Figure 2.24 Detrending of the thermal effect in strain‐gauge data: (a) before detrending; (b) the thermal trend; (c) after detrending.
The thermal trend in the raw signal can be removed and normalized to the same criterion by subtracting the mean value. As shown in Figure 2.25, the darker and lighter lines represent the signals before and after de‐noising using the wavelet mother function DB3, respectively. The signals collected during machining imply that the signals contain the dry‐run and tool‐use periods.
Figure 2.25 De‐noising signals to highlight differences between dry‐run and tool‐use periods.
By comparing with the values measured during the dry‐run period (range ± 0.035 mV), the values measured during tool‐use can be filtered from the background noise and derived in the range of ±0.083 mV. After de‐trending and de‐noising the raw data, the ratio between the dry‐run and tool‐use signals can be improved from 1 to 2.37, which makes the extraction of effective features for modeling considerably easier.
2.4.2 Automated Segmentation of Signal Data
To detect tool wear during machining, a vibration‐based evaluation method is developed and used on a CNC milling machine. During any machining operation, a tool holder chunked by the spindle holds the cutting tool in place as precisely and firmly as possible. The spindle stability affects the quality of tool holder and cutting tool. Thus, a high‐resolution accelerometer attached close to the spindle is adopted to monitor the cutting‐tool wear based on the changes of spindle vibration.
However, when M codes are not available in some cases, for example, the required number of DIO is too large to achieve some complicated operations, so that the raw data cannot be timely divided into several critical parts of final machining process by M Codes during the machining time. When dealing with the segmentation issue under the condition of an insufficient number of M codes, the feasible solution is to decrease the usage of the pairs of M codes by extending the duration of each data acquisition. In this way, however, not only the specified final machining parts that affect product quality most are included but various types of machining operations would be involved during each data acquisition.
In this manner, the acquired vibration data may be a long signal that contains the process during the idling (dry‐run) and the real machining (tool‐workpiece contact) periods. Thus, the challenge is how to automatically segment the collected data so as to estimate the tool‐wear status.
Three‐axis vibration data sampled at 2,048 Hz during the drilling operation of seven holes on a medal plate with material FDAC are respectively illustrated in Figure 2.26. To increase tool availability, a 0.5 mm hole is pecked by a two‐flute tool in each cycle until a total depth of 4 mm is achieved. The feed rate and spindle speed are 100 mm/min and 4,500 rpm, respectively. Obviously, the main loading happens in the Z‐axis vibration, and the bottom of Figure 2.26 shows real machining periods of seven parts, which are manually segmented and numbered from 1 to 7. The details of this application case can also be found in [18] via IEEE DataPort.
Figure 2.26 Collected vibration signals (including idling and machining periods).
To automatically segment the aforementioned Z‐axis data for identifying the actual drilling periods, an AEN model integrated with an encoder, code, and decoder is used to learn the idling characteristics under specific conditions. In this segmentation case, the encoder (four‐layer structure with 32, 16, 8, and 4 nodes, respectively) compresses the inputs into code in the middle layer, and the decoder (the inverse structure with 4, 8, 16, and 32 nodes) decompresses code into the outputs. Only one node in the code layer is used to evaluate the distance between the modeling and testing features.
First, 32 features are derived from vibration data collected during the idling periods by using the five‐level WPT. Figure 2.27 compares the two trends from 32 WPT features extracted from Z‐axis idling vibration (original data) and AEN training results using the same 32 WPT features (decoder data), under four different spindle speeds (3,000, 3,500, 4,500, and 5,000 rpm), where the node number starts counting from 0. Note that, the high similarity of both trends indicates that AEN is reliable.
Figure 2.27 Comparison of the original and decoded features under four idling conditions of spindle speeds: (a) 3,000 rpm; (b) 3,500 rpm; (c) 4,500 rpm; and (d) 5,000 rpm.
The reason why Z‐axis vibration is chosen to be a learning criterion for training the AEN model is that the main drilling loading occurs in Z‐axis but not in X‐axis or Y‐axis. The AEN accuracies would be worse if X‐axis or Y‐axis vibration is adopted to train AEN since loading difference between the idling section and the real drilling section is not significant enough.
Thus, once AEN learns the feature patterns of idling sections, it is able to achieve the period detection of tool–workpiece contact by comparing the certain distance between the idling section and the real machining section. Figure 2.28 illustrates the automated segmentation results of AEN. Figure 2.28a compares the distances between modeling features (Z‐axis vibration during idling) of the AEN model and testing features of real machining (X‐axis vibration during drilling) every 0.2 s from the beginning to the end of the data acquisition.
Figure 2.28 Automated segmentation of machining signals using an AEN: (a) distance derived by AEN based on idling vibration features of Z‐axis; (b) collected X‐axis vibration signal; and (c) zoom in segmented X‐axis signal.
As depicted in Figure 2.28a, a stable maximum distance, which means a high dissimilarity between the modeling and testing features, can be used to recognize a real drilling section. A certain duration of X‐axis vibration data can be segmented into the real drilling section according to Z‐axis vibration, as highlighted within the two red dotted lines in Figure 2.28a. In this manner, seven real drilling sections of Z‐axis vibration as in the bottom of Figure 2.26 can also be automatically segmented using AEN. To sum up, raw data can be segmented using the vibration characteristics