Cutting Force Prediction Research Articles

An analytical calculation method of instantaneous uncut chip thickness for cutting force prediction in five-axis flank milling

An analytical calculation method of instantaneous uncut chip thickness for cutting force prediction in five-axis flank milling

Cutting force prediction model considering tool-chip contact interface friction behavior in ULTVAM of Ti-6Al-4V

Ti-6Al-4V is widely used in the aviation industry because of its high strength, and good heat resistance. However, severe tool wear on the rake face occurs during the milling of Ti-6Al-4V, which is caused by intense friction between the tool rake face and the chips. To investigate tool wear in the milling of Ti-6Al-4V, ultrasonic vibration is introduced, and a cutting force prediction model that considers tool-chip contact interface friction behavior in Ultrasonic Longitudinal-Torsional Vibration-Assisted Milling (ULTVAM) is proposed in this paper. First, the tool tip motion trajectory and dynamic cutting thickness under ULTVAM were analyzed calculated, and compared with those in Common Milling (CM). Subsequently, the effects of ultrasonic vibration on the shear force under the ultrasonic softening effect, the friction force, and the friction reversal force on the tool-chip contact interface were investigated. A dynamic milling force model under ULTVAM was established before and after friction force reversal caused by ultrasonic longitudinal-torsional vibration. Finally, numerous experiments were conducted to validate the proposed model, and the experimental results indicated that the calculated dynamic milling forces agreed well with the measured values, with errors in the X and Y directions of 5.51% and 10.23%, respectively. In addition, the average roughness of the workpiece surface also decreased (1.08, 0.9, 0.6, 0.7 μm under ultrasonic amplitudes of 0, 1, 2, and 3 μm) and the tool wear state improved on the rake face under ULTVAM.

Multidimensional ultrasonic vibration machining modeling of SiCp/Al cutting forces with different volume fractions: Experiments and numerical simulations

The cutting properties of the particles of aluminum-based silicon carbide (SiCp/Al) and the base material are so disparate that it is challenging to ensure the surface quality of machining. The cutting force significantly influences the quality of the machined surface. Therefore, real-time cutting force prediction is paramount for ensuring the workpiece's desired surface quality. This paper presents a 3D ultrasonic milling force model, established from four aspects: cutting formation force, extrusion friction force, SiC particle crushing force, and ultrasonic impact force. The model is based on the ultrasonic action of the cutting process and the unique removal characteristics of the material. A 3D ultrasonic milling system was constructed to experimentally verify the milling force model. The effects of each parameter on the milling force were analyzed through orthogonal experiments, which resulted in the effects of depth of cut, rotational speed, volume fraction, ultrasonic amplitude, and feed rate. The ultrasonic amplitude, feed rate, and other factors were found to influence the milling force to varying degrees. The milling force was observed to be 42.47 %, 22.37 %, 10.85 %, 12.67 %, and 11.64 % affected by the factors mentioned above, respectively. The single-factor experiment was conducted to analyze the cutting force at different machining parameters. The experimental results and the MATLAB numerical simulation results exhibited a similar trend of change. The maximum absolute error between the two was 12.71 %, with an average absolute error of 3.735 %.

Finite element method based modeling of cutting forces and cutting temperature in micro-milling LF21

The demands for aluminum alloy LF21 micro precision parts are increasing in the fields of aerospace, high-tech electronic products, and the other fields. Micro-milling is an effective technology for machining small LF21 precision parts. Cutting forces and temperature are crucial factors in micro-milling process, directly affect tool vibration, tool wear, and surface quality of the workpiece, and even result in large deformation of the tool and workpiece. Direct measurement of cutting forces during micro-milling requires high-precision and expensive instruments. Moreover, due to the small cutting area in micro-milling, it is challenging to achieve accurate measurements of cutting area temperature. Therefore, accurate prediction of cutting forces and temperature in micro-milling is urgent and challenging. Nowadays, there are few studies on prediction of cutting forces in micro-milling LF21. The study on prediction of temperature in micro-milling LF21 is still blank. To solve the above problems, this paper proposes a finite element method based on modeling for prediction of cutting forces and temperature in micro-milling LF21. ABAQUS software is adopted. First, the geometry models of the micro-milling tool and workpiece are established. Then, the assembly and mesh division of the established models are completed. Johnson-Cook constitutive model and Johnson-Cook damage criteria are used to describe the material constitutive relationship and chip separation criteria, respectively. The suitable tool-workpiece friction models are determined. Finally, the simulation of the micro-milling LF21 process is achieved. Experiments of micro-milling LF21 are conducted and the cutting forces are measured using the dynamometer. The validity of the built process simulation model and the correctness of the cutting force prediction results are verified by the comparison of experiment and simulation cutting forces. Then, the prediction of temperature is achieved based on the verified process simulation model of micro-milling LF21.

MatDEM-based study of disc cutter force model in open TBM tunnels: Incorporating installation radius and synergistic effects

The prediction of rock cutting force is critical for tunnel boring machine performance and cutterhead design. This paper presents a novel model for rock cutting force prediction based on the Colorado School of Mines (CSM) model, which incorporates the installation position of disc cutters by introducing installation radius and synergistic effect factors. Linear cutting tests in the laboratory and large-scale rotary cutting simulations in MatDEM software were conducted to examine the impact of these factors. Results indicate that the normal and rolling forces increase and stabilize as the installation radius increases. The synergistic effect produces three force modes in a cutting circle, with mode α having the largest cutting force, mode β having a smaller force, and mode γ having the smallest force. The impact of installation radius and synergistic effect varies with rock-cutter parameters. Multiple regression analysis was used to determine the introduced factors. The proposed model was validated with rock strength and operation data from the Irtysh River conveyance project. The results demonstrate that the proposed model outperforms the CSM model in predicting cutting force in field conditions.

Time-varying dynamic modeling of micro-milling considering tool wear and the process parameter identification

Micro-milling technology is widely used in the manufacturing of micro three-dimensional complex parts in aerospace, mechanical equipment, biomedical devices, electronic components, and other fields due to its advantages of high machining accuracy, small size, and complex surface. Consequently, understanding its machining process is essential. This study proposes a time-varying dynamic micro-milling process model with the effects of tool runout, tool wear, and cutting status and explores the stable cutting domain of real-time machining. Considering the time-varying characteristics of the cutting process, combined with measured data and particle filtering method, the cutting force coefficients and tool wear parameters in the dynamic model at different times are identified. The dynamic equation of micro-milling is derived using a discrete method, and the stability of the cutting process is evaluated. Multiple cutting experiments are conducted on the Al6061, and the test data are compared with simulated values. The comparison results show that the accuracy of the proposed model in predicting the cutting state can reach 100%, and the average error of cutting force prediction is 7.72%. The research results can provide theoretical guidance for the safety evaluation of micro-milling and the selection of machining conditions.

A Review of Proposed Models for Cutting Force Prediction in Milling Parts with Low Rigidity

Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.

Comparison of regression, ANN, ANFIS, and ChatGPT prediction of turning cutting force

In this study, training and prediction performance on turning data were investigated with ChatGPT, which is a popular AI platform nowadays. In this context, the resultant cutting forces obtained as a result of different turning simulations with FEM, training and estimation were made using regression, ANN, ANFIS methods. Using the same data, training and predictions were made with ChatGPT-3 with different prediction algorithms. As a result, the lowest average error rates in the predictions made with the training data; 2E-6% for ANFIS in prediction methods and 0.19% for ANN1 conversation in GPT-3 were obtained. The lowest average error rates in the predictions made with the test data; 5.41% for regression using logarithmic Box–Cox transformation in prediction methods, and 22.66% for ANN1 conversation in GPT-3 were achieved. The highest prediction performance in GPT-3 conversations was observed when GPT-3 was asked to make predictions with ANN algorithm on both training and test data. As a result, GPT-3 has not yet generated acceptable solutions for machining problems due to its low performance in predicting test data. However, due to the fast advancement of artificial intelligence technologies, it is obvious that solutions to this and more engineering problems will be generated in near future. Highlights Prediction with ChatGPT-3 Prediction performance of artificial intelligence Cutting force prediction of turning operation

Physics-guided intelligent system for cutting force estimation in ultrasonic atomization-assisted micro-milling of porous titanium

Porous titanium is a new biomechanical implant material due to the good mechanical properties and biocompatibility. In order to meet the manufacturing needs of anisotropic porous titanium with complex micro-structures, the ultrasonic atomization with excellent cooling and lubrication effect is integrated with the micro-milling operation. Accurate prediction of cutting forces in the machining process is a principal factor as it exerts indispensable influence on the process efficiency and surface integrity. The prediction power of the traditional physics-based mechanistic cutting force model has been limited by the explicit relationship between force components and process variables based on the cutting mechanism and process geometries. In this paper, a comprehensive physics-guided intelligent system for stochastic cutting forces estimation is developed in the ultrasonic atomization-assisted micro-milling operation of porous titanium. The physics-based mechanistic cutting force model is proposed based on the determination of in-process random porosity of machined porous titanium with tool-workpiece contact. The influence of process uncertainty related to size effect and tool run-out is considered in the proposed physics-based mechanistic model. To overcome the poor nonlinear fitting and indeterminate cutting constants with numerous experiments, the machine learning algorithm has been merged into the physics-based mechanistic model to establish the physics-guided intelligent system of stochastic cutting force prediction. The predicted cutting force values of the proposed comprehensive physics-guided machine learning system are validated by the measured results with a wide variety of ultrasonic atomization-assisted micro-milling tests.

Unsupervised Domain Adversarial Adaptive Regression Network for Cutting Force Prediction at Varying Spindle Speeds

Unsupervised Domain Adversarial Adaptive Regression Network for Cutting Force Prediction at Varying Spindle Speeds

Back propagation neural network-based cutting forces prediction in dry turning SKD11 steel of heat treated with CBN inserts

Back propagation neural network-based cutting forces prediction in dry turning SKD11 steel of heat treated with CBN inserts

Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

Adiabatic shear behavior and cutting force prediction modeling of FV520B steel

Adiabatic shear behavior and cutting force prediction modeling of FV520B steel

An efficient vectorization solution to cutting dynamics modeling for face-hobbing of hypoid gears

The cutting vibration generated by cutting forces is critical to the accuracy and efficiency of face-hobbed hypoid gears. However, few relevant works are currently on cutting dynamics modeling for the face-hobbing process due to its complex spatial relationships. This study developed an efficient cutting dynamics model considering comprehensive characteristics of the non-generated face-hobbed hypoid gear machining process. A concise and efficient analytical solution to cutting dynamics modeling for face-hobbing of hypoid gears is obtained by using a vectorization method to characterize chip regenerating mechanisms. The machine tool vibration caused by cutting forces is predicted and validated according to an industrial case study. The proposed cutting dynamic model is 36.11% more efficient than the semi-analytical dynamic model derived from other cutting force prediction methods, and its accuracy is also improved with an error of less than 15%. Furthermore, the proposed vectorization solution to face-hobbing cutting dynamics modeling will be a practicable and effective method for analyzing the chatter in the machining of complex surfaces.

A feed direction cutting force prediction model and analysis for ceramic matrix composites C/SiC based on rotary ultrasonic profile milling

Ceramic matrix composites have immense applications in the aerospace, aircraft, and automobile industries. Belonging to this class, carbon-fiber reinforced ceramic matrix composites (C/SiC) are used for critical applications due to their superior properties. However, these materials have also stringent properties of heterogeneity, anisotropy, and varying thermal properties that affect machining quality and process efficiency. So, developing a cutting force prediction model and analyzing machining parameters is an essential need for the accurate machining of such materials. In this study, a mechanistic-based feed direction cutting force prediction model for rotary ultrasonic profile milling of C/SiC composites is developed and validated experimentally. The experimental and simulation results closely match each other. The mean error and standard deviation were recorded as 1.358 % and 6.003, respectively. The parametric sensitivity analysis showed that cutting force decreased with increased cutting speed, whereas it increased with increased feed rate and cutting depth. The proposed cutting force model for rotary ultrasonic profile milling of C/SiC composites is robust and can be applied to predict cutting forces and optimize the machining process parameters at the industry level.

Steady state prediction model and spectrum characteristics analysis of cutting force for CNC lathe considering variable machining parameters

This paper presents a steady state cutting force prediction model for CNC lathe considering feed displacement. The frequency spectrum characteristics of the cutting force in principal direction are analyzed. The turning test for 12Cr18Ni9 stainless steel workpiece is carried out by CNC machine tool ETC1625P firstly, and the real-time displacement data of worktable in the turning process are obtained. The cutting force data varying with the displacement of the worktable under different cutting parameters are obtained by cubic spline interpolation. The mechanical force model including metal shearing force and plowing force is established, so as to predict the steady state cutting force accurately corresponding to different displacement of the worktable. The influence of different cutting parameters on the amplitude frequency characteristics for the low frequency part of the cutting force in principal direction is studied by using wavelet packet decomposition and fast Fourier transform. The results show that the method can predict the steady cutting force accurately and reflect the amplitude frequency characteristics of the low frequency part for the cutting force in principal direction under different cutting parameters.

Mechanical analysis and machinability evaluation of a new EDM-turning hybrid machining process

This study developed an accurate electrical discharge-assisted turning (EDAT) technique for Ti–6Al–4 V alloy and a precise EDAT cutting force prediction model considering theoretical multi-physics fields with metallurgical transformation. A heat-fluid coupled model of multi-pulse discharge in the EDAT process was developed to obtain the thickness of the removal layer (RL) and modified layer (ML) together with the temperature field of the cutting layer. Subsequently, the shear flow stresses under different machining parameters were obtained using the oblique cutting and flow softening constitutive models. Within the cutting layer, the pure thermal softening effect reduces the flow stresses by more than 25% in the unmodified matrix layer (UML) and by more than 60% in the ML, which is dominated by the metallurgical transformation softening (MTS) effect. Finally, the cutting force model of EDAT under multi-physical fields was established and the accuracy was verified by relevant experiments. The results showed that the increase in discharge number accelerated the rise of workpiece surface temperatures, and the small cutting parameters were favorable to the machining performance of the EDAT. The MTS effect significantly reduces cutting forces while improving the quality of the machined surface.

Precision Grinding Technology of Silicon Carbide (SiC) Ceramics by Longitudinal Torsional Ultrasonic Vibrations.

Silicon carbide (SiC) ceramic material has become the most promising third-generation semiconductor material for its excellent mechanical properties at room temperature and high temperature. However, SiC ceramic machining has serious tool wear, low machining efficiency, poor machining quality and other disadvantages due to its high hardness and high wear resistance, which limits the promotion and application of such materials. In this paper, comparison experiments of longitudinal torsional ultrasonic vibration grinding (LTUVG) and common grinding (CG) of SiC ceramics were conducted, and the longitudinal torsional ultrasonic vibration grinding SiC ceramics cutting force model was developed. In addition, the effects of ultrasonic machining parameters on cutting forces, machining quality and subsurface cracking were investigated, and the main factors and optimal parameters affecting the cutting force improvement rate were obtained by orthogonal tests. The results showed that the maximum improvement of cutting force, surface roughness and subsurface crack fracture depth by longitudinal torsional ultrasonic vibrations were 82.59%, 22.78% and 30.75%, respectively. A longitudinal torsional ultrasonic vibrations cutting force prediction model containing the parameters of tool, material properties and ultrasound was established by the removal characteristics of SiC ceramic material, ultrasonic grinding principle and brittle fracture theory. And the predicted results were in good agreement with the experimental results, and the maximum error was less than 15%. The optimum process parameters for cutting force reduction were a spindle speed of 22,000 rpm, a feed rate of 600 mm/min and a depth of cut of 0.011 mm.

Mathematical Modeling and Experimental Study of Cutting Force for Cutting Hard and Brittle Materials in Fixed Abrasive Trepanning Drill.

Hard and brittle materials have excellent physical and mechanical performance, which are widely applied in the fields of microelectronics and optoelectronics. However, deep-hole machining of hard and brittle materials is very difficult and inefficient due to the high hardness and brittleness of these materials. To improve the quality and efficiency of deep-hole machining of hard and brittle materials, according to the brittle crack fracture removal mechanism of hard and brittle materials and the cutting model of the trepanning cutter, an analytical cutting force prediction model of hard and brittle materials processed using a trepanning cutter is established. This experimental study of K9 optical glass machining shows that as the feeding rate increase, the cutting force increase, and as the spindle speed increase, the cutting force decrease. By comparing and verifying the theoretical and experimental values, the average errors of axial force and torque are 5.0% and 6.7%, respectively, and the maximum error is 14.9%. This paper analyzes the reasons for the errors. The results indicate that the cutting force theoretical model can be used to predict the axial force and torque of machining hard and brittle materials under the same conditions, which provides a theory for optimizing machining process parameters.

Impact effect-based dynamics force prediction model of high-speed dry milling UD-CFRP considering size effect

The impact and size effect are generally neglected in force prediction model of cutting Carbon Fiber Reinforced Polymer (CFRP). Since carbon fibers are frequently cut in and out, the main cutting mechanisms of CFRP are mixed by impact, shear and other mechanisms, which are difficult to be clarified. Thus, we establish an impact effect-based cutting force prediction model of CFRP considering size effect. In the model, cutting regions are effected by chipping, pressing, impacting and bouncing, respectively. The cutting force is calculated based on the representative volume element (RVE) and minimum potential energy principle (MPEP). After chip breakage, the free-damped vibration force prediction model is established. The dynamics force prediction model is proofed with high prediction accuracy. Besides, the influences of cutting parameters on impact, cutting and vibration are analyzed by nonlinear regression analysis. It demonstrates that the impact and size effect are of great significance in revealing the cutting mechanisms of CFRP.