The act of purchasing a product is often shaped by a multitude of factors, both tangible and intangible. Among these, the sense of touch plays a significant and often underappreciated role in influencing the choices made by consumers. Understanding how the tactile experience affects consumers in this unique context can shed light on broader trends in global consumer behavior. This study delves into the intricateweb of touch criteria and their impact on the purchasing decisions of Tunisian consumers. Through a comprehensive analysis of touch-related factors, this study offers a nuanced perspective on the intricacies of consumer choice, making it a valuable contribution to the fields of marketing and consumer psychology.

In our fast-paced world, characterized by the unrelenting pressures of daily life, consumer well-being remains an ever-growing concern. The pursuit of well-being, a fundamental and universal human need, profoundly impacts human survival and life quality. Elasticity plays a pivotal role in consumer well-being. This study conducts an exploratory analysis of six factors related to flex fit well-being descriptors through typological and factor analyses. The aim is to identify the relationships between these factors and their contributions, enabling the creation of descriptors that simulate the perception process and predict the overall perception of clothing well-being. This study's exploratory statistical analysis deepens our understanding of the relationships and contributions of properties related to flex fit well-being, shedding light on the perception of clothing well-being.

Clothing well-being, a highly sought-after attribute by consumers, is a complex and subjective concept influenced by various factors. Thermal well-being, a key component, is tied to hot/cold sensations and reflects satisfaction with thermal conditions. Achieving a universally ideal thermal environment is challenging due to the individual variability in factors such as body temperature, activity level, and fabric properties. In this study, we conduct a comprehensive analysis of 12 thermo-physical factors related to thermal well-being descriptors through typological and factorial analyses. Our objective is to elucidate the intricate relationships between these factors and assess their contributions. Drawing insights from these statistical analyses, we aim to establish a set of descriptors that can replicate the perceptual process, facilitating the prediction of clothing's overall thermal well-being. This research holds the potential to inform the development of clothing that caters to diverse thermal preferences, offering consumers a more comfortable and satisfying experience.

Nowadays, customers expect that the products marketed will meet their needs, even those unexpressed. Therefore, every manufacturer must try to decipher its needs in order to ensure the survival of its business. This research work aims to help textile manufacturers determine the fundamental aspects of overall wellbeing which influence the purchasing decision of Tunisian consumers through the conduct of a questionnaire and the interpretation of the results. The results obtained allowed us, among other things, to classify the aspects of well-being from the least to the most influential.

We can observe that the use of abrasives has increased in recent years. In this study, the grain of perlites and pumice stones was harvested from industries and used as abrasive grains. From the tests carried out on these grains, it is possible to determine the application of the abrasives. The particles were sieved, cleaned, and sorted according to the particle size analysis which consists in determining the proportion of the different particle size classes. To determine the appearance of granules and to determine their diameter, scanning electron microscopy (SEM) was used. With both hollows and bumps present, the morphology of grains is very diverse. Pumice grains have more calcium atoms than perlite grains, according to investigations done using the inductively coupled plasma mass spectrometry (ICP/MS) technique. The perlite grains exhibit low resistance to wear in the micro-Deval test (MDE), whereas the pumice stone grains exhibit strong resistance.

The natural waste from two fibers has been gathered during the gypsum manufacturing sectors and recycled by fraying after cleaning fibers to reduce the detrimental effects on the environment. A thermogravimetric analysis (ATG) exhibits that the thermal degradation of fibers starts from 350 °C. The Differential scanning calorimeter analysis (DSC) curves show a peak at 85–90 °C which can be attributed to water loss. These cellulosic fibers have been blended with cotton fibers to make them non-woven and improve the performance of the material. The findings demonstrate that variations in the proportions of (cotton/tow) fibers have an effect on mechanical strength (Tear test, traction.). The chemical modification reduces resistance to water penetration chemical modification reduces resistance to water penetration. Roughness assessments of non-wovens using a User Surface Tester (UST) reveal that an increase in the amount of tow compared to cotton causes an increase in the roughness of samples. These fibers can be used in several fields such as construction, automotive, and also as composite reinforcements.

This paper presents the development of certain abrasives based on selected industrial wastes and gives an idea of their postuse behavior. These abrasives are manufactured using two methods: pulverization and coating. For these purposes, we used cellulosic nonwoven fibers as reinforcement, three types of resin (polyurethane, acrylic, and polyester) as a matrix, and abrasive grains of silicon carbide (SiC) and silica to obtain the abrasive character. We report the wear of the developed materials by abrasion, the evaluation of their roughness, the influence of the type of the abrasive grains, and their sizes on the wear performances. Scanning electron microscopy was performed to show the morphology of abrasives. The weight loss of abrasives was measured by thermogravimetric analysis and its derivative. Fourier-transform infrared spectroscopy allows the chemical characterization and identification of abrasive grains. Under the same test conditions, experimental results indicate that silica-based abrasives exhibit higher surface roughness and abrasion wear rate than SiC-based abrasives.

This paper aims to assess the potential for the use of waste cellulosic fibers and recycled grains as materials for the development of abrasives. However, abrasive wear produces low durability and a lot of waste. The improvement of adhesion between the components of composites is the key to extending their lifetime. For this purpose, the surface of the fiber was modified by cationic reactive agents. Composites were manufactured with treated fibers, polyurethane matrix, and iron shavings as abrasive grains. To obtain this product, the coating phenomenon is maintained. DSC and TGA analysis show the thermal stability of abrasives. Contact angle and zeta potential measurements have been used to evaluate the specific surface-chemical changes imparted by surface treatments of specimens. EDX and SEM observations indicate that matrix-reinforcing particle-phase boundaries have a very important effect on the using properties of these materials and revealed that the abrasion resistance of composite is sensitive to fiber/matrix adhesion.

Natural fibers represent renewable materials which, nowadays, are experiencing a great revival. They are low-density materials yielding considerably lightweight composites with highly specific properties. However, the disadvantage of these fibers is the low adhesion with most polymers. In composites, the matrix as well as the reinforcing preserve their physical and chemical properties, offer a better combination of mechanical properties. In the present study, we describe the modification of reinforcement surface by alkali treatment and its influence on the adhesion between the components of the composite, which is evaluated using zeta potential and contact angle tests to estimate the specific surface-chemical changes.

Stiffness is one of the most important utility properties of textile materials and plays a significant role in well-being due to its influence on physiological comfort. There are a lot of structural properties of textile materials also operating parameters (knitting + finishing) influencing stiffness. As part of our research, we proposed to help industry adjust the most relevant operating parameters prior to actual manufacturing to achieve the desired stiffness and satisfy consumers by the conception of a predictive artificial neural network’s models to predict the bending stiffness of knitted fabrics.

Due to its influence on physiological comfort, the appearance of fabric has a significant role in well-being. For apparel application, crease recovery is considered as a significant property of textiles. In this paper, the objective is to help industrials to predict the input variables (structural) from a fixed value of the crease recovery angle thanks to the use of ANNi.

This article aims to develop a non-woven abrasive material based on textile waste with suitable mechanical properties and surface conditions for the washing treatment of jeans. For this proposal, tow fibers mixed with cotton fibers were reinforced into a polyurethane resin and iron shavings using a coating technique. Abrasive morphology has been examined using MEB analysis. Their elemental composition was identified by EDX Test. The chemical characterization (FTIR) of iron grains was also mentioned. The influence of chemical fiber treatments (alkalinization and cationization) on the mechanical performance and surface properties of nonwovens has been analyzed. To examine fiber-matrix bonding and adhesion, a fiber pullout test was performed. The results showed that nonwoven created with cationized tow fibers improve interfacial properties compared to those made with untreated or alkalized fibers. While their mechanical qualities (Tear test, traction, etc.) were slightly reduced. The variation in the percentages of fibers (tow/cotton) has also an impact on the mechanical strength of the reinforcements. Using the Universal Surface Tester (UST), roughness measurements of nonwovens surface showed that the incorporation of Tow fibers led to significant improvement in the roughness. Cationization at 3% gives good interfacial adhesion and acceptable mechanical performance. This sample has been tried and validated by washing-out experts.

The manufacture of abrasives based on waste grains of pumice stones and pearlites represents the aim of this work is to minimize both the cost of the product and to recover industrial waste that harms the environment. We also used a non-woven based on cellulosic fibers as reinforcement and three types of resins (polyurethane, acrylic, and polyester). Obtaining these products is designed by two processes (by spraying and by coating). The effect of the manufacturing process, the type and size of the grains on the abrasive wear of denim fabrics, the material removal rate (MRR), and the surface morphologies obtained were quantitatively evaluated. A chemical analysis (FTIR) and a thermal analysis were carried out (ATG/DSC). Scanning electron microscopy (SEM) was performed to see the appearance of these grains and measure their diameter. Inductively Coupled Plasma Mass Spectrometry (ICP/MS) analyses show that pumice grains contain more calcium atoms than pearlite grains. The different abrasion grains were compared according to their polishing effect. We note that the abrasive manufacturing process, type, and size of abrasive particles affect the MRR. Pumice grits allow for deeper polishing compared to other grits.

In this paper, the development of a new composite as an abrasive material was reported. The composite was constituted of iron shavings as grains, a prepared non-woven from cellulosic fibers waste as support and a polyurethane resin as matrix. The studied composite was investigated using FT-IR spectroscopy and SEM analyses. FT-IR data confirmed that the non-woven composite was composed of cellulosic fibers and iron. SEM photos indicated that the resin was distributed on the surface of the composite and the iron grains were strongly fixed. The properties of the prepared composites were evaluated and compared with some commercial abrasives. The effect of some chemical modifications on the non-woven characteristics and on the lifetime of the prepared abrasives was studied. The concentration of the resin on the lifetime of abrasives was also discussed. The results showed that the composites made with polyurethane resin had longer life compared to some commercial abrasives. Chemically modified non-woven supports exhibited better results compared to untreated non-woven supports.

Stiffness is one of the most important utility properties of textile materials and plays a significant role in well-being due to its influence on physiological comfort [1]. On that point are a great deal of structural properties of textile materials also operating parameters (knitting+finishing) influencing stiffness and there are also statistically significant interactions between the principal factors determining the stiffness of textile materials. As part of our research, we proposed to facilitate the industry adjust the most relevant operating parameters before actual manufacturing to reach the desired stiffness and satisfy consumers. It warrants the application of artificial neural nets (ANNs) to predict the stiffness of finished knitted fabrics and the utilization of the Fuzzy Decision Tree in the selection procedure, to puzzle out the problem of insufficient data and boil down the complexity of predictive models. Moreover, a virtual leave one out approach dealing with overfitting phenomenon and allowing the selection of the optimal neural network architecture was applied.

Air permeability is one of the most important utility properties of textile materials as it influences air flow through textile material. Air permeability plays a significant role in well-being due to its influence on physiological comfort. The air permeability of textile materials depends on their porosity. There are a lot of structural properties of textile materials also operating parameters (knitting+finishing) influencing air permeability and there are also statistically significant interactions between the main factors influencing the air permeability of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. Two types of artificial neural networks (ANNs) model have been set up before modeling procedure by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. Moreover this study exhibited that air permeability could be predicted with high accuracy for stretch plain knitted fabrics treated with different finishing processes. Within the framework of the work presented, ANNs were applied to help industry to adjust the operating parameter before the actual manufacturing to reach the desired air permeability and satisfy their consumers.

Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a model able to simulate objectively the consumers’ perception. The global thermal comfort of stretch knitted fabrics is a multi-criteria phenomenon that requires the satisfaction of several properties at the same time. In this paper, we used the desirability functions to evaluate the satisfaction degree of global thermal comfort. Statistical method was used to investigate the interrelationship among knit thermo-physical properties, and group them into factors. Two models of artificial neural network (general and special) have been set up to predict the global thermal comfort from structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. By combining the strengths of statistics and fuzzy logic (data reduction and information summation) also a neural network (self-learning ability), hybrid model was developed to simulate the consumer thermal comfort perception. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown optimum input parameter. Finally, this forecasting can help industrials to anticipate the consumer’s taste. Thus, they can adjust the knitting production parameter to reach the desired global thermal comfort to satisfy this consumer.

An artificial intelligence-based system approach is presented in which the effects of the operating parameters and intrinsic features of yarn and fabric on Thermal Conductivity of Stretch Knitted Fabrics are investigated. These parameters were pre-selected according to their possible influence on the outputs which were the thermal conductivity. An original fuzzy logic based method was proposed to select the most relevant parameters. The results show that Knitted Structure’s is the most important input parameter, followed by Lycra Proportion (%), Loop length (cm), Yarn Count, Weight per Unit Area (g/m 2 ), Thickness (m), Gauge, Lycra Yarn Count (dtex) and Yarn Composition. According to our previous works, two types of model have been set up by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. The relative importance of the input variables was calculated using the connection weight approach. The results were found to agree with the fuzzy logic based sensitivity criterion. The trend analysis of the developed model revealed the influence of various input parameters on the thermal conductivity of knitted fabrics. Thus, it is believed that artificial intelligence System could efficiently be applied to the knit industry to understand, evaluate and predict thermal comfort parameters of stretch knitted fabrics.

In this paper, an artificial neural network (ANN) aided system for designing knit stretch materials based on the virtual leave one out approach is presented. This system aims at modeling the relation between functional properties (outputs) and structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. Knitted fabric structure type, yarn count, yarn composition, gauge, elasthane fiber proportion (%), elasthane yarn linear density, fabric thickness and fabric areal density, were used as inputs to ANN model. These models have been validated by a testing data. The developed neural model allows designers to optimize the structure of knit stretch materials according to the functional properties.

An artificial neural network (ANN) was developed to predict the thermal resistance of knit fabrics. Thickness, porosity, air permeability, weight per unit area, and fiber conductivity were taken as input variables of the ANN. Data on thermal resistance were measured on experiments carried out on jersey knitted structures. An original (virtual leave-one-out) approach dealing with the overfitting phenomenon and allowing the selection of the optimal neural network architecture was used. The optimal ANN model contained three hidden neurons in the hidden layer. This model was validated by testing data, and the confidence intervals on the predictions were evaluated. It shows good performance in prediction with better accuracy. The developed neural model is expected to be used for a wide industrial context.