## 6 Discussion
However, this correlation needs to be studied further before other recommendations can be made. A similar situation is seen in the accelerometer data, which can provide relevant information about the performance of a machine. As in the previous case, acceleration patterns need to be analyzed before any type of predictions can be made. Figure 16a,b shows histograms of the maximum accelerations that the positioning system can achieve during the measurement cycle in our machine design. These values are reported by the accelerometers placed on the Gantry machine and constitute a baseline for what can be considered normal behavior. The XDK sensor provides acceleration values, along with ambient temperature, pressure, and humidity. In this case, the XDK was set up to report peak values between measuring cycles, while the Gantry positioner is at rest. A typical histogram is shown in Figure 16c. An example of information that could be extracted out of the combined data, but that was not anticipated at the design stage, surfaced after several hundreds of measurement cycles in a particular trial run. At some point in the operation, part measurements deviated considerably out of range (Figure 16d). The operator stopped the machine and observed that one of the positioning plate support pins had been separated from the plate. Upon inspection, it was determined that the screw that was holding the pin in place became loose. An analysis of the environment data showed that prior to the faulty measurements, the accelerometer had picked up a large spike, in excess of 0.25 g in both X and Y, which could be interpreted as an impact. A few cycles after this event, measurements at specific points of the workpiece started to divert considerably. Had the acceleration parameter been monitored, a warning could have been given so that maintenance crews could have checked the machine. A feature of this type is a clear example of the improved intelligence that is derived from monitoring the environment and adapting the process as more knowledge is gained. While the problem was relatively minor in terms of the fix that was needed (it took about 10 min to reattach and glue the screw) it still knocked the machine out of production for more than one shift while the orders to diagnose the problem and implement the solution were made. Full data for this event were reported in [24]. Figure 15: (**a**) Ambient temperature and maximum accelerations of the positioner. (**b**) Cycle time and temperature variations measured during a typical shift. Connectivity in the plant was achieved through a Wi-Fi connection that was available for guests. It was possible to observe the efficacy of the process of sending data to the Cloud during R&R tests performed on the shop floor. It was standard practice to bring a laptop next to the machine and monitor how data were being registered on the Google Sheet. Data would be available within a couple of seconds of completion of a measuring cycle. However, during these tests, about once in every 250 cycles a measurement would not reach the Cloud. This performance could be improved by dedicating a connection for this process. Figure 16: (**a,b**) Histogram of acceleration of X and Y axes of the positioning system. (**c**) Histogram of accelerations reported by the XDK sensor. (**d**) Part deviation measured after machine was subject to shock. Overall, the design of the prototype in-line measuring machine proved to be able to meet the expectations of the shop conditions. The introduction of the clamping plate and moving sensor with a programmable path improved the flexibility/adaptability of the equipment. While highly desirable, these modifications represent an incremental improvement with respect to the equipment that is currently deployed on the shop floor. Connectivity on the other hand is what allows the new concept to establish itself as a new generation with respect to the equipment that it is intended to replace.