Cutting Force Ratio Research Articles

Surface creation and cutting characteristics during sugar maple wood scratching with spherical cone grit

A series of single-grit cutting tests were performed with spherical cone tools on sugar maple (Acer saccharum) wood to investigate its surface creation and cutting behavior. The tools with apex angles 2 $$\alpha$$ = 84 $$^\circ$$ and 2 $$\alpha$$ = 108 $$^\circ$$ were selected to clarify the effect of negative rake angles. It was found that there was no discernable pile-up on both sides of the scratches. Grit with 2 $$\alpha$$ = 108 $$^\circ$$ made less fractured fibers protrude from the top surface. The affluent porosity of wood was emphasized, and an analytical model of elastic deformation for single-grit cutting has been established. There seems to be a transitional depth of grit penetration, after which the elastic recovery keeps a constant value. The average cutting force ratio of grit with 2 $$\alpha$$ = 84 $$^\circ$$ was visibly higher, referring to better cutting performance. The size effect of grit with 2 $$\alpha$$ = 84 $$^\circ$$ was more pronounced in the two cutting directions. Compared with research on metal materials, the evident energy absorption capacity of sugar maple accounts for the relatively high overall specific energy.

The influence of radial engagement and milling direction for thin wall machining: a semi-analytical study

Milling stability prediction of regular milling processes has to be normally carried out by means of complex numerical methods due to the multidimensional nature of the limiting modes, hindering the direct study of the influence of the process parameters on milling stability. However, the dynamics of thin-walled part milling processes, especially flank milling operations, can often be described in a unique direction. Thus, the directional factor matrix is condensed onto a single coefficient whose mean value uniquely depends on the engagement limits and the radial-tangential cutting force ratio. By these means, this paper studies the influence of radial engagement and milling direction on thin wall milling stability. The superior performance of up-milling is demonstrated on account of its special geometric configuration, permitting both positive and negative mean directional factor situations. In addition, the expressions for optimal radial engagement for minimum Hopf chatter likelihood are provided, which serve as excellent tool for process planning regardless of the part dynamics or other process parameters. The obtained results are finally validated through semidiscretisation and initial value time domain simulations.

Development of a novel boring tool with anisotropic dynamic stiffness to avoid chatter vibration in cutting: Part 2: Analytical and experimental verification of the proposed method

In this study, we developed prototypes of boring tools with anisotropic dynamic stiffness, and their chatter stability was investigated analytically and experimentally. In Part 1, a novel design of boring tools with an anisotropic structure was proposed to improve the nominal stiffness in boring operation, thus resulting in higher chatter stability in simulation. In this part (Part 2), anisotropic boring tools with a holder length-to-diameter ratio (L/D) of 4 and 10 designed in Part 1 were prototyped, and their frequency response functions were evaluated. Then, the chatter stabilities were evaluated through turning experiments. With respect to a L/D4 boring tool with anisotropic structure, the nominal dynamic stiffness was significantly improved within the range of machining conditions that satisfies the appropriate combination of cutting force ratio and chip flow direction, compared to a conventional tool with isotropic structure. We confirmed that the proposed boring tool with an anisotropic structure increases the critical radial depth of cut by approximately 17 times compared with conventional tools. Even with an L/D10 anisotropic boring tool, a similar effect was observed wherein the dynamic stiffness of the tool was improved. In contrast, the effect of improving the dynamic stiffness was insufficient; thus, chatter free cutting could not be realized. Analytical investigations verified the importance of further improvement of dynamic characteristics. To realize stable boring with L/D10 boring tools, it is necessary to further reduce the system compliance while also improving the similarity of the frequency response function.

Comparison of Neural Networks and Regression Analysis to Predict In-process Straightness in CNC Turning

The objective of this research is to predict the in-process straightness of aluminum (Al 6063) and carbon steel (S45C) by monitoring the in-process cutting forces during CNC turning. The Fast Fourier Transform (FFT) is adopted to prove the relation between cutting force and straightness in frequency domain, which appear the same frequency. The cutting force ratio is proposed and normalized to predict the in-process straightness regardless of the cutting conditions. Firstly, the straightness is calculated by employing the two-layer feed-forward neural networks. The Levenberg-Marquardt backpropagation algorithm is utilized to train the system. Secondly, the multiple regression analysis has been applied to model the in-process prediction of straightness and the cutting force ratio under various cutting conditions with the use of least square method at 95% confidence level. Finally, the experimentally obtained results from the neural networks are compared with the ones obtained from the developed straightness model. It had been proved that the in-process straightness can be well predicted under various cutting conditions by using the trained neural networks.

Cutting Characteristics for Sugar Maple Using Single Grit with Spherical Cone and Triangular Pyramid Geometries.

Abrasive belt sanding plays an important role in wood processing. The abrasive grits on the belt perform similar to small cutting tools with negative rake angles. In this study, a series of single-grit scratching tests were carried out on Sugar maple workpieces to investigate the cutting characteristics of two different abrasive-grit shapes. The spherical cone grits had two kinds of included angles, and the triangular pyramid grits provided two cutting forms: one main cutting edge and two side cutting edges as well as two main cutting edges. Both scratching along and across the wood grain direction were conducted. In all cases, the material deformation and surface creation were analyzed, as well as cutting force. Several physical cutting models were established to help further understand the cutting mechanism. A new method was proposed to evaluate the energy consumption of single-grit scratching. The results showed that triangular pyramid grits with sharp cutting edges could sever wood fibers more efficiently, while spherical cone grits are prone to make material plastic deformation mainly manifested as superficial densification and pile-up. When scratching along the wood grain, the triangular pyramid grit with two main cutting edges showed the best cutting performance with better surface quality. It was also shown that the cutting force ratio of spherical cone grits was apparently less than that of triangular pyramid grits. The overall cutting power for spherical cone grits was remarkably higher than that for triangular pyramid grits for both scratching along and across the wood grain, which indicates that triangular pyramid grits have higher cutting efficiency and power utilization.

A Study of Relation between Tool Wear and Dynamic Cutting Force Ratio in CNC Turning

Since tool wear directly affects the manufacturing cost in CNC turning process, the aim of this research is to study the effects of cutting conditions on tool wear and dynamic cutting force ratio. It also describes relation of tool wear and dynamic cutting force ratio. The results of experiment show that cutting speed is the most significantly influential factor followed by feed rate, depth of cut and nose radius respectively. In addition, effect of dynamic cutting force on the tool wear analysis show that trend of the dynamic cutting force is not changed even though the cutting condition is changed. Accordingly, the tool wear can be predicted by using the dynamic cutting force ratio in turning process.

Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.

In-process prediction of roundness based on dynamic cutting forces

This paper describes a development of in-process prediction of roundness from the previous research of the author in CNC turning process. The relations of roundness, dynamic cutting forces, and cutting conditions have been investigated. In order to eliminate the effects of the cutting conditions, the dynamic cutting forces are dimensionless by taking the ratio of the average variations of dynamic radial force to that of the dynamic feed force. The exponential function is employed for the sake to model the in-process prediction of roundness which consists of cutting speeds, feed rates, depths of cut, tool nose radiuses, rake angles, and dynamic cutting force ratio. The experimentally obtained results showed that the better roundness errors can be obtained by increasing the cutting speed, the tool nose radius, and the rake angle. The new cutting tests have been conducted to verify the obtained model. It is understood that the in-process roundness can be predicted well during cutting with the high accuracy by using the proposed method.

A wavelet approach to predict surface roughness in ball-end milling

This research proposed an advance in the prediction of the in-process surface roughness during the ball-end milling process by utilizing the wavelet transform to monitor and decompose the dynamic cutting forces. The chatter detection system has been adopted from the previous research of the author to avoid the chatter first, and hence, the dynamic cutting force ratio is introduced to predict the in-process surface roughness during the normal cutting by taking the ratio of the decomposed dynamic cutting force in X axis to that in Z axis. The Daubechies wavelet transform is employed in this research to analyze the in-process surface roughness. The experimentally obtained results showed that the surface roughness frequency occurred at the same level of the decomposed dynamic cutting forces although the cutting conditions are changed. It is understood that the in-process surface roughness can be predicted effectively under various cutting conditions referring to the proposed monitoring system.

Intelligent Prediction of Tool Wear in Ball-End Milling Process Based on Dimensionless Cutting Force Ratio

This research proposed an in-process tool wear prediction during the ball-end milling process by utilizing the cutting force ratio. The dimensionless cutting force ratio is proposed to cut off the effects of the work material and the combination of cutting conditions. The in-process tool wear prediction model is developed by employing the exponential function, which consists of the spindle speed, the feed rate, the depth of cut, the tool diameter, and the cutting force ratio. The experimentally obtained results showed that the cutting force ratio can be utilized to predict the tool wear of ball-end milling tool. The new cutting tests have been employed to verify the model and the results run satisfaction. It has been proved that the in-process tool wear prediction model can be used to predict the tool wear regardless of the cutting conditions with the highly acceptable prediction accuracy.

Monitoring of Surface Roughness Based on Cutting Force and Cutting Temperature in Ball-End Milling Process by Utilizing Response Surface Analysis

The surface roughness is monitored using the cutting force and the cutting temperature in the ball-end milling process by utilizing the response surface analysis with the Box-Behnken design. The optimum cutting condition is obtained referring to the minimum surface roughness, which is the spindle speed, the feed rate, the depth of cut, and the tool diameter. The models of cutting force ratio and the cutting temperature are proposed and developed based on the experimental results. It is understood that the surface roughness is improved with an increase in spindle speed, feed rate and depth of cut. The cutting temperature decreases with an increase in tool diameter. The model verification has showed that the experimentally obtained surface roughness model is reliable and accurate to estimate the surface roughness.

Investigation of Relation between Straightness and Cutting Force in CNC Turning Process

The objective of this research is to examine the relation between the straightness and the cutting force ratio during the CNC turning process. The cutting force is monitored and obtained by installing the dynamometer on the turret of CNC turning machine. The relation between the cutting force ratio and the straightness is investigated under the various cutting conditions, which are the cutting speed, the feed rate, the depth of cut, the tool nose radius and the rake angle. The experimentally obtained results showed that the straightness can be improved with an increase in cutting speed, tool nose radius and rake angle. The relation between the dynamic cutting force and straightness profile can be proved by checking the frequency of the cutting force in frequency domain with the use of the Fast Fourier Transform (FFT), which is the same as the straightness profile. Hence, the cutting force ratio can be used to predict the straightness during the cutting regardless of the cutting conditions. The cutting force ratio is proposed to predict the straightness during turning process by employing the exponential function for the sake of straightness. The multiple regression analysis has been utilized to calculate the regression coefficients of the in-process prediction of straightness model by using the least square method at 95% confident level. It has been proved by the cutting tests that the in-process straightness can be predicted during the cutting within ±10% measured straightness with the high accuracy of 91.85%.

In-Process Prediction of Surface Roughness in Grinding Process by Monitoring of Cutting Force Ratio

The purpose of this research is to develop the models to predict the average surface roughness and the surface roughness during the in-process grinding by monitoring the cutting force ratio. The proposed models are developed based on the experimentally obtained results by employing the exponential function with four factors, which are the spindle speed, the feed rate, the depth of cut, and the cutting force ratio. The experimentally obtained results showed that the dimensionless cutting force ratio is usable to predict the surface roughness during the grinding process, which can be calculated and obtained by taking the ratio of the corresponding time records of the cutting force Fy in the spindle speed direction to that of the cutting force Fz in the radial wheel direction. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method at 95% confident level. The experimentally obtained models have been verified by the new cutting tests. It is proved that the developed surface roughness models can be used to predict the in-process surface roughness with the high accuracy of 93.9% for the average surface roughness and 92.8% for the surface roughness.

Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio

The aim of this research is to propose the practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the dynamic cutting force ratio. The proposed model is developed based on the experimentally obtained results by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the dynamic cutting force ratio. The experimentally obtained results showed that the frequency of the dynamic cutting force corresponds with the frequency of the surface roughness profile in the frequency domain. Hence, the dimensionless dynamic cutting force ratio is proposed regardless of the cutting conditions to predict the in-process surface roughness by taking the ratio of the area of the dynamic cutting force in X axis to that in Z axis. The multiple regression analysis is adopted to calculate the regression coefficients at 95 % confident level. The experimentally obtained model has been verified by using the new cutting conditions. It is understood that the developed surface roughness model can be used to predict the in-process surface roughness with the high accuracy of 92.82 % for the average surface roughness and 91.54 % for the surface roughness.

Development of Surface Roughness Prediction by Utilizing Dynamic Cutting Force Ratio

This paper presents the development of the in-process surface roughness prediction in the CNC turning process of the plain carbon steel with the coated carbide tool by utilizing the dynamic cutting force ratio. The dynamic cutting forces are measured to analyze the relation between the surface roughness and the cutting conditions. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with six factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the dynamic cutting force ratio. The dynamic cutting force ratio can be calculated and obtained by taking the ratio of the corresponding time records of the area of the dynamic feed force to that of the dynamic main force. The relation between the dynamic cutting force ratio and the surface roughness can be proved by the obtained frequency of them in frequency domain which are the same frequency. The proposed model has been proved by the new cutting tests with the high accuracy of 91.04% by utilizing the dynamic cutting force ratio.

Prediction of Surface Roughness on CNC Turning Based on Monitoring of Cutting Force and Cutting Temperature

This paper presents the surface roughness model which is proposed and developed to predict the surface roughness in the CNC turning of the carbon steel with the coated carbide tool under various cutting conditions by using the response surface analysis with the Box-Behnken design based on the experimental results. The in-process monitoring of the cutting force and the cutting temperature is utilized to analyze the relation between the surface roughness and the cutting condition. The tool dynamometer and the infrared pyrometer are employed and installed on the turret of CNC turning machine to measure the in-process cutting force and cutting temperature. The models of cutting force ratio and cutting temperature are also developed based on the experimental data. The optimum cutting condition is determined referring to the minimum surface roughness of the surface plot, which is obtained from the developed surface roughness model. The experimental results show that the higher cutting speed gives the better surface roughness due to the higher cutting temperature, however the tool life becomes shorter. The feed rate is the most significant factor which affects the surface roughness, while a small depth of cut helps to improve the surface roughness. The effectiveness of the surface roughness prediction model has been proved by utilizing an analysis of variance (ANOVA) at 95% confident level. Hence, the surface roughness can be predicted and obtained easily referring to the developed surface roughness model.

Integration of in-process monitoring and statistical process control of surface roughness on CNC turning process

In order to realise an intelligent machine tool, the aim of this research was to propose an integration of the in-process monitoring and statistical process control (SPC) of the surface roughness during the turning process by using the cutting force ratio. The previously developed in-process surface roughness models of the author are adopted and employed to predict the surface roughness during the cutting with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut and the cutting force ratio. The cutting force ratio is utilised to estimate the in-process surface roughness which is the ratio of the feed force to the main force. The dynamometer is installed on the tool turret of CNC turning machine to monitor the cutting force. The in-process SPC of the surface roughness has been developed and proposed to monitor and control the in-process predicted surface roughness from the adopted models by using the I-MR Charts. It has been proved by the cutting tests that the proposed and developed system of the in-process estimation and SPC of the surface roughness can be used to monitor the in-process predicted surface roughness by utilising the cutting force ratio with the high significance at 95% confident level.

Advanced Prediction of Surface Roughness by Monitoring of Dynamic Cutting Forces in CNC Turning Process

The aim of this research is to investigate the relation between the surface roughness and the dynamic cutting force ratio during the in-process cutting in CNC turning process. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, and the dynamic cutting force ratio. The dynamic cutting force ratio is proposed to predict the surface roughness during the cutting, which can be calculated and obtained by taking the ratio of the corresponding time records of the area of thedynamic feed force to that of the dynamic main force. The in-process relation between dynamic cutting force ratio and surface roughness can be proved by the frequency of the dynamic cutting force which corresponds to the surface roughnessfrequency. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method at 95% confident level. The proposed model has been verified by the new cutting tests. It is understood that the developed surface roughness model can be used to predict the in-process surface roughness with the high accuracy of 90.3% by utilizing the dynamic cutting force ratio.

Rounded cutting edge model for the prediction of bone sawing forces.

A new analytical model to predict bone sawing forces is presented. Development of the model was based on the concept of a single tooth sawing at a depth of cut less than the cutting edge radius. A variable friction model was incorporated as well as elastic Hertzian contact stress to determine a lower bound for the integration limits. A new high speed linear apparatus was developed to simulate cutting edge speeds encountered with sagittal and reciprocating bone saws. Orthogonal cutting experiments in bovine cortical bone were conducted for comparison to the model. A design of the experiment's approach was utilized with linear cutting speeds between 2600 and 6200 mm/s for depths of cut between 2.5 and 10 μm. Resultant forces from the design of experiments were in the range of 8 to 11 N, with higher forces at greater depths of cut. Model predictions for resultant force magnitude were generally within one standard deviation of the measured force. However, the model consistently predicted a thrust to cutting force ratio that was greater than measured. Consequently, resultant force angles predicted by the model were generally 20 deg higher than calculated from experimental thrust and cutting force measurements.

Analytical modeling and experimental investigation of ultrasonic-vibration assisted oblique turning, part III: Experimental investigation

The experimental investigation of the kinematics and dynamics of ultrasonic-vibration assisted oblique turning (oblique UAT) has been undertaken in the present study. For the purpose of comparison, the conventional oblique turning experiments were also carried out in parallel to the oblique UAT experiments. The burnishing effect of the vibrating motion of the cutting tool has been studied as a means of illustrating the kinematic interaction between the cutting tool and the workpiece in vibration cutting. The experimental investigation of the influence of oblique UAT parameters on the dynamics of the process has been another subject of the present study. The oblique UAT parameters taken into consideration consisted of vibration amplitude, cutting speed, inclination angle, tool cutting edge angle and feed rate. The cutting force components were differently influenced by these parameters. The inclination angle, tool cutting edge angle and feed rate did not have any significant effect on the cutting force ratio whereas the cutting speed and the vibration amplitude highly influenced this ratio. The experiments were carried out with aluminium 6061 and 2024.