface finish while position P1 (at beginning of the cut) had relatively poorer finish.

A Regression Neural Model for In-Process Surface Roughness Monitoring in End Milling Operations

The key element of the in-process surface roughness monitoring system is the decision-making model, which is utilized to analyze the input factors and then to generate a proper output. The success of the in-process monitoring system depends on the accuracy of the decision-making model. To increase the accuracy and reliability of model, it is important to reduce the variation of the inputs. To achieve this objective, an integration of regression and neural network was developed as a decision-making model in this research. In this integrated model, the regression model was applied as a filter to sort the input variables into groups. Furthermore, the grouped data was implemented to train and to generate different neural networks models to reduce the affection of input variation and increase the accuracy of the monitoring system. The input variables was first filtered by the threshold of regression model, and then analyzed by different neural networks model based on the filtered result. Finally, to evaluate the performance of the integrated model, the regression neural network and traditional neural networks were both developed for surface roughness monitoring system in an end milling operation to compare the accuracy of systems.

Cutting performance of nanocomposite Cr–C–N, Cr–Si–N and Cr–Si–C–N coated tools for micro end-milling operation

CrN-based multi-component coatings were deposited by a hybrid coating system combining arc ion plating (AIP) and a sputtering technique. In this paper, comparative studies on mechanical properties and cutting performance among Cr–C–N, Cr–Si–N and Cr–Si–C–N coatings were systematically conducted. Adding carbon and silicon atoms into CrN coatings had large effects on microstructural change and mechanical properties of CrN coatings by instrumental analyses such as XRD and HRTEM. The hardness value of Cr–Si–C–N coatings showed about 45 GPa, while those of Cr–Si–N and Cr–C–N coatings were 34 and 23 GPa, respectively. Cutting tests were carried out to evaluate the characteristics of micro cutting in a vertical machining center using an ultra-high-speed air turbine spindle. Consequently, the quaternary Cr–Si–C–N coated micro end-mill in dry machining showed good cutting performance under ultra-high-speed cutting conditions.

초경소재 선정을 위한 고속가공의 엔드밀 성능 평가

To satisfy the demand of higher cutting performance, mechanical properties with tungsten carbide (WC-Co) tool materials were investigated. Hardness and transverse rupture strength with WC grain size, Co content and density were measured. Compared to H, K, and S manufacture maker as tungsten carbide (WC-Co) tool materials were used for high-speed machining of end-milling operation. The three tungsten carbide (WC-Co) tool materials were evaluated by cutting of STD 11 cold-worked die steel (HRC25) under high-speed cutting condition. Also, tool life was obtained from measuring flank wear by CCD wear measuring system. Tool dynamometer was used to measure cutting force. The cutting force and tool wear are discussed along with tool material characteristics. Consequently, the end-mill of K, H manufacture maker showed higher wear-resistance due to its higher hardness, while the S maker endmill tool showed better performance for high metal removal.

Prediction of tool wear using regression and ANN models in end-milling operation a critical review

Palanisamy et al. (Int J Adv Manuf Technol Volume 37:29–41, 2008) focus on two different models in their research, namely, regression model (RM) artificial neural network (ANN) for predicting tool wear. The prediction values from RM and ANN models are compared with the experimental values conducted by them. The discussion shall focus on the following four main points that are not considered in the study.

A generation of more efficient CNC tool paths using simulation modelling

This work presents new two-phase optimisation strategy for efficient planning of finishing end-milling operations when machining pocket-type features. The novel optimisation approach incorporates the design tolerance into an error stack that relates directly to the CAD/CAM output. The optimisation mechanism controls dimensional tolerances through knowledge of cutting forces and the associated cutting tool deflections. The new off-line optimisation methodology creates a more efficient milling process with variable feed rate than conventional constant feed rate.

Simulation of three-dimension cutting force and tool deflection in the end milling operation based on finite element method

This paper presents a 3D simulation system which is employed in order to predict cutting forces and tool deflection during end-milling operation. In order to verify the accuracy of 3D simulation, results (cutting forces and tool deflection) were compared with those based on the theoretical relationships, in terms of agreement with experiments. The results obtained indicate that the simulation is capable of predicting the cutting forces and tool deflection.

A force-temperature model including a constitutive law for Dry High Speed Milling of aluminium alloys

In this study, a new analytical cutting force model was developed for the Dry High Speed Milling of aluminium alloys. The proposed model requires only work-material properties and cutting conditions to estimate the cutting forces and the temperature during end-milling processes. The transformation from orthogonal-to-oblique cutting was used to study the end-milling operation. A new method, which includes a Needleman-Lemonds constitutive equation, was introduced to simultaneously determine the average shear stress and the cutting temperature in the primary and secondary shear zones. The model validation was performed by comparing the prediction for machining Al6061-T6 and Al7075-T6 aluminium to mechanistic model results and experimental results. The computed temperature results were compared to published data. A good agreement was observed for both force and temperature results, for the investigated cutting conditions.

Prediction of tool life by statistic method in end-milling operation

The aim of the this study is to develop the tool life prediction model for P20 tool steel with aid of statistical method, using coated carbide cutting tool under various cutting conditions. This prediction model was then compared with the results obtained experimentally. By using Response Surface Method (RSM) of experiment, first and second order models were developed with 95% confidence level. The tool life was developed in terms of cutting speed, feed rate, axial depth and radial depth, using RSM and design of experiment. In general, the results obtained from the mathematical model are in good agreement with that obtained from the experiment data’s. It was found that the feedrate, cutting speed, axial depth and radial depth played a major role in determining the tool life. On the other hand, the tool life increases with a reduction in cutting speed and feedrate. For end-milling of P20 tool steel, the optimum conditions that is required to maximize the coated carbide tool life are as follow: cutting speed of 140 m/s, federate of 0.1 mm/rev, axial depth of 1.5 mm and radial depth of 2 mm. Using these parameters, a tool life of 39.46 min was obtained.

Directionally Independent Failure Prediction of End-Milling Tools by Tracking Increasing Chaotic Noise at the Machining Frequencies Due to Wear

Accurate on-line forecasting of a tool’s condition during end-milling operations is advantageous to the functionality and reliability of automated industrial processes. The ability to disengage the tool prior to catastrophic failure reduces manufacturing costs, excessive machine deterioration, and personnel hazards. Rapid computational feedback describing the system’s state is critical for realizing a practical failure forecasting model. To this end, spectral analysis by fast Fourier type algorithms allows a rapid computational response. The research described herein explores the development of nontraditional real fast Fourier transform (discrete cosine transform) based algorithms performed in unique higher-dimensional states of observed data sets. Moreover, the developed Fourier algorithm quantifies chaotic noise rather than relying on the more traditional observation of system energy. By increasing the vector dimensionality of the discrete cosine transform, the respective linear transform basis more effectively cross correlates the transform data into fewer (more significant) transform coefficients. Thus, a single vector in orthogonally higher-dimensional space is observed instead of multiple orthogonal vectors in single-dimensional space. More specifically, a novel modal reduction technique is utilized to track trends measured from triaxial force dynamometer signals. This transformation effectively achieves both modal reduction and directional independence by observing the chaotic noise instead of system energy. Algorithm output trends from six end-milling life tests are tracked from both linear and pocketing maneuvers in order to demonstrate the technique’s capabilities. In all six tests, the algorithm predicts impending tool failure with sufficient time for tool removal.