## 4 The Application of AWS Machine Learning (AML) for the Surface Finishing Process
### Research Motivation, Experimental Setup, and Data Acquisition  
Deburring in aerospace manufacturing processes is a treatment for removing burrs, sharp edges, and rough surfaces. The importance of deburring is growing as the product quality tolerance becomes tighter and high-precision devices become the norm [10]. The deburring process is important for the safety of the critical components of the aircraft engine, as well as for eliminating stress concentration. In this paper, the deburring process being analysed is the one that is applied on the boss holes of the combustor case in aircraft engines to remove any burrs and to chamfer the hole edges (see Figure 4). For an assembly process, the precision of the boss-hole diameter and chamfer is essential. The surface quality parameter is, therefore, the chamfer length of the boss-hole edges. To date, the chamfer length measurement is carried out by a direct measurement conducted by a skilled operator. However, this dependency might lead to product quality variations. In addition, a direct measurement requires a considerable amount of time as the measurement process needs to interrupt the production cycle. For the measurement, the combustor case needs to be detached from the fixture. When the chamfer length of the component does not meet the requirements, it needs to undergo an identical process again. As aerospace industries are looking for a process that would drive down the manufacturing time, they recognized that an in-process sensing solution is a potential tool to overcome the issue. One thing that should be highlighted is that in-process sensing requires a machine learning method to be implemented. A preliminary study on the offline machine learning method has been presented in Reference [11]. To answer the aforementioned challenge, an experimental setup to represent the actual process on the shop floor is established. The experimental setup requires the preparation of a work coupon, ABB robotic programming, and a vibration data acquisition system, including the use of an accelerometer and LabVIEW (2015, National Instruments, Austin, TX, USA). The work coupon was designed to replicate the combustor casing boss hole, as presented in Figure 4. It was designed to represent the actual Trent 700 combustor casing boss hole during the lab-scale deburring process. A detailed specification of the work coupon can be found in Reference [11]. The deburring experimental setup is shown in Figures 5 and 6. It comprises an ABB IRB 6660 robot with an electric power spindle (PDS Colombo RS 90). A standard mounted flap wheel (aluminium oxide) with a grit size of P80 is used as the deburring brush for removing the burrs and sharp edges of the boss-hole replica. The rotational speed of the spindle is controlled by a variable frequency drive set to 166.67 Hz, which corresponds to 10,000 RPM and a feed rate of 30 mm/s. The 3-axis accelerometer was attached to the work coupon as shown in Figure 6. The accelerometer used in this study is a Kistler 8763B IEPE (2016, Kistler, Winterthur, Switzerland) accelerometer with a sampling frequency of 40 kHz. The NI 9234 module from National Instruments with a maximum sampling frequency of 51.2 MS/s is utilized to record the signal. The data acquisition was performed using an NI-DAQ 9184 chassis, coupled with a supporting module for the respective sensor. Figure 4: The boss-hole location on a breakdown of a Trent 700 engine. The data acquisition was run synchronously with the robot spindle movement. The vibration signal was triggered as soon as the abrasive tool was in contact with the work coupon and it automatically stopped when the tool completed one cycle. The vibration data were then saved onto the PC once one cycle of the deburring process was completed. The chamfer length was also measured after each cycle of the deburring process was completed. The aim of measuring the chamfer length was to use this data in the learning process of the machine learning method.