Digital Twin-based Product Development and Manufacturing Processes in Virtual Space: Data Visualization Tools and Techniques, Cloud Computing Technologies, and Cyber-Physical Production Systems.

AuthorMichalkova, Lucia
  1. Introduction

    The purpose of our systematic review is to examine the recently published literature on data visualization tools and techniques, cloud computing technologies, and cyber-physical production systems and integrate the insights it configures on digital twin-based product development and manufacturing processes in virtual space. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that cloud-based digital twin technology is instrumental in industrial product lifecycle management across smart factories. The actuality and novelty of this study are articulated by addressing digital twin-based sustainable intelligent manufacturing and smart production management, that is an emerging topic involving much interest. Our research problem is whether modeling and simulation algorithms, process mining techniques, computer vision detection technology, and deep learning-enabled smart process planning (Andronie et al., 2021; Lazaroiu et al., 2022) enable production process optimization across autonomous production systems.

    In this review, prior findings have been cumulated indicating that digital twin-based product development and manufacturing processes in virtual space require performance optimization and maintenance scheduling. The identified gaps advance Modular and robust digital twin modeling. Our main objective is to indicate that sensor-based data acquisition and analysis are pivotal in diagnosis and simulation of digital twin-based product development. This systematic review contributes to the literature on digital twin-based smart manufacturing technologies and tools by clarifying that product digital twins require cloud computing technologies, virtually generated data, analytical functionalities, and advanced automation equipment. This research endeavors to elucidate whether digital twin network increases operational efficiency of manufacturing processes through supply chain production planning. Our contribution is by integrating research findings indicating that computer vision-based systems optimize operations in smart manufacturing by articulating autonomous decision-making.

  2. Theoretical Overview of the Main Concepts

    Workshop digital twin systems and Internet of Things-based cloud manufacturing harness data mining, cognitive artificial intelligence, and optimization algorithms to configure customizable products. Machine learning algorithms, simulation-based digital twins, and big data analytical tools enable coherent process tracking and product lifecycle management through data intensive modeling across smart shop floors and cyber-physical production systems. Smart manufacturing technologies can be pivotal in modeling virtual manufacturing systems by integrating digital twin capabilities, product lifecycle management, and preventive and predictive maintenance scheduling. Realtime production line monitoring is instrumental in smart shop floors and integrated cyber-physical systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), digital twin-based product development and manufacturing processes in virtual space (section 4), diagnosis and simulation of digital twin-based product development (section 5), virtual models and simulations in digital twin-based intelligent manufacturing (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

  3. Methodology

    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." The search terms were determined as being the most employed words or phrases across the analyzed literature. 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 (Tables 1 and 2). Extracting and inspecting publicly accessible files (scholarly sources) as evidence, before the research began no institutional ethics approval was required. 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 (Figures 1-6).

    Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were used that ensure the literature review is comprehensive, transparent, and replicable. The flow diagram, produced by employing a Shiny app, presents the stream of evidence-based collected and processed data through the various steps of a systematic review, designing the amount of identified, included, and removed records, and the justifications for exclusions.

    To ensure compliance with PRISMA guidelines, a citation software was used, and at each stage the inclusion or exclusion of articles was tracked by use of custom spreadsheet. Justification for the removal of ineligible articles was specified during the full-text screening and final selection.

  4. Digital Twin-based Product Development and Manufacturing Processes in Virtual Space

    Digital twin can be deployed in product lifecycle design and manufacturing across processes, machines, and plants (Huang et al., 2022; Son et al., 2022; Wang et al., 2022) through data-based digitalization, planned production schedule, and production and operation management. Digital twin processes integrate data collection and analysis while monitoring warehouse operations and critical machinery. Digital twin-based product development and manufacturing processes in virtual space require performance optimization and maintenance scheduling.

    Modular and robust digital twin modeling articulate a virtual mapping of manufacturing plants through heterogeneous data fusion and optimal resource sharing...

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