Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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Rethinking Production. Produktion als Treiber für eine Industriegesellschaft im Wandel

2023 , Uhlmann, Eckart

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Augmented Reality im Qualitätsmanagement in der Produktentstehung - Chancen und Risiken durch den Wandel der Industrie 4.0

2018 , Heinze, Adrian

German manufacturing companies have to face new challenges under the current develop-ments of Industry 4.0. Industrial production processes are changing because of an increasing digitalization. Individualized products and adaptive manufacturing systems can be realized but they question the competitive and leading position of German companies on the world market. Flexible and customer focused production processes seem to be one way to stay competitive. As a consequence, the Quality Management is challenged to find solutions that guarantee high product and process quality. In this regard, the Total Quality Management points out that qualified employees are important. Nevertheless, it is difficult to integrate employees into the changing and complex production field. Therefore, a new Quality Management approach is required. Augmented Reality has the potential to visualize context-sensitive information during the working process. Manufacturing Execution Systems can provide results of analysis for quality-based operations. These two approaches are merged into a concept by using a mobile AR-device to support quality-based operations in the working process. A structured procedure was designed within the concept that realizes a usable mobile AR-System for the use in the production. The focus is on combining the quality-based operations with the existing competences of the employee as well as on the technical implementation of Augmented Reality on a mobile device. As a result, the AR-System enables the employee to control and improve the product and process quality by initiating quality controlling cycles. As a consequence, manufacturing companies can remain their long-term competitive. Moreover, it opens up possibilities to qualify the employees for future quality-based operations. All in all, the competitiveness of manufacturing companies is secured by continuously improving the competences and company processes.

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A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance

2019 , Kovacs, Klaudia , Ansari, Fazel , Geisert, Claudio , Uhlmann, E. , Glawar, Robert , Sihn, Wilfried

Digital transformation and evolution of integrated computational and visualisation technologies lead to new opportunities for reinforcing knowledge-based maintenance through collection, processing and provision of actionable information and recommendations for maintenance operators. Providing actionable information regarding both corrective and preventive maintenance activities at the right time may lead to reduce human failure and improve overall efficiency within maintenance processes. Selecting appropriate digital assistance systems (DAS), however, highly depends on hardware and IT infrastructure, software and interfaces as well as information provision methods such as visualization. The selection procedures can be challenging due to the wide range of services and products available on the market. In particular, underlying machine learning algorithms deployed by each product could provide certain level of intelligence and ultimately could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a process-based model to facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing industries. This solution is employed for a structured requirement elicitation from various application domains and ultimately mapping the requirements to existing digital assistance solutions. Using the proposed approach, a (combination of) digital assistance system is selected and linked to maintenance activities. For this purpose, we gain benefit from an in-house process modeling tool utilized for identifying and relating sequence of maintenance activities. Finally, we collect feedback through employing the selected digital assistance system to improve the quality of recommendations and to identify the strengths and weaknesses of each system in association to practical use-cases from TU Wien Pilot-Factory Industry 4.0.

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Learning Factory for Industry 4.0 to provide future skills beyond technical training

2018 , Schallock, Burkhard , Rybski, Christoffer , Jochem, Roland , Kohl, Holger

The paper will describe the design of a learning factory for Industry 4.0 that addresses the growing demand for future skills of production staff. Existing learning factories often focus on the technical skills whereas this learning factory also trains decision making, group work and performance monitoring skills. The paper refers to the existing categorizes of learning factories and unveils its numerous features. The conceptual design includes theoretical and practical parts, which prove to be successful in a German learning factory that was realized by the authors. Especially, for the industry 4.0 environment, the layout consists of three stages of a production system, from manual to automatized manufacturing. The practical tasks cover the introduction of smart devices, connection of information flows as well as monitoring of performance. The didactical design of the training program provides a sustainable approach by not only realizing training courses but also includes preparation with management, mid-term coaching and success monitoring after the training. The learning factory is a part of a whole research institute for intelligent manufacturing in China including consultancy and application support. One of the underlying goals of the learning factory is to enable production staff for change management, decision making and innovation.

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Methodology for the in-process evaluation of software-based process failures in Selective Laser Melting machine tools

2018 , Pastl Pontes, Rodrigo

The intense use of Additive Manufacturing technologies, especially Selective Laser Melting (SLM), to produce complex functional components by several sectors of the industry evidenced that current machines are not able to ensure the product quality. The results are affected by failures occurred in manufacturing processes caused, for instance, by the controller software. Such failures influence either the time to manufacture a component or its final quality. Current system validation methodologies are not adapted to SLM machines and, therefore, they are not effective. Within this context, this dissertation focused on the development of a methodology to evaluate process failures originated by the software of SLM machines in order to measure the machine quality. For this purpose, a process map for the machine was described, Key Performance Indicators were designed, and the Conformance Timed Automata Effect Analysis (ConTEA) testing methodology was developed. By using ConTEA, models with functioning scenarios are created, the tests can be generated and applied to the SLM machine software, failures can be tracked and the machine quality can be assessed.

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