Machinability analysis in wire-EDM of cryogenically treated Ti6Al4V alloy and multi-objective optimization using MOAVOA and MOGA

Abstract The performance of Ti6Al4V alloy in engineering and biomedical applications is often limited by its poor wear and abrasion resistance, especially when compared to conventional materials such as CoCr-based alloys and stainless steels. In biomedical implants, such as hip and knee joints, this limitation results in a typical service life of only 10–15 years. Cryogenic treatment has emerged as a potential method to enhance the wear resistance of Ti6Al4V. However, machining this alloy using conventional methods remains challenging due to its low thermal conductivity, high cutting forces, and rapid tool wear, which lead to excessive heat generation and potential tool failure. Furthermore, its high electrical resistivity reduces its machinability using electrical discharge machining. This study investigates the machinability of cryogenically treated and untreated Ti6Al4V alloys using wire electrical discharge machining. A rotary central composite design, based on response surface methodology, was employed to develop quadratic models for material removal rate (MRR) and surface roughness (Ra), with discharge current (I), wire speed, and duty cycle (DC) as input parameters. Multi-objective optimization was carried out using a genetic algorithm (MOGA) and the African vultures optimization algorithm (MOAVOA) to simultaneously maximize MRR and minimize Ra. Results indicate that discharge current had the highest influence on MRR of cryogenically treated samples (MRR_CT) with a percentage contribution (PC) of 58.04%, followed by duty cycle at 20.28%. For surface roughness of cryogenically treated samples (Ra_CT), DC and I were the dominant linear terms with PCs of 11.91% and 7.83%, respectively, while current showed the highest influence in the square term with a PC of 34.44%. Optimization results demonstrated that MOAVOA outperformed MOGA in convergence speed and solution diversity, yielding a broader and more effective set of Pareto-optimal solutions

Analysis of cutting force coefficients in high-speed ball end milling at varying rotational speeds

In high-speed ball end milling, cutting forces influence machinability, dimensional accuracy, tool failure, tool deflection, machine tool chatter, vibration, etc. Thus, an accurate prediction of cutting forces before actual machining is essential for a good insight into the process to produce good quality machined parts. In this article, an attempt has been made to determine specific cutting force coefficients in ball end milling based on a linear mechanistic model at a higher range of rotational speeds. The force coefficients have been determined based on average cutting force. Cutting force in one revolution of the cutter was recorded to avoid the cutter run-out condition (radial). Milling experiments have been conducted on aluminum alloy of grade Al2014-T6 at different spindle speeds and feeds. Thus, the dependence of specific cutting force coefficients on cutting speeds has been studied and analyzed. It is found that specific cutting force coefficients change with change in rotational speed while keeping other cutting parameters unchanged. Hence, simulated cutting forces at higher range of rotational speed might have considerable errors if specific cutting force coefficients evaluated at lower rotational speed are used. The specific cutting force coefficients obtained analytically have been validated through experiments

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

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. </jats:p

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