Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Decentralised identification of used exchange parts with a mobile application
    ( 2020)
    Lehr, Jan
    ;
    Schlüter, Marian
    ;
    Krüger, Jörg
    Sustainable product development and use requires an extended life cycle of used and defective mechanical parts. Remanufacturing saves resources and helps the industry to utilise the product more efficiently. Reverse logistics is one of the most important challenges towards efficient remanufacturing. To improve this process, we propose an on-site part identification at the workshops. A fast on-site identification is essential for assisting repair shop personnel and saving time on searching for the right spare parts. Based on images taken by a mobile device our application provides various machine vision services, e.g., visual identification of used parts, already successfully tested in a sorting facility for remanufacturing parts. The mobile application provides a robust visual identification for different environments. We show that enhancing data for machine vision approaches with images from decentral sensors, i.e., mobile devices, leads to an improved identification accuracy.
  • Publication
    Automated Optical Inspection Using Anomaly Detection and Unsupervised Defect Clustering
    ( 2020)
    Lehr, Jan
    ;
    Sargsyan, Arlik
    ;
    Pape, Martin
    ;
    Philipps, Jan
    ;
    Krüger, Jörg
    Neural networks have proven to be extraordinarily successful in many computer vision applications. But the approaches used to train neural networks require large datasets of annotated images, which requires a solid amount of human time to prepare those datasets. To facilitate the adoption of machine learning based technologies in industrial computer vision applications, this paper presents a two-step unsupervised learning approach for anomaly detection with further defect clusterization. In the first stage, the defects are not explicitly learned, but are interpreted as an anomaly or novelty based on the dataset of defect-free samples. In a second stage, the anomalies detected in the first stage are clustered in unsupervised manner and classified into meaningful categories by experts with process knowledge (e.g. critical or non-critical defect). This paper presents a first small dataset containing one industrial object with a complex shape. The object is made of aluminiu m and is shown both free of defects and defective. Based on this, recommendations are given for an acquisition setup for a large, extensive dataset. Most of the existing papers are studying the approaches for uniform surface (texture) inspection. The specifics of this research is to identify defects on rigid bodies, which exhibit highly non uniform texture in the image. State of the art methods were evaluated and improved to increase the classification accuracy. With a fine-tuned ResNet-18 it was possible to achieve 100% accuracy for defective and defect-free images.
  • Publication
    Development of a Fire Detection Based on the Analysis of Video Data by Means of Convolutional Neural Networks
    ( 2019)
    Lehr, Jan
    ;
    Gerson, Christian
    ;
    Ajami, Mohamad
    ;
    Krüger, Jörg
    Convolutional Neural Networks (CNNs) have proven their worth in the field of image-based object recognition and localization. In the context of this work, a fire detector based on CNNs has been developed that detects fire by analyzing video sequences. The major additions of this work will primarily be realized through the use of temporal information contained in the video sequences depicting fire. In contrast to state of the art fire detectors, a large image database with 160,000 images with an even distribution of positive and negative samples has been created. To be able to compare image-based and video-based approaches as objectively as possible, different image-based CNNs will be trained under the same conditions as the video-based networks within the scope of this work. It will be shown that video-based networks offer an advantage over conventional image-based networks and therefore benefit from the temporal information of fire. We have achieved a prediction accuracy of 96.82%.
  • Publication
    Classification of Similar Objects of Different Sizes Using a Reference Object by Means of Convolutional Neural Networks
    ( 2019)
    Lehr, Jan
    ;
    Schlüter, Marian
    ;
    Krüger, Jörg
    Part identification is relevant in many industrial applications, either for direct recognition of components or assemblies, either as a fully automated process or as an assistance system. Convolutional Neural Networks (CNNs) have proven their worth in image processing, especially in classification tasks. It therefore makes sense to use them for industrial applications. There are major problems with parts that look very similar and can only be identified by their size. In this paper we have considered a subset of screws that all conform to the same norm but are of different sizes. The implicit learning of the screw size is only possible if the images are taken in a fixed distance setup and larger screws are shown larger on the images. In this paper we show that CNNs are able to implicitly measure target objects with the help of reference objects and thus to integrate the object size into the learning process.
  • 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 %.