Continued use of virtual commissioning models: A novel approach toward digital twins for automated production systems

Abstract Virtual Commissioning (VC) is an increasingly integral practice in the engineering of production systems, evidenced by growing research and industrial adoption. Advancements in VC model fidelity, computational performance, and automated generation are improving the validation of complex production systems and point toward further applicability beyond conventional control software validation. In industrial practice, however, VC models are still confined to their original use. While emerging applications for VC models in the operational phase highlight the potential for model reuse, a structured method for transforming these valuable engineering assets into Digital Twins (DTs) is still absent. This paper addresses this gap through three main contributions: first, by clarifying the VC/DT boundary within production contexts and identifying open research questions; second, by introducing a novel six-phase transformation method; and third, by identifying industrially-derived use cases and their specific requirements. The proposed method adopts the Design Science Research (DSR) methodology to systematically structure the problem space, derive actionable objectives, and design the iterative development process. Initial results include a set of industrial use cases with their corresponding requirements, as well as the exemplary execution of one development cycle. Finally supplemented by a qualitative cost-benefit analysis, this work offers practical guidance for realizing VC-based DTs that support operational tasks in production environments.

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Publication Details

Journal
The International Journal of Advanced Manufacturing Technology
Published
2026-01-19
DOI
https://doi.org/10.1007/s00170-025-17195-y
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00

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article

Continued use of virtual commissioning models: A novel approach toward digital twins for automated production systems

Alexander M. Kuhn, Shengjian Chen, Lars Klingel, Alexander Verl et al.
The International Journal of Advanced Manufacturing Technology
Digital Transformation in Industry
article

Continued use of virtual commissioning models: A novel approach toward digital twins for automated production systems

Alexander M. Kuhn, Shengjian Chen, Lars Klingel, Alexander Verl, Christer-Clifford Schenke, Felix Ferle, William Tekouo
article en

Abstract

Abstract Virtual Commissioning (VC) is an increasingly integral practice in the engineering of production systems, evidenced by growing research and industrial adoption. Advancements in VC model fidelity, computational performance, and automated generation are improving the validation of complex production systems and point toward further applicability beyond conventional control software validation. In industrial practice, however, VC models are still confined to their original use. While emerging applications for VC models in the operational phase highlight the potential for model reuse, a structured method for transforming these valuable engineering assets into Digital Twins (DTs) is still absent. This paper addresses this gap through three main contributions: first, by clarifying the VC/DT boundary within production contexts and identifying open research questions; second, by introducing a novel six-phase transformation method; and third, by identifying industrially-derived use cases and their specific requirements. The proposed method adopts the Design Science Research (DSR) methodology to systematically structure the problem space, derive actionable objectives, and design the iterative development process. Initial results include a set of industrial use cases with their corresponding requirements, as well as the exemplary execution of one development cycle. Finally supplemented by a qualitative cost-benefit analysis, this work offers practical guidance for realizing VC-based DTs that support operational tasks in production environments.

The International Journal of Advanced Manufacturing TechnologyVol. 142(9-10)
University of Stuttgart (DE), BMW (Germany) (DE), Leibniz Institute for Solid State and Materials Research (DE), Fraunhofer Institute for Machine Tools and Forming Technology (DE)
Bundesministerium für Wirtschaft und Energie
Industry, innovation and infrastructure
Openalex Percentile: Top 86%
Digital Transformation in Industry
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