Multi-Response Optimization of Resin Finishing by Using a Taguchi-Based Grey Relational Analysis

Area of Science:

• Textile Chemistry
• Materials Science
• Polymer Chemistry

Background:

• Formaldehyde-based resins pose environmental and health concerns.
• Developing safer, non-formaldehyde alternatives for textile finishing is crucial.
• Optimizing finishing processes is key to improving fabric performance and sustainability.

Purpose of the Study:

• To investigate and optimize non-formaldehyde resin finishing parameters for cotton fabric.
• To enhance fabric properties such as crease recovery, tearing strength, and whiteness.
• To apply Taguchi-based grey relational analysis for multi-response optimization.

Main Methods:

• Experimental investigation using Taguchi's design of experiments with an L27 orthogonal array.
• Coupling Taguchi technique with grey relational analysis to determine optimal parameters.
• Characterization of optimized fabric using FTIR, TGA, and SEM.

Main Results:

• Identified optimal parameters: resin concentration (80 g·L-1), polyethylene softener (40 g·L-1), catalyst (25 g·L-1), curing temperature (140 °C), and curing time (2 min).
• Achieved improved thermal stability and confirmed effective resin deposition on cotton fabric.
• Validated results using Analysis of Variance (ANOVA).

Conclusions:

• The non-formaldehyde resin finishing process was successfully optimized using Taguchi-based grey relational analysis.
• Optimized parameters significantly improved the functional properties of cotton fabric.
• The study provides a robust method for optimizing textile finishing processes with enhanced sustainability.