Yves-Simon Gloy

Self-optimising Production Systems

One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.

Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

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.

Tubular woven narrow fabrics for replacement of cruciate ligaments.

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.

Increasing the energy efficiency of air jet weaving based on a novel method to exploit energy savings potentials in production processes of the textile industry

This article deals with the energy efficiency of textile weaving machines. A method based on exergy balances has been developed at the Institute for Textile Technology RWTH Aachen University (ITA), Aachen, Germany in order to improve energy efficiency textile machines. The relay nozzles of the air-jet weaving technology need up to 80% of the energy of the weaving machine. At ITA, a new nozzle concept was developed. The developed geometry is a so called High-Volume-Low-Pressure nozzle (HVLP nozzle), based on convergent nozzle aerodynamic theory. With this concept, energy savings up to 30% are possible.

Meta-modeling for manufacturing processes

Meta-modeling for manufacturing processes describes a procedure to create reduced numeric surrogates that describe cause-effect relationships between setting parameters as input and product quality variables as output for manufacturing processes. Within this method, expert knowledge, empiric data and physical process models are transformed such that machine readable, reduced models describe the behavior of the process with sufficient precision. Three phases comprising definition, generation of data and creation of the model are suggested and used iteratively to improve the model until a required model quality is reached. In manufacturing systems, such models allow the generation of starting values for setting parameters based on the manufacturing task and the requested product quality. In-process, such reduced models can be used to determine the operating point and to search for alternative setting parameters in order to optimize the objectives of the manufacturing process, the product quality. This opens up the path to self-optimization of manufacturing processes. The method is explained exemplarily at the gas metal arc welding process.

INDUSTRIE 4.0 - Automation in weft knitting technology

Industry 4.0 applies to the knitting industry. Regarding the knitting process retrofitting activities are executed mostly manually by an operator on the basis on the operators experience. In doing so, the knitted fabric is not necessarily produced in the most efficient way regarding process speed and fabric quality aspects. The knitting division at ITA is concentrating on project activities regarding automation and Industry 4.0. ITA is working on analysing the correspondences of the knitting process parameters and their influence on the fabric quality. By using e.g. the augmented reality technology, the operator will be supported when setting up the knitting machine in case of product or pattern change - or in case of an intervention when production errors occur. Furthermore, the RFID-Technology offers great possibilities to ensure information flow between sub-processes of the fragmented textile process chain. ITA is using RFID-chips to save yarn production information and connect the information to the fabric producing machine control. In addition, ITA is currently working on integrating image processing systems into the large circular knitting machine in order to ensure online-quality measurement of the knitted fabrics. This will lead to a self-optimizing and selflearning knitting machine.

Simulation and Optimisation of Warp Tension in the Weaving Process

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.

Weaving machine as cyber-physical production system: Multi-objective self-optimization of the weaving process

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.

Self-optimizing Production Technologies

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Applying Multi-objective Optimization Algorithms to a Weaving Machine as Cyber-Physical Production System

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.