Nowadays apparel industries face ever-increasing global competition and unpredictable variations in demand. These pressures force manufacturers to consistently improve the efficiency of their manufacturing process to produce the finished product within the most reasonable timeline and the lowest cost of production. However, under a complex and fuzzy manufacturing environment, optimal, and consistent solutions are difficult to obtain. Consequently, in response to the need for new methods, a significant (steadily increasing) number of initiatives have sought to explore and optimize the use of artificial intelligence (AI) techniques in a range of industrial applications. This article demonstrates that research is still limited on AI application in the apparel industry by examining and analyzing the limitations of research challenges and previous studies. This article also addresses the problems facing the implementation of AI technologies in the apparel industry.

Additive manufacturing (AM) has emerged as a transformative technology for fabricating intricately tailored components, representing a paradigm shift in design versatility and material optimization. The fabrication of high-strength Al7XXX series alloys through this approach is attracting increasing interest due to their lightweight nature, exceptional mechanical properties, and corrosion resistance, making them essential for aerospace, automotive, and defense applications. Despite these advantages, manufacturing these alloys through AM remains challenging due to issues such as hot cracking, porosity, elemental evaporation, adhesion and bonding defects, and microstructural anisotropy. Recent studies have explored strategies to address these challenges, demonstrating successful fabrication and evaluating potential applications in advanced engineering. This review aims to report the current state of manufacturing techniques, categorizing them into solid-state and melting-based approaches while analyzing their processing mechanisms, challenges, and mechanical performance. Strategies for defect mitigation, including process optimization, post-processing treatments such as heat treatment and grain refinement, and novel alloy modifications, are critically reviewed. By consolidating recent advancements and identifying key challenges, this study establishes a foundation for future research, emphasizing defect control, microstructural stability, and industrial scalability for high-performance applications.

Improvement in the performance and compatibility of face masks has remained the focus of researchers in recent years, especially after the emergence of the COVID pandemic. Although a lot of progress in the design, tolerability, and comfort of the mask has been reported, there are certain limitations, requiring further improvement. The present review aims to highlight the filtration efficacy, comfort, and associated characteristic of various types of face masks and respirators as a function of their design and structure. In addition, the air pollutants, their adverse effects on health, certified respirators, and face masks are also discussed. The present review also provides an insight into different types of commercially available face masks in terms of their materials, filtration efficiency, and limitations. The role of emerging trends (such as nanotechnology and high-performance polymers) in the improvement and development of face masks and respirators is also discussed.

Additive Manufacturing (AM) has revolutionized the production industry by offering design freedom with shorter lead times and reduced material wastage. However, the damage tolerance (DT) of AM parts is a significant concern due to their microstructural and geometric complexities, which affect their mechanical performance. This article aims to provide a comprehensive overview of the manufacturing parameters affecting the components produced by AM specifically selective laser melting (SLM). Detailed discussions are presented on the effects of manufacturing attributes on the microstructure, defects, and mechanical characteristics of AM parts. Depending on these aspects, basic concepts are studied and critically explained specifically for AM materials. The basic criterion for damage‐tolerant component design, the criterion for fatigue and fracture properties, and the effect of the defects on fatigue life are critically presented. In addition, the effect of different types of gradation on the crack growth behavior of samples processed by SLM is also investigated in depth. There is currently a lack of a specific review study in the literature that establishes a connection between process attributes and metallographic properties, and their impact on the damage behavior of additively manufactured parts. This gap in research highlights the need for a comprehensive review to bridge this knowledge deficit and provide valuable insights for understanding the relationships between manufacturing processes, material characteristics, and the structural integrity of additively manufactured components. This review concludes by addressing the challenges and opportunities in designing and qualifying AM parts for damage tolerance.

Fatigue failure in additively manufactured components commonly initiates at critical defects that combine large size with proximity to the surface. Therefore, understanding the relation of extreme large sized defects with process parameters is essential for structural integrity. This study explores the correlation between the process parameters and statistically derived extreme defect sizes in Selective Laser Melting using Extreme Value Theory (EVT). AlSi10Mg samples are fabricated with varying bulk process parameters, while maintaining constant contouring conditions to generate different defect distributions and are characterized using computed tomography. Defect size is quantified via along the bearing plane for analysis. EVT is applied using the block maxima approach and modeled using the Gumbel distribution to estimate the 99th percentile maximum defect sizes (x _0.99 ) representing the statistical extremes. Correlations between x _0.99 and process parameter indicators such as modified volume energy density and melt pool width are established using quadratic regression and Gaussian process regression. Although based on a limited dataset, the results demonstrate a viable framework for linking process parameters to statistically derived extreme defect sizes in additively manufactured components.