@incollection{SarivanBaumannDiezAlvarezetal., author = {Sarivan, Ioan-Matei and Baumann, Stefan Andreas and D{\´i}ez {\´A}lvarez, Daniel and Euteneuer, Felix and Reichenbach, Matthias and Berger, Ulrich and Madsen, Ole and B{\o}gh, Simon}, title = {Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification}, series = {Advances in Automotive Production Technology - Theory and Application; Stuttgart Conference on Automotive Production (SCAP2020)}, booktitle = {Advances in Automotive Production Technology - Theory and Application; Stuttgart Conference on Automotive Production (SCAP2020)}, editor = {Weißgraeber, Philipp and Heieck, Frieder and Ackermann, Clemens}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-662-62961-1}, doi = {10.1007/978-3-662-62962-8_26}, pages = {221 -- 229}, abstract = {In this paper we present a novel method for performing in site real time quality inspection (QI) and consequently, digitalization of manual processes performed by human workers. It complements and improves our previous work in this area, which makes use of telemetry gathered from a smartwatch to classify manual actions as successful or unsuccessful. This new methodology provides the worker with a real time capable, robust and more accurate quality inspector. This work enhances the existing system through the elimination of input from the user by making use of a BIOX bracelet that detects gestures. The signal processing and classification methods are simplified and optimised by using assembled neural networks thus merging together the data gathered from multiple signal sources. Consequently, the overall QI system is improved with around 70\%, thus furthering the necessary development needed to have a system ready to be used on a production environment.}, language = {en} } @misc{ColliBergerBockholtetal., author = {Colli, Michele and Berger, Ulrich and Bockholt, Markus and Madsen, Ole and M{\o}ller, Charles and Vejrum W{\ae}hrens, Brian}, title = {A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era}, series = {Annual Reviews in Control}, volume = {Vol. 48}, journal = {Annual Reviews in Control}, issn = {1367-5788}, doi = {10.1016/j.arcontrol.2019.06.001}, pages = {165 -- 177}, abstract = {The digital transformation of the production sector is setting the scene for a major industrial change. The need for supporting companies in this transformation is currently covered by several maturity models, generally operationalized through standardized questionnaires, which provide, as an outcome, an assessment of the current maturity stage and a set of general improvement recommendations according to it. However, to provide companies with a more tangible support, there is a need for more individual approach. In order to deal with this need, this paper proposes, following a design science research framework, a novel approach based on Problem-Based Learning for structuring the assessment procedure as a dialectic process. This approach aims at facilitating the contextualization of the assessed company and, consequently, the identification of context-specific improvement recommendations. The proposed approach, supported by a maturity model used for framing information collected during the assessment process, is tested in three industrial cases. Although these have been assessed at the same maturity stage, different improvement recommendations have been proposed according to contextual factors such as strategic goals, core processes and key performance indicators.}, language = {en} } @incollection{BorckSchmittBergeretal., author = {Borck, Christian and Schmitt, Randolf and Berger, Ulrich and Hentschel, Christian}, title = {IIoT and Smart Sensors in Human-Centered Manufacturing}, series = {The Future of Smart Production for SMEs}, booktitle = {The Future of Smart Production for SMEs}, publisher = {Springer Nature Switzerland AG}, address = {Schweiz}, isbn = {978-3-031-15427-0}, doi = {10.1007/978-3-031-15428-7_18}, pages = {213 -- 224}, abstract = {Borck et al. evaluate the challenges and opportunities of Industrial Internet of Things (IIoT) and smart sensors in human-centered manufacturing. Particularly in small and medium-sized manufacturing with fewer machines and smart tools, it is significantly more difficult to automate processes and get the required information from the shop floor. Therefore, they give proven recommendations for the use of sensors based on a set of frequently occurring tasks in assembly, maintenance and logistics to achieve the support of smart data models in the context of Industry 4.0. "IIoT and smart sensors in human-centered manufacturing" concludes with concrete sample scenarios and describe the challenges and one solution using smart sensors and data models.}, language = {en} } @misc{WalterGuentherPrelletal., author = {Walter, Elias Fabian and G{\"u}nther, Norman and Prell, Bastian and Wilbers, Simon and Reiter, Philipp and Reiff-Stephan, J{\"o}rg and Berger, Ulrich}, title = {Optimierung visueller Benutzeroberfl{\"a}chen zur Stressreduzierung}, series = {Tagungsband AALE 2023}, journal = {Tagungsband AALE 2023}, publisher = {Hochschule f{\"u}r Technik, Wirtschaft und Kultur Leipzig}, address = {Luxemburg}, isbn = {978-3-910103-01-6}, doi = {10.33968/2023.13}, pages = {119 -- 129}, abstract = {Durch die Menschzentrierung in der Industrie 5.0 [1] sollten bestehende Ans{\"a}tze hinterfragt und gepr{\"u}ft werden [2]. Gerade der, durch die Digitalisierung hervorgerufene, Technostress sollte neben dem Fachkr{\"a}ftemangel einen zunehmenden Betrachtungsfaktor im Produktionsumfeld erhalten. Da trotz bisheriger Maßnahmen eine stetige Zunahme von Stress zu verzeichnen ist [3]. Auff{\"a}llig sind dabei die Arbeitsunf{\"a}higkeits-Fehltage, welche im letzten Jahr um weitere 6 \% bzw. um 18,2 Fehltagegestiegen sind. Womit durchschnittlich jede Erwerbsperson 3,17 Tage unter der Diagnose „psychische St{\"o}rung" krankgeschrieben wurde [4]. Um diesen Trend entgegenzuwirken, betrachtet der Beitrag die M{\"o}glichkeiten der Software-Ergonomie, womit die Gestaltung von HMIs (Mensch-Maschinen-Interfaces) den Menschen gezielter ber{\"u}cksichtigt um etwaige Belastungen zu reduzieren [5]. Im Rahmen der komplement{\"a}ren Industrie 4.0 und der einhergehenden Informationsflut, bestimmen immer komplexer werdende Schnittstellen und Bedienoberfl{\"a}chen einen Großteil des Arbeitsalltags. Diese erm{\"o}glichen den Anwenderinnen und Anwendern neben einem schnellen {\"U}berblick {\"u}ber Maschinendaten, Prozessabl{\"a}ufe und Handlungsempfehlungen die M{\"o}glichkeit zur einfachen Bedienung der technischen Anlagen. Der hier vorgestellte Ansatz betrachtet am Beispiel einer Bestandsanlage, wo sich Potenziale in der Stressreduzierung im Bereich der visuellen Benutzerschnittstellen befinden und welche Auswirkungen eine Ver{\"a}nderung der Leitf{\"u}hrung erm{\"o}glicht. Hierf{\"u}r wurde eine mehrphasige Studie durchgef{\"u}hrt dessen Ergebnisse im Bereich des Eye-Trackings sowie anhand von Vitalwerten im vorliegenden Beitrag diskutiert werden.}, language = {de} } @incollection{MollerMadsenBergeretal., author = {M{\o}ller, Charles and Madsen, Ole and Berger, Ulrich and Shou, Casper and Heidemann Lassen, Astrid and Vejrum W{\ae}hrens, Brian}, title = {The Smart Production Vision}, series = {The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs}, booktitle = {The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-031-15428-7}, pages = {13 -- 28}, abstract = {In this chapter, the Smart Production vision is discussed. The Smart Production approach is developed and described, and Smart Production is positioned in relation to Industry 4.0. Smart Production operationalize the journey towards Industry 4.0 and beyond. First, the need for a new approach to manufacturing is discussed, and from the perspectives of Industry 4.0, the Smart Production concept is derived. Then the framework is explored and finally, the approach is outlined. The Smart Production vision is an approach to make an integrated production system smarter by continuous digitizing, automating, and organizing towards supporting the company specific missions.}, language = {en} } @incollection{BergerMadsen, author = {Berger, Ulrich and Madsen, Ole}, title = {Introduction to Part 2 - Transformation of SMEs Toards Smart Production}, series = {The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs}, booktitle = {The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs}, editor = {Madsen, Ole and Berger, Ulrich and M{\o}ller, Charles and Heidemann Lassen, Astrid and Vejrum W{\ae}hrens, Brian}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-031-15428-7}, pages = {69 -- 75}, abstract = {This chapter will introduce the second part of the book. This part contains a collection of chapters aimed at supporting the SMEs in the transformation toward the Smart Production vision. In this part, different approaches are presented, which can assist SMEs in the formulation of a smart production vision and in the identification and prioritization of relevant initiatives, guiding the outline of a project roadmap. Furthermore, the part will introduce different regional innovation platforms in Denmark and Germany which support the SME transformations. Finally, it will be discussed how subscription-based methods could be used by SMEs to cut upfront investments and reduce requirements for digital competencies.}, language = {en} }