Real-Time Machine Vision System for an Automated Quality Monitoring in Mass Production of Multiaxial Non-Crimp Fabrics

Schmitt, Robert; Fürtjes, Tobias; Abbas, Bahoz; Abel, Philipp; Kimmelmann, Walter; Kosse, Philipp; Buratti, Andrea

doi:10.1016/j.ifacol.2015.06.446

Cited by 14 publications

(8 citation statements)

References 1 publication

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“…Within the extent of study, the defect detection device advanced to identify certain defect types on knit pile fabric carried out requested measurements at the expected accuracy value. In consequence of the literature review, it was observed that the histograms acquired from the defect detection device developed within this study are similar to those in the related studies (9,19,(33)(34)(35).…”

Section: Resultssupporting

confidence: 81%

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Yapay Görme Tabanli Kumaş Hata Tespi̇t Si̇stemi̇

Soyaslan¹,

Karataş²

2018

Tekstil Ve Konfeksiyon

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Due to the cost and complexity of existing defect detection systems, a fabric defect detection device based on an artificial vision system has been developed in this study. Using knitted pile fabric, six types of defects were studied: loop drop, fly defect, grease spot, cross-striped defect, hole defect and pilling defect. Obtained fabric images were converted into histograms by a computer program developed within the scope of this study and defect types were characterized.

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Section: Resultssupporting

confidence: 81%

“…The number of frame per second taken by the defect detection device is accounted as 10.6 fps in the best achieved integration time (536 milliseconds). In the similar studies using the artificial vision system, the fps values were measured in range of 2 and 25 fps (33)(34)(35).…”

Section: Methodsmentioning

confidence: 99%

Yapay Görme Tabanli Kumaş Hata Tespi̇t Si̇stemi̇

Soyaslan¹,

Karataş²

2018

Tekstil Ve Konfeksiyon

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“…For example, Sture et al (2016) showed that Salmon deformities and wounds can be identified using realtime machine vision achieving a detection rate of 86 per cent for deformities and 89 per cent for wounds. Furthermore, Schmitt et al (2015) designed a real-time machine vision system that can detect significant quality deficiencies in fibre-reinforced plastics under certain conditions. Li and Huang (2015) developed a method to inspect tyres, gathering geometrical data from images these authors then assessed the quality using tyre features identified from images.…”

Section: Assembly State Recognitionmentioning

confidence: 99%

Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence

Oyekan

Fischer

Hutabarat

et al. 2019

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Purpose The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques. Design/methodology/approach This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined. Findings This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable. Originality/value Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.

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“…Fibre orientation detection is challenging due to the high surface reflectivity and fine weaving of the material, and thus, it has still predominantly been accomplished manually in practice [31,41]. Traditional machine vision methods for fibre orientation detection of textiles prefer to utilise diffused lighting [45], such as diffuse dome [39] and flat diffuse [22] illumination measuring techniques. Polarisation model approaches have been particularly popular for measuring fibre orientation, where contrast between textile features such as fibres and seams are used to identify the structure of the material relative to the camera [40].…”

Section: Introductionmentioning

confidence: 99%

A cooperative mobile robot and manipulator system (Co-MRMS) for transport and lay-up of fibre plies in modern composite material manufacture

Yang

Wong

et al. 2021

Int J Adv Manuf Technol

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Composite materials are widely used in industry due to their light weight and specific performance. Currently, composite manufacturing mainly relies on manual labour and individual skills, especially in transport and lay-up processes, which are time consuming and prone to errors. As part of a preliminary investigation into the feasibility of deploying autonomous robotics for composite manufacturing, this paper presents a case study that investigates a cooperative mobile robot and manipulator system (Co-MRMS) for material transport and composite lay-up, which mainly comprises a mobile robot, a fixed-base manipulator and a machine vision sub-system. In the proposed system, marker-based and Fourier transform-based machine vision approaches are used to achieve high accuracy capability in localisation and fibre orientation detection respectively. Moreover, a particle-based approach is adopted to model material deformation during manipulation within robotic simulations. As a case study, a vacuum suction-based end-effector model is developed to deal with sagging effects and to quickly evaluate different gripper designs, comprising of an array of multiple suction cups. Comprehensive simulations and physical experiments, conducted with a 6-DOF serial manipulator and a two-wheeled differential drive mobile robot, demonstrate the efficient interaction and high performance of the Co-MRMS for autonomous material transportation, material localisation, fibre orientation detection and grasping of deformable material. Additionally, the experimental results verify that the presented machine vision approach achieves high accuracy in localisation (the root mean square error is 4.04 mm) and fibre orientation detection (the root mean square error is 1.84∘) and enables dealing with uncertainties such as the shape and size of fibre plies.

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Real-Time Machine Vision System for an Automated Quality Monitoring in Mass Production of Multiaxial Non-Crimp Fabrics

Cited by 14 publications

References 1 publication

Yapay Görme Tabanli Kumaş Hata Tespi̇t Si̇stemi̇

Yapay Görme Tabanli Kumaş Hata Tespi̇t Si̇stemi̇

Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence

A cooperative mobile robot and manipulator system (Co-MRMS) for transport and lay-up of fibre plies in modern composite material manufacture

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