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This work focuses on optimizing and predicting the tenacity of twin-sheath single-core hybrid yarn. This study aims to predict and maximize yarn performance by investigating key factors influencing yarn tenacity.

Three critical parameters − ultra-high molecular weight polyethylene (HPPE) denier, stainless steel micron size and twist per meter − were considered for making multicomponent yarn and optimized using the Box-Behnken design (BBD), a response surface methodology variant. The hybrid yarn studied consists of a stainless-steel core, a polyester inner layer and an HPPE outer layer with opposite twists. The ASTM D2256 method was applied on Instron 3365 machine to measure yarn tenacity.

The optimized yarn setup involved 200 twists per meter, 400 Den HPPE and 45-micron stainless steel, resulting in a 127.5 cN/Tex tenacity. The quadratic model best fits the data, with R² values close to 1.00 (R² = 0.9935, adjusted R² = 0.9817, projected R² = 0.8956), a lower PRESS value of 445, a higher adequacy precision of 19.6816 and a higher TPC percentage of 35.23%. The analysis of variance results confirmed the model significance (F-value = 84.75, P-value < 0.0001), and the average relative error was found to be 3.43%, indicating predictive accuracy.

This study demonstrates the effectiveness of the BBD in optimizing hybrid yarn tenacity, providing valuable insights in terms of core yarn and outer sheath yarn linear density with twist per meter. The work presents a novel approach to hybrid yarn optimization and prediction, expanding the potential for further research and development in textile engineering.

Spunlacing is a promising nonwoven technology for the production of fabric with good handle and better structural integrity. Structural parameters such as pore size, thickness and number of binding point/entanglement between fibres are decisive for good mechanical and comfort properties of nonwoven fabrics. This study aims to focus on the effect of different process parameters on the structural change in spunlace fabrics.

Spunlacing is purely a mechanical bonding technology where high-speed jets of water strike a web to entangle the fibres. Different spunlace nonwoven structures were produced by varying processing parameters such as waterjet pressure, delivery speed, web mass and web composition as per four-factor, three-level Box–Behnken design. The effect of these parameters on the structural arrangement was studied using scanning electron microscopy. An attempt has also been made to study the changes in pore geometry and thickness of the fabrics by using response surface methodology with backward elimination.

Significant structural changes were observed with variation in water pressure, web mass and web composition. The test results showed that fabric produced at higher waterjet pressure has lower mean pore diameter and lower thickness. The variation in mean pore diameter and mean thickness due to waterjet pressure is around 26 and 34 per cent, respectively, at 95 per cent significance level. The web composition and web mass also significantly influence the mean pore diameter and thickness at 95 per cent significance level. There is a strong positive correlation (r = 0.523) between mean air permeability and mean pore diameter of fabric, and this correlation is significantly linear. A strong negative correlation (r = −0.627) is found between weight and air permeability of fabric.

The delivery speed failed to show any significant effect; this is in contrary to the general expectation.

The effect of concurrent variation in waterjet pressure, web mass, delivery speed and web composition on the structure of spunlace nonwoven is studied, which was not reported in the literature. The effect of web composition on pore diameter of spunlace nonwoven is interesting finding. This study is expected to help in designing the spunlace nonwoven as per end uses and specifically for apparel application.

This study has demonstrated the effectiveness of the Internet of Things (IoT)-based technology using ANNs for localising AE sources in rail sections, providing a promising avenue for future research and practical applications.

The paper’s main focus is to develop an IoT based, energy efficient and smart sensor-based system that can detect AE sources accurately and effectively. In this study, the AE sensor is attached to an Arduino board for wireless data transmission. As the AE simulation process in this paper is an non-destructive testing (NDT) technique, pencil lead break (PLB) is done on the top flange (TF), side of top flange (STF), web and bottom flange (BF) to simulate artificial AE sources in the rail section. The generated AE signal due to PLB is collected by mounting the AE sensor over the rail web portion. It is found from the literature that optimal placement of the AE sensor is at the web portion of the rail section. Therefore, the good-quality signal from every segment (i.e. TF, web and BF) of the rail section can be captured from the web part. After capturing the signals wirelessly, the AE features like amplitude, energy, duration, etc. are used to find the AE source location using an artificial neural network (ANN). The developed ANN model is giving very promising results in terms of localisation of the AE source. The main challenge of this paper is transmitting AE signals wirelessly to DACs. As the ANN model is running perfectly, it can be said the Arduino can transmit the full packet AE signal to DAC through the Internet.

The experiment conducted to localise AE sources in a rail section using the developed ANN model has yielded very promising results. PLB is applied at 10 mm intervals up to 1,200 mm on the top flange, side of the top flange, web and bottom flange of the rail section. The MATLAB NNTOOL was used to develop the ANN model, which accurately detected AE sources in the rail section. The AE sensor mounted at the web provided excellent localisation of AE sources, with PLB at the TF, STF, web and BF detected without error. The percentage of error was found to be less than 1%, which is a highly promising result. Consequently, the developed ANN model has significant potential for use in detecting and localising damage in railway systems, which could ultimately improve the safety and reliability of railway transportation.

This study has shown that the use of ANNs for damage detection and localisation and the use of IoT in railway systems is more practical and can lead to significant improvements in efficiency and safety. With further research and development, the developed ANN model could become an essential tool for maintaining the rail section’s infrastructure, ultimately enhancing the railway system’s overall performance and reliability.