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The optimization of a process requires exact knowledge of the process, which is knowledge of correlations and inter-dependence between the process-determining variables and the knowledge over the actual condition of the process. In a data rich knowledge poor process like spinning, where the exact relationships between machine, material, climate and quality are yet to be concluded objectively, this research focuses on the use of artificial neural networks as a tool to find out the correlations between decisive variables and to determine the optimum settings. Drawing frame is considered to be the last fault correction point in spinning preparation chain, therefore, its settings has a vital role to play towards yarn quality. Leveling action point is one of the important auto-leveling settings involving an automatic search function at Rieter drawing frame RSB-D40 and requiring a large amount of sliver. In this study, attempts were made to optimize the leveling action point. Optimization of draft settings is also within the scope of this article. The ANNs were used to achieve such objectives and they were found to be very helpful in identifying the optimum settings and hence decreasing material loss and improving sliver quality.

This chapter proposes a protocol based on blockchain technology applied to corporate social responsibility (CSR). The first part discusses the characteristics associated with CSR actions and the main difficulties its development faces, such as transparency, security, fault tolerance, among others. Subsequently, the authors describe the characteristics and concepts related to blockchain-based developments to later describe our framework for the control and development of CSR actions based on blockchain. Herein, the authors also describe how to publicly and privately identify the participating elements of CSR and the operations and resources necessary for the implementation and operation of the proposed protocol.

This paper aims to provide an overview of the current manufacturing methods for three-dimensional textile preforms while providing experimental data on the emerging techniques of combining yarn interlocking with yarn interlooping.

The paper describes the key textile technologies used for composite manufacture: braiding, weaving and knitting. The various textile preforming methods are suited to different applications; their capabilities and end performance characteristics are analysed.

Such preforms are used in composites in a wide range of industries, from aerospace to medical and automotive to civil engineering. The paper highlights how the use of knitting technology for preform manufacture has gained wider acceptance due to its flexibility in design and shaping capabilities. The tensile properties of glass fibre knit structures containing inlay yarns interlocked between knitted loops are given, highlighting the importance of reinforcement yarns.

The future trends of reinforcement yarns in knitted structures for improved tensile properties are discussed, with initial experimental data.

The purpose of this paper is to propose the materials structure-wetting behaviour relationships and to show their peculiarities for some types of surgical woven fabrics and applications of liquids.

To show the effects of fabrics structure on wetting behaviour of surgical textile materials, the special structural indices in terms of yarns and filaments lateral area were used.

It was shown good correlation between total lateral area of filaments in unit area of woven fabrics and wetting contact angle of liquid drops on the tested samples. Probably due to different structure of woven fabrics at a level of fibres, another index, i.e. total lateral area of yarns in unit area of fabrics, is not suitable to show clear effect on wetting behaviour of the samples. The possibilities of applications of relationships for several types of textile materials and liquids were indicated.

To date there are no investigations concerning relationships between special structural properties of the surgical woven fabrics and their wetting behaviour. On a basis of the proposed approach into fabrics structure evaluation, this study developed analysis and some types of new equations for prediction of wetting contact angle of the materials.

The purpose of this paper is to investigate the mechanical properties’ behaviors of woven composite cut-out structures with specific parameters. Because of the complexity of micro-scale and meso-scale structure, it is difficult to accurately predict the mechanical material behavior of woven composites. Numerical simulations are increasingly necessary for the design and optimization of test procedures for composite structures made by the woven composite. The results of the proposed method are well satisfied with the results obtained from the experiment and other studies. Moreover, parametric studies on different plates based on the stacking sequences are investigated.

A multi-scale modeling approach is suggested. Back-propagation neural networks (BPNN), radial basis function (RBF) and least square support vector regression are integrated with efficient global optimization (EGO) to reduce the weight of assigned structure. Optimization results are verified by finite element analysis.

Compared with other similar studies, the advantage of the suggested strategy uses homogenized properties behaviors with more complex analysis of woven composite structures. According to investigation results, it can be found that 450/−450 ply-orientation is the best buckling load value for all the cut-out shape requirements. According to the optimal results, the BPNN-EGO is the best candidate for the EGO to optimize the woven composite structures.

A multi-scale approach is used to investigate the mechanical properties of a complex woven composite material architecture. Buckling of different cut-out shapes with the same area is surveyed. According to investigation, 45°/−45° ply-orientation is the best for all cut-out shapes. Different surrogate models are integrated in EGO for optimization. The BPNN surrogate model is the best choice for EGO to optimization difficult problems of woven composite materials.

This paper aims to extracted sericin from the cocoons of Bombyx mori silkworms, and sericin powder was applied onto drawn textured polyethylene terephthalate (PET) yarns as a spin finish. The reactivity on the surface of PET yarns was analyzed through Fourier transform infrared spectrophotometry–attenuated total reflectance (FTIR-ATR) and dyeing with methylene blue as a reactive dye. Also, investigations were conducted on the effects of sericin, citrc acid (CA) (as a crosslinking agent), and sodium hypophosphite (as a catalyst) concentrations on some properties of false-twist textured PET yarns.

A false-twist texturing machine (Scragg-Shirley minibulk, England) was used with the draw ratio of 1.05, heating temperature of 120°C, texturing speed of 100 m min⁻¹ and applied twist of 3,000 TPM. The aqueous extraction of sericin was carried out by the boiling of the raw silk in distilled water with L:R: 40:1 for 120 min. The aqueous solution was filtered with a filter paper to remove the impurities and insoluble fibroin. Finally, the sericin solution was freeze-dried to obtain the sericin powder. The sericin solution was applied on the drawn textured PET yarns using the “pad-dry-cure” method.

Sericin fixation onto the PET yarns was confirmed by FTIR-ATR. The results showed that there were no significant changes in the tensile strength, linear density, crimp contraction and crimp modulus, elongation at break and shrinkage. In contrast, a substantial increase was observed in moisture regain, vertical wicking, dye uptake and ultraviolet protection. There was also a reduction just in the electrical resistivity, in the presence of sericin.

Although sericin has been known to have numerous beneficial properties, its application in textile industry as a spin finish has not been reported yet.

Compared with the high stiffness of traditional CNC machine tools, the structural stiffness of industrial robots is usually less than 1 N/µm. Chatter not only affects the quality of robotic milling but also reduces the accuracy of the milling process. The purpose of this paper is to reduce chatter in the robotic machining process.

First, the mode coupling chatter mechanism is analyzed. Then the milling force model and the principal stiffness model are established. Finally, the robot milling stability optimization method is proposed. The method considered functional redundancies, and a new robot milling stability index is proposed to improve the quality of milling operations.

The experimental results prove a significant reduction in force fluctuations and surface roughness after using the proposed robotic milling stability optimization method.

In this paper, a new robot milling stability index and a new robot milling stability optimization method are proposed. This method can significantly increase the milling stability and improve the milling quality, which can be widely used in the industry.

This paper aims to introduce a method to couple truss finite elements to the material point method (MPM). It presents modeling reinforced material using MPM and describes how to consider the bond behavior between the reinforcement and the continuum.

The embedded approach is used for coupling reinforcement bars with continuum elements. This description is achieved by coupling continuum elements in the background mesh to the reinforcement bars, which are described using truss- finite elements. The coupling is implemented between the truss elements and the continuum elements in the background mesh through bond elements that allow for freely distributed truss elements independent of the continuum element discretization. The bond elements allow for modeling the bond behavior between the reinforcement and the continuum.

The paper introduces a novel method to include the reinforcement bars in the MPM applications. The reinforcement bars can be modeled without any constraints with a bond-slip constitutive model being considered.

As modeling of reinforced materials is required in a wide range of applications, a method to include the reinforcement into the MPM framework is required. The proposed approach allows for modeling reinforced material within MPM applications.

The wet-spinning process is very important for the development and production of new lightweight design materials. The washing process is determined as one of the most cost-expensive part of wet spinning. The purpose of this paper is to show the development of a new washing concept. It proposes to increase the washing performance by decreasing fiber-fiber-interfaces during the washing process.

For this purpose, conventional washing concepts are investigated by means of simulations and experiments to obtain process knowledge. Computational fluid dynamics simulation and particle image velocimetry measurements are used to investigate the process.

The overall deficit in conventional washing methods is the large number of fiber-fiber-interfaces, which inhibit the solvent transport out of the compact fiber bundle. Therefore, a new washing concept with included water nozzles is developed. Based on the simulations and observations it is found that the arrangement of the nozzles has direct influence on the fanning of the fiber bundle.

With increased fanning of the fiber bundle a more efficient solvent transport is expected. The developed washing box is a prosperous concept to achieve a higher washing performance during the wet-spinning process. The variable design of the washing box makes it possible to test different nozzle configurations and designs. In this paper the two most promising nozzle arrangements are shown and compared to each other.

The purpose of this paper is to reduce the strain and vibration during robotic machining.

An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.

The proposed intelligent approach can significantly reduce robotic machining strain and vibration.

The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.