Multi-Disciplinary Engineering for Cyber-Physical Production Systems

For the development of interdisciplinary technical systems such as CPS, systemic approaches which stringently summarize the logic of development are currently available. These approaches are suitable to support the complexity of both the CPS as well as the related developmental processes. However, these development methods are relatively generic. An adaptation or a tailoring to specific conditions of both the products under consideration as well as the development of boundary conditions is absolutely necessary to use them effectively and efficiently. For the development of CPS also a variety of IT tools which effectively support the product development but only if they are well coordinated with the corresponding processes, are already available. If the interfaces are described sufficiently and comprehensively, and the data characteristics of the results of the various development activities are taken into account, media discontinuities can be reduced. The major challenge in the development of complex technical systems is the overall system analysis and the system integration. To this end, modern methods such as model-based engineering in general and model-based Systems Engineering in specific, provide powerful approaches that must be applied and adjusted for the purposes of the product and process characteristics. This adjustment process to product development and the integration of MBSE approaches into the IT-structures may be seen as the main challenges for the future.

Cyber-Physical Production Systems (CPPS) foster new processes and production methods for reducing “time to market”, waste and failures, as well as improving quality and cost effectiveness. However, changes cannot be restricted to the technological side. An increasing share of services is offered with these systems in order to deliver new customized functions and other benefits. This trend has led to the introduction of Product Service Systems (PSS) as a promising framework describing the integrated development, realization and offering of specific product-service bundles as a solution. The integration of both CPPS and PSS concepts is becoming relevant for industry, because data monitoring, storage and processing allow creating a higher service layer able to deliver production systems with new “intelligent” behaviors and communicating capabilities. In this chapter, we use the term Cyber-physical Product-Service Systems (CPSS) for such an integrated approach. It gives a definition of CPS-based PSS and unveils the state-of-the-art for both concepts with major research issues for their integration. The evolution from products to solutions through servitization is shown, as well as the hardware, software, and service elements of CPSS, requiring an alignment of CPPS and service lifecycle models. Based on industrial use cases, this chapter also deals with challenges for engineering CPS-based PSS in terms of complexity, end user involvement with information exchange among stakeholders and linking views of multiple disciplines (mechanical engineering, information systems, service science etc.). This leads to implications for engineering processes, particularly cross-domain Requirements Engineering and design but also servitized Business Models enabled by CPS.

In the chapter Product Lifecycle Management (PLM) Challenges of CPPS, data and information management issues arising from the advanced use of modern product development and engineering methods are addressed. These advanced methods are required for engineering processes of smart systems and individualized products with high complexity and variability. Emphasis is put on challenges of the life-cycle oriented information integration of products and the respective Cyber-Physical Production Systems (CPPS). Furthermore, the chapter addresses data and information management problems coming from integration of the use and operation phase of products and systems in terms of forward and backward information flows.

Recent research and development activities within the field of production system engineering and use focus on the increase of production system flexibility and adaptability. One common issue of those approaches is the consideration of hierarchical and modular production system architectures where the individual components of the system are equipped with certain functionalities and information. Up to now there is no common understanding about what a component can constitute, i.e. which parts of a production system can be regarded as components within the hierarchy and which functionalities and information are assigned to it. This gap will be closed within this and the subsequent two chapters.

They will at first discuss the relevant layers of components in a production system, then the types of information required to be assigned to a component on the different layers to establish a digital representation of the component, and at last the description means exploitable to represent the identified information in the different life cycle phases of a production system.

This chapter in particular will consider hierarchies of production system components and their life cycle. Based on a literature survey and practical experiences candidates for hierarchy layers and their identification criteria are named. In addition, main life cycle phases of production systems are discussed.

Recent research and development activities within the field of production system engineering and operation focus on the increase of production system flexibility and adaptability. One common issue of those approaches is the consideration of hierarchical and modular production system architectures where the individual components of the system are equipped with certain functionalities and information. Up to now there is no common understanding about what a component constitutes, i.e. which parts of a production system can be regarded as components within the hierarchy and which functionalities and information are assigned to it. This gap will be closed within this, the prior, and the subsequent chapter.

They will at first discuss the relevant layers of components in a production system, then the types of information required to be assigned to a component on the different layers to establish a virtual representation of the component, and at last the description means exploitable to represent the identified information in the different life cycle phases of a production system.

This chapter in particular will consider in detail the information sets relevant for a production system component along the life cycle of a production system. Relevant artifacts are identified for each of the three main life cycle phases described in Chap. 5, assigned to the different layers of the production system hierarchy, and discussed against main cases of information reuse within the life cycle of production systems. Through this, it is intended to enable an identification of hierarchy layers based on relevant information sets.

Recent research and development activities within the field of production system engineering and use focus on the increase of production system flexibility and adaptability. One common issue of those approaches is the consideration of hierarchical and modular production system architectures where the individual components of the system are equipped with certain functionalities and information. Up to now, there is no common understanding about what a component can constitute, i.e. which parts of a production system can be regarded as components within the hierarchy and which functionalities and information are assigned to it. This gap will be closed within this and the two the prior chapters.

They will at first discuss the relevant layers of components in a production system, then the types of information required to be assigned to a component on the different layers to establish a digital representation of the component, and at last the description means exploitable to represent the identified information in the different life cycle phases of a production system.

This chapter, in particular, will consider the artifacts and description means related to them in each of the three life cycle phases on each layer of the hierarchical production system structure as proposed in Chap. 5. Furthermore, the artifacts are clustered and generic artifact classes are derived from the fragmented information artifact landscape. Finally, description means are assigned to the artifact classes, paving the way for future research on this topic.

Cyber-Physical-Systems (CPS) enable flexible and reconfigurable realization of automation system architectures, utilizing distributed control architectures with non-hierarchical modules linked together through different communication systems. Several control system architectures have been developed and validated in the past years by research groups. However, there is still a lack of implementation in industry. The intention of this work is to provide a summary of current alternative control system architectures that could be applied in industrial automation domain as well as a review of their commonalities. The aim is to point out the differences between the traditional centralized and hierarchical architectures to discussed ones, which rely on decentralized decision-making and control. Challenges and impacts that industries and engineers face in the process of adopting decentralized control architectures are discussed, analysing the obstacles for industrial acceptance and the new necessary interdisciplinary engineering skills. Finally, an outlook of possible mitigation and migration actions required to implement the decentralized control architectures is addressed.

Application fields of automated production systems are varied, e.g. automotive, aerospace and food industry, just to name a few. The complexity of such production systems has significantly been increased in the last years, (Koren et al., CIRP Ann Manuf Technol 48(2):527–540, 1999). This increase was a result of the increased complexity and variance of products. As a result of this, the engineering workflow of automated production system has continuously been adapted to new requirements. In this regards, this chapter shows and describes the current engineering workflow of automated production systems based on experience in the field of production system for the automotive industry. The main focus of this description is set on the established tool-chains and used tools to create engineering information as well as data formats to save and exchange information between tools and involved personnel. In the introduction of this chapter, differences between an automated production system and a cyber-physical system are given. Current production systems could be named CPPS but this term is not popular in the field of production system builder as well as production owners. But in spite of that the end of this chapter gives an outlook of the future of automated production systems in direction of CPPS.

Information exchange is one of the critical issues within the multi-disciplinary engineering chain of production system engineering. In the subsequent chapter the problem of identifying and standardizing an appropriate data exchange format for this field of application will be considered. It will be argued, why AutomationML can be an appropriate choice to fulfil current requirements.

To engineer large, complex, and interdisciplinary systems, modeling is considered as the universal technique to understand and simplify reality through abstraction, and thus, models are in the center as the most important artifacts throughout interdisciplinary activities within model-driven engineering processes. Model-Driven Systems Engineering (MDSE) is a systems engineering paradigm that promotes the systematic adoption of models throughout the engineering process by identifying and integrating appropriate concepts, languages, techniques, and tools. This chapter discusses current advances as well as challenges towards the adoption of model-driven approaches in cyber-physical production systems (CPPS) engineering. In particular, we discuss how modeling standards, modeling languages, and model transformations are employed to support current systems engineering processes in the CPPS domain, and we show their integration and application based on a case study concerning a lab-sized production system. The major outcome of this case study is the realization of an automated engineering tool chain, including the languages SysML, AML, and PMIF, to perform early design and validation.

A key requirement in supporting the work of engineers involved in the design of Cyber-Physical Production Systems (CPPS) is offering tools that can deal with engineering data produced across the various involved engineering disciplines. Such data is created by different discipline-specific tools and is represented in tool-specific data models. Therefore, due to this data heterogeneity, it is challenging to coordinate activities that require project-level data access. Semantic Web technologies (SWTs) provide solutions for integrating and making sense of heterogeneous data sets and as such are a good solution candidate for solving data integration challenges in multi-disciplinary engineering (MDE) processes specific for the engineering of cyber-physical as well as traditional production systems. In this chapter, we investigate how SWTs can support multi-disciplinary engineering processes in CPPS. Based on CPPS engineering use cases, we discuss typical needs for intelligent data integration and access, and show how these needs can be addressed by SWTs and tools. For this, we draw on our own experiences in building Semantic Web solutions in engineering environments.

Engineering Cyber-Physical Systems (CPS) is challenging, as these systems have to handle uncertainty and change during operation. A typical approach to deal with uncertainty is enhancing the system with self-adaptation capabilities. However, realizing self-adaptation in CPS, and consequently also in Cyber-Physical Production Systems (CPPS) as a member of the CPS family, is particularly challenging due to the specific characteristics of these systems, including the seamless integration of computational and physical components, the inherent heterogeneity and large-scale of such systems, and their open-endedness.

In this chapter we survey CPS studies that apply the promising design strategy of combining different self-adaptation mechanisms across the technology stack of the system. Based on the survey results, we derive recurring adaptation patterns that structure and consolidate design knowledge. The patterns offer problem-solution pairs to engineers for the design of future CPS and CPPS with self-adaptation capabilities. Finally, the chapter outlines the potential of collective intelligence systems for CPPS and their engineering based on the survey results.

This chapter focuses on the technological aspects involved in developing a service-oriented solution for interoperability in the context of cyber-physical production systems (CPPS). It addresses the typical state of industrial enterprises and the core technologies currently available for the development of a service-oriented (SO) solution for agile environments. The chapter therefore discusses features of the service-oriented paradigm as well as aspects related to enterprise and network architectures, constraints, and technologies to discern the current challenges facing modern enterprises. The chapter also explores the service-oriented reference architectures of recent EU projects to highlight their main characteristics. Finally, their respective realizations are decomposed to discern the connectivity strategies and standards employed by each to achieve an interoperability-focused technology stack for the operation of agile and flexible industrial plants.

Ramping up new products to volume production is a challenge for most manufacturing companies. The deviation between plan and reality of costs and duration of ramp-up projects is still significant, and the achievable quality of new products at the start-up of volume production is difficult to predict. Consequently, new products arrive too late at the customer, causing dissatisfaction or even loss of customers, additional operational costs or unplanned enhancement of ramp-up budget. The vertical knowledge exchange between product engineering and process engineering, as well as horizontally along the production process and the supply chain has turned out to be the major reason for deviations. This chapter describes how information from product engineering and process engineering has to be structured for automated recommendation of information reuse during the planning of ramp-up projects. It discusses the involvement of a multi-disciplinary knowledge base in a production environment but also organizational measures to be taken into account in order to address this challenge. Needs for standardization across enterprises is addressed as well. Through thus achievable improvement of planning quality, based on reused production knowledge, ramp-up projects can improve towards deterministic ramp-up processes. The article is of interest for industrial engineers, quality managers and ICT-managers in the industrial field.

In multi-disciplinary engineering (MDE) projects, information models play an important role as inputs to and outputs of engineering processes. In MDE projects, engineers collaborate from various disciplines, such as mechanical, electrical, and software engineering. These disciplines use general-purpose and domain-specific models in their engineering context. Important challenges include model synchronization and model quality assurance (MQA) that are covered insufficiently in current MDE practices. This chapter focuses on the needs and approaches for MQA in MDE environments. We address the following two research questions (RQs): The first RQ focuses on investigating needs and expected capabilities that are required for a systematic review process that focuses on changes in MDE design models (RQ-MQA1). The second RQ focuses on how to extend a standard modeling language for MDE, such as the AutomationML, to address needs for storing process-relevant attributes in the context of quality assurance and review process support (RQ-MQA2). This chapter presents concepts and an initial evaluation of MQA approaches in the context of selected MDE processes, i.e., the addition, change, or removal of a component in an engineering discipline and an impact analysis on the integrated plant model. Main results are that (a) an adapted review process helps to systematically drive model reviews for MDE and (b) the standardized language description of AutomationML can be extended with process-related attributes that are useful for quality assurance and reviewing.

This chapter summarizes and reflects on the material presented in this book regarding challenges and solutions for the required information processing and management in the context of the multi-disciplinary engineering of production systems.