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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI 15

Analysis of Defect Associated with Powder Bed Fusion with De...

引用

15th International Conference on Knowledge and smart Technology, KST 2023

作者： Pratap, Ayush Sardana, Neha Utomo, Sapdo John, A. Karthikeyan, P. Hsiung, Pao-Ann Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665477123

Research into the detection, classification, and prediction of internal defects using surface morphology data of parts created via powder bed fusion-type additive manufacturing has become a hot topic in the previous decade thanks to the development of deep learning. However, there is no other evidence to evaluate the model other than accuracy and metrics. In this paper, a novel data set is compiled from various literature and other sources to evaluate the black box model using explainable artificial intelligence (XAI). The data set contains three major powder bed fusion defects: gas porosity, lack of fusion, and balling. The anomaly was initially found using convolutional neural networks (CNN) and transfer learning. Based on test data, a model comparison was performed to determine the best accuracy and an F1 score. VGG16 has outperformed all other models in terms of accuracy, with an F1 score of 98.6 percent. Further, the model has been compared with the existing state-of-the-art model for classification in the domain of powder bed fusion defects. Finally, VGG16 was employed to interpret and explain the test data set. The LIME explanations revealed that the feature predicted by the model was present in conjunction with the fault. As a result, we are confident that the proposed model with XAI would considerably improve the fairness and trustworthiness of the output result in the powder bed fusion field. This can also aid in the automation of additive manufacturing in the realm of Industry 4.0. © 2023 IEEE.

关键词： Computer vision

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Explaining the Identification of Granular Crack with Deep Learning and XAI

Explaining the Identification of Granular Crack with Deep Le...

引用

IEEE Region 10 Symposium

作者： Ayush Pratap Neha Sardana Department of Metallurgical and Materials Engineering Indian Institute of Technology Ropar Rupnagar India Grad. Institute of Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan R.O.C

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Precise detection of granular fractures is crucial for various engineering applications, and Deep Learning approaches can transform imaging methodologies and material analysis. This study employed a convolutional neural network (CNN) that incorporated explainability elements to improve the trustworthiness and transparency of the model in classifying granular cracks. After conducting thorough model comparisons and analyses, a bespoke model was chosen. Extensive model comparisons and analyses led to the selection of a custom model with 91% training accuracy and optimized parameters. Fine-tuning of six pre-trained models identified VGG-16 as the top performer, achieving 99.96% training accuracy and 97.7% testing accuracy. Explainable AI (XAI) techniques, particularly the insertion method, provided robust interpretability, with attributed scores of 0.96 for insertion, 0.69 for deletion, and 0.37 for fidelity. Visualization using various attribution mapping methods reinforced the trustworthiness of this work in granular crack identification.

关键词： Training Deep learning Analytical models Accuracy Explainable AI Scalability Transforms Quality control Convolutional neural networks Testing

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AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding

AIX Implementation in Image-Based PM2.5 Estimation: Toward a...

引用

15th International Conference on Knowledge and smart Technology, KST 2023

作者： Utomo, Sapdo John, A. Pratap, Ayush Jiang, Zhi-Sheng Karthikeyan, P. Hsiung, Pao-Ann National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India

ISBN: (纸本)9781665477123

In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model's output, LIME has been implemented. According to LIME's explanation, the proposed model's output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model's decision. We hope that with this discovery, we can contribute to the theme of 'AI for social good,' notably in the domains of the environment and human welfare. © 2023 IEEE.

关键词： Air quality

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Federated Trustworthy AI Architecture for smart Cities 8

Federated Trustworthy AI Architecture for Smart Cities

引用

8th IEEE International smart Cities Conference, ISC2 2022

作者： Utomo, Sapdo John, A. Rouniyar, Adarsh Hsu, Hsiu-Chun Hsiung, Pao-Ann Grad. Institute of Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan National Chung Cheng University Dept. of Information Management Chiayi County Taiwan

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

As the number of smart citizens or users grows, several governments are eager to develop smart cities. All stakeholders, including intelligent users, demand an AI system that is trustworthy. To be considered trustworthy, an AI system must meet seven key criteria: (1) human agency and oversight, (2) robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, nondiscrimination, and fairness, (6) societal and environmental well-being, and (7) accountability. By merging the existing trustworthy AI framework from KServe and Federated Learning with a more reliable model aggregation protocol than earlier studies, we introduced the federated trustworthy Artificial intelligence (FTAI) architecture. With the integration of FedPSO and AIF360, we updated the FedCS technique. The proposed architecture meets all seven of the essential requirements to a high degree of satisfaction. This paper also includes a detailed explanation of this claim. The scenario demonstrates that the new global model comes with clear measures of fairness metrics, ensuring that the model is devoid of bias. © 2022 IEEE.

关键词： smart city

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AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding

AIX Implementation in Image-Based PM2.5 Estimation: Toward a...

引用

International Conference on Knowledge and smart Technology (KST)

作者： Sapdo Utomo A John Ayush Pratap Zhi-Sheng Jiang P. Karthikeyan Pao-Ann Hsiung Grad. Ins. Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan R.O.C Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan R.O.C Dept. of Metallurgical and Materials Engineering Indian Institute of Technology Ropar Rupnagar India

In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model’s output, LIME has been implemented. According to LIME’s explanation, the proposed model’s output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model’s decision. We hope that with this discovery, we can contribute to the theme of “AI for social good,” notably in the domains of the environment and human welfare.

关键词： Deep learning Pollution Atmospheric modeling Buildings Estimation Air quality Artificial intelligence

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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI

Analysis of Defect Associated with Powder Bed Fusion with De...

引用

International Conference on Knowledge and smart Technology (KST)

作者： Ayush Pratap Neha Sardana Sapdo Utomo A John P. Karthikeyan Pao-Ann Hsiung Dept. of Metallurgical and Materials Engineering Indian Institute of Technology Ropar Rupnagar India Grad. Ins. Ambient Intelligence and Smart Systems National Chung Cheng University Chiayi County Taiwan R.O.C Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan R.O.C

关键词： Deep learning Powders Transfer learning Surface morphology Predictive models Three-dimensional printing Data models

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