Mit dem Fortschreiten der Digitalisierung in der Industrie findet sich Augmented Reality (AR) in immer mehr Einsatzbereichen. Dennoch bleibt die industrielle Verbreitung trotz sich stetig entwickelnder Technik hinter den Prognosen zurück. Es existieren bereits Arbeiten, die sich mit der Klassifizierung von AR jedoch mit Fokus auf die tatsächliche Implementierung bzw. Umsetzung der Anwendung beschäftigen. Um Anwendungsgebiete und damit die eigentliche Problemstellung, in denen AR einen Mehrwert bieten kann, besser vergleichen und Anforderungen für industrielle Bereiche ableiten zu können, stellt dieser Beitrag ein Klassifizierungssystem für diese Einsatzgebiete vor. Auf vorhergehenden Arbeiten aufbauend wird gezeigt, dass eine Klassifizierung der Einsatzszenarien auf Basis der drei Dimensionen zu unterstützende Aktion, Lebenszyklusphase und Grad der Hilfestellung erfolgen kann. Dazu wird eine systematische Literaturrecherche von industriellen AR Anwendungen und Studien der Jahre 2016 bis 2020 durchgeführt und nach dem vorgeschlagenen Schema klassifiziert. Neben den daraus gewonnen Erkenntnissen werden in den Beiträgen verwendete Technologien, wie die Darstellungstechnik, der Detailierungsgrad, der Reifegrad der Anwendung und die Art der Inhaltserstellung analysiert. Außerdem werden Probleme bei der Umsetzung sowie künftige Forschungsthemen und -schwerpunkte herausgearbeitet.

The idiom by Thomas Fuller fantasizes the fact that seeing is believing, but the feeling is the truth. This ideology has fired the vision and innovation of the Mulsemedia, multiple-sensorial media, and Internet of Skills (IoS) which enable the exchange of control, skills, and expertise anytime/everywhere across the Internet. With the emergence of the new generation of mobile network (5G), Tactile Internet, as well as the deployment of Industry 4.0 and Health 4.0, multimedia systems are moving towards immersed haptic enabled human-machine interaction systems such as the Digital Twin (DT). Specifically, Industry 4.0 will be using DT and robots on a large scale. This will increase human-machine and interaction to a great extent. There will be multimodal communications used to interact with digital twins and robots, specially haptics. Hence, tactile internet will replace the conventional internet today. In fact, a DT system can also be extended in Health 4.0 domain to act as a COVID-19 early warning system. Tracking a person’s temperature and other symptom data in real-time can signal if as well as when it’s time to see a doctor or take a COVID test. Link to a COVID tracing app, the digital twin might help get more information about the virus relative to the person itself. Since there are currently no well-recognized models to evaluate the performance of these systems, to address this research lacuna, we proposed a Quality-of-Experience (QoE) model for DT systems containing multi-levels of subjective, objective, and physiopsychological influencing factors. The model is itemized through a fully detailed taxonomy that deduces the perceived user’s emotional and physical states during and after consuming spatial, temporal, proximal, and abstracted multi-modality media between humans and machines. Further, the taxonomy was modelled using the best practice of machine learning methods to show how QoE for digital twin applications can be inferred and predicted from interactions and biosignals in this class of applications. Furthermore, the taxonomy was applied to two use cases. The first one addresses the objective quality optimization for transmission in a large scale immersed haptic virtual reality over the Internet while the second one aims to objectively infer an important DT QoE physiological aspect i.e, fatigue.

The growing customer demands for product variety have put unprecedented pressure on the manufacturing companies. To maintain their competitiveness, manufacturing companies need to frequently and efficiently adapt their processes while providing high-quality products. Different advanced manufacturing technologies, such as industrial robotics, have seen a drastic usage increase. Nevertheless, traditional quality methods, such as quality inspection, suffer from significant limitations in highly customised small batch production. For quality inspection to remain fundamental for zero-defect manufacturing and Industry 4.0, an increase in flexibility, speed, availability and decision upon conformance reliability is needed. If robots could perform in-line quality inspection, defective components might be prevented from continuing to the next production stage. Recent developments in robot cognition and sensor systems have enabled the robot to carry out perception tasks they were previously unable to do. The purpose of this thesis is to explore the usage of robotic in-line quality inspection during changeable zero-defect manufacturing. To fulfil this aim, this thesis adopts a mixed-methods research approach to qualitative and quantitative studies, as well as theoretical and empirical ones. The foundation for this thesis is an extensive literature review and two case studies that have been performed in close collaboration with manufacturing companies to investigate how in-line quality inspection is perceived and utilised to enhance industrial robots. The empirical studies also aimed at identifying and describing what opportunities arise from having robotic in-line quality inspection systems. The result of this thesis is a synthesis of literature and empirical findings. From the literature review/study, the need for enhancing quality inspection was identified and a multi-layer quality inspection framework suitable for the digital transformation was proposed. The framework is built on the assumption that data (used and collected) needs to be validated, holistic, and online, i.e. when needed, for the system to effectively decide upon conformity to surpass the challenges of reliability, flexibility and autonomy. Empirical studies show that industrial robotic applications can be improved in precision and flexibility using the in-line quality inspection system as measurement-assisted. Nevertheless, this methodological changes and robot application face the hurdle of previous and current management decisions when passing from one industrial paradigm to another (e.g. mass production to flexible production). A discussion on equipment design and manufacturing process harmony and how in-line quality inspection and management can harmonise such a system was provided.

Diploma thesis presents an evaluation of the current state of the company system, identification of critical areas and areas suitable for improvement. Based on the theoretical knowledge and analysis results, commercial Business Intelligence software is designed to enhance the quality and efficiency of the company's decision-support system and the introduction of an advanced Quality Culture system. The thesis reveals critical locations in the corporate environment and opens up space to design improvements to the system.

This master thesis deals with position of the maintenance in the company structure and with modern management approaches. One of the highest levels of maintenance is the proactive maintenance, which is using the technical diagnostics to find out the causes of failures. The most inportant in case of machines are vibrodiagnostics and thermodiagnostics, which are going to be applied on the motors in company Bosch Diesel s.r.o.

The goal of my master‘s thesis is to create a digital twin of a testbed Barman. The Barman is a school model of autonomous mixed drinks production line that demonstrates the principles of Industry 4.0. In the theoretical part of the thesis, the choice of a suitable tool for virtualization is discussed. The Tecnomatix Process Simulate and the Mechatronic Concept Designer module integrated in the NX platform is compared. The practical part of the work is divided into two phases. The first phase was about looking for a way to integrate the robotic SCARA manipulator. The second phase was focused on the virtualization of the cell Shaker in the latter of the tools mentioned before. This work is designed as a guide for creating and verifying the concept of a digital twin.

The Master’s thesis deals with virtual commissioning of the CNC machine tool changer. First deals with current state of knowledge with a systematic analysis of virtual commissioning. The 3D model is created by physical properties, sensors, actuators and control signals. In the last part of this thesis, a PLC program and visualization is created. The programs needed to create a virtual commissioning are jsou NX 12.0 - Mechatronic concept designer, SIMIT Simulation Platform V10.0 a TwinCAT 3.

The purpose of this study is to investigate the relationship of two significant concepts withinthe manufacturing industry. Namely, the widely popular Lean production and the arising Industry 4.0. The lack of a common framework concerning their relationship requires further exploration, as stated by several researchers. For companies to stay competitive within a changing market it is of importance to adapt to new technologies, indicating that the understanding of this relationship is essential, as Lean production is extensively used in industries today. To increase the understanding of the concepts and their relationship three research questions were formulated, as followed: RQ 1: How can Industry 4.0 support Lean production? RQ 2: How can Lean production support the implementation of industry 4.0? RQ 3: What are the challenges of implementing Industry 4.0 in Lean production? The study was conducted using qualitative research methods such as the narrative literature review and thematic analysis, allowing for the identification of themes and structuring of the study in a narrative approach. Second-hand data was collected through academic databases using search terms highly relevant to the research questions. The theoretical framework of the study provides a foundation for the understanding of the concepts. Whereas the results are centered around the relationship of Industry 4.0 and Lean production with themes relevant to the research questions and subthemes to follow. The analysis highlights important aspects of the results in relation to the theoretical framework, while also discovering insights connecting the research questions together. The conclusion presents aspects of how Industry 4.0 technologies can support Lean production through factors such as increased continuous improvement, support of people, improved JIT, Kanban, maintenance and communications. Where the general benefits will be productivity, quality and speed of introducing new products, adapting to high variability of market demand. Furthermore, describing how Lean production can support the implementation of Industry 4.0, as the degree of Lean maturity directly influences the efficiency of Industry 4.0 implementation. The employment of Lean principles such as continuous improvement, focus on people and standardization can result in a more effective implementation and utilization of Industry 4.0. Challenges of implementing Industry 4.0 in Lean production such as the risk of counterproductive implementation because of the lack of common framework, limitations of certain Lean practices, Lean professionals and ICT professionals limited knowledge of eachother's fields and the understanding of how to apply Industry 4.0 based on lean principles. Lastly, recommendations are offered for companies aspiring to implement Industry 4.0, as they should evaluate their current level of Lean maturity in order to understand when it is most efficient to incorporate the new technologies. Companies seeking to aid their Lean objectives should concentrate on technologies such as IoT and CPS as they seem to have the most positive impact on Lean. In addition, recommending further research regarding the relationship of Industry 4.0 readiness and Lean maturity, exploring which degree of Lean maturity to efficiently integrate and utilize certain Industry 4.0 technologies and when to implement certain Industry 4.0 technologies to aid the advancement of Lean maturity.

Industry 4.0 and the development of novel digital technologies is forcing manufacturing companies to introduce drastic changes to their productions systems. These technologies provide unique opportunities for manufacturing companies to collect, process and store large data volumes, which can be used to facilitate the coordination of factory elements. Previous research indicate that decisions based on data can provide fact-based decisions which can contribute to an increased productivity. However, manufacturing companies are not fully exploiting data as support for decision-making, which is desirable for an increased competitiveness. Currently, much attention is pointed towards the technology instead of the humans responsible for interpreting data and making decisions. Adding to this, there is a lack of guidance on how manufacturing companies can go from current decision making practices (i.e., decisions based on gut feelings) to fact-based decisions driven by data. To address this gap, the purpose of this thesis is to propose a framework for achieving data-driven decision making in production development in the context of Industry 4.0. The purpose is accomplished by using a qualitative-based case study approach at a small and medium sized enterprise in the electronics industry. The results indicate that both challenges and enablers for achieving data-driven decision making in production development are related to perspectives and attitudes, processes for data quality, technology and processes for decision making. Four maturity levels of data-data driven decision making are also identified. The proposed framework can be used by manufacturing companies to help them plan and prepare for their own specific development path towards data-driven decision making. Contributing to current understanding, this thesis considers the human decision makers perspective to develop the ability to collect, process, analyze and use the data to support time efficient and high-quality decisions, an insight lacking in prior academic studies. Future research may include confirmation of the findings presented in this thesis with additional use cases and industry types. / Industri 4.0 och utvecklingen av nya digitala teknologier tvingar tillverkningsföretag att introducera drastiska förändringar i sina produktionssystem. Dessa teknologier skapar unika möjligheter för tillverkningsföretag att samla, processa och lagra stora datavolymer, vilka kan användas för att stödja koordineringen av fabrikselement. Tidigare forskning indikerar att beslut baserade på data kan innebära faktabaserade beslut vilket kan bidra till en ökad produktivitet. Tillverkningsföretag utnyttjar dock inte data som underlag för beslutsfattande, vilket är önskvärt för en ökad konkurrenskraft. I dagsläget är mycket uppmärksamhet riktat mot teknologier istället för de människor som är ansvariga för att tolka data och fatta beslut. Dessutom saknas ledning gällande hur tillverkningsföretag kan gå från nuvarande beslutsrutiner (exempelvis beslut baserade på magkänsla) till faktabaserade beslut på data. Syftet med detta examensarbete är därför att föreslå ett ramverk för att åstadkomma data-baserade beslut genom produktionsutveckling i ett Industri 4.0 kontext. Syftet har uppnått genom en kvalitativ fallstudie på litet och mellanstort företag i elektronikindustrin. Resultaten påvisar att både utmaningar och möjliggörare för att åstadkomma databaserade beslut i produktionsutveckling är relaterade till perspektiv och attityder, processer för datakvalitet, teknologi och processer för beslutsfattande. Fyra olika mognadsnivåer för data-baserade beslut har också identifierats. Det föreslagna ramverket för databaserade beslut kan användas av tillverkningsföretag i syfte att hjälpa dem planera och förbereda sig för deras egna specifika utvecklings mot databaserat beslutsfattande. Genom att bidra till nuvarande kännedom avser detta examenarbete de mänskliga beslutsfattarnas perspektiv gällande utveckling at förmågan att samla, processa, analysera och använda datan för att stödja tidseffektiva och högkvalitativa beslut. Detta är en insikt som saknas i tidigare akademiska studier. Framtida studier kan inkludera verifiering av resultaten presenterade i detta examensarbete med fler tillämpningsområden och typer av industrier.

Aus der Einleitung: "Auf der Hannovermesse 2011 wurde zum ersten Mal der Begriff "Industrie 4.0" der Öffentlichkeit bekannt gemacht. Die Akademie der Technikwissenschaften hat in einer Arbeitsgruppe diese Grundidee der vierten Revolution der Industrieproduktion weiterbearbeitet und 2013 in einem Abschlussbericht mit dem Titel „Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0“ veröffentlicht (BmBF, 2013). Die Grundidee besteht darin, wandlungsfähige und effiziente Fabriken unter Nutzung moderner Informationstechnologie zu entwickeln. Basistechnologien für die Umsetzung der intelligenten Fabriken sind: — Cyber-Physical Systems (CPS) — Internet of Things (IoT) und Internet of Services (IoS) — Big Data Analytics and Prediction — Social Media — Mobile Computing Der Abschlussbericht fokussiert den Wertschöpfungsschritt der Produktion, während die Fragen der Produktentwicklung weitgehend unberücksichtigt geblieben sind. Die intelligente Fabrik zur Herstellung intelligenter Produkte setzt aber auch die Weiterentwicklung der Produktentwicklungsmethoden voraus. Auch hier gibt es einen großen Handlungsbedarf, der sehr stark mit den Methoden des „Modellbasierten Systems-Engineering“ einhergeht. ..."