Compact Representation and Identification of Important Regions of Metal Microstructures Using Complex-step Convolutional Autoencoders
Compact Representation and Identification of Important Regions of Metal Microstructures Using Complex-step Convolutional Autoencoders
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DOI：
10.1016/j.matdes.2022.111236
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Dharanidharan Arumugam;R. Kiran
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文献类型：
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作者：
Dharanidharan Arumugam;R. Kiran
在这项研究中，我们提出了一种复杂步骤卷积自动编码器来识别金属微观结构中重要的区域，以实现紧凑表示和安全共享。首先，卷积自动编码器的架构是为了微观结构图像的紧凑表示而设计的。设计的自动编码器在不丢失重要信息的情况下实现了32的高图像压缩比。其次，在卷积自动编码器上实施使用复杂阶跃导数近似的自行开发的模型不可知敏感性分析，以识别对重建重要的微结构区域。最后，为三个等级的双相结构钢生成突出像素对于重建的重要性的显着图。显着图表明第二相区域和晶界对于微观结构图像重建很重要。与引导反向传播和逐层相关性传播方法相比，所提出的方法产生了更站得住脚的显着性解释。卷积自动编码器的解码器部分可用作密钥，可用于从编码图像信息重建实际的微观结构，从而有助于安全有效地共享微观结构数据。所提出的框架是通用的，可以扩展以识别其他金属、复合材料、生物材料和材料系统的重要微观结构区域。
In this study, we propose a complex-step convolutional autoencoder to identify the regions that are important in a metal microstructure for compact representation and secure sharing. Firstly, the architecture of a convolutional autoencoder is designed for the compact representation of microstructural images. The designed autoencoder achieved a high image compression ratio of 32 without loss of important information. Secondly, an in-home developed model agnostic sensitivity analysis using complex step derivative approximation is implemented on convolutional autoencoders to identify regions of the microstructure that are important for reconstruction. Finally, saliency maps that highlight the importance of pixels for reconstruction are generated for three grades of dual-phase structural steels. The saliency maps indicated secondary phase regions and grain boundaries are important for microstructure image reconstruction. The proposed approach produces more tenable saliency explanations compared to guided backpropagation and layer wise relevance propagation methods. The decoder part of the convolutional autoencoder can be used as a key that could be used to reconstruct the actual microstructure from encoded image information contributing to secure and efficient sharing of microstructure data. The proposed framework is generic and can be extended to identify important microstructural regions for other metals, composites, biomaterials, and material systems.