This paper aims to investigate the performance of narrow beam machined by MWEDM. The narrow beam machining experiments are carried out on self-development Micro-WEDM with Φ30μm electrode wire. Narrow beams with the width around 10μm are machined on E52100 steel under different machine conditions (discharge energy, servo reference voltage). Based on the analysis of the experiments results, the coupled temperature and stress analysis using ANSYS finite element analysis software is performed to investigate causes of the failures on process.
Meta-learning aims to teach the machine how to learn. Embedding model-based meta-learning performs well in solving the few-shot problem. The methods use an embedding model, usually a convolutional neural network, to extract features from samples and use a classifier to measure the features extracted from a particular stage of the embedding model. However, the feature of the embedding model at the low stage contains richer visual information, while the feature at the high stage contains richer semantic information. Existing methods fail to consider the impact of the information carried by the features at different stages on the performance of the classifier. Therefore, we propose a meta-learning method based on adaptive feature fusion and weight optimization. The main innovations of the method are as follows: firstly, a feature fusion strategy is used to fuse the feature of each stage of the embedding model based on certain weights, effectively utilizing the information carried by different stage features. Secondly, the particle swarm optimization algorithm was used to optimize the weight of feature fusion, and determine each stage feature’s weight in the process of feature fusion. Compared to current mainstream baseline methods on multiple few-shot image recognition benchmarks, the method performs better.
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