Comparison between response surface methodology and Taguchi method for dyeing process parameters optimization in...

Area of Science:

• Industrial Engineering
• Process Optimization
• Experimental Design

Background:

• Optimizing process parameters is crucial for efficiency and product quality.
• Selecting the right experimental design impacts cost, accuracy, and time.
• Four factors at three levels present a complex optimization challenge.

Purpose of the Study:

• To comparatively analyze Taguchi, Box-Behnken Design (BBD), and Central Composite Design (CCD) for process optimization.
• To determine the most effective experimental design for a system with four factors.
• To evaluate the trade-offs between experimental efficiency, accuracy, and cost.

Main Methods:

• Comparative analysis of Taguchi, BBD, and CCD.
• Utilized Analysis of Variance (ANOVA) to assess variable contributions.
• Employed R programming for model adequacy and lack-of-fit assessment.

Main Results:

• Taguchi method: 92% accuracy, cost-effective with fewer runs.
• Box-Behnken Design (BBD): 96% accuracy, higher precision.
• Central Composite Design (CCD): 98% accuracy, highest precision.

Conclusions:

• Taguchi designs are efficient and cost-effective for initial optimization.
• BBD and CCD offer superior accuracy and precision for fine-tuning parameters.
• The choice of design depends on balancing optimization needs with resource constraints.