Intelligent authoring and management system for assembly instructions

Continuously increasing complexity and variance within high variety low volume assembly systems causes a vast amount of work instructions. As the amount of new models and variants increases, the need of efficient generation of unambiguous instructions rises. Continuous instruction modifications are unavoidable due to design, customer or process changes. Case based research in cooperation with four manufacturing companies with manual assembly environments points out that assembly instructions authors currently are combining different authoring tools for creating and updating work instructions. Consequently, keeping the rising amount of work instructions up to date becomes less trivial. Furthermore, authors often create work instructions from scratch while instructions of product variants are mostly identical. This causes a large amount of similar work instructions stored as separate documents. As a result, the amount of inconsistent and outdated assembly instructions increases. Poor assembly instruction quality causes frustration and a lower performance of assembly operators. An automatic authoring system and intelligent operator feedback must eliminate these problems. The automatic authoring system provides the author with an overview of preprocessed information and related historical assembly instructions that can serve as a basis for the newly created instructions. In this way, the creation of instructions can be significantly accelerated and work instructions will become more consistent. An experimental lab setup is built in order to test the presented framework. Based on the first tests, the authoring process was significantly accelerated. Further tests within production environments are required in order to validate the presented framework

Transformation of Cognitive Assistance Systems into Augmented Reality

The field of Augmented Reality (AR) has received increasing attention in recent years, as AR can be applied to a wide range of problems. The use of AR offers great potential, especially in the industrial sector. Among many applications in this area, one promising application is the augmentation of cognitive assistance systems. Much research has already been done on the development of augmented support systems but it is still lacking on how to transform existing assistance systems into an AR application. This paper focuses on this transformation and presents a process model that intends to support the migration or digitalization of existing support systems into an AR application. An experimental study validates the proposed approach and derives recommendations for action

Towards a Classification of Learning Support Systems at the Digitized Workplace

Abstract: In light of the transformation to cyber-physical production systems in Industry 4.0, the increasing trend of digitalization and product customization together with the demographic changes in Germany reveal a clear need for supporting the employees' sustainable competences development at the workplace. In this context, technology-enhanced learning environments provide new approaches for developing the vocational training system at the workplace. This paper discusses the pedagogical, didactic and technical aspects that characterize learning systems at the manufacturing and digitized workplace. Furthermore, a set of current learning solutions for supporting the employees' informal learning under industrial settings will be classified based on the introduced scheme as a foundation for a more comprehensive overview in future. Keywords: Workplace Learning; Assistance System; Learning Platform; Industry 4.0. Motivation The increasing digitization of manufacturing enabled by cyber-physical systems (CPS) and of other processes in the supply chain will enforce changes in terms of qualification requirements, the quality of work, the organization forms of work and cooperation between humans and technology [Bo15]. The assembly industry will need innovative learning solutions to provide the workers with the required competences for assembling, picking and setting-up the products through short and fast learning activities. Furthermore, the move towards digitalization in the context of industry 4.0 will cause steady increase in the complexity of production process control as well as the operation and maintenance of the equipments [HK14]. Accordingly, the employees' tasks will primary involve activities of monitoring highly automated processes and regular intervention to keep the process under normal operating conditions. Recently, many research projects have addressed the major challenges for updating the vocational training system in order to meet the growing need for sustainable competences development. However, to the best of our knowledge, there is no comprehensive scheme to classify the various workplace learning solutions, which can be essential to identify areas that are currently disregarded or deemed challenging for research. In this work, we propose a first classification scheme which explains and relates the different concepts and terms that characterize learning solutions for the digitized workplace

Efficient human situation recognition using Sequential Monte Carlo in discrete state spaces

This dissertation analyses these challenges and provides solutions for SMC methods. The large, categorical and causal state-space is the largest factor for the inefficiency of current SMC methods. The marginal filter is analysed in detail for its advantages in categorical states over the particle filter. An optimal pruning strategy for the marginal filter is derived that limits the number of samples.Diese Dissertation analysiert diese Herausforderungen und entwickelt Lösungen für SMC-Methoden. Der große, kategorische und kausale Zustandsraum ist der größte Faktor für die Ineffizienz von aktuellen SMC-Methoden. Die Vorteile des Marginalen Filters in kategorischen Zustandsräumen gegenüber dem Partikelfilter werden detailliert analysiert. Eine optimale Pruning-Strategie wird für den Marginal Filter entwickelt

Dynamics of Long-Life Assets: From Technology Adaptation to Upgrading the Business Model

Knowledge management; Business information system

Dynamics of Long-Life Assets: From Technology Adaptation to Upgrading the Business Model

The editors present essential methods and tools to support a holistic approach to the challenge of system upgrades and innovation in the context of high-value products and services. The approach presented here is based on three main pillars: an adaptation mechanism based on a broad understanding of system dependencies; efficient use of system knowledge through involvement of actors throughout the process; and technological solutions to enable efficient actor communication and information handling.The book provides readers with a better understanding of the factors that influence decisions, and put forward solutions to facilitate the rapid adaptation to changes in the business environment and customer needs through intelligent upgrade interventions. Further, it examines a number of sample cases from various contexts including car manufacturing, utilities, shipping and the furniture industry. The book offers a valuable resource for both academics and practitioners interested in the upgrading of capital-intensive products and services

Information assistance for smart assembly stations

Information assistance helps in many application domains to structure, guide and control human work processes. However, it lacks a formalisation and automated processing of background knowledge which vice versa is required to provide ad-hoc assistance. In this paper, we describe our conceptual and technical work to include contextual background knowledge in raising awareness, guiding, and monitoring the assembly worker. We present cognitive architectures as missing link between highly sophisticated manufacturing data systems and implicitly available contextual knowledge on work procedures and concepts of the work domain. Our work is illustrated with examples in SWI-Prolog and the Soar cognitive architecture

Providing and Adapting Information Assistance for Smart Assembly Stations

Information assistance helps in many application domains to structure, guide and control human work processes. However, it lacks a formalisation and automated processing of background knowledge which vice versa is required to provide ad-hoc assistance. In this paper, we describe our conceptual and technical work to provide information assistance for smart assembly stations. Our contribution comprises a conceptual architecture which was implemented in an industrial prototype, the Plant@Hand smart assembly trolley. We describe three major aspects of our approach, the recognition of assembly tasks based on probabilistic models, decision making using a cognitive architecture, and strategies for dealing with sensor-based and knowledge-based errors. Finally, we present the industrial prototype setup