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    Abstract Digital manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create more lean, profitable, and data-driven manufacturing processes. This paper reviews modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to cyber-physical systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed, and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations. 
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    Monitoring of the health of water-based coolant used for machining requires measurement of various parameters of the coolant, including refractive index, temperature, pH, and turbidity. One of the primary parameters that is used to determine the concentration of the coolant is the refractive index, which is typically measured manually by an operator at regular intervals during machine operation. This paper describes the conceptualization and preliminary design of a coolant health monitoring system that will automatically measure the refractive index of the coolant and will digitize the resulting measurement for communication to a factory supervisory control and data acquisition (SCADA) system. To enable rapid integration into a factory’s network architecture, the coolant concentration measurement will be transmitted by the monitoring system using the MTConnect format. Having an MTConnect-enabled sensor will allow the data to be remotely aggregated and compared to other machine data to help give a better understanding of overall machine health. The economical approach to its design allows the coolant health monitor to be realizable for both small manufacturing enterprises (SMEs) and large manufacturers alike. This widespread implementation will further benefit industry’s movement toward Internet-of-Things (IoT)-equipped manufacturing facilities. 
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