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Wire-arc-based additive manufacturing (WAAM) is a promising technology for the efficient and economical fabrication of medium-large components. However, the anisotropic behavior of the multilayered WAAM-fabricated components remains a challenging problem.

The purpose of this paper is to conduct a comprehensive study of the grain morphology, crystallographic orientation and texture in three regions of the WAAM printed component. Furthermore, the interdependence of the grain morphology in different regions of the fabricated component with their mechanical and tribological properties was established.

The electron back-scattered diffraction analysis of the top and bottom regions revealed fine recrystallized grains, whereas the middle regions acquired columnar grains with an average size of approximately 8.980 µm. The analysis revealed a higher misorientation angle and an intense crystallographic texture in the upper and lower regions. The investigations found a higher microhardness value of 168.93 ± 1.71 HV with superior wear resistance in the bottom region. The quantitative evaluation of the residual stress detected higher compressive stress in the upper regions. Evidence for comparable ultimate tensile strength and greater elongation (%) compared to its wrought counterpart has been observed.

The study found a good correlation between the grain morphology in different regions of the WAAM-fabricated component and their mechanical and wear properties. The Hall–Petch relationship also established good agreement between the grain morphology and tensile test results. Improved ductility compared to its wrought counterpart was observed. The anisotropy exists with improved mechanical properties along the longitudinal direction. Moreover, cylindrical components have superior tribological properties compared with cuboidal components.

The purpose of this study is to extract and characterize a novel natural dye from the leaves of Lannea coromandelica and the extraction with finding ways of dyeing cotton fabric using three mordants.

The colouring agents were extracted from the leaves of Lannea coromandelica using an aqueous extraction method. The extract was characterized using analysis methods of pH, gas chromatography-mass spectrometry (GC-MS), Fourier transform infrared (FTIR), ultraviolet-visible (UV-vis) and cyclic voltammetry measurement. The extract was applied to cotton fabric samples using a non-mordant and three mordants under the two mordanting methods. The dyeing performance of the extracted colouring agent was evaluated using colour fastness properties, colour strength (K/S) and colour space (CIE Lab).

The aqueous dye extract showed reddish-brown colour, and its pH was 5.94. The GC-MS analysis revealed that the dye extract from the leaves of Lannea coromandelica contained active chemical compounds. The UV-vis and FTIR analyses found that groups influenced the reddish-brown colour of the dye extraction. The cyclic voltammetry measurements discovered the electrochemical properties of the dye extraction. The mordanted fabric samples showed better colour fastness properties than the non-mordanted fabric sample. The K/S and CIE Lab results indicate that the cotton fabric samples dyed with mordants showed more significant dye affinities than non-mordanted fabric samples.

Researchers have never discovered that the Lannea coromandelica leaf extract is a natural dye for cotton fabric dyeing. The findings of this study showed that natural dyes extracted from Lannea coromandelica leaf could be an efficient colouring agent for use in cotton fabric.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.