Marco Saggiomo

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.

Automation in quality monitoring of fabrics and garment seams

Abstract By increasing fraction of automation technologies, it is necessary to implement capable quality monitoring systems along the textile productions chain. In general, the observation of process parameters or the implementation of vision systems has been investigated. Because product quality is strongly depended on visual appearance the development of feasible vision systems is strongly pushed by industry demands. However, the use of cameras and proper lighting conditions needs to fulfill complex product requirements. Whenever defects occur during manufacturing products, extra costs incur due to the efforts and the time spent to fix these defects. So, it is a goal for manufacturers to minimize these costs by trying to detect those defects before they happen. Therefore, manufacturers started to equip their industrial machines with automatic defect detection systems. One of the major defects that can occur in weaving industry is yarn irregularity. Yarn irregularity presents a problem because it reduces the economic value of the woven product. The following chapter presents current technologies and innovative developments in the field of vision-based fabric and seam inspection.

Reduction of the Weaving Process Set-up Time through Multi-Objective Self-Optimization

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 multiobjective 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.

SozioTex-Sociotechnical systems in the Textile Industry: InterdisciplinaryCompetence Build-up in Human-machine Interaction Facing DemographicChange

High-wage countries are on the brink of change, due to social and technological effects. In this paper, we will first give an outlook on both these effects concerning the German textile industry. Second, we will shortly describe the interdisciplinary build-up of our research group which influences the way how we address our research issues. Finally, we will outline two prototypical applications that serve as demonstrators for further user tests and subsequent developments.

Fully Automatic Faulty Weft Thread Detection using a Camera System and Feature-based Pattern Recognition.

In this paper, we present a novel approach for the fully automated detection of faulty weft threads on airjet weaving machines using computer vision. The proposed system consists of a camera array for image acquisition and a classification pipeline in which we use different image processing and machine learning methods to allow precise localization and reliable classification of defects. The camera system is introduced and its advantages over other approaches are discussed. Subsequently, the processing steps are motivated and described in detail, followed by an in-depth analysis of the impact of different system parameters to allow chosing optimal algorithm combinations for the problem of faulty weft yarn detection. To analyze the capabilities of our solution, system performance is thoroughly evaluated under realistic production settings, showing excellent detection rates.

Soziotechnische Assistenzsysteme für die Produktionsarbeit in der Textilbranche

Die Entwicklung hin zu Industrie 4.0 basiert in erster Linie auf modernen Produktionsmaschinen in Verbindung mit digitalen Technologien. Diesem Trend folgend werden der Betrieb und die Entwicklung von modernen Textilmaschinen immer komplexer und erfordern komplexe Fahigkeiten und Arbeitsaufgaben des Bedienpersonals in den verschiedenen Qualifizierungsphasen. Unter dem Gesichtspunkt der zunehmenden Heterogenitat der Produktionsbelegschaft, insbesondere dem Wachstum der Gruppe der alteren Arbeitnehmer/-innen, scheint die differentiell-dynamische Arbeitsgestaltung in der Textilproduktion aktueller denn je. Die Verwendung von Assistenzsystemen ermoglicht eine altersgerechte Arbeit und qualifikationsspezifische Unterstutzung der Mitarbeiter/-innen. Diese Unterstutzung ermoglicht den Beschaftigten, ihre Berufsfahigkeit zu erhalten.

Automation in production of yarns, woven, and knitted fabrics

Abstract The production of yarns and sewing threads is highly automated. A long tradition in automation of the spinning process is mainly based on the experience of a few machine manufacturers. Improvements in automation in garment manufacturing are mainly focused on fabric production. Three knitting processes are compared regarding automation, productivity, and flexibility. In addition the quality inspection of knitted structures is evolving to a camera-based automated monitoring system that needs to cover the complexity and variety of knitted structures. An approach in self-optimization is described for weaving machines to autonomously find an operating point, which improves all objective functions, compared with conventional (reference) machine settings. By the development of on-loom imaging solution the online monitoring of the weaving quality becomes possible and reduces waste at an early stage of production.

Sociotechnical Systems in the Textile Industry

Abstract This article delineates the work of an interdisciplinary research group concerning the implementation of a digital assistance system in the German textile industry. Using a holistic approach, researchers from different disciplines contribute to the design of an integrated socio-technical method that guides industrial actors in developing and implementing digital assistance systems that are applicable on the shop-floor level and at the same time take into account various social and organizational demands. Following this approach, the development of new technologies is coordinated with innovative social practices, for example, learning techniques or organizational changes. Furthermore, aspired users of the assistance system participate in the project by contributing their expertise of the working progress as well as by defining requirements essential towards addressing the various challenges at hand. The conceptual outline and early findings of the project, including the development of a prototype of the assistance system, are presented in this article.