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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. 

The amount of data that can be gathered from a machining process is often misunderstood, and even if these data are collected, they are frequently underutilized. Intelligent uses of data collected from a manufacturing operation can lead to increased productivity and lower costs. While some large-scale manufacturers have developed custom solutions for data collection from their machine tools, small- and medium-size enterprises need efficient and easily deployable methods for data collection and analysis. This paper presents three broad solutions to data collection from machine tools, all of which rely on the open-source and royalty-free MTConnect protocol: the first is a machine monitoring dashboard based on Microsoft Excel; the second is an open source solution using Python and MTConnect; and the third is a cloud-based system using Google Sheets. Time studies are performed on these systems to determine their capability to gather near real-time data from a machining process.