Development of μ-ECDM System with Different Process Modes for Machining of Micro Features and Nanoparticles Synthesis

Abstract

Micromachining of difficult-to-machine materials is of prime focus nowadays. There are wide variety of advanced micromachining processes which are paving the way to achieve desired outcomes such as higher material removal rate (MRR), better surface quality, higher machining depths, reduced heat affected zones, and minimal workpiece damage. One such process is the micro electrochemical discharge machining (μ-ECDM) process which exhibits the capability to achieve the above-mentioned outcomes. But, in this process, proper replenishment of electrolyte at the machining zone is a challenging issue. This can be overcome by some assistance incorporation into the system and proper tuning of the process parameters. In this research work, an in-house developed μ-ECDM system incorporated with assistances like tool rotation and ultrasonication of electrolyte is used to micromachine nonconducting (borosilicate glass and Polymethyl methacrylate (PMMA)), composites (Carbon fiber reinforced polymer (CFRP) and Glass fiber reinforced polymer GFRP), and conducting materials (commercially pure titanium and Aluminum 7075 (Al7075) alloy) to analyze the machining responses like MRR, tool wear rate (TWR), overcut (OC), circularity (Cir), surface roughness (SR), channel width (Wd) and channel depth. To analyze these machining responses various experimental parameters are considered in this study. The parameters include voltage (V), type of electrolyte, concentration (wt%), frequency (kHz), duty factor (%DF), feed rate (μm/s), interelectrode gap (mm), immersion depth (mm), tool electrode material, auxiliary electrode material, tool rotation rate (rpm) and ultrasonication frequency. Among the experimental parameters, some are chosen to be variable process parameters and some to be constant parameters depending on the material that is being machined. The selection of variable and constant parameters is carried out by conducting rigorous pilot experimentation. The variable process parameters are chosen at three levels and experiments were designed based on the L9 orthogonal array or face center cubic-response surface methodology (FCC-RSM). The experiments are conducted according to the respective design layout and their corresponding analysis of variance (ANOVA) is performed. Then a linear or quadratic-based regression model is obtained from the statistical software which gives the relation between the process parameters and machining responses. Further, optimization is performed using a multi-objective JAYA algorithm (MOJAYA) to obtain a set of Pareto optimal solutions and then the multi-attributed decision making (MADM) R-method is used to obtain the best compromise among the available set of Pareto optimal solutions. A MATLAB code for both the MOJAYA algorithm and R-method is developed and validated with existing literature. The optimal solution obtained from MOJAYA coupled with R-method is validated by conducting an experiment at the nearest integer parametric setting and it is found that the error percentage is in good agreement not exceeding ±10. Further, in this work, attention has paid on the debris being treated as unwanted particles are dispersed in the electrolyte during micro-machining of titanium and borosilicate. The debirs of titanium and borosilicate obtained at the optimal process parameters setting is collected and filtered separately. Various diagnostic studies are performed for the characterization of debris (particles) and found that the particles obtained lie in the range of nano to submicron range. The particles obtained are nanoparticles of TiO2 and borosilicate which has numerous applications in various industries.