Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures.

During wire electrical discharge machining (WEDM), wire rupture may deteriorate workpieces' machined surfaces and increase the processing time. However, only a few referenced papers focused on wire rupture during past decades because of its complexity. In this research, Machine Learning (ML) technique was applied to analyze the relationship between manufacturing parameters and the chance of wire rupture. Three parameters, including gap voltage (GV), feed rate (FR), and water resistance (WR), were considered as training features, and a total of 298 sets were used to train an Artificial Neural Network (ANN). The results show that the prediction accuracy of wire rupture for 10 seconds in advance is above 85%.

This paper explored the application of fully connected neural networks and transfer learning techniques in laser forming, explicitly focusing on predicting scanning paths in multi-stage forming processes. Due to the time-consuming data collection, this research built imaginary data to replace experimental data with training in neural network efficiency. The research developed a machine learning model trained on imaginary datasets that can predict the scanning paths of experimental datasets. However, the test results on experimental data indicated that the imaginary data generation method led to a performance that did not meet expectations. Therefore, this paper also compared the differences between the imaginary and the experimental data. Furthermore, this research adopted transfer learning to address the issues of overfitting and low prediction accuracy encountered by fully connected neural networks at higher forming stages. It explored the fine-tuning and final-layer methods. The results suggest that the fine-tuning method can effectively enhance the model’s generalization capability, achieving better performance in complex forming stages. Compared to the fully connected network, it reduces nearly 35% of training iterations and improves test accuracy by 3–5 mm.