Christopher Turner

Cotton fiber length is a key determinant of yarn quality, and High Volume Instrument (HVI) measurements are widely used to assess fiber length characteristics. Recent research has shown that the complete HVI fibrogram contains substantially more information than the conventional HVI-reported parameters, enabling reconstruction of the full fiber-length distribution and calculation of additional length-related parameters. Before these measurements can be adopted for routine use, calibration procedures are needed to ensure agreement among instruments. This study developed and evaluated calibration procedures for fibrogram-derived length parameters calculated from reconstructed fiber-length distributions. Three calibration reference cottons representing short, medium, and long fiber lengths were established and tested on four HVIs over a six-month period. Calibration equations were generated using two-point and three-point linear regressions between reference and observed measurements and applied to USDA evaluation cottons and commercial samples. Instrument stability, calibration frequency, and the use of comb checks were also investigated. Stability analysis showed that measurement drift within individual HVIs was small over the study period, indicating that frequent calibration is unnecessary under well-maintained operating conditions. Calibration improved agreement among HVIs for both conventional HVI-reported parameters and fibrogram-derived length parameters. Two-point and three-point calibration produced similar results, suggesting limited benefit from the additional medium-length calibration standard. Calibration frequency and comb checks had minimal impact on calibration efficacy. Overall, the proposed procedures improve consistency among HVIs and support practical implementation of new fibrogram-derived length measurements.

Knowledge of cotton fiber length uniformity is important for the cotton industry. The accurate and reliable measurement of fiber length uniformity would allow cotton breeders to release new cotton varieties with improved fiber length variation. This knowledge would also help spinning mills to optimize their machine setup, which would improve yarn processing performance. Currently, the high volume instrument (HVI) is most commonly used to characterize the cotton fiber length variation. The HVI length measurement is based on the fibrogram principle. The HVI length measurement characterizes 2 points, 1.8% as the upper half mean length (UHML) and 7.8% span length as the mean length (ML) from the fibrogram, and reports UHML and uniformity index (UI). The ratio of ML to the UHML is used to calculate the UI and is expressed as a percentage. UI measurement does not represent the shorter fibers as the above two span lengths only represent the longest fibers within a sample. We propose to calculate the uniformity of the cotton fiber length using the complete fibrogram as an alternative. First, the area of the measured fibrogram curve is calculated. Second, the area of a theoretical mono-length fibrogram with a length equal to the maximum length of the fibers for the same sample is calculated. Finally, we calculate a new length uniformity as the ratio of the measured fibrogram area to the mono-length fibrogram area expressed as a percentage. Based on the results obtained using a set of 991 commercial samples, the new length uniformity shows promise. We also applied this new length uniformity to a set of 60 commercial-like samples and developed partial least square regression (PLSR) prediction models to predict yarn quality. The results obtained demonstrate that the new length uniformity predicts yarn quality better than the current UI.

Cotton fiber length is one of the most critical parameters of fiber quality for ring-spun and air-jet-spun yarns. Currently, two of the most common instruments for testing cotton fiber length are the USTER High Volume Instrument (HVI) and the USTER Advanced Fiber Information System (AFIS). The HVI bases its length measurement on the fibrogram concept. It is fast but reports only two length measurements—the upper half mean length (UHML) and the uniformity index (UI), where the UI is the percentage ratio of mean length (ML) to UHML. The AFIS is slower and more costly per test because it individualizes fibers to provide a complete fiber length distribution per sample. This paper presents a method that can reconstruct a complete fiber length distribution from an HVI fibrogram based on established fibrogram theory. Results show that the algorithm can accurately recover different types of distributions based on synthetically generated data. Results also show that reconstructed distributions of three different types of samples—upland, pima, and viscose—differ in ways that make sense based on known characteristics of their length distributions. Finally, a variety of statistics computed from the reconstructed distributions are compared to the HVI-reported length parameters. Results show a good correlation with HVI output with R² ranging from 0.730 to 0.965 across nine different methods of calculating the ML, UHML, and UI. Interestingly, statistics calculated from an approximation of the length distribution by weight are the most closely related to the HVI length parameters.

Fiber length is a crucial property throughout many textile processing steps. The high volume instrument (HVI) is the most commonly used tool to assess the properties of cotton fibers. The HVI currently reports two length parameters, the upper-half mean length (UHML) and uniformity index (UI). The UI is the ratio of mean length (ML) to the UHML expressed as a percentage. UHML and ML are extracted from the fibrogram. These parameters are used in the current U.S. cotton classification and global cotton marketing systems. The two values are highly correlated and characterize only the longer fibers in a sample. The fibrogram holds more descriptive information than the two measurements provided by the HVI. However, limited information is available about the stability and repeatability of the fibrogram measurement. This study aims to investigate the stability of the fibrogram, assess the reproducibility across multiple instruments, and determine if corrective actions are required. Three different raw cotton sample sets were tested for this three-stage experiment. The obtained results demonstrate that for a given HVI, the entire fibrogram is stable over both the short term and long term; however, differences among HVIs were observed. The proposed correction procedure effectively reduces the differences among the four HVI lines.

Cotton fiber length parameters are used across the cotton industry to select elite germplasm, purchase cotton bales and manage mill throughput. The High Volume Instrument (HVI) provides the most commonly used fiber length parameters, upper half mean length (UHML) and uniformity index (UI). UI is the ratio of mean length (ML) to the UHML expressed as a percentage. These length parameters, UHML and ML, are generated following the fibrograph principle and are highly correlated with two span lengths on the fibrogram curve. These two length parameters represent a small part of the fibrogram and do not characterize the total within-sample variation. In previous studies, we found that fiber length variation captured by the whole fibrogram improves the prediction of yarn quality. However, HVIs are currently calibrated for UHML and UI. In this study, we investigated a correction method using a set of 461 commercial samples to correct the whole fibrogram curve across HVIs and validated the method using an independent set of 932 commercial samples. The correction procedure lowers the Euclidian distance between fibrograms as much as 35%, bringing the fibrogram measurements into agreement across multiple HVIs. This indicates that the whole fibrogram could be used to improve HVI fiber length measurements across the cotton industry.

A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparels. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we have proposed a laser-based surface profiling system that utilizes a smart camera to sense the 3-D topography of the fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. In this paper, we detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system among 78 swatches of various color, type, and texture. The results show consistency among repeated scans of the same swatch as well as among different swatches taken from the same fabric sample. Also, since swatches taken from the same piece of fabric typically wrinkle similarly, this adds to the feasibility of the system. In other words, it adequately identifies and measures appropriate features of the wrinkles found on a sample.