Multi-response optimization and effect of tool rotation on micromachining of PMMA using an in-house developed µ-ECDM system

Abstract

Poly-methyl methacrylate (PMMA) is a lightweight and transparent thermoplastic material which is commonly used as an alternative for high-cost and resilient glass. PMMA has potential applications in the fields of microfluidics because of its high strength, low weight, optical transparency, and biocompatibility. Therefore, in this study, in-depth experimentation was carried out to generate microchannels on PMMA using an in-house developed micro Electrochemical Discharge Machining (µ-ECDM) system. The µ-ECDM process parameters used for the experimentation include voltage (V), electrolyte concentration (wt%), and duty factor (DF) (%). Experiments were designed at three levels of process parameters for the parametric study. The microchannels were machined on a 2.5 mm thick PMMA workpiece using a titanium tool of diameter 0.7 mm. The optical microscope images, along with SEM images, are used to characterize the machined channels. The machining characteristics such as material removal rate (MRR), tool wear rate (TWR), channel width, surface roughness (SR), and depth of the channel were studied using the process parameters. Individual response optimization is carried out using S/N ratios, but confounding of factors at different factor level settings is observed for each response. Therefore, to overcome this problem, multi-response optimization using the JAYA algorithm coupled with the multi-attributed decision-making (MADM) R-method has been adopted for maximizing MRR and depth of the channel and minimizing TWR, channel width, and surface roughness at single factor level settings. The optimal process parameters are obtained by multi-response optimization are 51 V, 24 wt%, and 55% DF, and the MRR, TWR, channel width, surface roughness, and depth of the channel are found to be 21.5 µg/min, 5.5 µg/min and 804.33 µm, 5.2412 µm, and 238.22 µm, respectively that are in close pact with the predicted observations. Further, the optimal machining parameters have been used along with tool rotation (in RPM) to observe the effect on machining features. The findings show that with increment in tool rotation rate improved the MRR, TWR, and depth of the channel decreased the channel width and surface roughness.