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A Rational Approach to Building Cohesive Zone Model for Use in modeling Impact analysis 19

A Rational Approach to Building Cohesive Zone Model for Use ...

19th Biennial International conference on Engineering, Science, Construction, and Operations in Challenging Environments (Earth and Space)

作者： Raihan, Mohammed Maurya, Ashutosh Rajan, Subramaniam Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85281 USA

ISBN: (数字)9780784485736

ISBN: (纸本)9780784485736

There is significant internal damage in structural composites when subjected to high impact loads. This internal damage is primarily the result of delamination caused by high normal and tangential stresses, and stress concentrations generated by the applied loading. Since delamination significantly reduces the structure's stiffness and strength, and often, cannot be detected because it occurs in the inter-laminar layers, understanding the mechanisms of delamination is therefore essential for preventing catastrophic structural failures. There are three different modes in delamination-Mode I or opening mode, Mode ii or shearing mode, and Mode iiI or tearing mode. Among these modes, Mode I, Mode ii, and mixed mode (derived from combination of Mode I and Mode ii) occur most commonly in laminated composites. In this paper, an end-to-end process is developed for generating the material models for modeling the composite as well as the cohesive zone model using an appropriate traction-separation law (TSL). First, material properties of a common aerospace composite, IM7-8552, are obtained using laboratory testing. The obtained data are used in the MAT_213 material model in LS-DYNA. Second, fracture-related tests, i.e., the double cantilever beam (DCB) and end notched flexure (ENF) tests, are conducted to obtain the load-displacement responses. Then using a combination of response surface methodology (RSM) and gradient-based numerical optimization technique, i.e., inverse analysis, the optimal values of the TSL parameters are obtained so that LS-DYNA's MAT_186 material model can be generated. Results obtained from this procedure show good agreement between the FE predictions and the experimental results.

关键词： Delamination

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Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis 38

Fast and accurate automatic wafer defect detection and class...

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conference on Metrology, Inspection, and process control XXXViiI

作者： Choi, Sanghyun Xie, Qian Greeneltch, Nathan Lee, Hyung Joo Govindaraj, Mohan Jayaram, Srividya Pereira, Mark Biswas, Sayani Bhamidipati, Samir Torunoglu, Ilhami Siemens EDA Seoul South Korea Siemens EDA Aliso Viejo CA USA Siemens EDA Pune Maharashtra India

ISBN: (纸本)9781510672178;9781510672161

Performing accurate and timely Scanning Electron Microscope (SEM) image analysis to identify wafer defects is crucial as it directly impacts manufacturing yield. In this paper, a machine learning (ML) based approach for analyzing SEM images (from wafer inspection machines) to locate and classify wafer defects is proposed. A state-of-the-art one-stage objection detection model called YOLOv8 (You Only Look Once version 8) is used as it offers a good balance between accuracy and inference speed. Experimental results confirm that an ensemble model composed of multiple YOLOv8 models can predict 6 types of defects with a mean Average Precision (mAP) of 0.789 (at IoU=0.5) for unseen test data consisting of real-world SEM images from 5 wafer fabs that have varying image qualities.

关键词： ADC (Automatic Defect Classification) Wafer defects SEM (Scanning Electron Microscope) ML modeling YOLO model Ensemble model Object detection

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Multi-instance data behavior in BPMN 42

Multi-instance data behavior in BPMN

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42nd International conference on Conceptual modeling: ER Forum, 7th Symposium on Conceptual modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： König, Maximilian Weske, Mathias Hasso Plattner Institute Prof.-Dr.-Helmert-Straße 2-3 Potsdam14482 Germany

process models have long-since been used to capture business processes for documentation, communication, analysis, and enactment purposes. Most of the commonly used modeling languages, e.g., BPMN, focus on the control flow of involved activities and participants, while mostly neglecting the data perspective despite the latter gaining more and more relevance for today’s companies. This is especially true for multiple instances of the same data, e.g., multiple items being ordered by the same customer. The BPMN standard’s concepts fail to capture such behavior sufficiently. In addition, their textual descriptions lack conciseness when it comes to the concepts’ semantics. Therefore, this paper proposes new semantics for multi-instance data objects including splitting and merging behavior which lays the foundation for further investigation of multi-instance behavior of process data flow in general. © 2023 CEUR-WS. All rights reserved.

关键词： BPMN Colored Petri Nets data Semantics Multi-Instance Behavior process modeling

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Investigation of Various data-Driven modeling Techniques for an Industrial Heat Exchanger 8th

Investigation of Various Data-Driven Modeling Techniques for...

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8th International conference on data Management, Analytics and Innovation, ICDMAI 2024

作者： Resma Madhu, P.K. Subbaiah, Jayalalitha Department of Electronics and Communication Engineering PSG Institute of Technology and Applied Research Coimbatore641 062 India School of Electrical and Electronics Engineering SASTRA Deemed University Thanjavur613401 India

ISBN: (纸本)9789819732418

Measurement data acquired from an industrial heat exchanger (HE) can provide a reliable inference on its dynamics and performance. Harnessing the information available on the acquired data, the data-driven modeling technique can create a dynamic model of an HE without any expert knowledge or complex mathematical analysis. Motivated by these factors, data-driven modeling techniques are investigated to model an industrial Naphtha cooler HE in this work. Model describing the HE dynamics is a primary requirement to simulate control strategies and enhance operational efficiency. Naphtha cooler is an industrial shell and tube HE, where the temperature of Naphtha available at the shell side is controlled using the cooling water flow rate at the tube side. Hence, the HE is modeled as a Multi-input Multi-output (MIMO) process with four inputs and two outputs. Three different data-driven modeling approaches namely, traditional, polynomial, and non-linear are investigated. A total of eight model structures are considered and the fit percentage is used as performance metrics to determine the accurate model. Experimental results illustrate that the ARMAX model exhibits a significant 88.2% fit on average. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heat exchangers

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analysis and Evaluation of Quality control Throughout Production of Real Scene 3D modeling Based on Oblique Aerial Photography 5

Analysis and Evaluation of Quality Control Throughout Produc...

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5th Asia-Pacific conference on Image processing, Electronics and Computers, IPEC 2024

作者： Han, Yachao Chen, Jie Gao, Zihong Zhao, Zheng Jiang, Xiao Yang, Dachang China Aero Geophysical Survey & Remote Sensing Center for Natural Resources Beijing China

ISBN: (纸本)9798350374407

As a new type of geographic data source, real 3D models can effectively complement the lack of facade and environmental information in traditional aerial remote sensing data captured from only downward perspective. Currently, there is a comprehensive push for real 3D construction in China. However, there are limited industry standards related to the quality inspection of 3D model outputs, which poses a challenge to ensuring the production quality of real 3D models. This study delves into the principles and workflow of oblique aerial photography for real 3D modeling, particularly focusing on techniques such as joint adjustment of multi-view imagery and dense matching of multi-view images. It identifies and analyzes the critical elements of quality control throughout the entire production process of oblique aerial photography 3D modeling. These elements and their specific impacts on the final product quality are examined one by one, leading to a summary of quality inspection methods for the entire production process. By conducting a qualitative inspection and evaluation of highprecision, large-scale oblique aerial photography real 3D modeling production examples in Shanghai, this study highlights five key advantages and disadvantages of this technology and offers recommendations. These insights provide valuable references for the development of industry standards for the quality inspection of oblique aerial photography real 3D model outputs. Furthermore, they offer a more sophisticated technical means for natural resource investigations, contributing to more precise and efficient natural resource management. © 2024 IEEE.

关键词： Aerial photography

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Semi-Supervised Soft Sensor modeling for Industrial Dynamic Information Mining Based on Two-Path Weight Comparison 13

Semi-Supervised Soft Sensor Modeling for Industrial Dynamic ...

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13th IEEE data Driven control and Learning Systems conference, DDCLS 2024

作者： Jia, Ruining Jin, Xinyi Ni, Xinru Ruan, Mengjiao Yang, Zeyu Wang, Peiliang School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou313000 China

ISBN: (纸本)9798350361674

Deep learning methods can extract reliable feature representations from massive process data to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and even limited in label samples due to untimely sampling and analysis. Aiming at the above problems, this paper proposes a semi-supervised soft sensor modeling method (SSACF-LSTM) for industrial dynamic information mining based on two-path weight comparison. An unsupervised long short-term memory network (LSTM) encoder-decoder is utilized to extract the hidden dynamic nonlinear information in unlabeled samples. This information is then spliced and masked with the initial input, which is subsequently fed into a deep neural network encoder-decoder for pretraining to learn a self-supervised task that mines correlation information between sequences as well as between samples that is useful for the model. In addition, the LSTM with attentional convolutional fusion serves as an auxiliary path that mines correlation information between variables directly from the initial input to complement the information extracted by self-supervision. Finally, the results of both are linearly weighted to obtain the final prediction. The method is validated in a debutanizer process. The results show that SSACF-LSTM can utilize both labeled and unlabeled data to extract quality-related features for soft sensor modeling, which is better than the semi-supervised LSTM network with historical feature fusion (HFF-ssLSTM), semi-supervised stacked auto-encoder (SSAE), and variational auto-encoder regression (SVAER). © 2024 IEEE.

关键词： Self-supervised learning

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Flood Risk analysis of Si River Based on Hydrodynamic modeling 24

Flood Risk Analysis of Si River Based on Hydrodynamic Modeli...

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4th International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2024

作者： Yang, Minzhi Shao, Yinxia Zhang, Longfei Zhu, He Gao, Qi Lou, Wei Power China Huadong Engineering Corporation Limited Hangzhou Zhejiang China

ISBN: (纸本)9798400710247

Mathematical model is the core of the digital twin basin, which can simulate the whole elements of the physical basin and the whole process of water conservancy governance and management activities, and provide intelligent and practical "algorithms"for the digital twin basin to support the basin flood control and other business applications. For the problem of frequent flood disasters in Jining Si River Basin, the application of EFDC hydrodynamic model, the establishment of the two-dimensional coupled hydrodynamic model of the main stream of the Si River and the flood storage and retention area, before and after the construction of the flood inundation scope of the simulation and analysis, the construction of the history, design, forecast, command four categories of flood conditions, to achieve the water model can be validated, the results of the research can be used for the development of the Si River Basin Flood control Plan, the application of the scheduling of flood control works, flood rescue and relief, etc. to provide the scientific basis, and for the effective alleviation of the impact of the floods in Si river to provide technical support. © 2024 Copyright held by the owner/author(s).

关键词： Risk analysis

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A conceptual framework integrating numerical simulation with system theory based method for quantitative explosion process hazard analysis

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process SAFETY AND ENVIRONMENTAL PROTECTION 2022年 166卷 202-211页

作者： Zhou, Shennan Wang, Zhongqi Li, Qizhong Beijing Inst Technol Univ State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China North China Inst Sci & Technol East Beijing Beijing 101601 Peoples R China

Explosion is a significant threat to chemical process safety, which often occurs suddenly with a wide range of impact, resulting in catastrophic disasters. Predictions of explosion hazards are required for regional safety assessment and disaster resilience enhancement of chemical industrial parks (CIPs). This paper, therefore, aims to develop a conceptual framework for systematically analyzing explosion evolutions and quantifying the po-tential hazards by combining system-theoretic process analysis (STPA) method with numerical simulation. The proposed framework consists of 5 steps: (i) establishment of hierarchical safe control structures (SCSs) of important chemical processing zones in the CIP, (ii) computational fluid dynamics (CFD) modeling for potential explosion evolutions in each zone by changing the examined parameters randomly, (iii) development of a convolutional neural network (CNN) prediction model through constant self-learning of CFD pressure field data, (iv) comprehensive assessment of blast damage by incorporating the outputs of the above numerical models into existing evaluation methods, (v) identification of unsafety control actions and causes, and safety constraints for the improvement of SCSs. Provided with monitoring data, the developed analysis architecture can predict ex-plosion process hazards and recommend appropriate safety strategies in real time. This would service the multi-level requirements for explosion prevention and protection, supporting better-informed decision-making. The paper describes the concepts and implementation process of the method as a first step.

关键词： Explosion hazard analysis Explosion flow field prediction STPA CFD simulation Convolutional neural network

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Parameter identification method for low voltage ride-through control of direct-drive wind turbines based on GRU-ISSA 4

Parameter identification method for low voltage ride-through...

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4th International conference on Intelligent Power and Systems, ICIPS 2024

作者： Yang, Huibiao Li, Xutao Xu, Hengshan Li, Hongqiang Electric Power Research Institute State Grid Ningxia Electric Power Co. Ltd. Yinchuan China School of Electrical Engineering and New Energy Three Gorges University Yichang China

ISBN: (纸本)9798331532765

In order to improve the accuracy of low voltage ride-through (LVRT) control parameter identification for direct-drive wind turbines, a identification method based on the GRU-ISSA algorithm is proposed. Firstly, the key parameters of LVRT are identified through theoretical analysis;Secondly, the GRU model is trained across various operating conditions to obtain identified control parameter values without requiring an optimization range, simplifying the identification process. Finally, the Improved Sparrow Algorithm (ISSA) is applied to refine the control parameters and enhance identification accuracy. The experimental results show that the error between the GRU-ISSA identification data and the measured data is only 0.27%, and the maximum error is 0.8%, which verifies the effectiveness and feasibility of the method. © 2024 IEEE.

关键词： Windmill

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Co-simulation approach integrating multibody and discrete element modeling for robomould process analysis 31

Co-simulation approach integrating multibody and discrete el...

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31st International conference on Noise and Vibration Engineering, ISMA 2024 and 10th International conference on Uncertainty in Structural Dynamics, USD 2024

作者： Goris, M. Deckers, E. Naets, F. KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium Flanders Make @ KU Leuven Belgium

ISBN: (纸本)9789082893175

This paper explores the implementation of a high-fidelity model for robomoulding, an advanced polymer processing technique derived from rotational moulding. Robomoulding produces large, hollow, stress-free polymer products but currently operates with an open-loop system due to the challenges in implementing measurement signals. The primary objective is to develop a comprehensive model integrating virtual sensing technologies for improved in-process assessment with limited sensor data. A combination of discrete element method (DEM) and multi-body dynamics (MBD) models simulate the process. The DEM model is calibrated using a rotating drum experiment with vision-based techniques to identify powder characteristics. The study compares the physical product's powder distribution to the simulation's estimated distribution, finding similar trends within a limited range of settings. These results demonstrate the model's potential and support further development of process control algorithms. © 2024 Proceedings of ISMA 2024 - International conference on Noise and Vibration Engineering and USD 2024 - International conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Molding

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