Identifying the influence of surface texture waveforms on colors of polished surfaces using an explainable AI approach
Identifying the influence of surface texture waveforms on colors of polished surfaces using an explainable AI approach
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使用可解释的 AI 方法识别表面纹理波形对抛光表面颜色的影响
DOI：
10.1080/24725854.2022.2100050
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发表时间：
2022
影响因子：
2.6
通讯作者：
Bukkapatnam, Satish T.S.
中科院分区：
文献类型：
--
作者：
Zhong, Yuhao;Tiwari, Akash;Yamaguchi, Hitomi;Lakhtakia, Akhlesh;Bukkapatnam, Satish T.S.
一个可解释的人工智能方法的基础上巩固本地可解释和模型不可知解释（LIME）模型输出的设计，以辨别表面形态的影响所表现出的颜色不锈钢304零件抛光与磁性磨料抛光（MAF）过程。MAF抛光工艺用于产生两个区域，每个区域对肉眼呈现蓝色或红色。颜色分布在显微镜下是不均匀的，即，一些红色微尺度斑块分散在蓝色区域中，反之亦然。在频域中表示表面形态（使用2D傅立叶变换）以捕获谐波表面图案，例如来自抛光过程的进给和铺设标记。采用卷积神经网络（CNN）从表面形态的频率特性中识别该区域的颜色。CNN能够预测观察到的颜色，测试准确率超过99%，这表明红色区域表面形态的频率特征与蓝色区域明显不同。围绕表面的每个区域内的每个小段构建LIME模型，以识别对区分颜色有影响的频率特征。为了处理异质性的影响，使用基于专家查询的算法来协调局部影响并收集通知蓝色与红色区域的频率特征的全局解释。我们发现，在红色区域中的主要形态特征是那些捕获的抛光层图案下的表面结构，而那些在蓝色区域捕获的不均匀和高频波形图案，如那些结果时，氧化膜形成由于激烈的抛光条件。
An explainable artificial intelligence approach based on consolidating the Local Interpretable and Model-agnostic Explanation (LIME) model outputs was devised to discern the influence of the surface morphology on the colors exhibited by stainless-steel 304 parts polished with a Magnetic Abrasive Finishing (MAF) process. The MAF polishing process was used to create two regions, each appearing either blue or red to the naked eye. The color distribution was microscopically heterogeneous, i.e., some red microscale patches were dispersed in blue regions, and vice versa. The surface morphology was represented in the frequency domain (using a 2D Fourier transform) to capture the harmonic surface patterns, such as the feed and lay marks from the polishing process. A Convolutional Neural Network (CNN) was employed to identify the color of the region from the frequency characteristics of the surface morphology. The CNN was able to predict the observed colors with test accuracies exceeding 99%, suggesting that the frequency characteristics of the surface morphology of the red regions are distinctly different from those of the blue regions. A LIME model was constructed around each small segment within each region of the surface to identify the frequency features that are influential for differentiating between the colors. To deal with the effect of heterogeneity, an algorithm based on the query by experts was used to reconcile the local influences and gather the global explanations of the frequency characteristics that inform the blue versus red regions. We found that the dominant morphological features in the red regions are those that capture the polishing lay patterns underlying surface structure, whereas those in the blue regions capture the non-uniform and high-frequency waveform patterns, such as those result when oxide films form due to the intense polishing conditions.
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2019 年 AAAI/ACM 人工智能、伦理与社会会议论文集
DOI：
--
发表时间：
2019
期刊：
AAAI/ACM Conference on AI, Ethics, and Society
影响因子：
--
作者：
P. Heald
通讯作者：
P. Heald
影响因子：
20.6
作者：
HARRIS, FJ
通讯作者：
HARRIS, FJ
DOI：
10.22214/ijraset.2022.47789
发表时间：
2022-12
期刊：
International Journal for Research in Applied Science and Engineering Technology
影响因子：
--
作者：
Aarush Saxena
通讯作者：
Aarush Saxena
纳什神经网络
DOI：
--
发表时间：
2022
期刊：
影响因子：
--
作者：
唐乾元;金子邦彦;John J. Molina
通讯作者：
John J. Molina
激光诱导表面着色的工艺研究
DOI：
--
发表时间：
2002
期刊：
影响因子：
--
作者：
H. Zheng;G. Lim;X. Wang;J. L. Tan;J. Hilfiker
通讯作者：
J. Hilfiker