Improving Yarn Quality Through Process Optimization using the Taguchi Approach and GRA (Case study: MAA Garment and Textile Company)

Abstract

In today’s competitive textile market, yarn quality plays a critical role in ensuring product performance, reducing waste, and maintaining customer satisfaction. This study focuses on improving yarn quality in the carding machine section of MAA Garment and Textile Company by optimizing key process parameters using the Taguchi Method and Grey Relational Analysis (GRA). The research identifies and investigates the influence of four key carding parameters: cylinder speed, flat speed, cylinder-to-doffer gauge, and cylinder-to-flat gauge on yarn imperfections such as neps, thick places, and thin places. A Taguchi L9 orthogonal array was used to design controlled experiments, and GRA was applied to handle multiple quality responses simultaneously. The analysis revealed that flat speed has the most significant effect on yarn imperfection. The optimal parameter settings (CS = 780 rpm, FS = 260 mm/min, CTD = 0.15 mm, CTF = Level A) led to substantial reductions in yarn defects. The confirmation test validated these findings, showing a significant improvement in yarn quality, as the Grey Relational Grade (GRG) increased from 0.41 to 0.61. The study demonstrates that a systematic, data-driven approach can effectively enhance yarn quality while minimizing experimentation cost and time. The findings provide valuable insights for textile manufacturers aiming to implement robust quality improvement strategies in their spinning processes.