The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material.

In laser-based powder bed fusion (L-PBF), the volumetric energy density (VED) controlled by laser power (LP) and scan speed (SS) significantly affects the desired surface quality. Any variations in the LP and SS on the VED notably affect the surface quality due to changes in laser powder interactions. Therefore, the present study concentrated on the effect of various combinations of LP and SS, i.e. low LP (160 W) with high SS (1250 mm/s), high LP (320 W) with low SS (410 mm/s), low LP (160 W) with low SS (550 mm/s) and high LP (320 W) with high SS (1100 mm/s). The wide range of varying VEDs was chosen between 29 and 177 J/mm³. The results showed the VED range between 66 and 90 J/mm³ exhibited minimum surface roughness parameters of 0.51 < Ra < 0.52 µm, 0.63 < Rq < 0.64 µm, and 2.53 < Rz < 2.79 µm. The specific impact of LP and SS combination between 160 and 320 W with 400–1250 mm/s by maintaining a constant VED of 66 J/mm³ was also investigated. At a constant VED of 66 J/mm³, the combination of 280 W and 960 mm/s shows minimum roughness parameters of Ra = 0.527 µm, Rq = 0.645 µm, and Rz = 2.446 µm. The results showed that the combination of low LP with low SS, high LP with high SS, low LP with high SS, and high LP with low SS, increases surface roughness.

The current research aims to optimize the multi-response during Wire Electrical Discharge Machining (Wire-EDM) of SLMed AlSi10Mg by using Taguchi integrated Grey Relational Analysis (GRA). Selective Laser Melting (SLM) is one of the most well-known and practical Additive Manufacturing (AM) methods, with the potential to replace many traditional production processes. The complex metallic support structures created during SLM require special attention as they are difficult to remove by hand. Therefore, this study performs post-processing using the Wire-EDM precision machining technique to evaluate the machinability of the SLMed AlSi10Mg as-built part. The multi-response optimization in this study aims to achieve the maximum material removal rate and the lowest surface roughness while considering four important influencing factors (pulse On time, pulse Off time, servo voltage, and wire feed rate) at four distinct levels. The Taguchi integrated Grey Relational Analysis (GRA) revealed that a pulse On time of 118 μs (Level 3), a pulse Off time of 44 μs (Level 1), a servo voltage of 60 V (Level 4), and a wire feed rate of 7 m s⁻¹ (Level 4) are recommended to achieve optimal machining of SLMed AlSi10Mg. Furthermore, the derived optimization results were carefully verified through a confirmatory experiment, which showed a 38.57% improvement in multi-response characteristics compared to the initial Wire-EDM parameter settings. The methodology proposed in this work offers a standardized approach that has the potential to be implemented for the rapid and precise prediction and optimization of surface roughness, while achieving better material removal during Wire-EDM of SLMed AlSi10Mg.

This study investigates the influence of Laser Powder Bed Fusion (LPBF) processing parameters of high-performance Ti6Al4V components on mechanical properties and microstructural uniformity. Experimental results indicate that lower laser power (200–203 W) and moderate scan speeds (600–604 mm/s) optimize the hardness and the Hatch distance (0.10–0.11 mm) and layer thickness (0.04–0.05 mm) significantly impact hardness, with specific parameter combinations yielding superior results. A comparative assessment of six metaheuristic algorithms, such as the JAYA algorithm, Cohort Intelligence (CI), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Genetic Algorithm (GA), and Simulated Annealing (SA), was performed for LPBF parameter optimization. JAYA and CI exhibited the fastest convergence, achieving peak fitness values within the first five iterations and demonstrating high stability with minimal oscillations. The results show a significant improvement in hardness, consistently ranging between 560 and 570 HV. JAYA and CI reached comparable maximal hardness measurements at 570 HV, which agreed with the laboratory-reported 428.6 HV value. The HYV values obtained by PSO and TLBO algorithms were competitive at about 569.5 HV yet displayed reduced accuracy in their results. GA and SA showed moderate to slow convergence speed, which led to unreliable precision in their application. JAYA and CI demonstrate better performance through their effective balancing capability of exploitation and exploration processes that enable rapid and stable convergence. Given its simplicity alongside its accuracy and robust performance, the JAYA algorithm proves the most appropriate method for LPBF parameter optimization. Real-time applications benefit from CI as a promising solution.