Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings.

Despite several additive manufacturing techniques are commercially available in market, Fused Deposition Modeling (FDM) is increasingly used by researchers and engineers for new product development. FDM is an established process with a plethora of advantages, but the visible surface roughness (SR), being an intrinsic limitation, is major barrier against utilization of fabricated parts for practical applications. In the present study, the chemical finishing method, using vapour of acetone mixed with heated air, is being used. The combined impact of orientation angle, finishing temperature and finishing time has been studied using Taguchi and ANOVA, whereas multi-criteria optimization is performed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The surface finish was highly responsive to increase in temperature while orientation angle of 0° yielded maximum strength; increase in finishing time led to weight gain of FDM parts. As the temperature increases, the percentage change in surface roughness increases as higher temperature assists the melt down process. On the other hand, anisotropic behaviour plays a major role during tensile testing. The Signal-to-noise (S/N) ratio plots, and ANOVA results indicated that surface finish is directly proportionate to finishing time because a longer exposure results in complete layer reflowing and settlement.

In polymer composites, synthetic fibers are primarily used as a chief reinforcing material, with a wide range of applications, and are therefore essential to study. In the present work, we carried out the erosive wear of natural and synthetic fiber-based polymer composites. Glass fiber with jute and Grewia optiva fiber was reinforced in three different polymer resins: epoxy, vinyl ester and polyester. The hand lay-up method was used for the fabrication of composites. L₁₆ orthogonal array of Taguchi method used to identify the most significant parameters (impact velocity, fiber content, and impingement angle) in the analysis of erosive wear. ANOVA analysis revealed that the most influential parameter was in the erosive wear analysis was impact velocity followed by fiber content and impingement angle. It was also observed that polyester-based composites exhibited the highest erosive wear followed by vinyl ester-based composites, and epoxy-based composites showed the lowest erosive wear. From the present study, it may be attributed that the low hardness of the polyester resulting in low resistance against the impact of erodent particles. The SEM analysis furthermore illustrates the mechanism took place during the wear examination of all three types of composites at highest fiber loading. A thorough assessment uncovers brittle fractures in certain regions, implying that a marginal amount of impact forces was also acting on the fabricated samples. The developed fiber-reinforced polymer sandwich composite materials possess excellent biocompatibility, desirable promising properties for prosthetic, orthopaedic, and bone-fracture implant uses.

This research focuses on a comprehensive exploration of the experimental and mechanical aspects of the electrical discharge machining (EDM) process, specifically targeting the machining characteristics of AA2014/Si₃N₄/Mg/cenosphere hybrid composites. The aim is to optimize the process parameters for enhanced machining performance through a combination of testing, optimization, and modelling methodologies. The study examines the effects of key EDM variables—peak current, pulse on time, and pulse off time—on critical output responses: surface roughness (Ra), electrode wear rate (EWR), and material removal rate (MRR). Leveraging an L9 Taguchi orthogonal array experimental design, the impact of controllable factors on these responses is analysed. An integrated approach utilizing MATLAB’s logic toolbox and Mamdani’s technique is employed to model the EDM process, and a multiple-response performance index is calculated using fuzzy logic theory, enabling multiobjective optimizations. Furthermore, a mechanical behaviour evaluation of AA2014/Si₃N₄/Mg/cenosphere hybrid composites is performed through mechanical testing, with a comparison between experimental machining results and predicted values. Scanning electron microscopy (SEM) images reveal the presence of filler reinforcements within the base alloy, displaying an improved microstructure and uniform reinforcement dispersion. An X-ray diffraction (XRD) analysis confirms the major elemental constituents—aluminium, silicon, and magnesium—in the hybrid composites. A microstructural analysis of the hybrid metal matrix composites (MMCs) prepared for EDM showcases closely packed reinforcement structures, circular ash-coloured spots indicating silicon and nitrates, and a fine dispersion of cenosphere reinforcement particles. The study’s outcomes demonstrate a promising application potential for these hybrid composites in various fields.

Fused filament fabrication (FFF) has numerous process parameters that influence the mechanical strength of parts. Hence, many optimization studies are performed using conventional tools and algorithms. Although studies have also been performed using advanced algorithms, limited research has been reported in which variants of the naked mole-rat algorithm (NMRA) are implemented for solving the optimization issues of manufacturing processes. This study was performed to scrutinize optimum parameters and their levels to attain maximum impact strength, flexural strength and tensile strength based on five different FFF process parameters. The algorithm yielded better results than other studies and successfully achieved a maximum response, which may be helpful to enhance the mechanical strength of FFF parts. The study opens a plethora of research prospects for implementing NMRA in manufacturing. Moreover, the findings may help identify critical parametric levels for the fabrication of customized products at the commercial level and help to attain the objectives of Industry 4.0.