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    Deep learning based soft sensors for industrial machinery
    (2020) Maschler, Benjamin; Ganssloser, Sören; Hablizel, Andreas; Weyrich, Michael
    A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.
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    Optical detection of random features for high security applications
    (1998) Haist, Tobias; Tiziani, Hans J.
    Optical detection of random features in combination with digital signatures based on public key codes in order to recognise counterfeit objects will be discussed. Without applying expensive production techniques objects are protected against counterfeiting. Verification is done offline by optical means without a central authority. The method is applied for protecting banknotes. Experimental results for this application are presented. The method is also applicable for identity verification of a credit- or chip-card holder.
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    ItemOpen Access
    Positioning of noncooperative objects using joint transform correlation combined with fringe projection
    (1998) Haist, Tobias; Schönleber, Martin; Tiziani, Hans J.
    Automated assembly and quality control require reliable systems for the detection of the position and orientation of complicated objects. Correlation methods are well suited, but they are affected by structured backgrounds, varying illumination conditions, and textured or dirty object surfaces. Using fringe projection to exploit the three-dimensional topography of objects, we improve the performance of a nonlinear joint transform correlator. Positioning of noncooperative objects with subpixel accuracy is demonstrated. Additionally, the tilt angle of an arbitrarily shaped object is measured by projecting object-adapted fringes that produce a homogeneous fringe pattern in the image plane. An accuracy of better than one degree is achieved.
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    ItemOpen Access
    User-friendly, requirement-based assistance for production workforce using an asset administration shell design
    (2020) Al Assadi, Anwar; Fries, Christian; Fechter, Manuel; Maschler, Benjamin; Ewert, Daniel; Schnauffer, Hans-Georg; Zürn, Michael; Reichenbach, Matthias
    Future production methods like cyber physical production systems (CPPS), flexibly linked assembly structures and the matrix production are characterized by highly flexible and reconfigurable cyber physical work cells. This leads to frequent job changes and shifting work environments. The resulting complexity within production increases the risk of process failures and therefore requires longer job qualification times for workers, challenging the overall efficiency of production. During operation, cyber physical work cells generate data, which are specific to the individual process and worker. Based on the asset administration shell for Industry 4.0, this paper develops an administration shell for the production workforce, which contains personal data (e.g. qualification level, language skills, machine access, preferred display and interaction settings). Using worker and process specific data as well as personal data, allows supporting, training and instating workers according to their individual capabilities. This matching of machine requirements and worker skills serves to optimize the allocation of workers to workstations regarding the ergonomic workplace setup and the machine efficiency. This paper concludes with a user-friendly, intuitive design approach for a personalized machine user interface. The presented use-cases are developed and tested at the ARENA2036 (Active Research Environment for the Next Generation of Automobiles) research campus.