Articulating stair systems provide temporary access across changing elevations by allowing the stair frame to rotate while the walking treads remain approximately level. The resulting structure is not geometrically fixed: deployment angle changes effective member length, load path, bending demand, and stability. This study develops an angle-aware structural verification framework for an 18-step, 36 in wide aluminum stair system and compares two documented finite-element design states at 55° and 30°. The baseline structure uses 6061-T6 aluminum and was evaluated in SAP2000 using the load framework and member resistance procedures documented in the source structural calculation package, including ASCE/SEI 7-16, the 2022 California Building Code, and the 2020 Aluminum Design Manual. The governing side beam/stringer combined demand-to-capacity ratio increased from 0.731 at 55° to 0.948 at 30°, corresponding to a 29.7% increase in critical utilization and a reduction in remaining unity margin from 26.9% to 5.2%. Decomposition of the interaction check showed that major-axis bending governed the change: the major bending demand-to-capacity contribution increased from 0.661 to 0.892, while major bending demand increased from 1.353 to 1.785 ft-kip. At the same time, the reported axial-compression capacity of the side member decreased from 1.756 to 0.673 kip because of the longer effective member length and associated buckling response in the shallow configuration. Tread-member utilization remained low, decreasing from 0.034 to 0.017. The results demonstrate that the shallow-angle configuration, rather than the steep-angle configuration, controls the baseline unbraced system. The scientific contribution is a configuration-dependent interpretation of articulating stair behavior in which inclination is treated as an explicit structural design variable. The proposed framework can be extended to full-angle parametric analysis, reinforced configurations, connection assessment, fatigue studies, and experimental validation.
This study explores the development of eco-friendly, high-performance fan blades using a hybrid composite of Enset and Bamboo fibers reinforced with unsaturated polyester resin. An Enset-Bamboo reinforced composite was fabricated by using hand layup method. The easiest method for preparing polymers is hand layup. Above all, in order to prevent polyester tar from adhering to the surface, the shape surface of the previously created mold was cleaned by applying discharge gel or wax. To achieve a high-quality surface finish on the item, smooth-surfaced Polyethylene Plastic was utilized at both the upper and lower portions of the form plate. The Enset - bamboo fiber reinforced polyester composite was fabricated using a hand lay-up process. An Enset-Bamboo reinforced fiber composite is the best material based on the result of strength and low weight ratio and safe for ceiling fan blade. To improve mechanical interlocking, fibers underwent alkaline (NaOH) treatment, and an optimal composition of 25% Enset, 20% Bamboo, and 55% polyester was determined through systematic analysis. The result of the composite blades achieved excellent durability, exhibited a low water absorption rate of 1.38%, and offered a significant 31% weight reduction compared to traditional aluminum alternatives, demonstrating their viability for aerodynamic applications. So, generally Enset-bamboo fiber reinforced composite matrix material is the suitable weight ratio, strength and durability for the ceiling fan blade.
Coffee remains Ethiopia's top export commodity and is the most significant crop in the country's economy. The crop's production and quality are extremely low, despite its economic significance. The decline in Ethiopian coffee quality is mostly caused by improper postharvest processing methods, such as harvesting immature cherries, not sorting during grading and processing, improper drying without considering drying time, drying place, layer thickness, and drying material, transportation, storage, over-fermentation, etc. The processing of coffee beans is a critical step that influences the quality of the final product. Effective demucilaging is essential to remove mucilage while preserving the integrity of the beans. This study focuses on the development and performance evaluation of a manually operated coffee bean demucileger, aimed at improving the efficiency of coffee processing while minimizing operational costs. The objective was to design a user-friendly, effective demucilaging device for small-scale coffee production settings. The demucileger was constructed using locally sourced materials, and its performance was evaluated based on parameters such as efficiency, ease of operation, and quality of demucilaged beans. Results indicated that the device successfully removed mucilage with an efficiency rate and washing capacity of 86% and 86.86 kg/hr. while maintaining the integrity of the beans. This manual operated demucileger presents a viable solution for smallholder coffee farmers, potentially enhancing their productivity and product quality in an economically sustainable manner.
Industrial waste, leftover materials and chemical residues constitute a major environmental challenge in Bangladesh where the textile industry annually produces some 400,000 tons of fabric waste that is a significant source of pollution. Manufacturing 4.0 technologies are making possible advanced manufacturing systems that can optimize production and reduce waste, using technologies such as those supported on data analytics and the Internet of Things (IoT). The objective of this study is to build machine-learning based predictive analytics framework for minimizing textile production waste, evaluate the developed framework using a practical context in Bangladesh and finally observe the environmental and socio-economic impact caused by the approach. The design employed a mixed-methods case study. Data were collected from a medium-sized textile dye-house in Dhaka from January to March 2025, with IoT tracked measurements on fabric consumption, machine productivity, and wastewater output (n = 1,000 production cycles). Python generates a Random Forest regression model to predict waste, while simulation is carried out through a digital twin to optimize production parameters. The model obtained a mean absolute error of 5.4% and was able to accurately predict the pattern of waste. Application of the optimized parameters resulted in 20% less fabric waste (from 500 to 400 kg/day), 15% less use of water in dyeing (from 10,000 to 8,500 liters/day) and 10% lower CO₂ emission (0.5 tons/day). The greatest waste reduction was observed in the urban area, due to better cutting techniques. These findings highlight the opportunities provided by Industry 4.0 analytics for sustainable manufacturing towards UN SDG 12. Additional investigation is also required on low-cost IoT deployment and policy enablers, to achieve widespread adoption and impactful change sustainably in developing economies.
The transition from Industry 4.0 to Industry 5.0 marks a paradigm shift from automation and digitalization toward a human-centric, sustainable, and resilient industrial ecosystem. This review paper explores the evolving role of Industrial Engineering (IE) within the framework of Industry 5.0, emphasizing the integration of advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), robotics, digital twins, and additive manufacturing with human intelligence and creativity. Unlike its predecessor, which primarily focused on efficiency and productivity through automation, Industry 5.0 aims to restore the human touch in manufacturing, ensuring that technology augments not replaces human capabilities. The paper synthesizes recent scholarly contributions and industrial case studies to identify how Industry 5.0 redefines traditional IE domains including operations optimization, production planning, quality control, ergonomics, and supply chain management. Furthermore, it discusses the alignment of Industry 5.0 with global sustainability goals, circular economy principles, and socio-technical resilience. The review highlights how Industrial Engineers act as key enablers in designing human-machine symbiotic systems, optimizing smart factories, and ensuring ethical and sustainable deployment of emerging technologies. Finally, the paper outlines research gaps and future directions, advocating for interdisciplinary frameworks that combine engineering, data science, and human factors to achieve a more inclusive and adaptive industrial future. By bridging technology with humanity, Industry 5.0 offers Industrial Engineering a renewed relevance in shaping the factories of tomorrow intelligent, empathetic, and sustainable.
