# Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance
## 2 Literature Review  
A comprehensive literature review was carried out using [PERSON] and [PERSON]'s approach. This integrative method of study is the only approach that combines different methodologies (including experimental and non-experimental research) and has the potential to play a greater role in practice. Literature research was conducted in scientific journals and monographs. The most basic criterion for inclusion was the relevance for this group of technologies, both for their theory, simulation and experimental research, and practical implementation in the economy. The authors of article [7] identify eight DT development prospects, including modular DTs, ensuring modeling coherency and accuracy, incorporating big data analytics into DT models, improving DT simulations, integrating virtual and augmented reality systems with DT, expanding the applications of DT, effective mapping of cyber-physical data and integration with cloud/edge computing. The DT technology is an effective tool that meets the requirements of smart manufacturing by way of recreating physical processes in virtual space [8; 9]. This is performed within a broad-spectrum of CPS (cyber-physical systems). The DT paradigm is well suited to a lifecycle-based paradigm [10]. Technology is increasingly considered to be of paramount importance to improving and evolving global production, including its globalization. Because DTs are now being used in new sectors to increase productivity, efficiency and competitiveness, a variety of tools and methods must be used, which include tools for managing data and connectivity, tools for representing and storing data, machine learning tools and analytical methods. Data acquisition and transmission are of key significance to DT as the technology requires real-time connectivity and data transfer. As an example, [PERSON] [11] proposes a data stream processing system in which data are analyzed and queried continuously. The above-mentioned data acquisition systems are of key significance to the implementation of DT in production environments containing data collected using temporary (due to the hardware used, type of materials, etc.) and permanent data storage processes (processing systems utilizing real-time sensors) [12]. Heterogeneous data and industry-specific knowledge acquired from production processes require modeling and integration with pro duction systems. The most important knowledge representation tools for developing DTs include NoSQL ontologies and databases [13; 14; 15; 16; 17], relational databases [18; 19], MySQL [20], SQLite 3 [21], real-time databases for storing various UML data structures [22], transactional graph databases [23] and databases handling data transformation and predictive queries [24]. In order to process data into knowledge, it is necessary to utilize machine learning and data exploration methods. The following methods are used in the case of digital twins:  
* Neural networks, used in quality prediction and operation control in metallurgy [25]; smart control in the paint and finishing works industry [26; 27], error prediction and diagnostics in CNC machining [28];
* Deep neural networks, used in autonomous production in smart production plants [29] and error diagnostics [30];
* Dynamic Bayesian networks, used by DTs to monitor the condition of aircraft wings [31];
* NSGA-II genetic algorithm used to optimize the efficiency of a machine [19; 26; 28; 32]. Businesses utilizing the DT technology also make use of what is referred to as microservices. These are programming tools developed as a set of loosely connected services. [PERSON] [12] defines them as functions that enable the development of an application as a set of relatively small, coherent, isolated and autonomous services that perform specific tasks.