Search

The warp tension is a critical variable of the weaving process. If the warp tension is too high or too low, the weaving process will be interrupted. In order to find a suitable setting for the weaving machine, the experience of the operator is needed. Self-optimization routines can support the operator in finding optimal settings. Within this paper, the model-based self-optimization of the weaving process developed at Institut fur Textiltechnik der RWTH Aachen University is presented. The self-optimization routine uses an automatic design of experiment to generate data for a full quadratic regression model of the characteristic values of the warp tension. Three weighted quality criteria are used to optimize the machine settings within given boundaries. An improvement is proposed by integrating the vertical warp stop motion position as a factor with high impact on the warp tension. The vertical warp stop motion position is automated and integrated into the optimization process. The adjusted routine is validated on an air jet weaving machine. The test results show that the integration of the warp stop motion position into a self-optimization routine leads to a 35% reduction of tension in the warp yarns. Compared to the existing routine, the integration of the warp stop motion position leads to a 23% higher effect on the warp tension as the target value of the optimization. The statistical validation shows that the quality of the used regression model is high. The described system also reduces the setup time of a weaving machine. Economically, the improvements mean a reduction of production costs by 22%, when producing small lot sizes. The system therefore contributes to the competitiveness of weaving mills in high-wage countries.

Purpose – The purpose of this paper is to deal with the energy efficiency of textile weaving machines. Increasing energy costs and environmental impact are a challenge for textile manufacturers as well as for the developers of textile production machines. As example, air jet weaving is the most productive but also most energy consuming weaving method. Design/methodology/approach – A method based on energy efficiency considered as the main requirements in the design phase has been developed at the Institut für Textiltechnik der RWTH Aachen University (ITA), Aachen, Germany, in order to improve energy efficiency of air-jet weaving machines. Technological developments are always concerned about low energy costs, low environmental impact, high productivity and constant product quality. The high degree of energy consumption of the method can be ascribed to the high need of compressed air required by the relay nozzles during the weft insertion process. Findings – The relay nozzles of the air-jet weaving technology consume up to 80 percent of the air required by the weft insertion process. At ITA a new nozzle concept was developed. The developed geometry is a so called high-volume-low-pressure nozzle, based on convergent nozzle aerodynamic theory. Originality/value – By employing such new concept of relay nozzles within the weft insertion process, energy savings are possible up to 30 percent.

Assistance systems for production machines have to fulfill several functions depending on specific tasks and users. The overall purpose of the use of an assistance system in production is to raise the efficiency of the production. In this text, one approach for an assistance system for a weaving machine is presented. Based on a systematic approach, a system is designed in order to facilitate the repair of broken weft yarns. Using this approach an assistance system with a high general user acceptance can be achieved. Furthermore, the use of assistance systems will have an impact on the necessary qualification of operators in the weaving mills.

Warp tension is a critical variable of the weaving process. If the warp tension is too high or too low the weaving process will be interrupted. In order to find suitable setting for the weaving machine, the experience of the operator or data base systems are used. Within this paper an automatic setup routine following model based self-optimization strategies is proposed. Within the routine, data for a regression model are collected by the weaving machine. For given quality criteria the weaving machine is able to calculate an optimal setting point. Validation of the routine shows that the chosen regression model is suitable; stress on the warp yarns is reduced. In addition, a statistical validation proves the usability of the regression models.

A vision-based measurement system to quantify the yarn density of woven fabrics during production is presented. As an extension to an earlier developed fabric flaw detection system, the proposed framework consists of a combination of basic and custom-made image-processing techniques that allow to precisely track single wefts and warps within fabric images—in real-time. Several adaptations facilitate the measurement of density changes for plain, satin, and twill weaves. In this paper, the algorithmic framework has been evaluated in several comprehensive on-line experiments on a real-world air-jet loom and is additionally compared with three alternative methods for fabric density measurement. It proved to be precise, robust, and applicable for industrial use as it overcomes many of the existing shortcomings of current methods.

Actuators 4(1), 2-16 (2015). doi:10.3390/act4010002, Published by MDPI, Basel

Real (physical) objects melt together with information-processing (virtual) objects to create Cyber-Physical Production Systems (CPPS). Through embedding of intelligent, self-optimizing CPPS in process chains, productivity of manufacturing companies and quality of goods can be increased. Textile producers especially in high-wage countries have to cope with the trend towards smaller lot sizes in combination with the demand for increasing product variations. One possibility to cope with these changing market trends consists in manufacturing with CPPS and cognitive machinery. This chapter presents a method for multi-objective self-optimization of the weaving process. Multi-objective self-optimization assists the operator in setting weaving machine parameters according to objective functions. The implementation of a self-optimization routine in a software-based Programmable Logic Controller (soft-PLC) is presented. The routine enables a weaving machine to calculate the optimal parameter settings autonomously. Set-up time is reduced by 75 % and objective functions are improved by at least 14 % compared to manual machine settings.

Smart manufacturing concepts merge modern production machinery and digital technologies in Cyber-Physical Production Systems (CPPS). CPPS consist of intelligent, real-time-capable, networked sensors and actuators. Operation of such advanced machinery requires substantial employees' skills in various qualification phases. This holds true for different industries, such as the German textile industry. An important measure that is intertwined with the implementation of advanced manufacturing concepts in textile production is supporting employees in their development of skills concerning smart manufacturing concepts. For this purpose, the development of Augmented reality (AR)-based assistance systems in connection with up-to-date textile machinery is regarded as a promising step towards the successful implementation of adequate, user-adaptable digital working environments in the textile industry. As the implementation of such assisted digital working environments in industrial organizations poses challenges, they might most adequately be addressed by adhering to standardized implementation procedures.

Real (physical) objects melt together with information-processing (virtual) objects. These blends are called Cyber-Physical Production Systems (CPPS). The German government identifies this technological revolution as the fourth step of industrialization (Industry 4.0). Through embedding of intelligent, self-optimizing CPPS in process chains, productivity of manufacturing companies and quality of goods can be increased. Textile producers especially in high-wage countries have to cope with the trend towards smaller lot sizes in combination with the demand for increasing product variations. One possibility to cope with these changing market trends consists in manufacturing with CPPS and cognitive machinery. This paper focuses on woven fabric production and presents a method for multi-objective self-optimization of the weaving process. Multi-objective self-optimization assists the operator in setting weaving machine parameters according to the objective functions warp tension, energy consumption and fabric quality. Individual preferences of customers and plant management are integrated into the optimization routine. The implementation of desirability functions together with Nelder/Mead algorithm in a software-based Programmable Logic Controller (soft-PLC) is presented. The self-optimization routine enables a weaving machine to calculate the optimal parameter settings autonomously. Set-up time is reduced by 75 % and objective functions are improved by at least 14 % compared to manual machine settings.

Tekstilec : glasilo slovenskih tekstilcev 59(2), 182-185 (2016). doi:10.14502/Tekstilec2016.59.182-185, Published by University of Ljubljana, Faculty of Natural Sciences and Engineering, Department of Textiles, Graphic Arts and Design, Ljubljana

Smart manufacturing concepts merge modern production machinery and digital technologies in Cyber-Physical Systems (CPS). CPS consist of intelligent real-time-capable and networked sensors and actuators. The operation of such advanced machinery requires new and substantial skills in employees in various qualification phases. Successful implementation strategies take these varying skills of the workforce, which result from diverse cultural, educational, age-or gender-related socio-demographic variables, into account. Thus, the need for a differential-dynamic job design in textile production seems more relevant than ever, especially regarding the increasing number of older employees due to demographic changes in high-wage countries,. An important measure that is intertwined with the implementation of advanced manufacturing concepts in textile production is supporting employees in their development of skills concerning these new production methods. For this purpose, the development of Augmented reality-based assistance systems in connection with up-to-date textile machinery is regarded as a promising step towards the successful implementation of adequate, user-adaptable Cyber-Physical Systems.

This chapter starts with the history of denim fabric and talks about the fibres and yarns used in the manufacture of denim. Challenges and opportunities in engineering and preparing yarns for denim manufacture are explained. Furthermore, the technologies used for denim weaving and different weaving machines are detailed. Sections follow detailing the possibility of manufacturing denim efficiently using intelligent machines, and technological developments for manufacturing high quality denim. Finally, future trends in the weaving of denim are included.

Warp tension is a major parameter of the weaving process. The system analysis of a weaving machine leads to a simulation model for calculating the warp yarn tension. Validation of the simulation has demonstrated that the results correspond well with the reality. In a second step, an improved model of this simulation was used in combination with a genetic algorithm and a gradient based method to calculate optimised setting parameters for the weaving process. A cost function was defined taken into account a desired course of the warp tension. It is known, that a low and constant warp tension course is suitable for weaving. Using the genetic algorithm or the gradient based method leads to optimised weaving machine parameters. Applying the optimised setting parameters on a loom demonstrated that the quality of the produced fabrics can be improved. Further analysis of produced fabrics did not show an influence of optimised weaving machine parameters on the mechanical properties or productivity of the weaving process.

17th World Textile Conference AUTEX 2017 - Shaping the Future of Textiles : 29-31 May 2017, Corfu, Greece 17th World Textile Conference AUTEX 2017 : Textiles - Shaping the Future, AUTEX 2017, Corfu, Greece, 29 May 2017 - 31 May 2017; London [u.a.] : Institute of Physics, IOP conference series / Materials science and engineering, 254, 132003, 1-6 (2017). doi:10.1088/1757-899X/254/13/132003, Published by Institute of Physics, London [u.a.]

17th World Textile Conference AUTEX 2017 - Shaping the Future of Textiles : 29-31 May 2017, Corfu, Greece 17th World Textile Conference AUTEX 2017 : Textiles - Shaping the Future, AUTEX 2017, Corfu, Greece, 29 May 2017 - 31 May 2017; London [u.a.] : Institute of Physics, IOP conference series / Materials science and engineering, 254, 132001, 1-4 (2017). doi:10.1088/1757-899X/254/13/132001, Published by Institute of Physics, London [u.a.]

The human knee is one of the most frequently injured joints. More than half of these injuries are related to a failure of the anterior cruciate ligament. Current treatments (allogeneic and autologous) bear several disadvantages which can be overcome through the use of synthetic structures. Within the scope of this paper the potential of tubular woven fabrics for the use as artificial ligaments has been evaluated. Twelve fabrics made of polyethylene terephthalate and polytetrafluoroethylene were produced using shuttle weaving technology. Mechanical and biological properties of the fabrics were assessed using static tensile testing and cytotoxicity assays. The results obtained within this study show that woven tubular fabrics can be potentially used as artificial ligament structures as they can provide the desired medical and mechanical properties for cruciate ligament replacements. Through the choice of material and weaving parameters the fabrics’ tensile properties can imitate the stress–strain characteristic of the human cruciate ligament. Further assessments in terms of cyclic loading behavior and abrasion resistance of the material are needed to evaluate the success in long term implantation.