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Synthetic materials have many drawbacks in high-performance garments because they absorb less moisture and cause allergies to sensitive individuals. Cotton materials cannot satisfy all the requirements and cannot provide the required high performance. This study aims to use eco-friendly materials with a common structure to analyse their suitability for high-performance garment application.

This study used two eco-friendly yarns (bamboo, modal and bamboo: modal 50:50) and yarns per needle (two- and four-ply yarns). with a single jersey knit construction and gauge of 7. The physical, mechanical, appearance, comfort, thermal and ultraviolet protection factor (UPF) protection characteristics were evaluated using 15 tests.

The produced knitted fabrics showed high performance for use as garments with physical, mechanical, appearance, comfort, thermal and UPF protection characteristics that were achieved, tested and analysed. The highest-achieved samples with a good UPF (<15) were made from bamboo material, which has other high-performance characteristics such as antibacterial characteristics, a soft surface, thermal insulation and others.

The single jersey structure was used for producing fabrics as it is the common structure in the garment. Also, only gauge 7 was used for its economics and ease of production.

Wearing clothes requires specifications for feeling comfortable, derived from the fibres, fabrics and finishing properties. This study aims to deal with the effect of economic blends containing hollow fibres, bamboo and cotton/polyester waste on the mechanical properties of the produced fabrics and the appropriate end use.

This research included two blends: one consisted of cotton/polyester wastes blended with bamboo and the other to which Chorisia fibres were added. Two weft counts 10,6/1 Ne were made from each blend, which were used to produce four fabric samples (S1 Chorisia-free and S2 with Chorisia); additionally, another two samples were dyed that contain Chorisia (S3) from each count. The six samples were tested by Kawabata Evaluation System (KES).

The samples gave a good total hand value (THV) for use as men's winter suits, where the thicker count 6/1, with and without Chorisia had better properties, also both counts 6, 10/1 with dye. The hollow fibres affected the fabrics’ properties, including thickness, shear, bending, thermal conductivity and weight. Both blends had a positive effect on THV.

Cotton/polyester waste, Chorisia and bamboo fibres were tested, and 2% Remazol Yellow GNL dye was used.

The ratio of blending, weft counts and dye affected the fabric’s properties, with consequences for the use of the Kawabata system and its applications.

The fabrics used in this research may be considered to be economical and have good THV.

The study proved the usefulness of fabrics made of two blends. The Chorisia component may be seen as a good alternative to cotton fibres to reduce the cost of producing high-consumption winter suit fabrics.

Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to estimate and evaluate without the complexity and time-consuming experimental procedures. The purpose of this study is to develop and select the best regression model equations for the prediction and evaluation of surface roughness of plain-woven fabrics.

In this study, a linear and quadratic regression model was developed for the prediction and evaluation of surface roughness of plain-woven fabrics, and the capability in accuracy and reliability of the two-model equation was determined by the root mean square error (RMSE). The Design-Expert AE11 software was used for developing the two model equations and analysis of variance “ANOVA.” The count and density were used for developing linear model equation one “SMD1” as well as for quadratic model equation two “SMD2.”

From results and findings, the effects of count and density and their interactions on the roughness of plain-woven fabric were found statistically significant for both linear and quadratic models at a confidence interval of 95%. The count has a positive correlation with surface roughness, while density has a negative correlation. The correlations revealed that models were strongly correlated at a confidence interval of 95% with adjusted R² of 0.8483 and R² of 0.9079, respectively. The RMSE values of the quadratic model equation and linear model equation were 0.1596 and 0.0747, respectively.

Thus, the quadratic model equation has better capability accuracy and reliability in predictions and evaluations of surface roughness than a linear model. These models can be used to select a suitable fabric for various end applications, and it was also used for tests and predicts surface roughness of plain-woven fabrics. The regression model helps to reduce the gap between the subjective and objective surface roughness measurement methods.

This paper aims to investigate the influence of picking sequence, weave design and weft yarn material on the thermal conductivity of the woven fabrics.

This work includes the development of 36 woven samples with two weave designs (1/1 plain and 3/1 twill), three picking sequences (single, double and three pick insertion) and six different weft yarn materials (cotton, polyester having 48 filaments, polyester with 144 filaments, spun coolmax having Lycra in core and coolmax in sheath, filament coolmax and polypropylene). The thermal conductivity was measured using ALAMBETA tester.

The results showed that weft yarn material, weave design and picking sequence have a meaningful impact on the thermal conductivity of woven fabric. The value of thermal conductivity was lowest for the fabrics with three pick insertion and 3/1 twill weave in all weft yarn materials.

Plain woven fabric with single pick insertion is feasible for summer wear to enhance the comfort of wearer. By changing the warp yarn grouping and material, improved thermal conductivity/resistance can also be achieved.

The authors have studied the combined effect of different weft yarn materials with different picking sequences and different weave designs on thermal conductivity of the woven fabrics.