Cutting Force Generation Research Articles

A New Cutting Mechanics Model for Improved Shear Angle Prediction in Orthogonal Cutting Process

Abstract In metal cutting processes, accurately determining the shear angle is essential, as it governs chip formation and cutting force generation. Despite extensive research conducted on this topic, the accurate prediction of the shear angle remains a subject of ongoing investigation. This paper presents a new analytical model for predicting the shear angle, taking into account the direction difference between the shear stress at the boundary of the primary shear zone and the maximum shear stress. The constitutive property of the workpiece material with respect to the strain, strain rate, and temperature is considered in predicting the shear angle. The results show that the solution for the shear angle is not unique for a given rake and friction angle, and is highly dependent on the flow stress response of the workpiece material. Orthogonal cutting experiments were conducted on steel and aluminum alloys under various uncut chip thicknesses, cutting speeds, and tool rake angles to characterize the chip thickness and shear angle. Based on a comparison between model predictions, experimental results, and data from the literature for various workpiece materials and cutting conditions, it is shown that the proposed model results in an improved prediction for shear angle by considering the stress transformation within the primary shear zone.

Some Studies on Cutting Force Generation in Machining of Mg-Ca1.0 Alloy

Magnesium alloy has qualities that are similar to those of bone, it is now commonly acknowledged as a biodegradable material. Mg-Ca1.0 is a recently developed biodegradable substance that doesn’t cause any harmful substances to be produced in the body. It is frequently utilized in fixation screws and supporting plates for bone implants. To far, very little research has been published on the machining of biodegradable Magnesium Calcium alloy. This investigation’s goal is to evaluate the cutting forces produced during a face milling procedure. The experimental work used a one-factor-at-a-time strategy to assess how process factors affect performance attributes. The current study uses a CVD diamond-coated insert to examine how the depth of cut, feed rate, and cutting speed affect the radial (Fx), tangential (Fy), and axial (Fz) cutting forces during face milling in a dry machining environment.

A comparative study on milling-induced surface roughness during milling of jute–epoxy and carbon–epoxy composites and optimization for better surface finish through Taguchi and RSM techniques

During the present experimentation, milling machining was performed on two different composites, namely carbon fiber-reinforced polymer composite and jute fiber-reinforced polymer composite, using a computer numerical control vertical machining center. The selected machining parameters were spindle speed (S), feed rate (FR), depth of cut (DOC), and flute number or cutting edge number (FN). The output parameter is the machined surface roughness (Ra). Analysis of variance was used to predict the percentage influence of each parameter on machining quality. The parameter feed rate exhibited a higher influence on the machined surface roughness, followed by spindle speed, flute number, and depth of cut in sequence. Similarly, while milling the carbon fiber composite, the feed rate had the highest influence, followed by the parameter flute number. As for the surface roughness, the feed rate had a greater effect, followed by the spindle speed. Under the same machining conditions, it was observed that the sequence of parameters influencing the jute composite and carbon composite changed in the case of cutting force generation, but the sequence of parameters was the same for both cases in terms of roughness. The outcome of the work confirmed that to achieve a smaller value of roughness in the milling of jute–epoxy composite, the optimum combination should be S = 3000 rpm, FR = 800 mm/min, DOC = 0.25 mm, and FN = 6. Similarly, to achieve the minimum surface roughness value in the milling of carbon–epoxy composite, the optimum combination of parameters should be S = 600 rpm, FR = 100 mm/min, DOC = 0.25, and FN = 6. The average roughness values obtained during the milling of jute–epoxy composite and carbon–epoxy composites are 6.685 and 3.08 μm, respectively. In this present work, it is proved that the optimum combination of parameters to get the minimum surface roughness and the amount of surface roughness produced during milling are highly influenced by the type of reinforced material. The graphs are prepared for the entire range of input parameters to identify the intermediate Ra value at any input value without the use of software.

Nonlinearity and periodicity in machining carbon fiber reinforced polymer

In the machining of carbon fiber reinforced polymer (CFRP) materials, the relationship between the cutting forces and uncut chip thickness can be linear or nonlinear, depending on the fiber orientation ranges. In addition, the variation of cutting forces in two consecutive cuts is periodic at a certain fiber orientation of 120 deg and the uncut chip thicknesses of 30μm60μm. It is suggested that the material removal rate changes even for the same uncut chip thickness in consecutive cuts, such as milling and drilling. Through orthogonal experiments for unidirectional CFRP at various fiber orientations and uncut chip thicknesses, this study aims to analyze the observed nonlinearity and periodicity of cutting forces which occur at certain fiber orientation and uncut chip thickness. It is found that both the nonlinearity and periodicity are primarily dependent on the machined surface damage left by the previous cut. This damage state varies with the uncut chip thickness and is influenced by the interaction between the material deformations at the tool rake face and flank face. Thus, the reported findings in the chip formation and cutting force generation reported by this study can guide the tooling and process planning to enhance the machining performance for CFRP.

The Effects of Nanoparticle Reinforcement on the Micromilling Process of A356/Al2O3 Nanocomposites

Improving scientific knowledge around the manufacturing of nanocomposites is key since their performance spreads across many applications, including those in meso/micro products. Powder metallurgy is a reliable process for producing these materials, but usually, machining postprocessing is required to achieve tight tolerances and quality requirements. When processing these materials, cutting force evolution determines the ability to control the microcutting operation toward the successful surface and part quality generation. This paper investigates cutting force and part quality generation during the micromilling of A356/Al2O3 aluminum nanocomposites produced via powder metallurgy. A set of micromilling experiments were carried out under various process parameters on nanocomposites with different nano-Al2O3 reinforcements (0–12.5 vol.%). The material’s ductility, internal porosity, and lack of interparticle bonding cause the cutting force generation to be irregular when nanoparticle reinforcements were absent or small. Reinforcement ratios higher than 2.5 vol.% strongly affect the cutting process by regularizing the milling force generation but lead to a proportionally increasing average force magnitudes. Hardening due to nano-reinforcement positively affects cutting mechanisms by reducing the plowing tendency of the cutting process, resulting in better surface quality. Therefore, a threshold on the nano-Al2O3 particles’ volumetric loadings enables an optimal design of these composite materials to support their micromachinability.

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.

Multi response hybrid optimization of sustainable high-speed end milling on 89.7Ti-6Al-4V

This research investigates machining parameters involving the high-speed CNC end milling to improve the material removal rate, to reduce the surface roughness and cutting force. A large amount of cutting forces is generated while machining the high strength and high hardness materials. In order to reduce the cutting force generation during the machining of difficult to machine materials with large diameter cutting tools, improve the material removal rate and reduce the surface roughness, the present study has been framed. For these objectives, sixteen experimental combinations based on the design of the experiment (L16 Orthogonal array) constructed in Minitab were performed by varying the parameters. The multi-response optimization, Taguchi-based grey relation analysis, was used to perform the various responses based on suitable weightage. It shows optimal machining parameters such as spindle speed of 800 rpm, depth cut of 0.5 mm, and 0.4 mm/rev of feed rate for enhancing the quality, productivity, and machinability. The contribution of each parameter toward response variables was found by using an analysis of variance.

3-D finite element modeling of sequential oblique cutting of unidirectional carbon fiber reinforced polymer

Machining of carbon fiber reinforced polymers (CFRP) such as milling and drilling is in the form of oblique and sequential cutting at the tool edge. The material removal mechanism and surface quality are highly dependent on the fiber orientation and oblique angle. This paper presents a 3-D finite element model of oblique cutting of unidirectional CFRP. The effects of the fiber orientation and oblique angle on the chip formation mechanism, cutting force generation, and surface damage are determined. To investigate the effect of sequential cutting, a second cut on the machined surface material is simulated. The effects of sequential cutting and random fiber distribution on the cutting forces and surface damage are analyzed. Oblique cutting experiments were conducted to evaluate the FE model by comparing the simulated and experimental cutting forces and chip morphology.

Mechanistic force modeling in drilling of SiCp/Al matrix composites considering a comprehensive abrasive particle model

SiCp/Al composite is one of the key light metal matrix composites widely used in aerospace and electronics industries due to its excellent material properties. Understanding the mechanism of abrasive particle action and the cutting force generation is crucial for achieving desired hole quality and machining accuracy. However, there is no research on cutting force modeling in drilling of SiCp/Al composites. This paper proposes an energy-based mechanistic modeling method to predict the cutting force in drilling of SiCp/Al composites for the first time. A new comprehensive abrasive particle model considering different particle action mechanisms, such as cracking, debonding, and squeezing, is presented to depict the energy consumed by abrasive particle action. Experiments with a wide range of particle volume fractions and feed rates, and different particle sizes were carried out to validate the accuracy of the proposed model. Results show that the proposed model can accurately predict the cutting force with an average error of 6.55% in drilling of SiCp/Al composites. Furthermore, the energy consumed on abrasive particle action is estimated to be 8.2–13.6% of total cutting energy. The proposed model on SiCp/Al composites can be extended to other particle reinforced metal matrix composites.

Mechanistic force modelling in drilling of AFRP composite considering the chisel edge extrusion

Aramid fiber–reinforced plastic (AFRP) composites have been widely used in automotive, aerospace, and defense industries. The common AFRP drilling process tends to cause damage to the composite structures which subsequently affects their fatigue lives and in-service performance. Understanding the mechanism of cutting force generation is crucial in controlling the cutting process for achieving desired hole quality and machining accuracy. This study proposes a novel mechanistic model considering both the cutting action and the extrusion action of the chisel edge. For the first time, the extrusion force generated by the chisel edge has been considered as a rigid wedge penetrating into an elastic half space based on the Hertz contact theory. The total thrust force in AFRP drilling is divided into three components: (i) thrust force generated by the cutting lips, (ii) thrust force generated by the chisel edge cutting action, and (iii) extrusion force generated by the chisel edge extrusion action. The proposed model was then validated by experiments and data was compared with the case where extrusion was not considered. The results show that our novel mechanistic model can provide a more accurate thrust force prediction. The average error of our model was 2.54% against the experimental data, whereas the error seen in conventional model without accounting extrusion was 8.22%. This suggests that the chisel edge extrusion plays a significant part in the drilling of AFRP and hence confirms the necessity of considering extrusion in establishing the associated mechanistic model.

On stability analysis for micro milling of P-20 steel: Enhancement through application of TiAlN coated WC tool

The utilization of micro milling process is increasing in micro fabrication sector owing to its applicability for various types of engineering materials and capable for manufacturing of complex shapes. However, less stiffness of the small diameter micro end mill becomes the major hurdle during machining of hard materials such as P-20 steel. In addition, during high speed machining temperature at the machining zone raises which spurs to tool wear and higher cutting force generation. Therefore, application of lower coefficient of friction and abrasion resistant coating material over un-coated WC tool can be a viable solution for reduction of cutting force and enhancement of stability limit. In this paper, an analytical model for prediction of dynamic stability has been instituted and influence of TiAlN coated tool has been compared with un-coated WC tool. The proposed model has been justified by analysing FFT spectrum of the vibration data for various combinations of spindle speed and depth of cut. Finally, it is observed that Influence of TiAlN coated tool proved its significance by uplifting the stability limit by 20.71% as compared to uncoated WC tool.

Analytical Model of Cutting Force in Micromilling of Particle-Reinforced Metal Matrix Composites Considering Interface Failure

During the micromachining processes of particle-reinforced metal matrix composites (PMMCs), matrix-particle interface failure plays an important role in the cutting mechanism. This paper presents a novel analytical model to predict the cutting forces in micromilling of this material considering particle debonding caused by interface failure. The particle debonding is observed not only in the processed surface but also in the chip. A new algorithm is proposed to estimate the particles debonding force caused by interface failure with the aid of Nardin–Schultz model. Then, several aspects of the cutting force generation mechanism are considered in this paper, including particles debonding force in the shear zone and build-up region, particles cracking force in the build-up region, shearing and ploughing forces of metal matrix, and varying sliding friction coefficients due to the reinforced particles in the chip-tool interface. The micro-slot milling experiments are carried out on a self-made three-axis high-precision machine tool, and the comparison between the predicted cutting forces and measured values shows that the proposed model can provide accurate prediction. Finally, the effects of interface failure, reinforced particles, and tool edge radius on cutting forces are investigated by the proposed model and some conclusions are given as follows: the particles debonding force caused by interface failure is significant and takes averagely about 23% of the cutting forces under the given cutting conditions; reinforced particles and edge radius can greatly affect the micromilling process of PMMCs.

Online monitoring of delamination mechanisms in drilling of Mwcnts reinforced Gfrp nanocomposites by acoustic emission

Among various unconventional Non-Destructive testing (NDT) methods, Acoustic Emission (AE) referred as the most suitable NDT method for detection and characterization of material defects during machining on nanocomposites. AE technique can also be applied for dynamic machining operation called drilling for better understanding of different drilling events from start to the end of the process. The present paper mainly focuses on on-line monitoring of drilling-induced delamination of multi-walled carbon nanotubes (MWCNTs) reinforced glass fiber reinforced plastic (GFRP) nanocomposites laminates through AE technique. A 4 mm thickness of 0.3wt% MWCNTs reinforced GFRP laminates were fabricated by 16 layers of glass fabric cloth with modified epoxy resins for drilling experimentation and trails were conducted on these laminates with uncoated, TiCN and TiAlN coated 6mm diameter carbide twist drills. Design of experiments were planned based on Taguchi L9 orthogonal array and speed, feed were considered as input process parameters. The AE output response parameters such as Root Mean Square (RMS) voltage, AE rise, energy, peak amplitude, hits, AE count, duration and counts to peak were helped to analyse the delamination mechanisms as well as cutting force generation during drilling on nanocomposites. From the experimental results, it was concluded that low feed rate (0.02 mm/rev) and high spindle speed (1500 RPM) with TiCN coated carbide drill gives the reduced delamination factor and thrust force values. Additionally, AE parameters were also highly influenced by the cutting parameters as well as delamination mechanisms. Out of all AE parameters AE-RMS voltage designated as the important parameter to analyse the delamination factor and increased RMS voltage observed with higher feed rate and cutting forces. The other AE parameters were very closely associated to the RMS voltage. Finally, it was confirmed that AE is most suitable NDT technique for on-line monitoring of drilling induced delamination for production of defect free holes on GFRP-MWCNTs composites laminates.

An improved cutting force model for micro milling considering machining dynamics

Micro milling, as a versatile micro machining process, is kinematically similar to conventional milling; however, it is significantly different from conventional milling with respect to chip formation mechanisms and uncut chip thickness modelling, due to the comparable size of the edge radius to the chip thickness, and the small per-tooth feeding. Considering tool runout and dynamic displacement between the tool and the workpiece, the contour of the workpiece left by previous tool paths is typically in a wavy form, and the wavy surface provides a feedback mechanism to cutting force generation because the instantaneous uncut chip thickness changes with both the vibration during the current tool path and the surface left by the previous tool paths. In this study, a more accurate uncut chip thickness model was established including the precise trochoidal trajectory of the cutting edge, tool runout and dynamic modulation caused by the machine tool system vibration. The dynamic regenerative effect is taken into account by considering the influence of all the previous cutting trajectories using numerical iteration; thus, the multiple time delays (MTD) are considered in this model. It is found that transient separation of the tool-workpiece occurring at a low feed per tooth, caused by MTD and the existing cutting force models, is no longer applicable when transient tool-workpiece separation occurs. Based on the proposed uncut chip thickness model, an improved cutting force model of micro milling is developed by full consideration of the ploughing effect and elastic recovery of the workpiece material. The proposed cutting force model is verified by micro end milling experiments, and the results show that the proposed model is capable of producing more accurate cutting force prediction than other existing models, particularly at small feed per tooth.

Investigations on Surface Milling of Hardened AISI 4140 Steel with Pulse Jet MQL Applicator

In this article, an experimental investigation was performed in milling hardened AISI 4140 steel of hardness 40 HRC. The machining was performed in both dry and minimal quantity lubricant (MQL) conditions, as part of neat machining, to make a strong comparison of the undertaken machining environments. The MQL was impinged int the form of pulse jet, by using the specially developed pulse-jet-attachment, to ensure that the cutting fluid can be applied in different timed pulses and quantities at critical zones. The tool wear, cutting force and surface roughness were taken as the quality responses while cutting speed, table feed rate and flow rate of the pulse were considered as influential factors. The depth of cut was kept constant at 1.50 mm because of its less significant effects and the straight oil was adopted as cutting fluid in pulse-jet-MQL. The effects of different factors, on the quality responses, are analyzed using ANOVA. It is observed that MQL applicator system exhibits overall better performance when compared to dry milling by reducing surface roughness, cutting force and prolonging tool life but a flow rate of 150 ml/h has tremendous effects on the responses. This investigation and afterward results are expected to aid the industrial practitioner and researcher to adopt the pulse-MQL in high speed milling to prolong tool life, reduce tool wear, diminish cutting force generation and promote better surface finish.

Experimental and finite element simulation based investigations on micro-milling Ti-6Al-4V titanium alloy: Effects of cBN coating on tool wear

Micro-milling process is a direct and flexible fabrication method in producing functional three dimensional micro-products. The advance of micro-milling process ultimately depends on the development of micro cutting tools since it is a tool-based process. Therefore, in this study an attempt to improve the performance of carbide micro-end mills by applying cubic boron nitride (cBN) coating was carried out. Experiments and finite element method (FEM) based simulations were used to study the effect of cBN coated tool in micro-machining of Ti-6Al-4V titanium alloy. The experiments were conducted to compare the performance of cBN coated and uncoated micro-end mills in terms of surface roughness, burr formation and tool wear. FE simulations were employed to investigate chip formation process in micro-milling to reveal the effects of cBN coated micro-end mills with increased edge radius in terms of cutting force generation, tool temperature and contact pressure, sliding velocity and hence tool wear rate. The simulation results were further utilized for estimating tool life using a sliding wear rate model and compared with experiments. This study clearly showed that the cBN coated carbide tool outperformed the uncoated carbide tool in generation of tool wear and cutting temperature.