Multi-parameter optimization of 3D-printed polyphenylene sulfide (PPS) for enhanced mechanical performance

• High-performance Polyphenylene sulfide polymer mechanical performance optimization of six 3D printing settings in Additive Manufacturing • Taguchi L25, analysis of variances and reduced quadratic regression modeling were utilized. • The raster deposition angle was the most critical parameter, and the infill density was the least important. • The optimum set of parameters improved the mechanical performance metrics by more than 250%, whereas the toughness was improved ten times. • Less than 10% error was found in the confirmation runs. Additive manufacturing (AM) has contributed to many industrial applications, owing to the properties of advanced materials, such as high-performance polymers (HPPs). One such HPP is Polyphenylene sulfide (PPS) HPP, which was investigated herein to locate the critical 3D printing settings affecting the mechanical performance of the fabricated parts, introduce the optimum set of their values, and propose prediction models for direct industrial use. PPS parts were three-dimensional (3D) printed under various sets of control parameter levels using the material extrusion (MEX) method. Six 3D printing settings were evaluated: infill density, raster deposition angle, nozzle temperature, printing speed, layer thickness, and bed temperature. The mechanical properties assessed were tensile and yield strengths, modulus of elasticity, toughness, specimen weight, and tensile strength per weight. A Taguchi L25 design of experiment was used for the analysis. Analysis of variance and regression tools were used. Thermal, morphological, and structural characterizations were also performed. The raster deposition angle was the most influential factor for all properties assessed. The optimum set of parameters improved the mechanical performance metrics by more than 250% (optimum parameters yielded a tensile strength of 65.4 MPa), whereas the toughness was improved ten times (57.64MJ/m 3 ). Confirmation runs verified the prediction models, with less than 10% deviation in the values. The R² values were approximately 85%, indicating that the modeling process accurately captured the behavior of the MEX AM PPS parts. The findings can be exploited in real-life applications when designing parts to be manufactured with PPS HPP using MEX AM.