Metal-based microelectromechanical systems are widely used in applications such as micro-energy harvesters, micro-heat exchangers and micro-electromagnetic that require high strength and flexibility. In the fabrication of such systems, micro wire electrical discharge machining (MicroWEDM) is majorly used. This paper studies the effect of the MicroWEDM process parameters on the dimensional deviation of machined MEMS structures including microcantilevers and micro-beams using the Taguchi method. Using optimal levels of the parameters including pulse duration (0. 8 μ s), cutting speed (8. 4 mm/min), voltage (17 V) and wire tension (0. 5 kg), the dimensional deviation is reduced about 8. 65 times compared with the average of experiments results. The order of effect importance of the process parameters on the dimensional deviation of microstructures obtained by the ANOVA analysis of S/N ratios is as follows: pulse duration, wire tension, process voltage and cutting speed. Dimensional deviation of the micro-features was reduced to 1 μ m using the optimal levels of the process parameters.

Abstract: Wire Electrical Discharge Machining (WEDM) is a non-traditional thermal machining process used to manufacture irregularly profiled parts. Machining of ductile cast iron (ASTM A536) under several machining factors, which affect the WEDM process, is presented. The considered machining factors are pulse on time (Ton), pulse off (Toff), peak current (Ip), voltage (V), and wire speed (S). To optimize the machining factors, their setting is performed via an experimental design using the Taguchi method. The optimization objective is to achieve maximum Material Removal Rate (MRR) and minimum Surface Roughness (SR). Additionally, the analysis of variance (ANOVA) is used to identify the most significant factor. Also, a regression analysis is carried out to forecast the MRR and SR dependent on defined machining factors. Depending on consequences, the best regulation factors for reaching the maximum MRR are Ton = 32 μs, Toff = 8 μs, Ip = 4 A, S = 40 mm/min. and V = 70 volt. Whereas, the optimal control factors that achieve the minimum SR is Ton = 8 μs, Toff = 8 μs, Ip = 2 A, S = 20 mm/min, and V= 30 volt. It is hypothesized that the perfect combination of control factors that achieves minimum SR and maximum MRR is Ton = 8 μs, Toff = 8 μs, Ip=5 A, S=50 mm/min. The microstructure of the machined surface in the optimal machining conditions shows a very narrow recast layer at the top of the machined surface.

Wire erosion discharge machining (WEDM) is a modification of electro discharge machining (EDM) and has been widely used for a long time for cutting punches and dies, shaped pockets and other machine parts on conductive work materials. The surface finish of the machined surface mainly depends on the pulse duration, open voltage, wire speed and dielectric flushing pressure. In the present work, two of the techniques, namely factorial design and neural network (NN) were used for modeling and predicting the surface roughness of AISI 4340 steel. Surface roughness was taken as a response variable measured after WEDM and pulse duration, open voltage, wire speed and dielectric flushing pressure were taken as input parameters. Relationships between surface roughness and WEDM cutting parameters have been investigated. The level of importance of the WEDM cutting parameters on the surface roughness was determined by using the analysis of variance method (ANOVA). The mathematical relation between the work piece surface roughness and WEDM cutting parameters were established by regression analysis method. This mathematical model may be used in estimating the surface roughness without performing any experiments. Finally, predicted values of surface roughness by techniques, NN and regression analysis, were compared with the experimental values and their closeness with the experimental values determined. Results show that, NN is a good alternative to empirical modeling based on full factorial design.