# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 4 Industrial Internet of Things (IIoT) Platform for Hybrid Edge and Cloud Computing
### The Cloud  
The cloud is a huge, interconnected network of powerful servers that performs services for business. The Savvy Data Systems cloud, used in this system, can be divided in three major parts: communication to the cloud, cloud processing, storage and modeling, and cloud data analysis and presentation.  
_Communications:_ The cloud provider, Savvy Data Systems, uses transport layer security (TLS) to offer secure data transmission to the cloud. TLS is a widely adopted security protocol designed to facilitate privacy and data security for communication over the Internet. A primary use of TLS is encrypting the communication between web applications and servers, such as web browsers loading a website.  
_Cloud processing, storage and modeling:_ There are three main parts. The first one, named the data exchange block, allows connectivity to the smart box, the IoT hub, through optimal security actions. Companies gain secure and real-time access to new data and lightweight, instant interactions with other blocks; the second block is formed by the data persistence block that deals with persisting (storing and retrieving) data from a data lake. The machine CNC data (power consumption, depth of cut, feed rates, spindle speeds, program name, alarms... ) and PAC data (vibration data) are stored in an unstructured way in the Savvy Data Systems data lake. Finally, the machine learning (ML) block is a managed service that allows developers and data scientists to create superior models of machine learning and take them to production. The Cloud ML Engine offers preparation and prediction services that can be used together or separately. In the proposed use, the gathered data from the CNC and the PAC for the many train wheels machined were aggregated using the Cloud ML Engine, which helped to find weak points during the machining operation for different workpiece references.  
_Cloud data analysis and presentation:_ The data visualization block is responsible for the graphical display of the acquired information (historical data) for two purposes: sense-making (also called data analysis) and communication. The data can be effectively visualized following design principles that are derived from an understanding of human perception. In the train wheel repair manufacturing process, machining data fingerprint visualization was proposed (see Figure 11). The unstructured data of the cloud were transformed for human understanding, mixing process data, manufacturing orders, and quality results. On the other hand, the live remote monitoring block was used for the visualization of machine operation data in the moment. Live machine data could be visualized remotely in real-time. Finally, interoperability with third party software, manufacturing execution system (MES) and enterprise resource planning (ERP), was also possible using the full-rest API. Interoperability with human devices is also possible; the cloud could send an e-mail in case of failure or damage detection during the machining operation.  
Figure 10: Installed hardware: (**a**) real-time controller, IoT monitoring hub, and (**b**) triaxial accelerometer.