Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Deep learning for part identification based on inherent features
    ( 2019)
    Krüger, Jörg
    ;
    Lehr, Jan
    ;
    Schlüter, Marian
    ;
    Bischoff, Nils
    The identification of parts is essential for the efficient automation of logistic processes such as part supply in assembly and disassembly. This paper describes a new method for the optical identification of parts without explicit codes but based on inherent geometrical features with Deep Learning. The paper focusses on the improvement of training of Deep Learning systems taking into account conflicting factors such as limited training data and high variety of parts. Based on a case study in turbine industry the effects of steadily growing training data on the robustness of part classification are evaluated.
  • Publication
    Vision-based Identification Service for Remanufacturing Sorting
    ( 2018)
    Schlüter, Marian
    ;
    Niebuhr, Carsten
    ;
    Lehr, Jan
    ;
    Krüger, Jörg
    One of the main goals of sustainability is to reduce the ecological footprint. As a result the automotive industry has been encouraged to become more efficient in using existing resources to reach a target value of at least of 85 % of a car's weight for reuse and recycling as of 2015. The trade of used parts is expanding in total amount as well as in diversity of items. In industry practice employees have to decide upon the further use of a product based on experience or a reference list. We introduce a machine vision -based service for the identification of exchange parts. Images and weights of used parts serve as input whereby extracted inherent object features determine the identification of respective parts. First, in two main steps data is pre-filtered by its dimensions and volume out of a low-level 3D-model, created by a Shape-From-Silhouette algorithm. Secondly, a feature-based matching process is performed on the images. Two different feature matching approaches, a classic key point-based as well as a convolutional neural network, are evaluated. First results show the proof of concept recognition rates up to 96 %.