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108 results on '"Yang, Haw-Ching"'

1. Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability

Author

Xie, Jiarui, Yang, Zhuo, Hu, Chun-Chun, Yang, Haw-Ching, Lu, Yan, and Zhao, Yaoyao Fiona

Subjects
Computer Science - Computational Engineering, Finance, and Science, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Powder bed fusion (PBF) is an emerging metal additive manufacturing (AM) technology that enables rapid fabrication of complex geometries. However, defects such as pores and balling may occur and lead to structural unconformities, thus compromising the mechanical performance of the part. This has become a critical challenge for quality assurance as the nature of some defects is stochastic during the process and invisible from the exterior. To address this issue, digital twin (DT) using machine learning (ML)-based modeling can be deployed for AM process monitoring and control. Melt pool is one of the most commonly observed physical phenomena for process monitoring, usually by high-speed cameras. Once labeled and preprocessed, the melt pool images are used to train ML-based models for DT applications such as process anomaly detection and print quality evaluation. Nonetheless, the reusability of DTs is restricted due to the wide variability of AM settings, including AM machines and monitoring instruments. The performance of the ML models trained using the dataset collected from one setting is usually compromised when applied to other settings. This paper proposes a knowledge transfer pipeline between different AM settings to enhance the reusability of AM DTs. The source and target datasets are collected from the National Institute of Standards and Technology and National Cheng Kung University with different cameras, materials, AM machines, and process parameters. The proposed pipeline consists of four steps: data preprocessing, data augmentation, domain alignment, and decision alignment. Compared with the model trained only using the source dataset, this pipeline increased the melt pool anomaly detection accuracy by 31% without any labeled training data from the target dataset., Comment: 8 pages, 7 figures, 3 tables. IEEE CASE 2024

Published
2024

3. Implementation of a Model-Tracing-Based Learning Diagnosis System to Promote Elementary Students' Learning in Mathematics

Author

Chu, Yian-Shu, Yang, Haw-Ching, and Tseng, Shian-Shyong

Abstract

Of all teaching methods, one-to-one human tutoring is the most powerful method for promoting learning. To achieve this aim and reduce teaching load, researchers developed intelligent tutoring systems (ITSs) to employ one-to-one tutoring (Aleven, McLaren, & Sewall, 2009; Aleven, McLaren, Sewall, & Koedinger, 2009; Anderson, Corbett, Koedinger, & Pelletier, 1995; Anderson & Reiser, 1985; Blessing, Gilbert, Ourada, & Ritter, 2009; Mitrovic et al., 2009; Mitrovic & Ohlsson, 1999; Suraweera, Mitrovic, & Martin, 2007; VanLehn et al., 2005). However, most ITSs have restricted user interfaces, which confine reasoning strategies of students during problem solving, thus ignoring the fact that students could use dissimilar strategies to solve a given question. Furthermore, student learning problems could be diagnosed from the derivation of their answers. In order to interpret students' mathematical problem-solving behaviors, this study developed a Model-tracing Intelligent Tutor (MIT) to diagnose students' learning problems and provide learning feedback for individual students. A quasi-experiment was conducted in an elementary school to evaluate the effectiveness of the proposed approach, in which 124 fifth graders participated. The experimental results show that the model-tracing-based learning diagnosis system is significantly more helpful to the students in learning mathematics than the conventional web-based test in terms of learning achievements.

Published
2014

5. Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components

Author

Tran, Hong-Chuong, Lo, Yu-Lung, Yang, Haw-Ching, Hsiao, Hung-Chang, Cheng, Fan-Tien, and Kuo, Tsung-Han

Abstract

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of $1.6~\mu \text{m}$ and a standard deviation is $0.7~\mu \text{m}$ . By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of $13.45~\mu \text{m}$ and a standard deviation of $2.5~\mu \text{m}$ . In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa Note to Practitioners—As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of previous layer have a strong influence on the printing quality of current layer. The variations of surface roughness will lead to the fluctuation of mechanical properties of the fabricated components. Additionally, to the best of author’s knowledge, current commercial L-PBF machines can not actively control the surface roughness of samples during the process. The proposed IAMA in this work can predict and control the roughness on the top surface of each layer during L-PBF process. Therefore, the constructed architecture has strong potential for integrating into the commercial L-PBF machine for controlling the surface roughness on the top of the parts during the manufacturing process. As a result, the quality of the fabricated components is expected to be in consistent. Accordingly, L-PBF machine equipped with IAMA will have a strong potential in applying for mass production of the components in aerospace and automobile industries.

Published
2023
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7. A Novel Implementation Framework of Digital Twins for Intelligent Manufacturing Based on Container Technology and Cloud Manufacturing Services

Author

Hung, Min-Hsiung, primary, Lin, Yu-Chuan, additional, Hsiao, Hung-Chang, additional, Chen, Chao-Chun, additional, Lai, Kuan-Chou, additional, Hsieh, Yu-Ming, additional, Tieng, Hao, additional, Tsai, Tsung-Han, additional, Huang, Hsien-Cheng, additional, Yang, Haw-Ching, additional, and Cheng, Fan-Tien, additional

Published
2022
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13. Automatic virtual metrology for wheel machining automation.

Author

Yang, Haw-Ching, Tieng, Hao, and Cheng, Fan-Tien

Subjects
METROLOGY, VIRTUAL reality, MACHINING, AUTOMATION, CONTINUOUS improvement process, MASS production, PRODUCT quality, WHEELS
Abstract

Total inspection after wheel machining becomes essential for safety consideration and continuous improvement. However, conducting wheel-by-wheel actual metrology is very expensive and time-consuming. A novel idea is to use virtual metrology (VM) that predicts wheel quality based on process data collected from machine tool with a slight supplement of actual metrology data. The technology of automatic virtual metrology (AVM) has been proposed by the authors and successfully deployed in hi-tech industries, such as semiconductor, display and solar cell. The purpose of this study was to propose an approach to apply the AVM system factory-wide to wheel machining automation (WMA) for achieving total inspection of all the precision items of WMA under mass production environment. [ABSTRACT FROM PUBLISHER]

Published
2016
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28. Modeling and Analysis of Equipment Managers in Manufacturing Execution Systems for Semiconductor Packaging

Author

Cheng, Fan-Tien, Yang, Haw-Ching, Kuo, Tsung-Liang, Feng, Chengche, and Jeng, MuDer

Abstract

Equipment Managers (EMs) play a major role in a Manufacturing Execution System (MES). They serve as the communication bridge between the components of an MES and the equipment. The purpose of this paper is to propose a novel methodology for developing analytical and simulation models for the EM such that the validity and performance of the EM can be evaluated. Domain knowledge and requirements are collected from a real semiconductor packaging factory. By using IDEF0 and state diagrams, a static functional model and a dynamic state model of the EM are built. Next, these two models are translated into a Petri net model. This allows qualitative and quantitative analyses of the system. The EM net model is then expanded into the MES net model. Therefore, the performance of an EM in the MES environment can be evaluated. These evaluation results are good references for design and decision making. Index Terms--Equipment Manager, IDEF0, Manufacturing Execution System (MES), Petri nets.

Published
2000

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