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Abstract In the current investigation, enhancement in the mechanical properties, thermal stability, and decrement in the moisture absorption capabilities of polyvinyl alcohol was observed by cross‐linking it with different weight percentages (wt. %) of fumaric acid and reinforcing them along with sulfuric acid‐modified graphite particles. The effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design method. As in this contemporary era of rapid industrial expansion and fierce competition, the primary goals of manufacturers revolve around the production of high‐quality goods at optimal costs within a specified timeframe. Therefore, to save both cost and time, the authors have utilized a technique known as “gray‐fuzzy optimization” to balance competing goals between mechanical (toughness, elongation and strength) and physical (water intake capacity) attributes. The present approach aimed to reduce the experimentation expenses and increase the effectiveness of composite manufacturing by considerably reducing the intricacy of practical multi‐response optimization issues. Response graphs and analysis of variance tests were also utilized to calculate the percentage contribution of the input variables and provide model significance. Highlights Polyvinyl alcohol is cross‐linked with different weight percentages (wt. %) of fumaric acid, and reinforced them with sulfuric acid‐modified graphite particles. Enhancement in mechanical properties, thermal stability, and decrease in moisture absorption capabilities were perceived for the fabricated composite. Effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design. Gray‐fuzzy optimization technique was utilized to balance competing goals between water intake and mechanical attributes like toughness, elongation and strength. Response graphs and analysis of variance (ANOVA) tests were also utilized to evaluate the percentage contribution of input variables and provide model significance.
Kafaltiya et al. (Thu,) studied this question.