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Product-Process-Resource Asset Networks as Foundation for Improving CPPS Engineering

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In the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.
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The paradigm shift from mass production to mass customisation and reduced product lifecycles requires continuous re-engineering/configuration of modern manufacturing systems. Although efforts are being made to design and build manufacturing systems based on the paradigms of changeability, reconfigurability, and flexibility, the knowledge of the system's capability remains unstructured and isolated from product design and engineering tools. As a result, introducing product design changes are costly, time-consuming and error-prone. To address this problem, this research utilises a Product, Process, and Resource (PPR) ontology with a view to supporting changes through information integration and knowledge generation. The approach moves away from product-centric tools such as Product Lifecycle Management (PLM) and thus a heterarchical model of the system is created. The contribution of this work is to demonstrate how modular ontologies can be utilised in a practical and industrially relevant manner by integrating the data structure of a set of component-based virtual engineering tools into the Resource domain. The research presents a proof-of-concept of the proposed approach using an automated engine assembly station as a case study. Inferences are made from explicit knowledge through rules and mapping as to whether both Product and Process requirements are met by Resource domain capabilities. The approach used in this work has the potential to significantly improve the workflow as and when new products are introduced or modifications need to be made as the scope of change can be assessed rapidly resulting in more focused engineering and design work.
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Mechatronic system is considered as the resulting integration of electrical/electronic system, mechanical parts and information processing. Therefore, to enable a systematic design process of mechatronic systems with a high-level integration, the so-called multidisciplinary integrated design is required. However, neither academia nor industry has yet provided an effective solution, which can fully support the whole design process to achieve such multidisciplinary integrated design. In order to organise the design activities from different disciplines and to aid the designers to achieve the multidisciplinary integrated design, the authors propose a design methodology based on a multidisciplinary interface model. In line with the systems engineering practices, an extended V-model is used as the macro-level process in the proposed design methodology. It starts with identification of requirements on the entire system and ends with a user-validated system. The hierarchical design model is adopted as the micro-level process. It supports the specific design phases where individual designers can structure design sub-tasks and proceed and react in unforeseen situations. To ensure the consistency and traceability between the two levels, the multidisciplinary interface model is proposed. This design methodology is demonstrated by studying the design process of a quadrotor.
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Industry 4.0 production systems must support flexibility in various dimensions, such as for the products to be produced, for the production processes to be applied, and for the available machinery. In this article, we present a novel approach to design and control smart manufacturing systems. The approach is reactive, that is responds to unplanned situations and implements an iterative refinement technique, that is, optimizes itself during runtime to better accommodate production goals. For realizing these advances, we present a model-driven methodology and we provide a prototypical implementation of such a production system. In particular, we employ Planning Domain Definition Language (PDDL) as our artificial intelligence environment for automated planning of production processes and combine it with one of the most prominent Industry 4.0 standards for the fundamental production system model: IEC 62264. We show how to plan the assembly of small trucks from available components and how to assign specific production operations to available production resources, including robotic manipulators and transportation system shuttles. Results of the evaluation indicate that the presented approach is feasible and that it is able to significantly strengthen the flexibility of production systems during runtime.
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Changing conditions on costumer, material, and technology market force producing companies to decrease duration of product and production system development. Especially in case of complex products like cars, this reduction leads to a strategic need for parallel development of products and production systems. Thus automotive industry organizes a complex interplay between product engineering and production system engineering within the new product and production system development processes (NPPDP).
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This book discusses challenges and solutions for the required information processing and management within the context of multi-disciplinary engineering of production systems. The authors consider methods, architectures, and technologies applicable in use cases according to the viewpoints of product engineering and production system engineering, and regarding the triangle of (1) product to be produced by a (2) production process executed on (3) a production system resource. With this book industrial production systems engineering researchers will get a better understanding of the challenges and requirements of multi-disciplinary engineering that will guide them in future research and development activities. Engineers and managers from engineering domains will be able to get a better understanding of the benefits and limitations of applicable methods, architectures, and technologies for selected use cases. IT researchers will be enabled to identify research issues related to the development of new methods, architectures, and technologies for multi-disciplinary engineering, pushing forward the current state of the art.
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Objects to change within a manufacturing enterprise can be products, technological or logistical processes, parts of the manufacturing facilities or a company's organization. For this paper we assume, that IT systems are also objects to change – they have to be adapted to changes to products and facilities on the shop floor. Today the adaption of IT systems is managed and done manually – therefore the authors propose an automated way of changing the production's IT-systems. For this purpose two main ideas are described: reading and interpreting a self-description of production equipment and enrichment of these descriptions with data from the “digital factory” bridging the gap between planning and operating IT-systems and thus enabling higher adaptivity of manufacturing systems.
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