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Minitab LLC

Six Sigma

Warding Off a New Wrinkle with Design of Experiments Tools

Statistical software predicts best fabric properties for wrinkle-free cotton

Published: Monday, July 18, 2011 - 16:08

C

otton. Given that it’s the most widely used fiber in clothing, you’re probably wearing some right now. We love cotton’s comfortable properties and soft feel. But as everyone who’s ironed a cotton shirt or pants knows, these same properties can make cotton-based fabric particularly prone to wrinkling. That’s why researchers from the National Textile University in Faisalabad, Pakistan, embarked on a study aimed at predicting the best fabric properties for strong, crease-free cotton.

Cloth manufacturing plants strive to create a high-quality, durable product, but the mix of fibers, resins, softeners, and catalysts for perfect cotton fabric requires a fine balance that can be difficult to obtain. The application of resins results in a loss of fabric strength, making cloth more inclined to tearing. So manufacturers add softeners to retain fabric strength—but this promotes wrinkling. Striking the perfect combination for strong and wrinkle-free cotton, without sacrificing softness and comfort, is a tough challenge for textile producers.

National Textile University researchers set out to help producers find the ideal fabric mix by using the design of experiment (DOE) tools in Minitab Statistical Software. Their ultimate goal: create the optimal recipe for soft, sturdy, and wrinkle-resistant cotton fabric.

With DOE, researchers can change more than one factor at a time, then use statistics to determine which ones are important and even identify the optimum levels for these factors. Because DOE reduces the number of experimental runs needed compared to one-at-a-time experimentation, it’s an efficient and economical way to improve any process (see figure1).


Figure 1: Minitab’s main effects plots showed how variables affected wrinkle recovery, while interaction plots made it easy for researchers to assess which variables most affected tear strength.

Researchers used Minitab to construct a two-level factorial design and explore the effect of each factor at low and high settings. For each experimental run, they gathered data about crease recovery angle and tear strength for multiple fabric specimens. Analyzing the data revealed how these properties were affected by differing amounts of resin, magnesium chloride, softener, fabric curing time, and temperature. To make it easy to see how the variables and their interactions affected fabric quality, the researchers used Minitab to create main effects and interaction plots.

The analysis showed that increasing curing time and temperature, as well as increasing resin concentration, produced the most wrinkle-resistant cotton. It also showed, for example, that a longer curing time and higher curing temperature reduced tear strength, with a sharper decrease detected at the higher concentration of magnesium chloride. Using the software's regression tools, the team generated equations they could use to predict both optimal wrinkle recovery and tear strength.

Textile producers are now using these equations to create optimal fabric mixes for high-quality, comfortable cotton that is less inclined to wrinkle. The predictive capability of the equations also allows textile producers to forecast and evaluate current and new fabric mixes before putting them into production. With continued use and some fine-tuning, the researchers believe this predictive approach can be used more extensively throughout the textile industry. When that happens, you can thank the National Textile University researchers—and Minitab Statistical Software—for helping you spend a little less quality time with your ironing board.

Note: Research discussed in this article was originally published in “Predicting the Crease Recovery Performance and Tear Strength of Cotton Fabric Treated With Modified N-methylol Dihydroxyethylene Urea and Polyethylene Softener,” Coloration Technology, April 2010.

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Minitab LLC

Minitab Inc. develops statistical analysis and process improvement software for academic and commercial users. Minitab Statistical Software, a data analysis tool for businesses, has been used to implement virtually every major Six Sigma quality improvement initiative, and to teach statistics in more than 4,000 colleges and universities worldwide. Quality Companion is used to plan and execute Six Sigma projects. Use the subscription-based online learning course, Quality Trainer, to learn or refresh your knowledge and to access quality statistics expertise anytime. Interactive lessons based on real problems make concepts easy to retain.