FG Automatisierungstechnik
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As Convolutional Neural Network based models become reliable and efficient, two questions arise in relation to their applications for industrial purposes. The usefulness of these models in industrial environments and their implementation in these settings. This paper describes the autonomous generation of Region based CNN models trained on images from rendered CAD models and examines their applicability and performance for part handling application. The development of the automated synthetic data generation is detailed and two CNN models are trained with the aim to detect a car component and differentiate it against another similar looking part. The performance of these models is tested on real images and it was found that the proposed approach can be easily adopted for detecting a range of parts in arbitrary backgrounds. Moreover, the use of syntheic images for training CNNs automates the process of generating a detector.
Industry 4.0 is still in its development phase and it promises to bring remarkable benefits to the manufacturing industry around the world when employing the Smart Factory application in large organizations and their supply chains. However, there is a risk of a miss-match when trying to introduce Industry 4.0 to Small and Medium Enterprises (SME) as the concept is mainly being developed around large manufacturing companies. The purpose of this research is to analyze the readiness level and feasibility of implementing Industry 4.0 technologies for SME’s in the federal state of Brandenburg (Germany). The work is based on the survey of 20 SME’s assessing their current problems emphasizing on automation, Enterprise Resource Planning (ERP), CAD/CAM, factory layout planning and logistics. Five SME’s from different domains out of the 20 surveyed are taken as case studies to evaluate the potential benefits, trade-offs and barriers from an implementation of these integrated technologies. The findings revealed that the companies are still coping with the issues relating to planning, logistics and automation. It was also found that all the concepts of i4.0 may not be necessary or even beneficial to an enterprise in the current scenario and new strategies need to be developed for its realization in SME’s.
Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can influence further decisions and analysis, e.g., sensor selection and understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machines and random forest were employed. We used a five-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA’s repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: (1) Different FS methods can yield comparable macro-F1 yet considerably different FS stability values. (2) Fisher score (single and/or ensemble) is superior in most of the cases. (3) mRMR’s stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble.
Die in diesem Artikel beschriebene, praktische, Verwendung einer Augmented Reality Umgebung findet statt für die Fehlerbeseitigung und Fehlerkorrektur bei der Arbeit in einer Laboranlage, die verschiedene Fehler simuliert kann. Innerhalb der Anlage sind alle wesentlichen Komponenten mit Sensorik ausgestattet, so dass jederzeit Informationen über
den Zustand des Systems in Echtzeit bereitliegen. Sobald sich einen Fehler innerhalb der Anlage befindet, kann das Steuerungssystem durch die vorliegenden Sensordaten dieses
Problem identifizieren. Die verschiedenen Teile der Anlage sind mit Positions-Markern zur Identifizierung eines
Anlagenbereiches ausgerüstet. Wird die Anlage durch einen Fehler gestoppt ist, muss der Mitarbeiter die Anlage mit Hilfe des Kamerasystems eines mobilen Endgerätes (mit dem
Tablet) erfassen. Die erfassten Daten aus diesem Scan werden mit Hilfe der Software verarbeitet. Durch die visuelle Rückmeldung (z.B., auf dem Tablet) erhält der Anwender die Information über den aufgetretenen Fehler innerhalb der Anlage und zur Behebung des Zustandes. Auf
Grundlager der erkannten Marker können positionsgenau Objekte eingeblendet werden die dem Anwender den Ort des Fehlers anzeigen und Hinweise zur Bedienerführung zur
Aufnahme des ordnungsgemäßen Betriebs der Anlage geben. Der Anlagenführer wird in die Lage versetzt, die Fehlerbeseitigung und Fehlerkorrektur schnell und erfolgreich zu realisieren. Ein Einsatz des Systems für Schulungsprozesse ist vorgesehen, da sich dies
positiv auf die Verringerung von Stillstandszeiten auswirkt. So demonstriert das beschriebene, realisierte Verfahren wie die praktische Verwendung der
AR zur Reduzierung des zeitlichen Aufwandes für die Fehlerbeseitigung und die Fehlerkorrektur der Maschinenumgebung bei der Mensch-Maschinen-Integration in Industrie 4.0 Umgebungen beiträgt und den gesamten Integrationsprozess zwischen Mensch und Maschine vertiefet.