## 4 Research Question Results
### Second Research Question  
A summary of the ML algorithm applied to DT is shown in Figure 12. As shown in the figure, ML algorithms can be classified into several classes based on their working strategy, that is, (a) synaptic connectionists, which work based on the synaptic signalling method of the human nervous system, for example, an artificial neural network. (b) Evolutionary: Based on [PERSON]'s theory of natural evolution, for example, genetic algorithms. (c) Multirelation learning: representation of knowledge-preserving linguistic meaning, for example, the kg embedding model. (d) Logical inference: process of logical conclusion from premises, for example, decision tree or rule mining. (e) Analogy-based: separates data points based on analogical features, for example, Support Vector Machine (SVM). (f) Probabilistic: inferring the probability of an event or variable based on a few values of that variable or event, for example, Bayesian learning and Markov-chain-based models. It has been shown that synaptic connectionist ML algorithm, i.e., neural network, is dominant in the period 2015-2022. In 2015 and 2016, no work was published on ML-based DT. However, over time, neural networks have been used for data fusion [12], data error compensation [29], real-time control [34], etc. The neural network architecture has become application-oriented over time, such as in the conventional neural network in 2018 [3], Bayesian neural network [29] in 2019, deep Q network [35] in 2020, GAN [36] in 2021, and Gaussian kernel extreme-learning machine [37] in 2022 (Figure 13). Neural network architecture is evolving by incorporating new types of transfer function, uncertainty, and optimization. All these changes can be mapped to application-specific requirements and improved performance. Additionally, logical inference-based algorithms such as random forest, extra random forest, and AdaBoost, and analogy-based algorithms such as SVM and k-means were discussed in the publications. However, random forest and SVM clustering have marginal variation over time, thus they are excluded from Figure 13. In the future, ML-based automation applications will be dominant in reducing pandemic effects, demand-based manufacturing, and human- and computer-integrated manufacturing. Figure 12: Summary of ML Algorithm over time. Figure 13: Neural network evolution. Figure with arrows indicating change in ANN over time. The tasks performed by ML algorithms in ML-based DT can be classified into three categories (shown in Figure 14)  
* Predictive data analytics. * Descriptive data analytics. * Prescriptive data analytics. * Model-based task. * Data-based task. **Data analytics:** As can be seen from the figure, for the period 2015-2017, predictive data analytics were limited to machine monitoring. However, both in 2018-2019 and 2020-2022, predictive maintenance emerged as fault prediction method, with a significant number of publications focusing on it. In the period 2020-2022, predictive maintenance evolved into resource performance prediction and machine availability prediction. In the case of descriptive data analytics, the period 2015-2017 showed basic tasks, such as machine monitoring [12,38]. In the period 2018-2019, only one publication was published with descriptive analytics, which was job scheduling [7]. Conversely, in the period 2020-2022, several descriptive analytics emerged, such as visualisation [39], rescheduling [24], and analysis [25]. No publication in the period 2015-2017 focused on prescriptive data analytics. However, in the period of 2018-2019, control and optimisation of the manufacturing system was the focus [29,34]. In the period 2020-2022, high-level adaptive control and closed-loop control with the help of ML was focused on [9,35,40,41,42]. **Model-based task:** In the period 2015-2017, only one publication focused on model-based tasks, such as design, manufacturing, and service (PLM) [4]. Several models have  
Figure 14: Evolution of tasks performed by ML algorithm.