High-speed Milling Process Research Articles

Optimization of Cutting Parameters on High-Speed CNC Milling for Precision parts of Non-ferrous Castings using RSM

This research paper analyses the cutting parameter optimisation suitable for precision parts manufacturing units, which mainly uses non-ferrous cast blocks for their job orders using Box-Behnken's response surface method. This solution method developed based on case studies conducted from industrial units involved in precision components manufacturing in 100% export oriented units around Coimbatore. The problem is converted into a simultaneous optimisation model for getting better material removal rate (MRR) on high-speed CNC milling process while maintaining the surface roughness requirements less than 20 microns. The experiments were carried out for different alloy materials of non-ferrous group frequently used for manufacturing precision components aluminium 6,061, brass, gunmetal, phosper bronze. The group of materials is of heterogeneous but they share similar mechanical properties required for a precision component, it is generally difficult to machine these material due to either soft ductile/hard brittle characters. However this research is to give an adaptable solution to the small and medium type job order companies, which struggle to maintain the precision at a minimised cost of manufacturing. The results show that MRR could be maximised by optimisation of input parameters while keeping Ra in the range less than 0.25 microns.

Chatter detection in milling process based on synchrosqueezing transform of sound signals

Chatter is a self-excited vibration between the workpiece and tool with negative effects. In this work, a chatter detection method is proposed based on synchrosqueezing transform (SST) of sound signals. Firstly, the SST is used to analyze the sound signals recorded with the microphone and a time-frequency representation is obtained. Then, filtering is conducted to remove the disturbance of tooth passing frequency and its harmonics in time-frequency domain. Next, singular value decomposition (SVD) method is employed to condense the TF matrix and the first-order singular value is calculated as the chatter indicator. Finally, chatter threshold is set based on 3σ criterion for the detection of chatter occurrence. The proposed method is validated with cutting tests, and the results show that the method has great potential to be used for the online chatter detection of high-speed milling process.

Effects of Cutting Atmosphere on High-Speed End Milling Process for Titanium Alloy Ti6Al4V

As titanium alloys have a high strength-to-weight ratio and superior corrosion resistance, they are widely used in the aerospace, biomedical, and automotive industries. However, these alloys exhibit very poor machinability, which results in problems such as short tool life. This study investigates the effect of the cutting atmosphere on tool wear during high-speed end milling of the titanium alloy Ti6Al4V. Dry cutting, cold air jet cutting, cutting fluid mist jet cutting, and cutting fluid flush cutting were considered in order to determine the optimum cutting atmosphere and conditions. For down-cutting speeds of 200−300 m/min, the cutting atmosphere and cutting speed were adopted as experimental parameters. Down-cutting was performed in order to measure the width of the tool flank wear land as the cutting length was increased. The results indicated that the optimum cutting method was cold air jet cutting. For a cutting length of 500 mm, this method produced a narrower flank wear land than dry cutting. In addition, for longer cutting lengths of up to 4000 mm, the wear rate for cold air jet cutting was less than or equal to that for dry cutting, and no chipping or excessive wear was observed.

Optimization of process parameters in the high-speed milling of titanium alloy TB17 for surface integrity by the Taguchi-Grey relational analysis method

TB17 is an ultra-high-strength titanium alloy, a material typically difficult to machine. The surface quality and integrity of titanium alloy TB17 are poor after machining, which can seriously affect its service performance and restrict its fields of application. Therefore, the optimization of high-speed milling process parameters for the new ultra-high-strength titanium alloy TB17 was investigated based on multiple performance characteristics, including surface roughness, surface microhardness, and surface residual stress. The Taguchi method with grey relational analysis was utilized for the experiments. Additionally, analysis of variance was employed to evaluate the most influential factors for surface integrity in the high-speed milling of titanium alloy TB17. The results of the analysis using the Taguchi-Grey relational analysis method indicate that the preferred combination of high-speed milling process parameters are as follows: the cutting fluid condition of H-1 fine grinding fluid, using a milling speed of 100 m/min, a feed per tooth of 0.02 mm/z, an axial depth of cut of 1 mm, a radial depth of cut of l.5 mm, a rake angle of 18°, a clearance angle of 12°, and a helix angle of 60°. Moreover, the analysis of variance reveals that milling speed has the greatest effect on the surface integrity in the high-speed milling of the TB17 titanium alloy, and the contribution percentages for each factor are as follows: the cutting fluid condition (3.98%), milling speed (25.89%), feed per tooth (8.96%), axial depth of cut (1.29%), radial depth of cut (13.71%), rake angle (17.17%), clearance angle (6.62%), and helix angle (15.64%).

Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes

Tool condition monitoring (TCM) is a necessary action in a high-speed milling (HSM) process. As a worn milling tool might irreversibly damage a workpiece, there is a vital demand for a TCM system to evaluate the tool wear progress, or equivalently the resultant surface roughness, nonintrusively. To build up a condition monitoring system for HSM processes, sensor signals are to be utilized to form a reference model that reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence techniques to be more repeatable and generalizable. This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Studied clustering methods are applied to the wavelet features of force and vibration signals to illustrate the repeatability of their results. It is shown that clustering methods can coarsely capture the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed how the application of clustering methods may improve the performance of existing reference models toward the more efficient utilization of available experimental data and to develop easily generalizable reference models. Finally, a possible application of clustering results is discussed comparing with state-of-the-art papers.

Performance optimization for cemented carbide tool in high-speed milling of hardened steel with initial microstructure considered

Investigation on cutting parameter optimization was performed to enhance the cemented carbide tool life in high-speed milling of hardened steel. Image analysis of the tool material microstructure was conducted to determine the average grain diameter and the size of the representative element. The initial state of the tool material microstructure was evaluated based on micromechanics and damage mechanics. Finite element simulation of the milling process was performed to acquire the tool stress. The original damage of the tool and simulated tool stress were integrated based on the concept of damage equivalent stress. Distribution and evolution of the damage equivalent stress on the tool body was analyzed and a new indicator for cutting parameter optimization was brought forward. A cutting parameter optimization method was proposed on the basis of the new indicator. Experimental tests showed that the proposed optimization method can be used to identify the optimum cutting parameter combination and prolong the cemented carbide tool life in high-speed milling process.

Dynamic modeling of spindle-rolling bearings systems in peripheral milling operations

In High-Speed Milling Process (HSM) the machining precision is directly affected by spindle bearing system which has a crucial role and it impacts directly the dynamic performance of the machining system. This study is about an accurate simulation of such system response. In fact, the Timoshenko beam theory with different circular sections is used to propose a finite element model of the spindle system. The effect of the rolling bearings and the gyroscopic moment at high speeds as well as the centrifugal forces and the cutting forces are considered in the formulation. The rigid and flexible movements of the spindle are taken into account. The dynamic responses of tool-tip under the dynamic cutting forces are numerically investigated.

Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis

In recent years, increasing demands for more efficient high speed milling (HSM) processes have propelled the application and development of more effective modeling methods and intelligent machining approaches. Hence, a desired reference model has to incorporate more efficient and sophisticated feature extraction and artificial intelligence (AI) techniques aiming for more repeatability and generalizability.In our work, wavelet analysis is applied for feature extraction to provide a better insight into time–frequency changes of the process. Considering the high dimension of extracted wavelet features, dimension reduction methods, such as clustering techniques are inevitable. They are applied as an interpretation layer between the feature extraction and artificial intelligence subsystems to form a new model structure for HSM with generalizability and sequential learning capability.We introduce a new architecture that incorporates the advantages of fuzzy clustering into well known dynamic fuzzy neural networks (DFNN) to form an online condition monitoring system which is tolerant to slight drifts in process dynamics and adaptable to variations in parameters and device. Sequential Fuzzy Clustering Dynamic based Fuzzy Neural Networks (SFCDFNN) is developed and successfully applied for monitoring of an HSM process. It is able to sequentially learn the model and adapt itself to variations and also provide an estimation or prediction on the status of the process. It facilitates for nonintrusive fault diagnosis and prognosis. Lastly, its performance on modeling of experimental data is practically illustrated and compared to its other counterparts.

Frequency-domain stability lobe prediction for high-speed face milling process under tool–workpiece dynamic interaction

Self-excited vibrations of the face milling process can result in instability, poor surface finish and machine tool failure. In order to avoid chatter vibrations, this article develops an algorithm for predicting the stability lobes for face milling processes. It considers the factors including radial instantaneous chip thickness, entry and exit angles and the dynamic interaction between cutting tool and workpiece which is often neglected by many researchers. An electronic impact hammer is used to identify the dynamic parameters of the face milling system. Milling experiments have been conducted to validate the predictive capability of the developed algorithm for stability lobes. The results show that the prediction model can estimate the stable and unstable zones for face milling process. This article provides a frequency-domain method for establishing stability lobes which can predict stability zones rapidly. The outcome of this research will bring about methodologies for cost-effective monitoring of face milling processes and maximize the material removal rate.

Investigation of surface roughness and tool wear length with varying combination of depth of cut and feed rate of Aluminium alloy and P20 steel machining.

High-speed milling technique is often used in many industries to boost productivity of the manufacturing of high-technology components. The occurrence of wear highly limits the efficiency and accuracy of high- speed milling operations. In this paper, analysis of high-speed milling process parameters such as material removal rate, cutting speed, feed rate and depth of cut carried out by implemented to conventional milling. This experiment investigate the effects of varying combination of depth of cut and feed rate to tool wear rate length using metallurgical microscope and surface roughness using portable surface roughness tester after end milling of Aluminium and P20 steel. Results showed that feed rate significantly influences the surface roughness value while depth of cut does not as the surface roughness value keep increasing with the increase of feed rate and decreasing depth of cut. Whereas, tool wear rate almost remain unchanged indicates that material removal rate strongly contribute the wear rate. It believe that with no significant tool wear rate the results of this experiment are useful by showing that HSM technique is possible to be applied in conventional machine with extra benefits of high productivity, eliminating semi-finishing operation and reducing tool load for finishing.

Identification of Cutting Shear Stress, Shear and Friction Angles Using Flat End Milling Tests

Orthogonal-to-oblique transformation model, which is formulated based on the cutting database including shear stress, shear and friction angles, can be used to predict cutting forces in high speed milling process and any other machining process. The involved shear stress, shear and friction angles are traditionally identified from abundant number of turning experiments. For the purpose of saving experimental cost, this paper presents a novel method to identify these parameters directly from flat end milling processes. Identification procedures are established by transforming the cutting forces measured in Cartesian coordinate system into a local system. The advantage lies in that in spite of the cutter geometries and cutting conditions, only a few tests are required to develop the model, which is experimentally validated to be effective for predicting the cutting force in terms of magnitude and shape in other machining cases.

Study on fluctuation of cutting force in process of high-speed milling of Inconel 718

By the single-factor testing method, some research are conducted on the curved surface in the process of high-speed milling of Inconel 718, and the change regularities of the cutting force fluctuation against the variation of spindle speed, curvature, concavity, convexity, depth of cut and feed engagement are obtained. The conclusions will be instructive and meaningful to the follow-up selection of technological parameters in the high-speed milling process of curved surface of Inconel 718.

Study on fluctuation of cutting force in process of high-speed milling of Inconel 718

By the single-factor testing method, some research are conducted on the curved surface in the process of high-speed milling of Inconel 718, and the change regularities of the cutting force fluctuation against the variation of spindle speed, curvature, concavity, convexity, depth of cut and feed engagement are obtained. The conclusions will be instructive and meaningful to the follow-up selection of technological parameters in the high-speed milling process of curved surface of Inconel 718.

Modeling and Analysis of Spindle with Active Magnetic Bearings for High-Speed Milling Process

AbstractThe high speed milling represents the most important process to produce parts in different fields such as aeronautics, automotive and mould. It allows obtaining parts with complex form and with the best surface quality. So, it remains essential to understand the impact of both the cutting force model and its parameters on the tool tip response. Consequently, the mastering of chatter vibration in milling and surface properties of the work piece is essential. In this paper, the simulation of machining is applied to determine the cutting forces distribution. A spindle system modeling is presented using a new approach: Both rigid and flexible modes of the spindle's shaft are taken into account. The shaft is discretized with the Timoshenko beam finite elements with different circular sections. Nonlinear electromagnetic loads exerted by AMBs are computed in terms of the nominal air gap between bearings and spindle, the control current and the displacement of each node. A parametric study is performed to determine the influence of some parameters, such as the feed rate, the tangential cutting coefficient, the spindle speed and the axial depth of the cut on the cutting forces and the chatter vibrations in milling.

Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation

Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.

A Survey on Artificial Intelligence-Based Modeling Techniques for High Speed Milling Processes

The process of high speed milling is regarded as one of the most sophisticated and complicated manufacturing operations. In the past four decades, many investigations have been conducted on this process, aiming to better understand its nature and improve the surface quality of the products as well as extending tool life. To achieve these goals, it is necessary to form a general descriptive reference model of the milling process using experimental data, thermomechanical analysis, statistical or artificial intelligence (AI) models. Moreover, increasing demands for more efficient milling processes, qualified surface finishing, and modeling techniques have propelled the development of more effective modeling methods and approaches. In this paper, an extensive literature survey of the state-of-the-art modeling techniques of milling processes will be carried out, more specifically of recent advances and applications of AI-based modeling techniques. The comparative study of the available methods as well as the suitability of each method for corresponding types of experiments will be presented. In addition, the weaknesses of each method as well as open research challenges will be presented. Therefore, a comprehensive comparison of recent developments in the field will be a guideline for choosing the most suitable modeling technique for this process regarding its goals, conditions, and specifications.

The milling simulation and experimental research on high volume fraction of SiCp/Al

A multi-phase two-dimension finite element model has been developed by using ABAQUS/Explicit in order to describe the effect of reinforcement particles’ existence in SiCp/Al6063 composites on the machining process. It is used to simulate the high-speed milling process of high volume fraction SiCp/Al6063 composites. As comparison, the same machining process of matrix Al6063 is simulated with the HEM (homogenous equivalent material) model. These two models are verified with quasi-orthogonal slot milling tests. It is assumed that the variation of predicted milling forces with the feed rate is consistent with the experiments and the prediction error of the peak value of F y can be controlled within 10 %. Moreover, multi-phase model is helpful for discovering the miscellaneous interactions of matrix, particle and tool and the induced complicated stress distribution in the cutting area, for further obtaining an in-depth understanding of removal mechanism of SiCp/Al6063 composites.

Tool-Chip Friction and Two-Dimensional Numerical Simulation Analysis of High-Speed Milling AISI304

The physical friction system model was established between the tool and the chip based on the analysis of tri-bological behavior of high speed milling process of the end mill. The finite element simulation method was employed to study the tool-chip friction model, and the two-dimensional(2D) finite element model of milling was created. The numerical results revealed the chip morphology, stress and temperature distribution of the tool-chip contact surface. The tool temperature field distribution provided supports for tool-chip friction state theory and the 3D milling model.

Dynamic Analysis for High-speed Cutters of Five-axis CNC Milling Machine

Cutter vibration characteristics of five-axis milling machine in high-speed milling process were studied. Finite Element Modal Analysis for high-speed cutters with different parameters is conducted using finite element software.The impact of tooth number, extended length, diameter and material of milling cutter on the mode shapes and natural frequencies of milling cutter is researched in detail. Harmonic response curves of High-speed cutter under different frequency are calculated. Based on the response curve, the resonance frequency range of high-speed cutter is obtained in order to achieve high-precision milling and longer life of the tool. Analysis shows that: mode shapes of milling cutter are divided into strong vibration mode shapes and weak vibration mode shapes and its natural frequency become smaller with the increase of the cutter teeth number. Natural frequency of milling cutter decreases with the increase of extended length of milling cutter. The number of strong vibration mode is smaller with the increase of milling cutter diameter so that the milling cutter with large diameter can play a role of anti-vibration. Natural frequency of the high-speed steel cutter is lower than that of the diamond and carbide cutter. This research provides theoretical basis for the design of high speed milling cutter and reducing milling chatter.

Stability Prediction in High-Speed Milling Process Considering the Milling Force Coefficients Dependent on the Spindle Speed

In order to further research the chatter vibration in high-speed milling, in this paper, a new regenerative chatter vibration model, considering the effect of milling force coefficients dependent on the spindle speed (MFCDSS) on the stability of high-speed milling process is proposed, and then milling stability lobe diagram is obtained, based on full-discretization method (FDM). The variable tendency of the stability of milling system is analyzed by comparisons in case of different radial immersion ratios in low-speed and high-speed milling regions, respectively. It is found that great stability predicting differences occur, especially in high-speed region when the MFCDSS is considered. This model can further supplement the theory of stability of high-speed milling process, it has certain engineering guidance significance in the selection of high-speed milling parameters.