# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 3 State-of-the-Art
### Data Science Applications in Sensor Analysis
#### 3.1.4 Process Control  
Process control and monitoring sensors is highly relevant for Industry 4.0. The literature is relatively limited and mostly comes from the field of time series forecasting or supervised machine learning based on images.  
A number of references describe supervised learning applications especially on small-scale 3D printers (commonly based on fused-deposition-modeling principle). Some notable works that help in detecting quality of 3D printed part are presented in [31; 32; 33; 34; 35; 36]. Although these works provide valuable contributions to 3D printing especially in detecting and monitoring part quality, they assume the possibility of using tagged data to train the models, and they are mainly based on images of video processing. These approaches are out of the scope of this research for several reasons:  
* The need to obtain supervised data every time a model has to be trained is unrealistic in real Industry 4.0 manufacturing.
* In a customer environment with a fleet of 3D printers installed, obtaining global knowledge of the machines' health from the manufacturer's perspective would require analyzing data anonymously, as any confidential data (involving 3D print design or the print images) other than the sensor records cannot be shared or stored outside the customer site.
* Additionally, some of these works assume the possibility of installing additional cameras into the 3D printers; however, in an industrial setting, this is not always possible, as only customers have the sole decision-making authority to make any changes.  
The time series approach based on sensor data seems a more suitable reference for our research. In this regard, some works merit a reference. In [37], an application from the iron and steel industries processes sensor data through a discrete-time extended _Kalman_ filter for both process state estimation and sensor data fusion. Understanding the sensors' patterns and invoking real-time process control can tremendously improve print job success on a whole. In the context of 3D printing, process control helps monitor the evolution of the part by printing layers and guarantees the overall success of the job. In process control, some works can be found in the field of 3D printing, although they are not suitable for our research goals. In [38], exponential weighted moving average (EWMA) charts are used to monitor the process parameters and detect shifts in the fused deposition printing process and thereby assess geometrical deformations of the printed part. In [39], [PERSON] et al. present a novel scheme of simulating surface properties of printed part by defining in-control and out-of-control specifications and use bootstrap sampling to estimate the control limit of several control charts. Both of these works consider small-scale 3D printers. In the context of a large-scale 3D printer, such as multi-jet fusion, such approaches do not apply, as the machine is far more complex, so process monitoring cannot be limited to just one surface of the part, and external cameras are also not applicable. However, it is interesting to see the idea of introducing bootstrap techniques in the processing to scale up the modeling.