Digital Twin-based Cyber-Physical Production Systems in Immersive 3D Environments: Virtual Modeling and Simulation Tools, Spatial Data Visualization Techniques, and Remote Sensing Technologies.

• Author: Cug, Juraj

1. Introduction

   Virtual simulation tools and algorithms deploy sensor data to optimize digital twin systems. The purpose of our systematic review is to examine the recently published literature on digital twin-based cyber-physical production systems in immersive 3D environments and integrate the insights it configures on virtual modeling and simulation tools, spatial data visualization techniques, and remote sensing technologies. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that smart manufacturing systems integrate modeling and simulation tools for data-driven digital twins. The actuality and novelty of this study are articulated by addressing real-time digital twin-based optimization of cyber-physical production systems, that is an emerging topic involving much interest. Our research problem is whether robotized manufacturing systems deploy autonomous decision-making (Lazaroiu et al., 2019) in digital twin development.

   In this review, prior findings have been cumulated indicating that multi-source heterogeneous product lifecycle data, machining equipments and tools, and digital manufacturing technology (Lazaroiu et al., 2020) can be harnessed in digital twin-driven applications. The identified gaps advance digital twin deployment in smart manufacturing systems. Our main objective is to indicate that visualization tools and intelligent manufacturing equipment improve efficiency in process manufacturing systems through performance monitoring. This systematic review contributes to the literature on digital twin-based virtual shop floor operations and product development by clarifying that Product lifecycle data optimize firm performance by harnessing intelligent manufacturing systems. This research endeavors to elucidate whether decision support tools and smart control systems (Andronie et al., 2021a, b, c) enable resource and production scheduling through data simulation and prediction. Our contribution is by integrating research findings indicating that simulation optimization algorithms, digital twin systems, and deep learning-enabled smart process planning (Lazaroiu et al., 2022) are pivotal in real-time process monitoring.

2. Theoretical Overview of the Main Concepts

   Product simulation models, interactive data visualization, and optimal system configurations are essential in data-driven industrial process monitoring by use of remote sensing technologies in the virtual reality-based visualization environment. Visual analytics enables process monitoring and control of data perception and transmission as regards virtual simulation and products. Data mining algorithms, virtual modeling technology, smart devices, and 3D convolutional neural networks can increase productivity across digital twin shop floor by integrating object detection, action and emotion recognition, and remote sensing systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), virtual machining systems, digital twin technology, and predictive simulation tools (section 4), data-driven digital twin modeling and cognitive data visualization (section 5), spatial data visualization tools, prognostic and diagnostic algorithms, digital twin data, and predictive maintenance (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 March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "digital twin" + "cyber-physical production systems," "immersive 3D environments," "virtual modeling and simulation tools," "spatial data visualization techniques," and "remote sensing technologies." The search terms were determined as being the most employed words or phrases across the analyzed literature. As research published in 2022 was inspected, only 144 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 19 mainly 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: AMSTAR, Dedoose, Distiller SR, and SRDR (Figures 1-6).

4. Virtual Machining Systems, Digital Twin Technology, and Predictive Simulation Tools

   Digital twin-based machining process monitoring, virtual manufacturing system, and smart process planning (Huang et al., 2022; Li et al., 2022a; Xia et al., 2022) develop on cyber-physical production systems across digital twin shop floor. Smart manufacturing development supports organizational competitiveness and production efficiency improvement in machinery and equipment engineering through predictive smart manufacturing, real-time data interaction and maintenance services, and cloud-based intelligent remote operations. Simulation optimization algorithms, digital twin systems, and deep learning-enabled smart process planning are pivotal in real-time process monitoring.

   Virtualization tools further real-time digital twin-based optimization of cyber-physical production systems (Kombaya Touckia et al., 2022; Onaji et al., 2022; Qamsane et al., 2022) by data scalability and modularity. Product simulation models, interactive data visualization, and optimal system configurations are essential in data-driven industrial process monitoring by use of remote sensing technologies in the virtual reality-based visualization environment...