Kristina Simonis

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.

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.

Automated enhancement and detection of stripe defects in large circular weft knitted fabrics

Stripes are periodic defects that are difficult to detect during production even by experienced human inspectors. Therefore, we introduce an image processing method for automatically detecting stripe defects in circularly knitted fabric. We show how a barely visible defect can be optically enhanced to improve manual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated stripe detection. Image enhancement is performed by applying gabor and matched filters to histogram-equalized fabric images. Subsequently, we extract image information with different descriptors (LBP, GLCM, HOG) and feed these into random forest and SVM classifiers. The full pipeline is validated by training and testing it on three sets of fabric produced with different knitting machines and parameter settings. Results show that the proposed enhancement combined with a statistics-based descriptor such as GLCM or HOG allows to train both tested classifiers with good classification rates of up to 98.9%.

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.

3D knitting using large circular knitting machines

For the first time 3D structures can now be produced on large circular knitting machines. Till date, such structures could only be manufactured on flat knitting machines. Since large circular knitting machines operate much faster, this development increases the overall productivity of 3D knits. It thus opens up a totally new avenue for cost reduction for applications in sportswear, upholstery, aerospace and automotive industry. The following paper presents the state of the art regarding the realisation of three dimensional fabrics. In addition, current knitting technologies regarding three dimensional formations will be explained. Results of the pretrials explaining the change in knitted fabrics ́ dimension, executed at the Institut für Textiltechnik of the RWTH Aachen University, will be presented. Finally, the description of the 3D knit prototype developed will be provided as a part of this paper.

Efficient Image Processing System for an Industrial Machine Learning Task

We present the concept of a perceptive motor in terms of a cyber-physical system (CPS). A model application monitoring a knitting process was developed, where the take-off of the produced fabric is controlled by an electric motor. The idea is to equip a synchronous motor with a smart camera and appropriate image processing hard- and software components. Subsequently, the characteristics of knitted fabric are analysed by machine-learning (ML) methods. Our concept includes motor-current analysis and image processing. The aim is to implement an assistance system for the industrial large circular knitting process. An assistance system will help to shorten the retrofitting process. The concept is based on a low cost hardware approach for a smart camera, and stems from the recent development of image processing applications for mobile devices [1–4].