Experimental investigation and optimization of technological performance characteristics in 3D printing of ABS/PC composite polymer

Fused filament fabrication is a material extrusion process, widely adopted additive manufacturing method, recognized for its versatility, simplicity, and cost effectiveness in producing customized composite polymer components across industries such as electronics, biomedical, automotive, and aerospace. This research addresses two technological performance characteristics (machine tool vibration Tv and surface roughness R a ) in 3D printing of acrylonitrile butadiene styrene/polycarbonate (ABS/PC) composite polymer, which hasn’t been addressed before. Additionally, this research explores the influence of machine tool vibration on surface morphology of printed parts aiming to optimize the 3D printing settings while balancing print quality and process stability. Four key process variables, including nozzle temperature, nozzle speed, infill density and layer thickness, are taken into consideration during the trials involving the thirty trials as per central composite design of experiments. The statistical method including analysis of variance and response surface methodology (RSM) in associated with desirability function approach (DFA) has been utilized for assessment, predictive modelling and multi-response optimization of performance characteristics (Ra, Tv) in 3D printing. Results revealed that nozzle speed (36.08%) and infill density (10.26%) predominantly affect surface roughness in ABS/PC 3D printing, and reducing these parameters lowers Ra, while higher nozzle temperature improves surface finish. Machine tool vibration is mainly governed by infill density (49.80%), nozzle speed (17.14%), and layer thickness (2.01%); higher infill density and nozzle speed increase vibration, and thinner layers further intensify the Tv. Increased nozzle speeds cause mechanical vibrations in the print head and build platform, causing material deviations, layer distortion, and poor surface finish. The developed RSM models accurately predict 3D printing performance, supported by low p -value (