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Runqiong Wang Ph.D Associate Professor

My research interests include tool condition monitoring for thin-walled parts cutting, intelligent manufacturing, and digital twins of manufacturing systems.

What's new

Education and Working Experience

  • 2025.03~Now, Associate Professor, College of Electromechanical Engineering, Qingdao University of Science and Technology

  • 2020.09~2024.12, Ph.D., School of Mechanical Engineering, Shandong University

  • 2023.10~2024.10, Visiting Ph.D. Student, Department of Mechanical and Mechatronics Engineering, University of Auckland

  • 2018.08~2020.08, Mechanical Engineer, Hyundai Motor R\&D Center (China)

  • 2015.09~2018.01, M.E., Northeastern University (China)

  • 2011.09~2015.06, B.E. & B.A., Ludong University

Research Outputs

Articles

First author

  1. Wang R, Song Q, Peng Y, et al. Milling surface roughness monitoring using real-time tool wear data[J]. International Journal of Mechanical Sciences 2025;285:109821 (IF=7.1, JCR Q1)

  2. Wang R, Song Q, Peng Y, et al.Toward digital twins for high-performance manufacturing: Tool wear monitoring in high-speed milling of thin-walled parts using domain knowledge. Robotics and Computer-Integrated Manufacturing_2024;88:102723 (IF=9.1, JCR Q1)

  3. Wang R, Song Q, Peng Y, et al. A milling tool wear monitoring method with sensing generalization capability. Journal of Manufacturing Systems 2023;68:25-41. (IF=12.2, JCR Q1)

  4. Wang R, Song Q, Peng Y, et al. Self-adaptive fusion of local-temporal features for tool condition monitoring: A human experience free mode. Mechanical Systems and Signal Processing 2023;195:110310. (IF=7.9, JCR Q1)

  5. Wang R, Song Q, Liu Z, et al. Multi-condition identification in milling Ti-6Al-4V thin-walled parts based on sensor fusion. Mechanical Systems and Signal Processing 2022;164:108264.(IF=7.9, JCR Q1)

  6. Wang R, Zhu L, Zhu C. Research on fractal model of normal contact stiffness for mechanical joint considering asperity interaction. International Journal of Mechanical Sciences 2017;134:357–69. ( IF=7.1, JCR Q1)

  7. Wang R, Song Q, Liu Z, et al. A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts. Sensors 2021;21:5779. ( IF=3.4, JCR Q2)

  8. Wang R, Song Q, Peng Y, et al. Tool Condition Monitoring for High Performance Milling Based on Feature Adaptive Fusion and Ensemble Learning. Journal of Mechanical Engineering, 2024;60(1):149-158. (in Chinese, EI)

  9. Wang R, Zhu L, Zhu C. Investigation of Contact Stiffness Model for Joint Surfaces Based on Domain Expansion Factor and Asperity Interaction. Journal of Mechanical Engineering, 2018;54(19):88-95. (in Chinese, EI)

Co-author

  1. Peng Y, Song Q, Wang R, et al. A tool wear condition monitoring method for non-specific sensing signals. International Journal of Mechanical Sciences 2023:108769

  2. Peng Y, Song Q, Wang R, et al. Intelligent recognition of tool wear in milling based on a single sensor signal. The International Journal of Advanced Manufacturing Technology 2023;124:1077–1093

  3. Ji H, Song Q, Wang R, et al. Evaluation and prediction of pore effects on single-crystal mechanical and damage properties of selective laser melted Inconel-718. Materials & Design 2022;219:110807

  4. Xue P, Zhu C, Wang R, et al. Research on dynamic characteristics of oil-bearing joint surface in slide guidesl. Mechanics Based Design of Structures and Machines 2022;50(6):1893-1913

Patents

  1. Song Q, Wang R, Liu Z, et al. Characteristic strengthening method and system for on-line monitoring of state of milling cutter of thin-wall part. CN202210366357.6. 2023-03-14

  2. Song Q, Wang R, Liu Z, et al. Multi-sensing-signal fusion monitoring thin-wall part milling data dimension reduction method and system. CN202110179639.0. 2022-04-22

  3. Wang R, Zhu L, Ni C, et al. Method for determining normal contact rigidity of loaded joint part by considering interaction effect of micro-bulges on rough surfaces. CN201710029431.4. 2020-06-16

  4. Song Q, Wang R, Liu Z, et al. Cutting signal multi-domain feature high-quality fusion and fusion feature performance evaluation method, CN202211156530.6. 2022-09-22

Awards

  • National Scholarship for Doctoral Students

  • Outstanding Research Achievement Award for Graduate Students, Shandong University

  • Excellent Doctoral Training Plan of School of Mechanical Engineering, Shandong University

  • Excellent Award for Mid-term Assessment of Doctoral Project, Shandong University

  • Excellent Master's Thesis of Liaoning Province, Selected by Liaoning Province Education Department

  • Excellent graduate student, SDU & NEU

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