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Multi-Disciplinary Engineering for Cyber-Physical Production Systems

pp 433-457

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Towards Model Quality Assurance for Multi-Disciplinary Engineering

Needs, Challenges and Solution Concept in an AutomationML Context
  • Dietmar WinklerAffiliated withSBA-Research gGmbHTechnische Universität Wien, Institute of Software Technology and Interactive Systems, CDL-Flex Email author 
  • , Manuel WimmerAffiliated withTechnische Universität Wien, Institute of Software Technology and Interactive Systems, CDL-Flex
  • , Luca BerardinelliAffiliated withTechnische Universität Wien, Institute of Software Technology and Interactive Systems, CDL-Flex
  • , Stefan BifflAffiliated withTechnische Universität Wien

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Abstract

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.

Keywords

Model Quality Assurance Multi-Disciplinary Engineering Model Review Defect Detection AutomationML