Analysis of rotational speed variations on cutting force coefficients in high-speed ball end milling

In high-speed ball end milling, cutting forces influence machinability, dimensional accuracy, tool deflection, tool failure, machine tool chatter and vibration, etc. Thus, an accurate prediction of cutting forces prior to actual machining is very much essential for a good insight into the process to produce good quality machined parts. In ball end milling, the cutting forces are proportional to the chip cross-sectional area and constant of proportionalities are referred as cutting force coefficients and they depend on many factors, like cutter geometry, cutting conditions, tool material and workpiece material properties. However, determining these specific cutting force coefficients in ball end milling process is not at all straightforward; rather it is fairly complex. Machining with higher cutting speed affects the chip formation mechanisms and finally causes a significant change in the cutting force coefficients. In the present study, the effect of rotational speeds has been investigated on the cutting force coefficients. A series of experiments have been performed at higher rotational speed. It has been found that the cutting force coefficients are influenced by rotational speed significantly. The results are also verified using experiments

Modelling and application of response surface optimization to optimize cutting parameters for minimizing cutting forces and surface roughness in high-speed, ball-end milling of Al2014-T6

In this research study, empirical mathematical models for cutting forces and surface roughness have been developed to investigate the effect of axial depth of cut, feed, radial depth of cut and cutting speed in high-speed ball-end milling of Al2014-T6. Ball-end milling experiments have been planned using central composite design based on response surface methodology. The mathematical models have been established and tested for adequacy. A full quadratic model has been adopted for modelling. It has been found that axial depth of cut is the most dominant cutting parameter for the tangential and axial cutting forces, accounting for 49.38 and 47.12% contributions, respectively. Radial depth of cut is the most dominant parameter for radial force and contributes 69.94% for it. Results also revealed that force components decrease with increase in cutting speed. There is very small variation in cutting force components in the cutting speed range of 75–150 m/min at lower values of axial and radial depth of cut. Surface roughness is effected by cutting speed largely followed by feed. Multi-objective optimization has been performed using composite desirability to optimize the cutting parameters for minimum surface roughness and cutting forces simultaneously. Confirmation tests have been conducted using optimal set of cutting parameters. The results of confirmation tests are very close to the predicted results

Chatter and dynamic cutting force prediction in high-speed ball end milling

Machine tool chatter is a serious problem which deteriorates surface quality of machined parts and increases tool wear, noise, and even causes tool failure. In the present paper, machine tool chatter has been studied and a stability lobe diagram (SLD) has been developed for a two degrees of freedom system to identify stable and unstable zones using zeroth order approximation method. A dynamic cutting force model has been modeled in tangential and radial directions using regenerative uncut chip thickness. Uncut chip thickness has been modeled using trochoidal path traced by the cutting edge of the tool. Dynamic cutting force coefficients have been determined based on the average force method. Several experiments have been performed at different feed rates and axial depths of cut to determine the dynamic cutting force coefficients and have been used for predicting SLD. Several other experiments have been performed to validate the feasibility and effectiveness of the developed SLD. It is found that the proposed method is quite efficient in predicting the SLD. The cutting forces in stable and unstable cutting zone are in well agreement with the experimental cutting forces

Optimization of surface roughness in ball-end milling using teaching-learning-based optimization and response surface methodology

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth (fz) = 0.06 mm, axial depth of cut (Ap) = 0.74 mm, cutting speed (Vc) = 145.8 m/min, and radial depth of cut (Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 µm and has been validated by confirmation experiments

Sustainable mechanical properties evaluation for graphene reinforced Epoxy/Kevlar fiber using MD simulations

AbstractUniform filler dispersion and fillers, such as nanomaterials, with distinct but superior qualities compared to the typical matrix component can be used to create high performance composites. The current study used molecular dynamics simulations to investigate the effect of graphene reinforcement on the mechanical properties of Kevlar/Diglycidyl Ether of Bisphenol-A (DGEBA). Diethyl toluene diamine (DETDA) serves as the curing agent for DGEBA, and graphene is randomly reinforced at 1, 1.5, and 2 weight percent in the DGEBA/DETDA epoxy matrix. A layered (epoxy-Kevlar-epoxy, EKE) is constructed by maintaining Kevlar fibre in horizontal position in between the graphene-reinforced DGEBA/DETDA as illustrated in the figure. Energy minimization, geometry optimization, and dynamics are used to further stabilise the constructed structure. The method of constant strain is used to determine mechanical parameters. The MD simulation revealed that mechanical properties increased with increasing graphene content, with 21.42%, 36.47%, and 42.49% for 1, 1.5, and 2 weight percentages of graphene, respectively, when compared to pure EKE. The increasing trends in elastic moduli corroborate the experimental findings. The ultimate strength increases as graphene content increases