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In recent years, the numer of hybrid work systems using human robot collaboration (HRC) increased in industrial production environments - enhancing productivity while reducing work-related burden. Despite growing availability of HRC-suitable manipulation and safety technology, tools and techniques facilitating the design, planning and implementation process are still lacking. System engineers who strive to implement technically feasible, ergonomically meaningful and economically beneficial HRC application need to make design and technology decisions in various subject areas, whereas the design alternatives per morphological analysis is applied to establish a description model that can serve as both a supporting design guideline for future HRC application of value-adding, industrial quality as well as a tool to characterize and compare existing applications. It focuses on HRC within assembly processes, and illustrates the complexity of HRC applications in a comprehensible manner through its multi-dimensional structure. The morphology has been validated through its application on various existing industrial HRC applications, research demonstrators and interviews of experts from academia.
Future intralogistics systems need to adapt flexibly to changing material flow requirements in line with future versatile factory environments, producing personalized products under the performance and cost conditions of today's mass production. Small batch sized down to a batch size of "1" lead to a high complexity in the design and economical manufacturing of these customized products. Intralogistics systems are integrated into higher-level areas (segment level) as well as into upsteam and downstream performance units (system-wide areas). This includes the logistic activities relevant for the system (organized according to storage, picking, transport) such as transportation or storage tasks of tools, semi-finished products, components, assemblies and containers, and waste. Today's centralized material flow control systems, which work based on predefined processes, are not capable and more specifically not suitable to deal with the arising complexity of changeable intralogistics systems. Autononomous, decentralized material flow control systems distribute the required decision-making and control processes on intelligent logistic entities. A major step for the development of an autonomous control method for hybrid intralogistics systems (manual, semi-automated and automated) is the development of a generic archetype for intralogistics systems regarding the system boundaries, elements and relations resulting in a descriptive model taking into account amongst others the time of demand, availability of resources, economic efficiency and technical performance parameters. The ESB Logistics Learning Factory at ESB Business School (Reutlingen University) serves for this as a close-to-reality development and validation environment.
Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.
Teaching at assembly workstations in production in SMEs (small and medium sized companies) often does not take place at all or only insufficiently. In addition to the lack of technical content, there are also aggravatingly incorrect movement sequences from an ergonomic point of view, which "untrained" people usually automatically acquire. An AI based approach is used to analyze a definite workflow for a specific assembly scope regarding the behavior of several employees. Based on these different behaviors, the AI gives feedback at which points in time, work steps and movement’s particularly dangerous incorrect postures occur. Motion capturing and digital human model simulation in combination with the results of the AI define the optimized workflow. Individual employees can be trained directly due to the fact that AI identifies their most serious incorrect postures and provide them with a direct analogy of their “wrong” posture and “easy on the joints posture”. With the assistance of various test persons, the AI can conduct a study in which the most frequently occurring incorrect postures can be identified. This could be realized in general or tailored to specific groups of people (e.g. "People over 1.90m tall must be particularly careful not to make the following mistake...). The approach will be tested and validated at the Werk150, the factory of the ESB Business School, on the campus of the Reutlingen University. The new gained knowledge will be used subsequently for training in SMEs.
Manufacturing companies are confronted with external (e.g. short-term change of product configuration by the customer) and internal (e.g. production process deviations) turbulences which are affecting the performance of production. Predefined, centrally controlled logistics processes are limiting the possibilities of production to initiate countermeasures to react in an optimized way to these turbulences. The autonomous control of intralogistics offers a great potential to cope with these turbulences by using the respective flexibility corridors of production systems and applying intelligent logistic objects with decentralized decision and process execution capabilities to maintain a target-optimized production. A method for AI-based storage-location- and material-handling-optimization to achieve performance-optimized intralogistics system through continuous monitoring of performance-relevant parameters and influencing factors by using AI (e.g. for pattern recognition) has been developed. To provide the basis to investigate and demonstrate the potentials of autonomously controlled intralogistics in connection with turbulences of production and in combination with AI, an intelligent warehouse involving an indoor localization system, smart bins, manual, semi-automated/collaborative and autonomous transport systems has been developed and implemented at Werk150, the factory on campus of ESB Business School (Reutlingen University). This scenario, which has been integrated into graduate training modules, allows the analysis and demonstration of different measures of intralogistics to cope with turbulences in production involving amongst others storage and material provision processes. The target fulfilment of the applied intralogistics measures to master arising turbulences is assessed based on the overall performance of production considering lead times and adherence to delivery dates. By applying artificial intelligence (AI) algorithms the intelligent logistical objects (smart bin, transport systems, etc.) as well as the entire logistics system should be enabled to improve their decision and process execution capabilities to master short-term turbulences in the production system autonomously.
The production environment experiences copious challenges, but likewise discovers many new potential opportunities. To meet the new requirements, caused by the developments towards mass-customization, human-robot-cooperation (HRC) was identified as a key piece of technology and is becoming more and more important. HRC combines the strengths of robots, such as reliability, endurance and repeatability, with the strengths of humans, for instance flexibility and decision-making skills. Notwithstanding the high potential of HRC applications, the technology has not achieved a breakthrough in production so far. Studies have shown that one of the biggest obstacles for implementing HRC is the allocation of tasks. Another key technology that offers various opportunities to improve the production environment is Artificial Intelligence (AI). Therefore, this paper describes an AI supported method to improve the work organization in HRC in regards to the task-allocation. The aim of this method is to build a dynamic, semi-autonomous group work environment which keeps not just employee motivation at a high level, but also the product quality due to a decreased failure rate. The AI helps to detect the perfect condition in which the employee delivers the best performance and also supports at identifying the time when the worker leaves this optimal state. As soon as the employee reaches this trigger event, the allocation of the tasks adapts based on the identified stress. This adaptation aims to return the employee to the state of the optimal performance. In order to realize such a dynamic allocation, this method describes the creation of a pool with various interaction scenarios, as well as the AI supported recognition of the defined trigger event.
The high system flexibility necessary for the full automation of complex and unstructured tasks leads to increased technological complexity, thus to higher costs and lower performance. In this paper, after an introduction to the different dimensions of flexibility, a method for flexible modular configuration and evaluation of systems of systems is introduced. The method starts from process requirements and, considering factors such as feasibility, development costs, market potential and effective impact on the current processes, enables the evaluation of a flexible systems of systems equipped with the needed functionalities before its actual development. This allows setting the focus on those aspects of flexibility that add market value to the system, thus promoting the efficient development of systems addressed to interested customers in intralogistics. An example of application of the method is given and discussed.
The paper describes a new stimulus using learning factories and an academic research programme - an M.Sc. in Digital Industrial Management and Engineering (DIME) comprising a double degree - to enhance international collaboration between four partner universities. The programme will be structured in such a way as to maintain or improve the level of innovation at the learning factories of each partner. The partners agreed to use Learning Factory focus areas along with DIME learning modules to stimulate international collaboration. Furthermore, they identified several research areas within the framework of the DIME program to encourage horizontal and vertical collaboration. Vertical collaboration connects faculty expertise across the Learning Factory network to advance knowledge in one of the focus areas, while Horizontal collaboration connects knowledge and expertise across multiple focus areas. Together they offer a platform for students to develop disciplinary and cross-disciplinary applied research skills necessary for addressing the complex challenges faced by industry. Hence, the university partners have the opportunity to develop the learning factory capabilities in alignment with the smart manufacturing concept. The learning factory is thus an important pillar in this venture. While postgraduate students/researchers in the DIME program are the enablers to ensure the success of entire projects, the learning factory provides a learning environment which is entirely conducive to fostering these successful collaborations. Ultimately, the partners are focussed on utilising smart technologies in line with the digitalization of the production process.
While academia and industry see large potential for human-robot collaboration (HRC), only a small number of realized HRC application is currently found in industry. To gather more data about current hindrances to wider implementation of collaborative robots, a study among 15 robot manufactureres and 14 system integrators of collaborative robot technology has been conducted through a predesigned questionnaire procedure. Additionally, five industrial users of human-robot collaboration have been interviewed on the main challenges they experienced during the initial implementation process. The quantitative data has been analyzed using the Wilcoxon-Signed-Rank-Test. Accoring to the study participants, the main challenges within the implementation currently are the identification of HRC-suitable processes, the application of relevant safety norms (such as ISO 10218, ISO/TS 15066) and the application-individual risk assessment.
The level of automation in intralogistics has steadily increased over recent years. For small and medium-sized enterprises (SMEs), however, the associated digital change is a major challenge. Since most SMEs are facing increasing sales volumes (e.g. due to e-commerce and good overall economy) in combination with decreasing lot sizes due to the market demand for individualized products, SMEs have to find innovative solutions to cope with these challenges in production as well as in logistics. Innovative technologies, like 3D printing technologies for the production for small lot sizes and future-oriented intralogistics technologies can serve as enablers in logistics to realize flexible logistic processes for increasing market requiremments. Considering that, this paper examines innovative and future-oriented technologies for intralogistics such as smart containers, driverless forklift systems, data glasses, smart shelves and smart pallets regarding their potential for SMEs. This explorative research paper shows that digital technologies are already suitable and available for SMEs.However, challenges are still seen in areas like the identification and digitalization potential and the financing of these new projects. The primary reason escpecially for SMEs for this is that they have to make investments based on an economically feasible payback period and less based on prestigious reasons like digitalization flagship projecs done by large corporations. In addition, the identification of feasible starting points for digitalization within intralogistic systems embedded in specific factory processes is a major challenge not only for SMEs.
Gesellschaftliche und industrielle Trends im Zuge der Digitaliserung induzieren Veränderungsprozesse in der Industrie. Eine hohe Flexibilität und schnelle Entscheidungsfindungsprozesse stellen entscheidende Wettbewerbsvorteile für Unternehmen dar, um zukünftig erfolgreich am Markt agieren zu können. Um dies zu ermöglichen, müssen aggregierte Echtzeitdaten und Prognosen unmittelbar sowohl am Ort der Wertschöpfung als auch dezentral zur Verfügung stehen. Die Entscheidungsunterstützung mit Hilfe geeigneter Visualisierungen ist ein maßgeblicher Bestandteil von Shopfloor Management Systemen. Aufgrund der steigenden Anforderungen wurde das konventionelle und analoge Shopfloor Management in den letzten Jahren verstärkt durch digitale Lösungen ersetzt. Ein ganzheitlicher Shopfloor Management Ansatz, der die Trends und die daraus resultierenden Herausforderungen für die Industrie abdeckt, ist aktuell nicht vorhanden. Zukünftige Shopfloor Management Lösungen sollen diese Lücke schließen. Hierfür wurde ein ganzheitliches System entwickelt, welches Produktionsinformationen in Echtzeit unmittelbar am Shopfloor visualisiert, eine integrierte flexible Planung und Steuerung der Produktion beinhaltet sowie die Mitarbeiterbedürfnisse berücksichtigt. Eine flexible und individuelle Schichtplanung durch die Mitarbeiter und eine umfassende automatische Beanspruchungsbeurteilung sind dazu integriert worden. Zudem ermöglicht das System die Prognose und Visualisierung von Produktionsinformationen und unterstützt die Anwender bei der Durchführung strukturierter Shopfloor-Meetings. Dadurch werden Entscheidungen direkt auf den Ort der Wertschöpfung verlagert.
Changing requirements and qualification profiles of employees, increasingly complex digital systems up to artificial intelligence, missing standards for the seamless embedding of existing resources and unpredictable return on investments are just a few examples of the challenges of an SME in the age of digitalisation. In most cases there is a lack of suitable tools and methods to support companies in the digital transformation process in the value creation processes, but also of training and learning materials. A European research project (BITTMAS - Business Transformation towards Digitalisation and Smart systems, ERASMUS+, 2016-1 DE02-KA202-003437) with international partners from science, associations and industry has addressed this issue and developed various methods and instruments to support SMEs. Within the scope of a literature search, 16 suitable digitalisation concepts for production and logistics were identified. In the following, a learning platform with a literature database with multivariable sorting options according to branches and keywords of digitalisation, a video gallery with basic and advanced knowledge and a glossary were created in order to provide the user with consolidated and structured specialist knowledge. The 16 identifying concepts for transforming value-added processes in the context of digitalisation were transferred to a learning platform using developed learning paths in coaching and training to online course modules including test questions. A maturity model was developed and implemented in a self assessment tool for the analysis to identify the potential of digitalisation in production and logistics in relation to the current technological digitalisation level of the company. As a result, the user receives one or more of the 16 potential digitalisation concepts suggested or the delta for the necessary, not yet available enabler technologies is presented as a spider diagram. For a successful implementation of the identified suitable digitalisation concepts in production and logistics, a further tool was developed to identify supplementary requirements for all company divisions and stakeholders in relation to the "digital transformation" in the form of a self-evaluation. This paper presents the methods and tools developed, the accompanying learning materials and the learning platform.
Due to Industry 4.0, the full value creation has the chance to undergo a fundamental technological transformation, the realisation of which, however, requires the commitment of every company for its own benefit. The new approaches of Industry 4.0 are often hardly evaluated, let alone proven, so that SMEs in particular often cannot properly estimate the potentials and risks, and often waiting too long with the migration towards Industry 4.0. In addition, they often do not pursue an integrated concept in order to identify possible potentials through changes in their business models. . As part of the research project "GEN-I 4.0 – Geschäftsmodell-Entwicklung für die Industrie 4.0” ", the ESB Business School at Reutlingen University of Applied Sciences and the Fraunhofer Institute for Industrial Engineering and Organization FHG IAO were engaged by the Baden-Württemberg Foundation from 2016 to 2018 to develop tools and an approach how the local economy can develop digital business models for itself in a methodical, beneficial and targeted manner. Through international analyses and interviews GEN-I 4.0 gained and concretized the knowledge required for the evaluation and selection of solutions and approaches for the transfer to develop digital business models. Together with the know-how of the project partners on Industry 4.0 and business model development, the findings were incorporated into the development of two software tools with which SMEs are shown the potentials of Industry 4.0 for their individual business model, online and in selfassessment, and given a comprehensive structured, concrete approach to development, as well as their individual risk. Users of the tools are supported by the selected platform for the networking of different players to implement innovative business models accompanied by coaching concepts for the companies in the follow-up and implementation of the assessment results.
After the initiator of the ESB Logistics Learning Factory, Prof. Vera Hummel had made experience in developing and implementing a concept for a Learning Factory for Advanced Industrial Engineering (aIE) at the University of Stuttgart, Institute IFF between 2005 and 2008, she was appointed as a full professor at ESB Business School, a faculty of Reutlingen University in March 2010. Lacking a realistic, hands on learning and teaching environment of industrial scale for its industrial engineering students, first ideas for a Learning Factory that would strongly focus on all aspects of production logistics were drafted in 2012. Already back then, a strong integration of virtual and physical factory was desired: While the Learning Factory itself would be physical, the neighboring partners along the supply chain, such as suppliers or distribution warehouses, could be added in a fully virtual way. Considering implementation of the ESB Logistics Learning Factory a strategic initiative of the university, initial funding was provided by the faculty ESB Business School itself. Following its own creed, to provide future-oriented training for the region, also primarily local suppliers and manufacturers were selected as equipment providers to the new Learning Factory. During the initialization phase, 2014, a total of three researchers and nine students worked approximately four months to set up a first assembly line, storage racks, AGVs, or pick-by-light systems in conjunction with the underlying didactical concept. Since then, several hundred of students have participated in trainings and lectures held in the ESB Logistics Learning Factory, several research projects were carried out, and multiple high-level politicians and industry executives have been touring the shop floor. Also, more than EUR 2 million in research and infrastructure funds could be secured for expansion and upgrade — allowing the ESB Logistics Learning Factory today to represent many core aspects of an Industrie 4.0 production environment.
The 21st century: an era where emojis and hashtags find their way into every sentence, where taking selfies, live tweeting and mining bitcoin are the norm, and where Insta-culture dictates what we say and do. This is the era into which the digital native was born. With so many changes in every aspect of our lives, how is it that one of the most influential aspects, our education, has remained unchanged? Our education system not only fails to appeal to today’s students, but more importantly, it fails to equip them with the skills required in the 21st Century. It is thus of no surprise that industries feel graduates entering the workplace lack skills in critical thinking, problem solving and self-directed learning. AI, machine learning and big data: Tools and mechanisms we so eagerly incorporate to create smart factories yet are hesitant to use elsewhere. Gamification and games have shown great results in education and training; with most research suggesting a stronger focus on personalization and adaptation. When combined with analytics and machine learning, the potential of games is yet to be realized. A real-time adaptive game would not only always present an appropriate degree of challenge for the individual but would allow for a shift in focus from the recitation of facts, to the application of information filtered to solve the particular problem at hand. South Africa, a country faced with a severe skills gap, could benefit greatly from games. If used correctly, they may just offer a desperately needed contribution toward equipping both current and future employees with the skills needed to survive in the 21st century. This paper explores the feasibility of using such games for enhanced knowledge dissemination and the upskilling of the workforce.
Development of an easy teaching and simulation solution for an autonomous mobile robot system
(2019)
With mass customized production becoming the mainstream, industries are shifting from large-scale manufacturing to flexible and customized production of small batch sizes. Agile manufacturing strategies adopted by SMEs are driving the usage of collaborative robots in today's factories. Major challenges in the adoption of cobots in the industry are the lack of a highly trained workforce to program the robot to perform complex tasks and integration of robot systems to other smart devices in the factory. In addition, the teaching and simulation by non-robotics experts of many industrial collaborative robot systems like the KUKA LBR iiwa is a major challenge, since these systems are designed to be programmed by robot experts and not by shop floor workers or other non-experts. This paper describes the research and development activities done for reducing the barriers in operation and ensure holistic integration of LBR iiwa cobot in the assembly on the example of the ESB Logistics Learning Factory. These include a visual programming solution for the easy teaching of various tasks. Robotic tasts are classified based on common robotics applications and application-specific blocks abstracting specific actions are implemented. A factory worker with no programming competency cour create robot programs by combining these blocks using a Graphical User Interface. In addition, a simulation solution was developed to visualized, analyse, and optimize robotic workflow before deployment. an autonomous mobile robot is integrated with the LBR iiw to improve reconfigurability and thus also the productivity. The system as a whole is controlled using an event-driven distributed control system. Finally, the capabilities of the system are analysed based on the design principles of Industrie 4.0 and potential future research ideas are discussed to further improve the system.
The global demand for individualized products leading to decreasing production batch sizes requires innovative approaches how to organize production and logistics systems in a dynamic manner. Current material flow systems mainly rely on predefined system structures and processes, which result in a huge increase of complexity and effort for system and process changes to realize an optimized production and material provision of individualized products. Autonomous production and logistics entities in combination with intelligent products or logistic load carriers following the vision of the “Internet of Things” offer a promising solution for mastering this complexity based on autonomous, decentralized and target size-optimized decision making and structure formation without the need for predefined processes and central decision-making bodies. Customer orders are going to prioritize themselves and communicate directly with the required production and logistics resources. Bins containing the required materials are going to communicate with the conveyors or workers of the respective intralogistics system organizing and controlling the material flow to the autonomously selected workstation. A current research project is the development of a collaborative tugger train combing the potential of automation and human-robot collaboration in intralogistics. This tugger train is going to be integrated into a self organized intralogistics scenario involving individualized customer orders (low to high batch sizes). To classify the application of self-organization within intralogistics systems, a criteria catalogue has been developed. The application of this criteria catalogue will be demonstrated on the example of a self-organization scenario involving the collaborative tugger train and an intelligent bin system.
The persistent development towards decreasing batch sizes due to an ongoing product individualization, as well as increasingly dynamic market and competitive conditions lead to new changeability requirements in production environments. Since each of the individualized products mgith require different base materials or components and manufacturing resources, the paths of the products giong through the factory as well as the required internal transport and material supply processes are going to differ for every product. Conventional planning and control systems, which rely on predifined processes and central decision-making, are not capable to deal with the arising system's complexity along the dimensions of changing goods, layouts and throughput requirements. The concepts of "self-organization" in combination with "autonomous ocntrol" provide promising solutions to solve these new requirements by using among other things the potential of autonomous, decentralized and target-optimized logistical objects (e.g. smart products, bins and conveyor systems) wich are able to communicate and interact with each other as well as with human wokers. To investigate the potential of automation and human-robot collaboration for intralogistics, a research project for the development of a collaborative tugger train has been started at the ESB Logistics Learning Factory in lin with various student projects in neighboring research areas. This collaboraive tugger train system in combination with other manual (e.g. handcarts) and (semi-) automated conveyoer systems (e.g. automated guided forklift) will be integrated into a dynamic, self-organized scenario with varying production batch sizes to develop a method for target-oriented sefl-organization and autonomous control of intralogistics systems. For a structured investigation of self-organized scenarios a generic intralogistics model as well as a criteria cataloghe has been developed. The ESB Logistics Learning will serve as a practice-oriented research, validation and demonstration environment for these purposes.
Increasing flexibility, greater transparency and faster adaptability play a key role in the development of future intralogistics. Ever-changing environmental conditions require easy extensibility and modifiability of existing bin systems. This research project explores approaches to transfer the Internet of Things (IoT) paradigm to intralogistics. This allows a synchronization of the material and information flow. The bin is enabled by the implementation of adequate hardware and software components to capture, store, process and forward data to selected system subscribers. Monitoring the processes in the intralogistics by means of the smart bin system ensures the implementation of appropriate actions in case of defined deviations. By using explorative expert interviews with representatives from the automotive and pharmaceutical industries, seven practical application scenarios were defined. On this basis, the requirements of smart bin systems were examined. For each individual case of application, a system model was created in order to obtain an overview of the system components and thus reveal similarities and differences. Based on the similarities of the system models, a general requirement profile was derived. After the hardware components of the bin system had been determined, a utility analysis was carried out to find the adequate IoT software. The utility analysis was conducted with a focus on data acquisition and data transfer, data storage, data analysis, data presentation as well as authorization management and data security. The results show that there is great interest in easily expandable and modifiable bin systems, as in all cases, the necessary information flow in the existing bin system has to be improved by means of new IoT hardware and software components.
The approach of self-organized and autonomous controlled systems offers great potential to meet new requirements for the economical production of customized products with small batch sizes based on a distributed, flexible management of dynamics and complexity within the production and intralogistics system. To support the practical application of self-organization for intralogistics systems, a catalogue of criteria for the evaluation of the self-organization of flexible logistics systems has been developed and validated, which enables the classification of logistics systems as well as the identification and evaluation of corresponding potentials that can be achieved by increasing the degree of self-organization.