A Deep Learning Approach for High Speed Machining Tool Wear Monitoring

Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning

“…Due to it's importance in the manufacturing industry, TCM has been an active area of research for the past few decades [16][17][18]. In particular, TCM for machining [19][20][21][22][23][24] and forming [25][26][27] processes have received a lot of attention. Some of the papers have investigated tool wear mechanism and progression for various manufacturing processes.…”

“…Others ML techniques such as fuzzy logic systems, Bayesian networks, decision trees, support vector machine (SVM), artificial neural networks (ANN). Recent trends are shifting towards the use of deep learning techniques which combine feature generation and model training steps in a single process [23]. Some attempts have been made for adaptive process control using sensing signals [33,34].…”

Recent Advances on Machine Learning Applications in Machining Processes

“…Beside this application, the actual use of DL algorithms in machining in the substitution of ML reduces the number of the required steps, since feature extraction and selection (4 to 5) is typically done by the same model [31,32]. These steps may improve the final results of the DL applications, as highlighted in References [33,34]. The main limits of DL algorithms incur when the acquired data is not enough to train the models correctly, for example, breakages or malfunctioning data tend to be reduced compared to the massive amount of data collected in conventional operational conditions of a machine tool, which may lead the model to biased situations.…”

A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

“…It should be pointed out that deep learning is a promising technique capable of automatically searching information features behind the raw data. In [14][15][16][17], the feasibility of convolutional neural networks (CNNs) on extracting information features has been presented. In fact, one can convert input signals into images using Gramian Angular Summation Fields and then leverage CNN to classify the tool wear condition [18].…”

“…By decomposing original 1D signals to reconstruct 2D ones, CNNs can accurately predict the tool wear condition [20]. However, there are some critical shortcomings of the CNN-based tool condition detection [14][15][16][17]. For example, a large labeled dataset is required to improve the performance of the CNN, and the signal-to-image conversion procedure might result in a great information loss of the raw signals [18].…”