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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.