The influence of mechanical action on felting shrinkage of wool fabric in the tumble dryer

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Felting shrinkage of untreated wool fabric occurs easily during tumble drying. Mechanical action applied on fabrics plays a significant part in felting shrinkage of wool fabric. In general, the more severe the mechanical action of a washing or drying machine, the more rapid is felting shrinkage. However, both the degree of mechanical action applied on fabric and the type of mechanical action could influence felting shrinkage of untreated wool fabric. In the current study, fabric movement and felting shrinkage of untreated wool fabric at different rotation speeds of the drum in a tumble dryer under no heating condition were studied. Based on the different fabric movements at different rotation speeds of the tumble drum, the extent of impact force and rubbing force at different rotation speeds were assessed through their ranking. The total mechanical action applied on the fabric was expressed by the percentage of thread removal of “thread removal fabric” during drying process. The results showed that lowest mechanical force on fabrics could be achieved when the higher rotation speed of the drum was used for drying wool fabrics in tumble dryers, and it could prevent wool felting shrinkage. It was also found that falling of the fabric followed by impact to the drum wall caused less felting shrinkage than sliding with rubbing between fabrics. Therefore, falling movement of fabric could be a potential method to dry wool fabric in drying machines without causing severe felting shrinkage

Dimensional change of wool fabrics in the process of a tumble-drying cycle

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Currently domestic tumble dryers are popularly used for drying garments; however, excessive drying and the inappropriate way of tumble agitation could waste energy and cause damage to or the dimensional change of garments. Shrinkage of wool fabrics during tumble drying causes a serious problem for wool garments. The current study investigated the shrinkage of untreated and Chlorine-Hercosett–finished wool fabrics at different drying times. Temperature of air in the tumble dryer, temperature of fabric, moisture content of fabric, and dimensional change at different drying times were measured. For the duration of the tumble drying, the rise of fabric temperature and the reduction of moisture content on the wool fabric were investigated to explore their relationship to the shrinkage of wool fabrics in the tumble-drying cycle. It was found that the tumble-drying process can be divided into different stages according to the temperature change trend of wool fabrics. The shrinkage mechanisms of the untreated and the treated fabrics were different. The dimensional change of untreated wool fabric was caused mainly by felting shrinkage during tumble drying. Chlorine-Hercosett–finished wool fabric can withstand the tumble-drying process without noticeable felting shrinkage due to the surface modification and resin coating of surface scales of wool fibers. The finding from the current research provides further understanding of the shrinkage behavior of wool fabrics during the tumble-drying process, leading to optimizing operational parameters at specific stages of a tumble-drying cycle

Exergy analysis of porous cotton fabric drying process during the domestic air vented dryer

This study reveals energy and exergy efficiencies of the fabric drying processes during air vented dryer. Exergy models of thedrying processes have been formed and each stage is examined in terms of exergetic parameters. Additionally, parametricstudies, including the exergy destruction rates, exergy efficiencies, and exergy loss ratios of the system and its components,have been investigated under various operating conditions. The results indicate that the exergy efficiency increases with theincrease in drying rate. Heater of dryer is the highest exergy destruction component of the whole dryer and its powersignificantly affects exergy destruction of whole drying process; while fan and motor of driving drum are lower exergydestruction component of dryer. Use of staged heating model of adjusting heater power based on drying period is found to be aneffective method to reduce the exergy destruction rate of dryer and fabric damage caused by over-drying. Specifically, exergyefficiency of dryer can be improved by increasing the heater power during the warm up and the constant rate period, or bydecreasing the drying-power during the falling rate and the blow-air period. The findings are found to be useful to systemdesign and performance optimization of domestic dryer in term of reducing irreversibility of the drying system.

Low Cost Interconnected Architecture for the Hardware Spiking Neural Networks

A novel low cost interconnected architecture (LCIA) is proposed in this paper, which is an efficient solution for the neuron interconnections for the hardware spiking neural networks (SNNs). It is based on an all-to-all connection that takes each paired input and output nodes of multi-layer SNNs as the source and destination of connections. The aim is to maintain an efficient routing performance under low hardware overhead. A Networks-on-Chip (NoC) router is proposed as the fundamental component of the LCIA, where an effective scheduler is designed to address the traffic challenge due to irregular spikes. The router can find requests rapidly, make the arbitration decision promptly, and provide equal services to different network traffic requests. Experimental results show that the LCIA can manage the intercommunication of the multi-layer neural networks efficiently and have a low hardware overhead which can maintain the scalability of hardware SNNs

Efficacy of normodyne-magnesium sulfate combination treatment on pregnancy-induced hypertension, and its effect on VEGF and Flt-1 levels

Purpose: To investigate the efficacy of the combined use of normodyne and magnesium sulfate in the treatment of pregnancy-induced hypertension, and its effect on vascular endothelial growth factor (VEGF) and factor receptor-1 (Flt-1) levels in serum.Methods: A total of 100 patients with pregnancy-induced hypertension attending Maternal and Child Health Hospital of Xinjiang Uygur Autonomous Region, Xinjiang, China, were categorized as Group A, and then further subdivided into control sub-group (who were treated with magnesium sulfate only) and study sub-group (treated with magnesium sulfate plus normodyne). Furthermore, 100 healthy pregnantwomen attending the hospital for prenatal examination during the same period were categorized as Group B. Serum expressions of VEGF and Flt-1 in all patients were determined and compared. The therapeutic effect, adverse reactions, adverse pregnancy outcomes, blood pressure before and after treatment, 24 h proteinuria, and serum expression levels of VEGF and Flt-1 in the study and control groups were determined and compared.Results: Serum VEGF levels in patients with pregnancy-mediated hypertension were significantly lower than those of healthy pregnant women, and Flt-1 was raised in healthy pregnant women (p < 0.05). In the study group, treatment was markedly more effective, and the degree of amelioration of blood pressure, 24 h proteinuria, serum VEGF, and Flt-1 were significantly higher than for control sub-group. There were lower adverse pregnancy outcomes in study sub-group than in control (p < 0.05).Conclusion: The combination of magnesium sulfate and normodyne produces greater clinical efficacy in the treatment of patients with pregnancy-induced hypertension than magnesium sulfate alone, and also shows a high safety profile

Anomaly Detection in Batch Manufacturing Processes using Localised Reconstruction Errors from 1-Dimensional Convolutional AutoEncoders

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams

Convolutional AutoEncoders for Anomaly Detection in Semiconductor Manufacturing

Semiconductor manufacturing, characterised by its complex processes, demands efficient anomaly detection (AD) systems for quality assurance. This study extends from previous work utilising unsupervised Convolutional AutoEncoders for AD in Semiconductor batch manufacturing by applying the technique to a novel dataset supplied by a local Semiconductor Manufacturer. Our method uses an approach that employs 1-dimensional Convolutional Autoencoders (1d-CAE) to improve AD performance and interpretability through the numerical decomposition of reconstruction errors. Identifying anomalies this way allows engineering resources to explain anomalies more effectively than traditional methods. We validate our approach with experiments, demonstrating its performance in accurately detecting anomalies while providing insights into the nature of these irregularities. The experiments also demonstrate the impact of training setup on detection capability, outlining an efficient framework for determining an optimal hyperparameter set-up in an industrial dataset. The proposed unsupervised learning approach with AE reconstruction error improves model explainability, which is expected to be beneficial for deployment in semiconductor manufacturing, where interpretable and trustworthy results are critical for solution adoption by process engineering teams