Ti-6Al-4V is widely used in the aerospace, automobile, and biomedical fields, but is a difficult-to-machine material. Electrical discharge machining (EDM) is regarded as one of the most effective approaches to machining Ti-6Al-4V alloy, since it is a non-contact electro-thermal machining method, and it is independent from the mechanical properties of the processed material. This paper aims to combine grey relational analysis and Taguchi methods to solve the problem of EDM parameters optimization. From the viewpoint of health and environment, tap water as working fluid has good working environment, since it does not release harmful gas. The process parameters include discharge current, gap voltage, lifting height, negative polarity and pulse duty factor. The electrode wear ratio (EWR), material removal rate (MRR) and surface roughness (SR) as objective parameters are chosen to evaluate the whole machining effects. Experiments were carried out based on Taguchi L 9 orthogonal array and grey relational analysis, and then verified the results through a confirmation experiment. Compared the machining parameters A 1 B 1 C 3 D 2 with A 1 B 2 C 2 D 2 , MRR increased from 1.28 mm 3 /min to 2.38 mm 3 /min, EWR decreased from 0.14 to 0.10 mm 3 /min and SR decreased from Ra 2.37 μm to Ra 1.93 μm. The process parameters sequenced in order of relative importance are: the ratio of pulse width to pulse interval, discharge current, lifting height and gap voltage. The results showed that using tap water machining Ti-6Al-4V material can obtain high MRR, decrease the machining cost and have no harmful to the operators and the environment.
Ceramic particle-reinforced metal matrix composites (PRMMCs) have been widely applied in modern industry with excellent mechanical characteristics. Meanwhile, the addition of high hardness reinforcements also produces a significant challenge for precision machining of PRMMCs and leads to poor machinability and higher time consumed in the subsequent surface treatment process. To investigate the machining mechanism of PRMMCs, a 2D mesoscopic-based finite element (FE) model reinforced with randomly distributed polygon particles was developed. Elastoplastic and failure behavior of aluminum alloy, high hard-brittle and fracture characteristics of reinforcements and particle-matrix-tool interactions were considered comprehensively. Systematic cutting experiments show that the proposed FE model accurately predicts deformation mechanism and surface quality of PRMMCs. Particle fracture and debonding are mainly determined by the stress distribution and strain value. Serrated chips primarily are formed along shear plane and accompanied with the generation and propagation of microcracks. In addition, cracks around particles are easier to propagate to chip root and promote the formation of segmented chips. Cutting depth significantly affects the surface quality, subsurface damage, and cutting force. Moreover, a proper cutting speed is beneficial to improve efficiency and machinability of PRMMCs.
Keywords Ceramic particle • Metal matrix composites • Precision machining • Deformation mechanism • Finite element analysis
AbbreviationsYield stress (MPa) t Tensile stress (MPa) cCompressive stress (MPa) 0
Abstract. Elliptical vibration cutting (EVC), as a precision machining
technology, is used in many applications. In precision machining, control
accuracy plays an essential role in improving the machinability of
difficult-to-machine materials. To improve the control accuracy, dynamic
and static characteristics of the system need to be tuned to obtain the
optimal parameters. In this paper, we use a glowworm algorithm with an improved adaptive step size to tune the parameters of a robust adaptive fuzzy controller. We then obtain the optimal controller parameters through
simulation. The optimal solution of the controller parameters is then
applied to a 3D EVC system model for simulation and closed-loop testing
experiments. The results indicate that a good agreement between the ideal
curve and the tracking signal curve verifies the optimality of the
controller parameters. Finally, under certain cutting conditions, the
workpieces of three different materials are cut with two different cutting
methods. The study revealed that the surface roughness value is reduced by
20 %–32 %, which further verifies the effectiveness of the optimal
controller's parameters.
In micromachining, the Quasi-intermittent Vibration-assisted Swing Cutting technology alleviates the residual height problem of vibration-assisted machining, and inherits the intermittent machining characteristics of Elliptical Vibration-assisted Cutting (EVC). The minimum chip thickness has a significant impact on cutting forces, tool wear, and process stability when working with difficult-to-machine materials. This study thoroughly examines the impact of cutting parameters and tool parameters on the quality of the workpiece during machining in order to better understand the time-varying characteristics of the QVASC machining process and the size effect on micro cutting of SiC crystal. This paper created the minimum chip thickness prediction model suited for QVASC machining process. The effects of variables like cutting speed and tool inclination on the minimum chip thickness were discussed as well as the scribing tests that were conducted on such challenging materials as SiC. The research findings show that: during the machining process, the critical undeformed chip thickness of silicon carbide decreases continuously as the cutting velocity sequentially increases (1.51 mm/min, 1.88 mm/min, 2.26 mm/min); under the down inclination angle (0–10°), the critical undeformed chip thickness also continuously decreases. Some conclusion can be drawn that cutting at too fast a velocity and reduces the thickness of the instantaneous undeformed chip, which is not conducive to ductile removal.
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