Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Abstract

In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations.

Transformación digital en sistemas de gestión de calidad en empresas tecnológicas

“…The documentation of audits and corrective and preventive actions (CAPA) means that the flow of documents only keeps growing, which makes it even more necessary to implement digitization at the organizational level and even more so when it is an organization that has a vertical integration system that requires it to have information regarding allied companies. With an automated system, risk management is integrated into all processes [37], more importantly, it is a driving force in the process of corrective and preventive actions, within the investigation and root cause analysis. This gives organizations an advantage in prioritizing responses to adverse events and various situations presented in the enterprise [38].…”

Modellunterstützte Qualitätssicherung von Engineering-Daten industrieller Produktionssysteme

“…Integrations-oder Datenaustauschplattformen (Biffl et al 2019a, b) können einen effizienten Datenaustausch zwischen heterogenen Fachbereichen ermöglichen. Zur Qualitätsverbesserung können Maßnahmen der Qualitätssicherung helfen, Fehlerspeziell in fachbereichsübergreifenden Datenmodellenfrühzeitig zu finden und dadurch das Risiko für den Projekterfolg zu minimieren (Biffl et al 2011(Biffl et al , 2019a; (Winkler et al 2017b). Ergänzend zu funktionalen Anforderungen an Software-Werkzeuge im Entwicklungsprozess (Schmidt et al 2014) ergeben sich auch Bedarfe an die Qualitätssicherung, wie beispielsweise (a) die Sicherung einer durchgängigen Qualität der Ergebnisse einzelner Fachbereiche; (b) die Sicherung einer durchgängigen Qualität über Fachbereichsgrenzen; und (c) eine effiziente Überwachung von kritischen Projekt-und Produktparametern, deren aktuelle Daten aus unterschiedlichen Werkzeugen stammen.…”

“…Diese Ebenen können im Anlagen-Engineering unterschiedlich adressiert werden. dells übergreifende Prüfungen ermöglichen (Fay et al 2013;Winkler et al 2017b). Beispielsweise ermöglichen das EPLAN Engineering Center 1 und der SIMATIC Automation Designer 2basierend auf einem gemeinsamen Datenmodelldurchgängiges Engineering mit einheitlicher Dokumentation und Standardisierung durch wiederverwendbare Komponenten.…”