Synergistic Optimisation of Surface Properties and Surface Quality of FDM ABS Specimens Based on RSM and CWOA

The surface roughness (Ra) and coefficient of friction (COF) of acrylonitrile butadiene styrene (ABS) parts fabricated through fused deposition modelling (FDM) are key determinants of their functional service performance. However, these two objectives often present a trade-off relationship in single-objective optimisation. To achieve synergistic optimisation of surface quality and surface performance of FDM ABS parts, this paper proposes a multi-objective optimisation framework integrating response surface methodology (RSM) with a chaotic whale optimisation algorithm (CWOA). A Box–Behnken design (BBD) was employed to establish quadratic regression models for Ra and COF as functions of layer thickness (0.16–0.24 mm), extrusion ratio (0.9–1.0), infill density (20–100%) and extrusion temperature (240–270 °C). Based on analysis of variance (ANOVA) and response surface analysis, a non-linear mathematical model with dual responses was constructed. Pareto-dominated CWOA was introduced for multi-objective optimisation, and the optimal process parameter combination was selected using the TOPSIS method. The results showed that the optimal parameters were: layer thickness 0.16 mm, extrusion ratio 0.9, infill density 66%, and extrusion temperature 270 °C. Under these conditions, the experimentally measured Ra was 7.7921 μm (2.04% error from the predicted value) and COF was 0.1504 (2.80% error from the predicted value). Compared with the benchmark reference (the average value of the BBD central experimental runs), a synchronous decrease in Ra and COF was achieved (Ra reduced by 27.4% and COF reduced by 14.9%). Metallographic morphology analysis revealed that the optimal specimen surface exhibited wide filament ridges, narrow and well-defined inter-filament valleys, and no obvious forming defects, achieving synergistic low roughness and low friction. The proposed RSM-CWOA framework provides an effective method for multi-objective optimisation of FDM processes and can be extended to other polymer additive manufacturing systems.