# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 6 Applications to 3D Printer Data
### Post-Processing in Bootstrap-CURE Method
Sensor_1 has the recommended pressure reading to initiate the job. The temperature measurement in sensor_3 is also adequate as per the domain knowledge, as is sensor_39. In general, the behavior of sensor_39 and sensor_1 should match what is seen in this cluster. Sensor_35 also has the correct measurements; however, sensor_34 does not match the behavior all the time. Hence, this cluster is expected to lead to successful jobs, but there may be a few failures due to a mismatch in the behavior of sensor_35 and sensor_34. Further, analysis of the data reveals that the cluster indeed has mostly successful jobs with few that are 'failed' or 'operator-canceled'. The main reason found for the failure of the job is 'power-cut', while some failed to print because an operator canceled the jobs. **Cluster 2 (failed jobs due to malfunction of components):**  
In this cluster, sensor_1 does not reach the recommended pressure value to start the job and this should result in system error. Although the measurements of sensor_3 are within spec, the behavior of sensor_1 and sensor_39 does not match (the pressure values should be reversed to each other), which is evident almost throughout the data. Additionally, sensor_34 shows measurements that are higher than normal. Overall, the cluster shows quite unfavorable readings that would hamper a job to start or would lead to failure. Further analysis revealed that most of the jobs found in this cluster failed with the main reason being'system error'. **Cluster 3 (failed jobs due to imbalance of sensors):**  
This cluster is peculiar, as sensor_1 and sensor_3 both are found to be within the recommended specifications, as are sensor_4 and sensor_5. This indicates sufficient conditions for a print job to start. Further, the measurements of sensor_1 and sensor_39 also correspond to each other, which is necessary for a print job to continue. Sensor_34 shows very high readings and thus points to a higher level of humidity; sensor_32 and sensor_33, which control the overall airflow in the machine, run at a lower temperature. This creates an imbalance in the internal cooling environment, which might result in system error. Further verification of the jobs in this cluster disclosed that most of them failed due to'system error'. **7. Discussion**  
This paper presents a sensor data science contribution in the field of Industry 4.0 and comprises all steps from the very first of identifying operation modes in a machine \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & **Hierarchical Clustering** & **Original CURE** & **Bootstrap–CURE** \\ \hline Sample size & 10,000 & 10,000 & 10,000 \\ No. of clusters & 4 & 4 & 4 \\ Calinski–Harabasz & 9208.0117 & 2885.2101 & 5198.4981 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calinski–Harbasz index comparison. monitoring scenario, where large multivariate sensor data are continuously reported. Understanding these operation modes and identifying the patterns of failures can be of interest for further development of statistical models for predictive maintenance, and also to analyze the relationships among other sensors that suggest future improvements in the design of the 3D printer. It is important to note that the methodology proposed here is general for any large-scale sensor data environment, and this applies to other Industry 4.0 domains, such as gas turbines or air quality in smart cities. The specific application presented here is with regards to the industrial 3D printers, where customers' print-layer images or videos are considered confidential, and the method has to rely solely on the sensor data to obtain a global understanding of machine behavior. This is only possible through unsupervised analysis. In fact, in a real industry context, rapid product innovation makes it difficult to prepare supervised data continuously to learn models. When unsupervised learning is faced, there is no possibility to compute either the misclassification rates or the confusion matrices.