Haw‐Ching Yang

784 total citations
55 papers, 570 citations indexed

About

Haw‐Ching Yang is a scholar working on Industrial and Manufacturing Engineering, Mechanical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Haw‐Ching Yang has authored 55 papers receiving a total of 570 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Industrial and Manufacturing Engineering, 24 papers in Mechanical Engineering and 10 papers in Electrical and Electronic Engineering. Recurrent topics in Haw‐Ching Yang's work include Industrial Vision Systems and Defect Detection (16 papers), Advanced machining processes and optimization (15 papers) and Manufacturing Process and Optimization (11 papers). Haw‐Ching Yang is often cited by papers focused on Industrial Vision Systems and Defect Detection (16 papers), Advanced machining processes and optimization (15 papers) and Manufacturing Process and Optimization (11 papers). Haw‐Ching Yang collaborates with scholars based in Taiwan, United States and Yemen. Haw‐Ching Yang's co-authors include Fan‐Tien Cheng, Min‐Hsiung Hung, Yu‐Chuan Lin, Hsien‐Cheng Huang, Chao‐Chun Chen, Kenneth Scott, H.P. Singh, Toshiki Hirano, Ali M. Niknejad and Roger T. Howe and has published in prestigious journals such as Geology, Expert Systems with Applications and IEEE Access.

In The Last Decade

Haw‐Ching Yang

52 papers receiving 547 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Haw‐Ching Yang Taiwan 14 358 157 87 61 58 55 570
Alexander Epple Germany 11 99 0.3× 149 0.9× 53 0.6× 28 0.5× 11 0.2× 40 344
Peng Duan China 18 574 1.6× 42 0.3× 66 0.8× 93 1.5× 26 0.4× 93 1.1k
H.S. Cho South Korea 10 115 0.3× 62 0.4× 35 0.4× 28 0.5× 12 0.2× 46 321
Vishnu Narayanan India 13 145 0.4× 141 0.9× 137 1.6× 48 0.8× 7 0.1× 33 482
Hasan Tercan Germany 10 189 0.5× 189 1.2× 46 0.5× 11 0.2× 28 0.5× 23 482
R. Bell United Kingdom 12 265 0.7× 188 1.2× 23 0.3× 9 0.1× 52 0.9× 42 538
Bruno Maione Italy 13 355 1.0× 92 0.6× 25 0.3× 58 1.0× 64 1.1× 47 754
Jin Xie China 14 434 1.2× 30 0.2× 116 1.3× 140 2.3× 45 0.8× 45 702
Dhruv Gupta United States 13 136 0.4× 43 0.3× 134 1.5× 174 2.9× 9 0.2× 31 560
Cheng Pang Sweden 15 322 0.9× 33 0.2× 253 2.9× 66 1.1× 4 0.1× 70 757

Countries citing papers authored by Haw‐Ching Yang

Since Specialization
Citations

This map shows the geographic impact of Haw‐Ching Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Haw‐Ching Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haw‐Ching Yang more than expected).

Fields of papers citing papers by Haw‐Ching Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Haw‐Ching Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Haw‐Ching Yang. The network helps show where Haw‐Ching Yang may publish in the future.

Co-authorship network of co-authors of Haw‐Ching Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Haw‐Ching Yang. A scholar is included among the top collaborators of Haw‐Ching Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Haw‐Ching Yang. Haw‐Ching Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hsu, Chih‐Yu, Haw‐Ching Yang, & Yu‐Lung Lo. (2025). Predicting TSV depth using laser drilling by simulation model and neural network. Expert Systems with Applications. 299. 130018–130018.
2.
3.
Yan, Bing, et al.. (2023). Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices. IEEE Robotics and Automation Letters. 9(4). 3878–3885. 2 indexed citations
4.
Yang, Haw‐Ching, et al.. (2022). Suboptimal Explainable Scheme for Machining Outcome Estimation. IEEE Robotics and Automation Letters. 7(3). 7834–7841. 1 indexed citations
5.
Lo, Yu‐Lung, et al.. (2022). Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components. IEEE Transactions on Automation Science and Engineering. 20(4). 2527–2538. 13 indexed citations
6.
Hung, Min‐Hsiung, Yu‐Chuan Lin, Hung‐Chang Hsiao, et al.. (2022). A Novel Implementation Framework of Digital Twins for Intelligent Manufacturing Based on Container Technology and Cloud Manufacturing Services. IEEE Transactions on Automation Science and Engineering. 19(3). 1614–1630. 48 indexed citations
7.
Yang, Haw‐Ching, et al.. (2021). MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters. IEEE Robotics and Automation Letters. 6(4). 6553–6560. 8 indexed citations
8.
Yang, Haw‐Ching, et al.. (2020). An Automated Dynamic-Balancing-Inspection Scheme for Wheel Machining. IEEE Robotics and Automation Letters. 5(2). 2224–2231. 6 indexed citations
9.
Yang, Haw‐Ching, et al.. (2020). An Online AM Quality Estimation Architecture From Pool to Layer. IEEE Transactions on Automation Science and Engineering. 18(1). 269–281. 16 indexed citations
10.
Yang, Haw‐Ching, et al.. (2019). An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing. IEEE Robotics and Automation Letters. 4(3). 2886–2893. 18 indexed citations
11.
Yang, Haw‐Ching, et al.. (2019). A Gradual Refreshing Scheme for Improving Tool Utilization. IEEE Robotics and Automation Letters. 4(2). 515–522. 4 indexed citations
12.
Chen, Chao‐Chun, Min‐Hsiung Hung, Yu‐Chuan Lin, et al.. (2019). A Novel Efficient Big Data Processing Scheme for Feature Extraction in Electrical Discharge Machining. IEEE Robotics and Automation Letters. 4(2). 910–917. 12 indexed citations
13.
Yang, Haw‐Ching, et al.. (2018). Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing. IEEE Robotics and Automation Letters. 3(2). 934–941. 18 indexed citations
14.
Yang, Haw‐Ching, et al.. (2017). A cyber-physical scheme for predicting tool wear based on a hybrid dynamic neural network. Journal of the Chinese Institute of Engineers. 40(7). 614–625. 10 indexed citations
15.
Yang, Haw‐Ching, et al.. (2016). Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell. IEEE Access. 5. 3369–3375. 10 indexed citations
16.
Cheng, Fan‐Tien, et al.. (2016). Industry 4.1 for Wheel Machining Automation. IEEE Robotics and Automation Letters. 1(1). 332–339. 43 indexed citations
17.
Yang, Haw‐Ching, et al.. (2011). Automatic feature selection and failure diagnosis for bearing faults. Society of Instrument and Control Engineers of Japan. 235–239. 1 indexed citations
18.
Yang, Haw‐Ching, et al.. (2003). A Lower Paleozoic Plate Tectonic Model for the North Qilian Mountains, NW China. AGUFM. 2003. 1 indexed citations
19.
Huang, Eric, Fan‐Tien Cheng, & Haw‐Ching Yang. (2003). Development of a collaborative and event-driven supply chain information system using mobile object technology. 3. 1776–1781. 7 indexed citations
20.
Cheng, Fan‐Tien, et al.. (2000). Modeling and analysis of equipment managers in manufacturing execution systems for semiconductor packaging. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 30(5). 772–782. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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