Stainless steel with high surface quality is required in many industries and chemical mechanical polishing can achieve both local and global planarization of the substrate surface. However, it is difficult to realize both high material removal rate and high surface quality by a single step polishing. In this regard, a two-step polishing process, coarse polishing with α-Al2O3 abrasives first and then fine polishing with silica abrasives, was proposed to solve the trade-off between material removal and surface quality. The effects of pH (1~12) and H2O2 (0~0.5wt%) on the polishing of 304 stainless steel disk (area ~6.7 cm2) were systematically studied and CMP mechanism of stainless steel was discussed. The results indicated that, at pH 4, with the addition of 0.01wt% H2O2, the surface roughness of stainless steel was successfully reduced from 0.702 μm to 44.6 nm (the first step using α-Al2O3 abrasives) and 1.61 nm (the second step using silica abrasives). Finally, an ultra-smooth surface was obtained with decent material removal rate.
Detecting tiny defects is not easy for industrial product manufacturers. Classical models have witnessed remarkable progress in detecting defects. Nevertheless, these models may fail to detect tiny defects effectively and efficiently when trained with few samples. Accordingly, we propose an efficient two-stage detector, Ψ-Net, to solve the problem of detecting tiny defects with few samples. In the first stage, we present an efficient model for extracting region proposals from large images. In the second stage, we propose the ResNeLt integrated with Ψ-Attention to classify the region proposals. ResNeLt is a lightweight network that enables the model to be trained with few samples. Meanwhile, Ψ-Attention, a model-agnostic plug-in, improves the featureencoding capability of tiny defects detectors. The proposed model outperforms several state-of-the-art models on NEU-CLS dataset. In addition, the accuracy and sensitivity of Ψ-Net have been significantly improved with few samples over Surface Crack Detection dataset and our own-collected Robber-S dataset.
Background and Purpose: Accurate and efficient medical image segmentation plays an important role in subsequent clinical applications such as diagnosis and surgical planning. This paper proposes an efficient interactive framework based on a graph convolutional network (GCN) for medical image segmentation. Methods: The initial segmentation results showed that a set of boundary control points can be generated for further interactive segmentation. We presented an adaptive interactive manner that allows the user to click on the boundary for fast interaction or drag the erroneous predicted control points for accurate correction. Furthermore, we proposed an interactive segmentation network (referred to as IVIF-GCN) to learn user experience in the interactive process by transforming interactive cues into annotations. In IVIF-GCN, a module of information fusion of image features and vertex position features (IVIF) is proposed to learn the location relationship between the current vertex and the neighboring vertices. Finally, the locations of control points around the interaction point is predicted and updated automatically. Results: The proposed method achieves mean Dice of 96.6% and 91.3% on PROMISE12 and our in-house nasopharyngeal carcinoma (NPC) test sets, respectively. The experimental results showed that the proposed method outperforms the state-of -the-art segmentation methods. Conclusions:The proposed interactive medical image segmentation method can efficiently improve segmentation results for clinical applications in the absence of training data. The GUI tool based on our method is available at https://github.com/Tian-lab/IGMedSeg.
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