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Close and safe interaction of humans and robots in joint production environments is technically feasible, however should not be implemented as an end in itself but to deliver improvement in any of a production system’s target dimensions. Firstly, this paper shows that an essential challenge for system integrators during the design of HRC applications is to identify a suitable distribution of available tasks between a robotic and a human resource. Secondly, it proposes an approach to determine task allocation by considering the actual capabilities of both human and robot in order to improve work quality. It matches those capabilities with given requirements of a certain task in order to identify the maximum congruence as the basis for the allocation decision. The approach is based on a study and subsequent generic description of human and robotic capabilities as well as a heuristic procedure that facilities the decision making process.
Automatic content creation system for augmented reality maintenance applications for legacy machines
(2024)
Augmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual.
The early involvement of experiences gained through intelligence and data analysis is becoming increasingly important in order to develop new products, leading to a completely different conception of product creation, development and engineering processes using the advantages that the dedication of the digital twin entails. Introducing a novel stage gate process in order to be holistically anchored in learning factories adopting idea generation and idea screening in an early stage, beta testing of first prototypes, technical implementation in real production scenarios, business analysis, market evaluation, pricing, service models as well as innovative social media portals. Corresponding product modelling in the sense of sustainability, circular economy, and data analytics forecasts the product on the market both before and after market launch with the interlinking of data interpretation nearby in real-time. The digital twin represents the link between the digital model and the digital shadow. Additionally, the connection of the digital twin with the product provides constantly updated operating status and process data as well as mapping of technical properties and real-world behaviours. A future-networking product, by embedded information technology with the ability to initiate and carry out one's own further development, is able to interact with people and environments and thus is relevant to the way of life of future generations. In today's development work for this new product creation approach, on one hand, "Werk150" is the object of the development itself and on the other hand the validation environment. In the next step, new learning modules and scenarios for trainings at master level will be derived from these findings.
It has not yet been possible to achieve the desired aim of decoupling economic growth from global material demand. Small and medium sized enterprises (SMEs) represent the backbone of most industrialized economies. Although material efficiency is of vital importance for many SMEs, few of them actually treat it as their top priority. There is a cornucopia of tools and methods available, which can be used for material efficiency purposes. These, however, have gained little groud in the SME-field. This work deals with the enabling factors for material efficiency improvements in manufacturing SMEs and projections towards aspects of supply chain and circular economy. A multi-disciplinary decoupling approach for manufacturing SMEs and an implementation roadmap for further practical development are proposed. The approach combines appropriate complexity of technology and socio-economic considerations. It enables a connection to existing methods and the implementation of established information technologies.
Today's logistics systems are characterized by uncertainty and constantly changing requirements. Rising demand for customized products, short product life cycles and a large number of variants increases the complexity of these systems enormously. In particular, intralogistics material flow systems must be able to adapt to changing conditions at short notice, with little effort and at low cost. To fulfil these requirements, the material flow system needs to be flexible in three important parameters, namely layout, throughput and product. While the scope of the flexibility parameters is described in literature, the respective effects on an intralogistics material flow system and the influencing factors are mostly unknown. This paper describes how flexibility parameters of an intralogistics system can be determined using a multi-method simulation. The study was conducted in the learning factory “Werk150” on the campus of Reutlingen University with its different means of transport and processes and validated in terms of practical experiments.
Decreasing batch sizes in production in line with Industrie 4.0 will lead to tremendous changes of the control of logistic processes in future production systems. Intelligent bins are crucial enablers to establish decentrally controlled material flow systems in value chain networks as well as at the intralogistics level. These intelligent bins have to be integrated into an overall decentralized monitoring and control approach and have to interact with humans and other entities just like other cyber-physical systems (CPS) within the cyber-physical production system (CPPS). To realize a decentralized material supply following the overall aim of a decentralized control of all production and logistics processes, an intelligent bin system is currently developed at the ESB Logistics Learning Factory. This intelligent bin system will be integrated into the self developed, cloud-based and event-oriented SES system (so-called “Self Execution System”) which goes beyond the common functionalities and capabilities of traditional manufacturing execution systems (MES).
To ensure a holistic integration of the intelligent bin for different material types into the SES framework, the required hard- and software components for the decentrally controlled bin system will be split into a common and an adaptable component. The common component represents the localization and network layer which is common for every bin, whereas the flexible component will be customizable to different requirements, like to the specific characteristics of the parts.
Railway operators are being challenged by increasing complexity and safeguarding the availability of passenger rolling stock, bringing maintenance and especially emerging technologies into the focus. This paper presents a model for selection and implementation of Industry 4.0 technologies in rolling stock maintenance. The model consists of different stages and considers the main components of rolling stock, the related appropriate maintenance strategies and Industry 4.0 technologies considering the maturity level of the railway operators. Relevant criteria and main prerequisites of the technologies were identified. The model proposes relevant activities and was validated by industry experts.
Increasing complexity in manufacturing processes poses new challenges for industrial maintenance. In addition, advanced machine monitoring and lifetime forecasting options expand the tools and maintenance strategies available. Today, maintenance strategy selection is performed sequentially usually based on prioritised machines and components. These selections are optimized locally for each machine isolated, not considering the context of other machines within the value-adding network. To overcome these challenges, this paper presents an approach for an integrated maintenance strategy selection in one-step by an integrated model considering possible machine failures and the context of other machines within the value-adding network in parallel.
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.
Digitalization changes the manufacturing dramatically. In regard of employees’ demands, global trends and the technological vision of future factories, automotive manufacturing faces a huge number of diverse challenges. Currently, research focuses on technological aspects of future factories in terms of digitalization. New ways of work and new organizational models for future factories have not been described yet. There are assumptions on how to develop the organization of work in a future factory but up to now, literature shows deficits in scientifically substantiated answers in this research area. Consequently, the objective of this paper is to present an approach on a work organization design for automotive Industry 4.0 manufacturing. Future requirements were analyzed and deducted to criteria that determine future agile organization design. These criteria were then transformed into functional mechanisms, which define the approach for shopfloor organization design