<b>Cost-Effective Optimization Strategies for Yarn Sizing: Enhancing Efficiency and Reducing Costs</b>

This research presents a comprehensive investigation into cost-effective optimization strategies for yarn sizing, with a primary focus on enhancing process efficiency and reducing operational costs within the textile manufacturing sector. Through a combination of field surveys, in-depth interviews with industry professionals, and comparative analyses of both traditional and contemporary sizing methodologies, the study systematically identifies the critical factors influencing sizing performance and fabric quality. Key parameters under scrutiny include the selection of sizing materials, size pick-up percentage, viscosity of sizing baths, and the uniformity of application, all of which directly impact yarn strength, smoothness, hairiness, and weaving efficiency. The research further evaluates the increasingly important role of artificial intelligence (AI), automation, and data analytics in optimizing sizing processes. These technologies are shown to enable more precise control over process variables, reduce waste, and support data-driven decision-making for continuous improvement. In addition, the study explores sustainable alternatives to conventional sizing agents, highlighting the environmental and economic benefits of adopting eco-friendly materials and reducing chemical consumption without compromising yarn performance. Findings demonstrate that optimizing sizing formulations and process parameters can significantly enhance weaving efficiency exemplified by a notable reduction in machine stops and warp breaks while simultaneously lowering production costs and minimizing environmental impact. The study concludes with actionable recommendations for textile manufacturers to adopt advanced optimization strategies, leverage AI and automation, and integrate sustainable practices into their sizing operations. These insights provide a roadmap for future research aimed at further improving textile manufacturing efficiency and sustainability.