Cutting Force Prediction Model Research Articles

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.

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.

Offline Feed-Rate Scheduling Method for Ti–Al Alloy Blade Finishing Based on a Local Stiffness Estimation Model

In the aerospace field, Ti–Al alloy thin-walled parts, such as blades, generally undergo a large amount of material removal and have a low processing efficiency. Scheduling the feed rate during machining can significantly improve machining efficiency. However, existing feed-rate scheduling methods rarely consider the influence of machining deformation factors and cannot be applied in the finishing stages of thin-walled parts. This study proposes an offline feed-rate scheduling method based on a local stiffness estimation model that can be used to reduce machining errors and improve efficiency in the finishing stage of thin-walled parts. In the proposed method, a predictive model that can rapidly calculate the local stiffness at each cutter location point and a cutting-force prediction model that considers the effect of cutting angle are established. Based on the above model, an offline feed-rate scheduling method that considers machining deformation error constraints is introduced. Finally, an experiment is performed by taking the finishing of actual blade parts as an example. The experimental results demonstrate that the proposed feed-rate scheduling method can improve the machining efficiency of parts while ensuring machining accuracy. The proposed method can also be conveniently applied to feed-rate scheduling in the finishing stage of other thin-walled parts without being limited by machine tools.

Model-based feed rate optimization for cycle time reduction in milling

Feed rate is an important determinant of the cutting force and cycle time. Selection of an appropriate feed rate can increase the processing quality and production efficiency. To determine an optimized feed rate, many methods have been developed to maintain a constant cutting force by applying a real-time feedback system constructed with data such as the tool condition, temperature, and cutting load of the spindle. However, these methods have low reliability because of difficulty in accurately measuring the spindle load. Moreover, real-time changes in the feed rate alter the cycle time. This study optimized the feed rate to a value that maintains the cutting force based on a cutting force prediction model and an algorithm to extract the cutting conditions from each line of the G-code. The feed rate optimization was applied to a flat end mill and ball end mill. An interpreter for the G-code was configured to calculate the tool center point and cutting condition. A real-time spindle power measurement system was constructed to measure the cutting force in each line of the G-code by identifying the parameters of the cutting force prediction model. The cutting force criterion was selected as the highest value among the predicted cutting forces. The optimized feed rate that maintains the cutting force was extracted based on the cutting conditions in other lines and the cutting force prediction model. The extracted feed rate was applied to the G-code by modifying only the feed rate command. Experimental results obtained with a commercial machining tool show that the proposed feed rate optimization significantly reduces the overall cycle time and effectively maintains the cutting force.

Deep learning-based instantaneous cutting force modeling of three-axis CNC milling

Accurate cutting force modeling is the basis for good planning and optimization of the process and parameter of Computerized Numerical Control (CNC) milling. Traditional cutting force prediction models suffer from problems of oversimplifications on the model's input and framework, making it difficult to predict the cutting force accurately in the complex machining process. This paper proposes a novel deep learning-based instantaneous cutting force prediction model with superior modeling precision. According to the mechanism of cutting force generation, the comprehensive geometric and processing information in the machining process is creatively expressed as multi-channel digital images named Image of Comprehensive Geometric Processing Information (ICGPI). A deep learning network called Milling Force Convolutional Neural Network (MF-CNN) is then designed that takes the ICGPI as the input and the three-dimensional instantaneous cutting forces as the output. To address the challenging problem of interpretation of the deep learning network, the MF-CNN is analyzed toward the theoretical mechanistic cutting force model, validating that the proposed method can fully cover all the geometric information and mathematical operations involved in the theoretical model. Finally, some physical cutting experiments are conducted to validate the effectiveness and superiority of the proposed method, showing that our MF-CNN can predict the instantaneous cutting force with outstanding accuracy and is much superior to the three most popular benchmarks.

Neural Network-based Model Parameter Estimation for End Milling of Carbon Fiber Reinforced Polymer (CFRP) Composites

A predictive cutting force model is critical during the end milling of Carbon Fiber Reinforced Polymer (CFRP) composites to achieve optimum fiber delamination, tool wear, and surface accuracy. The mechanistic force model showed robust prediction abilities; however, cutting constant estimation is challenging due to the involvement of multiple variables resulting in higher-order mathematical formulations. This paper presents augmenting the Feed Forward Neural Network (FFNN) with a mechanistic force model to approximate independent relationships of the chip load and edge contact length with shearing and rubbing constants. The prediction abilities of the proposed approach are compared with the traditional Fourier fitting model and experimentally measured values over diverse cutting conditions. It has been demonstrated that the proposed approach can accurately estimate cutting forces during the end milling of CFRPs.

Determination of the optimal milling feed direction for unidirectional CFRPs using a predictive cutting-force model

Determination of the optimal milling feed direction for unidirectional CFRPs using a predictive cutting-force model

A 2.5D C/C composite cutting force prediction model based on brittle fracture mechanism

A 2.5D C/C composite cutting force prediction model based on brittle fracture mechanism

Tool Cutting Force Prediction Model Based on ALO-ELM Algorithm.

Aiming at the problems of low learning efficiency, slow convergence speed, and low prediction accuracy of traditional data-driven model applied to tool cutting force prediction, a prediction method of tool cutting force based on ant lion optimizer (ALO) extreme learning machine (ELM) is proposed. ALO was used to improve the weights of input layer and hidden layer of ELM, so as to improve its prediction accuracy. The tool cutting force prediction models were established by using ALO-ELM, ELM, BP (backpropagation) neural network, and support vector machine, respectively. The experimental results show that the mean square error, mean absolute percentage error, and mean absolute error of ALO-ELM prediction model are 0.9911%, 0.0011%, and 1.0863%, respectively, which are far lower than the other three prediction models. ALO-ELM prediction model has stronger prediction accuracy and generalization ability, which can be effectively applied to the prediction of cutting force.

A new dynamic boring force calculation method using the analytical model of time-varying toolpath and chip fracture

The accurate prediction of dynamic cutting force has great significance for the quality control of the boring process for deep-cavity thin-walled parts. However, most of the classic and improved cutting force models commonly neglect the mechanism analysis of dynamic cutting force, resulting in the dynamic prediction of cutting force being almost impossible. To resolve this problem, a novel analytical dynamic cutting force prediction model for the boring process was proposed to consider the time-varying toolpath and chip fracture in the machining process. The generation mechanism of dynamic cutting force was analyzed by cutting theory and mathematical formula. The theory of maximum shear stress and the law of conservation of energy were applied to derive the dynamic cutting force for an entire rotation cutting cycle. Then, the proposed dynamic cutting force model was validated using a designed boring experiment and a classical cutting force model. The compared results showed that the dynamic cutting force model based on the proposed method had good agreement with the experimental data. Meanwhile, the advantage of accurate prediction was also further demonstrated by comparing the classic cutting force model. This paper presents an analytical dynamic cutting force prediction model for the boring process considering the toolpath and chip breakage. The underlying mechanism of the dynamic change of the cutting force was studied, and data supporting the improved quality of boring deep-cavity thin-walled parts was provided.

Orthogonal Cutting Force Modeling Using the Nonequipartition Parallel Shear-Zone Distribution Model

This paper proposed a modified Oxley’s model using the nonequipartition parallel shear-zone theory. By introducing a strain and strain rate distribution model in the first shear band, the assumption that primary deformation zone was divided equally by the main shear plane was eliminated. Then the expressions of shear-zone thickness and resultant angle were derived according to Oxley’s cutting theory. Finally, a force prediction model for orthogonal cutting was established by analyzing the mechanical-thermal characteristics in the deformation zone. The thin-walled cylinder made of AISI 304 was used to carry out the orthogonal turning experiment. A variety of cutting force prediction models were used to compare with the experimental results. The calculation results from the proposed model show that the prediction accuracy of cutting force in both cutting and feed directions is improved.

Instantaneous cutting force model considering the material structural characteristics and dynamic variations in the entry and exit angles during end milling of the aluminum honeycomb core

The structural characteristics of the material and dynamic changes in the entry and exit angles are of great significance for the accurate prediction of the cutting force during milling of the aluminum honeycomb core. In this study, an instantaneous cutting force prediction model for the aluminum honeycomb core that comprehensively considers the porous structural characteristics of the material and dynamic changes in the entry and exit angles during the machining process was developed. A unified structural model for an aluminum honeycomb core with a regular hexagonal cell structure and excellent mechanical property was established. Based on the structural characteristics of the aluminum honeycomb core, three types of thin-walled edges were considered, and the dynamic changes in their entry and exit angles were investigated by considering the cutter radius, cutting width, honeycomb core wall thickness, and relative position between the cutter and thin-walled edge. An orthogonal-to-oblique cutting transformation approach that can effectively reflect the cutting characteristics of the material was adopted to calibrate the cutting force coefficients in which cutting experiments using different cutter geometries and parameters were conducted. The aluminum honeycomb core was fixed using green, high-efficiency cryogenic liquid nitrogen. To verify the reliability of the proposed model, a series of milling experiments were carried out and the experimental and simulation results were compared. It was found that the simulated cutting force is highly consistent with the experimental result. The proposed model can accurately predict the peak values of the cutting force in the x, y, and z directions, with an average prediction error of less than 10%.

An analytical cutting force model of quasi-intermittent vibration assisted swing cutting based on predictive machining theory

Quasi-intermittent vibration assisted swing (QVASC) method was aimed to improve the cutting machinability of difficult-to-machine materials. Inherited the intermittent cutting characteristics of the elliptical vibration cutting (EVC) technology, this method reduces the impact of residual height on the machined surface quality. But there is still a lack of in-depth research on cutting force modeling of QVASC. Based on the conventional cutting force model, a cutting force prediction model in QVASC processing, which considers the time-variable characteristics of QVASC, adopts the non-equidistant shear zone model and regards the workpiece material properties, tool geometry, and cutting conditions as the input data are proposed in this paper. Using the micro-element method, the proposed model decomposed the cutting edge to calculate the cutting force and the cutting force value by superimposing them. It finally predicts the chip flow angle. Experimental results show that when the cutting velocity increases, the average cutting force and the Z -direction increases by 20%, the average cutting force and the Y direction decreases by 17%. The average cutting force along X direction remains unchanged; the depth of cutting became larger. Besides, the average cutting force X decreased by 18%, Y decreased 21%, and Z increased by 13%. As the feed rate increases, the X direction is increased by 26%, and the Z direction is reduced by 22%, while the cutting force in the Y direction is unchanged. After comparing and verifying the measured value and the predicted value, the minimum error of the prediction model reaches 4.01%, which validates the force model of QVACS.

Composite light ropes model-based dynamics force prediction model of high speed dry milling UD-CF/PEEK considering size effect

As an emerging material, Carbon fiber reinforced polyetheretherketone (CF/PEEK) is widely used in aviation, automotive, sports products, medical equipment and other high-end manufacturing industries due to its high shock resistance, thermostability, and recyclability. However, its dry-machining is an unclear dynamic material removal process which limits the machining efficiency and surface quality. To this end, we first presented composite light ropes model in machining composites considering size effect. Based on composite light ropes model, cutting region is affected by bending, stretching, pressing, bouncing and delamination, as the cutting mechanisms of carbon fiber and matrix are different. The carbon fibers distribution in chip is first introduced into the model to confirm the fiber deformation at different position. Based on composite light ropes model and elastic-plastic theory, the cutting force prediction model is obtained. To complete the model, the free-damped vibration force prediction model is established in empty cutting stage. The dynamics force prediction model is proofed with high prediction accuracy. Besides, the influences of cutting parameters on cutting and vibration force are analyzed by nonlinear regression analysis. It demonstrated that size effect is of great importance in establishing cutting force prediction model and revealing cutting mechanisms of CF/PEEK.

Microstructured surface generation and cutting force prediction of pure titanium TA2

Titanium and its alloys have widespread applications in aerospace and medical fields due to their outstanding mechanical properties and corrosion resistance. However, very limited attention has been focused on machining the microstructure on their surface and the cutting force prediction during machining. Motivated by this, this study used the ultraprecision diamond surface texturing (UDST) process to generate microstructure on the commercially pure titanium TA2 and established a cutting force prediction model. Firstly, the two special characteristics of UDST are systematically investigated by the finite element simulation and the vibration trajectory analysis. And as a core component of the UDST process, a two-degree-of-freedom vibration generator is presented. Considering the periodical vibration motion, tool geometry, and material properties, a practicable cutting force model is next developed for predicting the dynamic cutting force. Finally, two types of unique microstructures are successfully fabricated on the TA2 surface. Experimental results show that the proposed cutting force model agrees well with the measured results, validating its effectiveness.