Digital Twin-based Product Development and Manufacturing Processes in Virtual Space: Data Visualization Tools and Techniques, Cloud Computing Technologies, and Cyber-Physical Production Systems
Digital Twin-based Product Development and Manufacturing Processes in Virtual Space: Data Visualization Tools and Techniques, Cloud Computing Technologies, and Cyber-Physical Production Systems
Author(s): Lucia Michalkova, Veronika Machová, Daniel CarterSubject(s): Business Economy / Management, ICT Information and Communications Technologies
Published by: Addleton Academic Publishers
Keywords: digital twin; cloud computing; cyber-physical production system;
Summary/Abstract: Despite the relevance of digital twin-based product development and manufacturing processes in virtual space, only limited research has been conducted on this topic. In this article, we cumulate previous research findings indicating that digital twin-based product development and manufacturing processes in virtual space require performance optimization and maintenance scheduling. We contribute to the literature on digital twin-based smart manufacturing technologies and tools by showing that sensor-based data acquisition and analysis are pivotal in diagnosis and simulation of digital twin-based product development. Throughout February 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “digital twin” + “product development,” “manufacturing processes,” “data visualization tools and techniques,” “cloud computing technologies,” and “cyber-physical production systems.” As we inspected research published in 2022, only 154 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 23, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT.
Journal: Economics, Management, and Financial Markets
- Issue Year: 17/2022
- Issue No: 2
- Page Range: 37-51
- Page Count: 15
- Language: English
- Content File-PDF