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The capability of using Focused Ion Beam (FIB) for milling microchannels is experimentally and theoretically investigated. Microchannel structures are fabricated by a NanoFab 150 FIB machine, using an Arsenic (As²⁺) ion source. A beam current of 5 pA at 90 keV accelerating energy is used. Several microchannel patternings are milled at various dwell times at pixel spacing of 14.5 nm on top of a 60 nm gold-coated silicon wafer. An analytical/numerical model is developed to predict the FIB milling behavior. By comparing with the experimental measurements, the model predictions have been demonstrated to be reliable for guiding and controlling the milling processes.

Manufacturing industries are rapidly growing with varying customer needs, and efficient quality control tools are widely used to optimize product/process performances. This paper highlights the modified quality control module to optimize the milling performances of polymer nanocomposites. The carbon fabric and reduced graphene oxide reinforced (CF/rGO) polymer composites are machined at varying process constraints. The experimentation was designed according to Taguchi’s orthogonal array. The Milling performances were optimized using a multi-criterion decision-making (MCDM) tool based on a combination distance-based assessment (CODAS) optimization method. The desired value of surface roughness (Ra) and cutting force (Fc) is examined during the machining of the developed polymer. CODAS optimization module efficiently combined the various contradictory parametric outcomes into a single objective assessment value (Hi), which could not be possible by utilizing the usual conventional Taguchi method. Specifically, the optimal machining conditions were found to be rGO wt.%—1, speed—2000rpm, feed—80mm/min, DoC—1.5mm. Overall, the findings demonstrate the practicality of the recommended MCDM tool, which outperformed the usual conventional Taguchi method. The optimal assessment score of CODAS was noted as 1.904, which confirms the better viability of the current MCDM approach. This study contributes to the advancement of efficient quality control tools that can be widely used to optimize product/process performances in manufacturing industries.