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.

Aluminum hybrid composites have the potential to satisfy emerging demands of lightweight materials with enhanced mechanical properties and lower manufacturing costs. There is an inclusion of reinforcing materials with variable concentrations for the preparation of hybrid metal matrix composites to attain customized properties. Hence, it is obligatory to investigate the impact of different machining conditions for the selection of optimum parameter settings for aluminum-based hybrid metal matrix composite material. The present study aims to identify the optimum machining parameters during wire electrical discharge machining of samples prepared with graphite, ferrous oxide, and silicon carbide. In the present research work, five different process parameters and three response parameters such as material removal rate, surface roughness, and spark Gap are considered for process optimization. Energy-dispersive spectroscopy and scanning electron microscopy analysis reported the manifestation of the recast layer. Analytical hierarchy process and genetic algorithm have been successfully implemented to identify the best machining conditions for hybrid composites.

Fused deposition modelling (FDM) is a technique of additive manufacturing used to fabricate a 3D (three-dimensional) model with layer-by-layer deposition of required materials with less material wastage. FDM is used to make any objects with a meager cost, but also there are some negative points related to less strength, less accuracy, and less surface finish. In this study, acrylonitrile butadiene styrene (ABS) is printed using an FDM printer to investigate the effects of various changing parameters like nozzle temperature (°C), infill pattern, and printing speed (mm/s) on surface roughness and thickness measurement. Experiments are designed using the Taguchi L9 orthogonal array method and ANOVA method. For obtaining an increase in surface roughness, the most influencing factor is printing speed with 83.41% contribution, and the effect of nozzle temperature is 9.04%. Lesser printing speed enhances the surface finish and, in the case of thickness and outer dimension of all the printed samples, results are almost constant. Regression analysis is performed to formulate the single-objective equations, and a genetic algorithm (GA) is applied to optimize the values of process parameters.

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.

Concrete is a composite material that is commonly used in the construction industry. It will certainly be exposed to fires of varying intensities when used in buildings and industries. The major goal of this article was to look into the influence of mineral additions such as foundry sand and marble dust on the residual characteristics of concrete. To examine the behavior of residual characteristics of concrete after fire exposure, marble dust was substituted for cement and fine sand was substituted for foundry sand in varying amounts ranging from 0% to 20%. It aided in the better disposal of waste material so that it might be used as an addition. The purpose of the experiment was to see how increased temperatures affected residual properties of concrete, including flexural strength, compressive strength, tensile strength, static as well as dynamic elastic modulus, water absorption, mass loss, and ultrasonic pulse velocity. At temperatures of 200 °C, 400 °C, 600 °C, 800 °C, and 1000 °C, the typical fire exposure behavior of concrete was investigated. The effects of two cooling techniques, annealing and quenching, on the residual properties of concrete after exposure to high temperatures were investigated in this study. Replacement of up to 10% of the cement with marble dust and fine sand with foundry sand when concrete is exposed to temperatures up to 400 °C does not influence the behavior of concrete. At temperatures above 400 °C, however, the breakdown of concrete, which includes marble dust and foundry sand, causes a rapid deterioration in the residual properties of concrete, primarily for replacement of more than 10%.