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Top1. Introduction
Around 1976, the concepts of sensor monitoring in a machine for machining operations were developed, since then, the correlation between the tool wear and the cutting parameters have been seeking through factors such as cutting force, vibration, surface roughness, and surface temperature (Micheletti, Koenig, & Victor, 1976). Tlusty and Andrews (1983) conducted a study of the sensors of that time and those that were being developed for the supervision of machining processes. Byrne et al. (1995) proposed monitoring the processes of the machine tools, with the implementation of intelligent sensors of open architecture with signal processing and the ability to make decisions with higher speed to control and improve the cutting processes. Thenceforth, a large number of articles have published focused on obtaining control of processes and the creation of intelligent sensors, some of them with some signal processing and decision-making strategies for the monitoring of multiple manufacturing processes (Teti, Jemielniak, O’Donnell, & Dornfeld, 2010).
From this combination of hardware, software, and data, the so-called fourth industrial revolution (industry 4.0) is derived, obtaining an integration of cyber-physical systems (CPS), the Internet of Things (IoT), and the storage and processing of data in the cloud as an alternative solution to the disadvantages of the industrial sector (Toro, Correa, & Ferreira, 2018 and Fei Tao, Cheng, & Qi, 2017). All the above presupposes a fundamental challenge in the growth of digital manufacturing since in order to be successful in the acquisition and analysis of data, technologies and processing systems must be developed with the ability to monitor any productive process, allowing it to be efficient and profitable. Under this circumstance, it implies obtaining a great variety of data that must be stored and analyzed so that they can be converted into useful data in decision-making, improving the competitiveness of the different industrial sectors (Wang & Wang, 2016). For this reason, industry 4.0 has become the center of attention worldwide since its birth in Germany for the year 2011. In this direction, China and the United States are betting on research as a strategy to boost productivity, to the point of becoming an essential reference for emerging countries in the use of technology developed in the short term (Li, 2017).
These models will change the way the industry behaves, integrating products, processes, sensors, and machines, and incorporating customers in the production process with companies focused on the customer (Hermann, Pentek, & Otto, 2016 and Wang & Wang, 2016).
Acquiring the data of any machine is not easy for a company; much less analyze such information directly in the cloud. This data acquisition is vital since it can provide a definitive value in decision making. The processing of data in the cloud provides a wide range of applications in manufacturing in order to supervise and control the productive process, such as process planning, supervision of machining centers, robots, or any other type of machine (Zhang et al., 2017; Lee et al., 2015 and Caggiano, Segreto, & Teti, 2016). This way it contributes to the industries in improving the efficiency, reliability, and the quality of their products, which is reflected in the reduction of manufacturing and production costs, making them quantifiable processes (Toro et al., 2018 and Correa, Toro, & Ferreira, 2018). Monitoring the data in the specific case of a CNC machining center could help to control and optimize the total energy consumption of the process depending on the cutting parameters (Haridy, Wu, & Shafik, 2012 and Caggiano et al., 2016), in addition, the use of predictive techniques to choose the optimal cutting parameters contributes to increasing the adaptability and efficiency of resources (Stork, 2015; Tapoglou et al., 2015 and Toro et al., 2018).