THE CONTRIBUTION OF ARTIFICIAL INTELLIGENCE TO DECISION SUPPORT

The global nonwoven market is experiencing sustained growth, driven by demand in filtration, hygiene, healthcare, and packaging sectors. This boom requires faster product improvement cycles, tighter fee control, and stronger product stop space, primarily basis weight (g/m2), which directly controls mechanical and functional performance This paper design for predicting line-base weight tops of 100% nonwoven polyesters version product3. the development of the artificial intelligence (AI) selection assistant done, considers key method variables as input: area of layer (stacking), draw ratio, and fabric level (line speed). A fuzzy good-judgment-based absolute method, experimentally statistically calibrated, is specified to output their joint non-linearly with finite base load The overall performance of the model is evaluated using accuracy uncertainty metrics, although the commercial impact is quantified in the learning phase and experimentally improves the market price to. Expected outcomes include a robust basis weight prediction method, a significant decrease in required physical trials, and enhanced industrial efficiency in nonwoven production.