# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 5 Hybrid Edge-Cloud-Based Smart System for Chatter Detection and Suppression
### Cloud Computing for Machining Fingerprint Analytics  
The cutting conditions for each workpiece were recorded. The recorded data consisted of the instantaneous position, feed rate, and power consumption for each axis, the rotational speed and power consumption of the spindle head, and the vibration severity values in X, Y, and Z directions. Thus, each workpiece had its own fingerprint, which was stored in the data persistence block in the cloud. Traceability of the cutting process was therefore made possible thanks to the cloud's massive storage capacity. Using the data visualization block, the user was able to access the desired workpiece, for instance. Piece2-Type Class-36 2018-02-06 07:00:02 (see Figure 11). It was then possible to check the number of cutting passes required for machining the wheel profile and to observe relevant data such as required machining time, number of alarms, and vibration severity. If one of the cutting passes was selected, then all the recorded data were shown, including a vibration map indicating the maximum vibration levels on the wheel profile, a power consumption map showing the load required at each position for the axes and the spindle head, and finally a feed rate map showing the different speeds during the trajectory of the tool (see Figure 11). Machine analytics was used to describe the chatter problem, crossing the data coming from the accelerometers with the cutting position obtained from the machine sensors. Only descriptive analytics were used in this study. It is important to remember that this process is a maintenance operation where the part is not homogenous, and the depth of cut and hardness of the surface can vary considerably. The aggregation of the same type of workpiece fingerprints can highlight hidden insights that would be very difficult to detect any other way. For instance, Figure 12 shows the probability of a certain machining position having a vibration higher than the alarm level when the smart system is working. The ratio between the number of occurrences (cases where the heuristically extracted vibration limit was exceeded) and the total number of possible cases (the total number of machined wheels) was analyzed for each position and reference. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Status** & **Severity [mm/s rms]** & **Action** \\ \hline STABLE & 0–2.4 & Normal operation \\ WARNING & 2.5–4 for 4 s & Process damping: 80\% of spindle speed \\ ALARM & \textgreater{}4 for 4 s & Process damping: 80\% of spindle speed + CSSV \\ DAMAGE & \textgreater{}4 for 10 or more seconds & Cutting process interruption \\ RESET & 0–2.4 & Resetting conditions to normal operation. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm logic. It was noticed that the points with the highest probability of having the maximum vibration level were always the same (Figure 12). Two points concentrated the risk of having high vibration from the same references and were not close to reset points. The trajectory of the first insert crossed the wheel in the negative Z direction and found the first critical zone between reset point 1 and 2 in the initial radius. The wheel withstood a plastic deformation during its operative life, and a considerable difference in terms of depth of cut between the central part and this radius were found. Therefore, the depth of cut was bigger during the reshaping process and the risk of chatter increased. The second critical point was located before the fourth reset point. In this area, the depth of cut did not have any important increase, but the material was hardened due to the deformation created during operation. The deformation process of the wheel profile was comparable with a rolling process in the positive Z direction [47]. Once a great number of fingerprints had been recorded, the smart system had a machine learning block prepared to house any machine learning technique.