The transformation of virtual product development to Digital Engineering (DE) requires the successful integration of Digital Engineering or data-driven methods into existing product development processes. Those methods allow for the analysis and usage of existing data. However, missing knowledge about these methods, as well as their performance or limitations, is a major burden for their application, especially in small and medium-sized enterprises. In order to close this gap, this paper proposes the AI4PD ontology, linking product development processes (PD) and Digital Engineering methods (AI). This knowledge representation gives companies an overview of the available methods to support them in selecting a suitable solution for their problems. The representation of AI4PD is performed in Protégé using the W3C standard OWL syntax. The opportunities of AI4PD are shown by a use case of identifying a DE-Method for predicting manufacturing possibilities based on test data and CAD files. Furthermore, after possible problems in existing product development processes are identified, AI4PD covers the necessary knowledge for a successful method of identification and integration to transform virtual product development to Digital Engineering.

This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.

Die Produktentwicklung befindet sich in einer Transformation. Das etablierte Paradigma der virtuellen Produktentwicklung weicht zunehmend dem Digital Engineering. Insbesondere kleine und mittelständische Unternehmen (KMU) haben jedoch Probleme bei der Optimierung ihrer Prozesse durch Integration datengetriebener Methoden, da Ansätze zur zielgerichteten Prozessanalyse und Methodenidentifikation bislang fehlen. Der vorgestellte Ansatz stellt einen strukturierten Prozess zur Unterstützung der Integration datengetriebener Methoden in der Produktentwicklung zur Verfügung. Der Fokus liegt hierbei darauf, in bestehenden und etablierten Prozessen Anwendungsfälle für neuartige Methoden zu identifizieren und passende Methoden auszuwählen.

Design processes are always in motion, since more and more data-driven methods are used for various design and validation tasks. However, small and medium enterprises especially struggle with enhancing their processes with data-driven methods due to a lack of practical and easy-to-use analysis and redesign methods which can handle design process characteristics. In this paper, we present PADDME, which stands for process analysis for digital development in mechanical engineering, as a novel method that, in contrast to currently available analysis methods, considers those design process characteristics with respect to the integration of data-driven methods. Furthermore, a novel technology-readiness framework for digital engineering is introduced. Using the PADDME method, an industrial case study on introducing data-driven methods into the design and evaluation process chain is presented. The usability and novelty of the method are shown by the case study. Thus, PADDME allows a detailed capturing of current design processes and paves the way for process optimisation through data-driven methods. PADDME is a valuable method for advancing digital mechanical engineering processes in small and medium enterprises, and future work will focus on refining and expanding its application and evaluation.