# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
## 4 Digital Twins: Enabling Interoperability in Manufacturing
### Machine Learning in Smart Manufacturing  
Industries often collect large amounts of raw data from different types of sensor networks [23; 24]. The massive heterogeneous data is often then sent to the cloud where an information dashboard is expected. This presents an enormous challenge to clean and homogenize the raw data to make it suitable for analysis. The availability of the data is often taken for granted by the industries and it is expected to provide a real-time predictive analytic model with a dump of data which is often insufficiently exploited by companies.  
The use of current machine learning (ML) approaches is anticipated to be an appealing solution to address manufacturing security challenges due to the large amounts of data, high computing power, and large storage capacity that have been deployed in factories through the connection of smart devices and machinery. Machine learning coupled with real-time data analysis is gaining huge popularity as mainstream support for the provision of on-the-spot (or near immediate) relevant information and actionable insights in strategic monitoring.  
Artificial intelligence's branch of machine learning relies on the notion that computers can discover patterns in data and make judgments without much human input. Many studies have been carried out on how to make machines learn by themselves. A recent study by [PERSON] et al. [9] presents a wide variety of ML applications in Industry 4.0. However, these ML applications generally focus on aspects of the production process itself, such as planning and scheduling, smart maintenance, quality control, etc. [25]. An overview of the multitude of ML types is shown below in Figure 6.  
By analysing and transforming stored data into knowledge that can be used to generate predictions, ML approaches are intended to extract knowledge from existing data. Making decisions that can be put into action more quickly and precisely than a human can is what machine learning in manufacturing refers to. Forecasting and comprehending anomalies or outliers are two areas where this proves to be eminently sensible. Forecasting can add value in some stages of the production process. There is a good possibility that forecasts can be made with enough historical data and context regarding the choices and procedures used in relation to the data. A human analyst may find the data from a single machine to be overwhelming, which is where ML might be useful [9]. Five domains have been identified where ML has a big influence.  
1. Predictive maintenance. The amount of downtime can be considerably decreased by using historical data from maintenance logs to estimate how a machine will react under a future payload and whether it will need to be changed based on what previously resolved that issue.
2. Predictive quality. Significant cost savings can be achieved by anticipating and reducing costs.
3. Scrap reduction. It is possible to reduce waste and increase product quality by using measurements to predict behaviour across product requirements.
4. Increasing yield/throughput. Knowing if and when a machine or process will not conform to a set of requirements enables proactive action to be taken to bring it back into compliance, lowering the number of quality passes.  
Figure 6: Types of machine learning.  
Demand and inventory forecasting. It is possible to estimate the demand for and movement of essential parts with a complete understanding of factory operations and the production data, leading to significant inventory savings [5; 26; 27; 28; 29; 30].