Publications

1. Deng, W., Le, H., Nguyen, K. T., Gogu, C., Medjaher, K., Morio, J., & Wu, D. (2025). A Generic physics-informed machine learning framework for battery remaining useful life prediction using small early-stage lifecycle data. Applied Energy, 384, 125314.
2. Richter, F., & Wu, D. (2025). Interfacial adhesion of dissimilar thermoplastics fabricated via extrusion-based multi-material additive manufacturing. Materials & Design, 113688.
3. Sun, Y., Akçay, F. A., Wu, D., & Bai, Y. (2025). Analytical and numerical modeling on strengths of aluminum and magnesium micro-lattice structures fabricated via additive manufacturing. Progress in Additive Manufacturing, 10(2), 1421-1434.
4. Liu, Q., Duan, C., Richter, F., Shen, W., & Wu, D. (2024). Interfacial behavior between thermoplastics and thermosets fabricated by material extrusion-based multi-process additive manufacturing. Additive Manufacturing, 96, 104568.
5. Jin, L., Zhai, X., Wang, K., Zhang, K., Wu, D., Nazir, A., & Liao, W. H. (2024). Big data, machine learning, and digital twin assisted additive manufacturing: A review. Materials & Design, 113086.
6. Liu, Q., & Wu, D. (2024). Machine Learning and Feature Representation Approaches to Predict Stress-Strain Curves of Additively Manufactured Metamaterials with Varying Structure and Process Parameters. Materials & Design, 241, 112932.
7. Wei, Y., Grau, G., & Wu, D. (2024). Sheet Resistance Prediction of Laser Induced Graphitic Carbon with Transformer Encoder-Enabled Contrastive Learning. Journal of Intelligent Manufacturing, 1-15.
8. Wei, Y., & Wu, D. (2024). State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries with Conditional Graph Convolutional Network. Expert Systems with Applications, 238, 122041.
9. Wei, Y., Wu, D., Terpenny, J. (2024). Remaining Useful Life Prediction Using Graph Convolutional Attention Networks with Temporal Convolution-Aware Nested Residual Connections. Reliability Engineering and System Safety, 242, 109776.
10. Wei, Y., & Wu, D. (2024). Conditional variational transformer for bearing remaining useful life prediction. Advanced Engineering Informatics, 59, 102247.
11. Shi, J., Okolo, W. A., & Wu, D. (2023). Remaining Flying Time Prediction of Unmanned Aerial Vehicles Under Different Load Conditions. Journal of Aerospace Information Systems, 1-10.
12. Wei, Y., & Wu, D. (2023). Model-based Real-time Prediction of Surface Roughness in Fused Deposition Modeling with Graph Convolutional Network-based Error Correction. Journal of Manufacturing Systems, 71, 286-297.
13. Shi, J., Wei, Y., & Wu, D. (2023). Data-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-ion Battery. Journal of Computing and Information Science in Engineering, 1-14.
14. Le, H., Yavas, D., & Wu, D. (2023). Flexural and Fracture Behavior of Additively Manufactured Carbon Fiber Reinforced Polymer Composites with Bioinspired Bouligand Meso-Structures. Composite Structures, 323, 117436.
15. Wei, Y., & Wu, D. (2023). Remaining Useful Life Prediction of Bearings with Attention-Awared Graph Convolutional Network. Advanced Engineering Informatics, 58, 102143.
16. Liu, Q., Wei, F., Coathup, M., Shen, W., & Wu, D. (2023). Effect of Porosity and Pore Shapes on the Mechanical and Biological Properties of Additively Manufactured Bone Scaffolds. Advanced Healthcare Materials, 2301111.
17. Le, H., Minhas-Khan, A., Nambi, S., Grau, G., Shen, W., & Wu, D. (2023). Predicting the Sheet Resistance of Laser-Induced Graphitic Carbon using Machine Learning. Flexible and Printed Electronics, 8(3), 035013.
18. Liu, Q., Zhang, Z., Yavas, D., Shen, W., Wu, D. (2023). Multi-Material Additive Manufacturing: Effect of Process Parameters on Flexural Behavior of Soft-Hard Sandwich Beams, Rapid Prototyping Journal, https://doi.org/10.1108/RPJ-07-2022-0231.
19. Wei, Y., Wu, D., & Terpenny, J. (2023). Bearing remaining useful life prediction using self-adaptive graph convolutional networks with self-attention mechanism. Mechanical Systems and Signal Processing, 188, 110010.
20. Wei, Y. and Wu, D. (2022). Prediction of State of Health and Remaining Useful Life of Lithium-Ion Battery Using Graph Convolutional Network with Dual Attention Mechanisms, Reliability Engineering and System Safety, 230: 108947. 
21. Shi, J., Rivera, A., & Wu, D. (2022). Battery Health Management Using Physics-Informed Machine Learning: Online Degradation Modeling and Remaining Useful Life Prediction, Mechanical Systems and Signal Processing, 179: 109347.
22. Wei, Y., & Wu, D. (2024). Material removal rate prediction in chemical mechanical planarization with conditional probabilistic autoencoder and stacking ensemble learning. Journal of Intelligent Manufacturing, 35(1), 115-127.
23. Zhang, Z., Liu, Q., & Wu, D. (2022). Predicting Stress-Strain Curves Using Transfer Learning: Knowledge Transfer Across Polymer Composites, Materials & Design, 218: 110700.
24. Yavas, D., Liu, Q., Zhang, Z., & Wu, D. (2022). Design and Fabrication of Architected Multi-Material Lattices with Tunable Stiffness, Strength, and Energy Absorption, Materials & Design, 217: 110613.
25. Wang, Z., Yang, W., Liu, Q., Zhao, Y., Liu, P., Wu, D., Banu, M., & Chen, L. (2022). Data-Driven Modeling of Process, Structure and Property in Additive Manufacturing: A Review and Future Directions, Journal of Manufacturing Processes, 77: 13-31.
26. Wei, Y., Wu, D., & Terpenny, J. (2022). Constructing Robust and Reliable Health Indices and Improving the Accuracy of Remaining Useful Life Prediction, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of  Engineering Systems, 5(2): 021009.
27. Wang, D., Liu, Q., Wu, D., & Wang, L. (2022). Meta Domain Generalization for Smart Manufacturing: Tool Wear Prediction with Small Data, Journal of Manufacturing Systems, 62: 441-449. 
28. Shi, J., Peng, D., Peng, Z., Zhang, Z., Goebel, K., & Wu, D. (2022). Planetary Gearbox Fault Diagnosis Using Bidirectional-Convolutional LSTM Networks, Mechanical Systems and Signal Processing, 162: 107996.
29. Wei, Y., Wu, D., & Terpenny, J. (2021). Learning the Health Index of Complex Systems Using Dynamic Conditional Variational Autoencoders, Reliability Engineering and System Safety, 216:108004.
30. Zhang, Z., Yavas, D., Liu, Q., & Wu, D. (2021). Effect of Build Orientation and Raster Pattern on the Fracture Behavior of Carbon Fiber Reinforced Polymer Composites Fabricated by Additive Manufacturing, Additive Manufacturing, 47: 102204.
31. Xie, R. and Wu, D. (2021). Optimal Transport-based Transfer Learning for Smart Manufacturing: Tool Wear Prediction Using Out-of-Domain Data, Manufacturing Letters, 29:104-107. 
32. Hyer, H., Zhou, L., Liu, Q., Wu, D., Song, S., Bai, Y., McWilliams, B., Cho, K., & Sohn, Y. (2021). High Strength WE43 Microlattice Structures Manufactured by Laser Powder Bed Fusion, Materialia, 16: 101067.
33. Yavas, D., Zhang, Z., Liu, Q., & Wu, D. (2021). Fracture Behavior of 3D Printed Carbon Fiber Reinforced Polymer Composites, Composites Science and Technology, 208: 108741.
34. Zhang, Z., Liu, Z., & Wu, D. (2021). Prediction of Melt Pool Temperature in Directed Energy Deposition Using Machine Learning, Additive Manufacturing, 37: 101692. 
35. Xu, H., Liu, Q., Casillas, J., Mcanally, M., Mubtasim, N., Gollahon, L., Wu, D., & Xu, C. (2021). Prediction of Cell Viability in Dynamic Optical Projection Stereolithography-based Bioprinting Using Machine Learning, Journal of Intelligent Manufacturing, https://doi.org/10.1007/s10845-020-01708-5.
36. Shi, J., Yu, T., Goebel, K., & Wu, D. (2021). Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features, Transactions of the ASME, Journal of Computing and Information Science in Engineering, 21(2): 021004.
37. Yavas, D., Zhang, Z., Liu, Q., & Wu, D. (2021). Interlaminar Shear Behavior of Continuous and Short Carbon Fiber Reinforced Polymer Composites Fabricated by Additive Manufacturing, Composites Part B: Engineering, 204: 108460.
38. Zhang, Z., Shi, J., Yu, T., Santomauro, A., Gordon, A., Gou, J., & Wu, D. (2020). Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning, Transactions of the ASME, Journal of Computing and Information Science in Engineering, 20(6): 061015. 
39. Wei, Y., Wu, D., & Terpenny, J. (2020). Robust Incipient Fault Detection of Complex Systems Using Data Fusion, IEEE Transactions on Instrumentation and Measurement, 69(12), 9526-9534.
40. Alam, MDE, Wu, D., & Dickerson, AK. (2020). Predictive Modeling of Drop Ejection from Damped, Dampened Wings by Machine Learning, Proceedings of the Royal Society A, 476(2241).
41. Wei, Y., Wu, D., & Terpenny, J. (2020). Decision-Level Data Fusion in Quality Control and Predictive Maintenance, IEEE Transactions on Automation Science and Engineering, in press. 
42. Yu, T., Zhang, Z., Liu, Q., Kuliiev, R., Orlovskaya, N., & Wu, D. (2020). Extrusion-Based Additive Manufacturing of Yttria-Partially-Stabilized Zirconia Ceramics, Ceramics International, 46 (2020), 5020-5027. 
43. Zhang, Z., Poudel, L., Sha, Z., Zhou, W., & Wu, D. (2020). Data-Driven Predictive Modeling of Tensile Behavior of Parts Fabricated by Cooperative 3D Printing, Transactions of the ASME, Journal of Computing and Information Science in Engineering, 2020, 20(2): 021002. 
44. Li, Z., Liu, R., & Wu, D. (2019). Data-Driven Smart Manufacturing: Tool Wear Monitoring with Audio Signals and Machine Learning, Journal of Manufacturing Processes, 48, 66-76.   
45. Zhang, L., Li, Z., Królczyk, G., Wu, D., & Tang, Q. (2019). Mathematical Modeling and Multi-attribute Rule Mining for Energy Efficient Job-shop Scheduling, Journal of Cleaner Production, 241, 118289. 
46. Yu, T., Hyer, H., Sohn, Y., Bai, Y., & Wu, D. (2019). Structure-Property Relationship in High Strength and Lightweight AlSi10Mg Microlattices Fabricated by Selective Laser Melting, Materials and Design, 108062. 
47. Yu, T., Zhang, Z., Song, S., Bai, Y., & Wu, D. (2019). Tensile and Flexural Behaviors of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites, Composite Structures, 111147. 
48. Hao, J., Li, C., Meng, Q., Li, Z., & Wu, D. (2019). Effect of Tilt Angle on Bead Morphology in Multi-Axis Laser Cladding, Journal of Manufacturing Processes, 43, 311-322. 
49. Yu, T., Wu, D., & Li, Z. (2019). Predictive Modeling of Material Removal Rate in Chemical Mechanical Planarization with Physics-Informed Machine Learning, Wear, 426, 1430-1438. 
50. Li, Z., Zhang, Z., Shi, J., & Wu, D. (2019). Prediction of Surface Roughness in Extrusion-based Additive Manufacturing with Machine Learning, Robotics and Computer-Integrated Manufacturing, 57, 488-495. 
51. Li, Z., Wu, D., & Yu, T. (2019). Prediction of Material Removal Rate for Chemical Mechanical Planarization Using Decision Tree-Based Ensemble Learning, Transactions of the ASME, Journal of Manufacturing Science and Engineering, 141(3), 031003. 
52. Li, Z., Goebel, K., & Wu, D. (2019). Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning, Transactions of the ASME, Journal of Engineering for Gas Turbines and Power, 141(4), 041008. 
53. Wu, D., Wei, Y., & Terpenny, J. (2018). Predictive Modeling of Surface Roughness in Fused Deposition Modeling Using Data Fusion, International Journal of Production Research. 
54. Wu, D. & Xu, C. (2018). Predictive Modeling of Droplet Formation Processes in Inkjet-Based Bioprinting, Transactions of the ASME, Journal of Manufacturing Science and Engineering, 140(10), 101007. 
55. Wu, D., Ren, A., Zhang, W., Fan, F., Liu, P., Fu, X., & Terpenny, J. (2018). Cybersecurity for Digital Manufacturing, Transactions of the SME, Journal of Manufacturing Systems, 48, 3-12. 
56. Wang, J., Ma, Y., Zhang, L., Gao, R., & Wu, D. (2018). Deep Learning for Smart Manufacturing: Methods and Applications, Transactions of the SME, Journal of Manufacturing Systems, 48, 144-156. 
57. Li, Z., Wu, D., Hu, C, & Terpenny, J. (2018). An Ensemble Learning-based Prognostic Approach with Degradation-Dependent Weights for Remaining Useful Life Prediction, Reliability Engineering and System Safety, 184, 110-122. 
58. Wu, D., Jennings, C., Terpenny, J., Kumara, S., & Gao, R. (2018). Cloud-Based Parallel Machine Learning for Tool Wear Prediction, Transactions of the ASME, Journal of Manufacturing Science and Engineering, 140(4), 041005. 
59. Wu, D., Jennings, C., Terpenny, J., Gao, R., & Kumara, S. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests, Transactions of the ASME, Journal of Manufacturing Science and Engineering, 139(7), 071018. 
60. Wu, D., Liu., S., Zhang, L., Terpenny, J., Gao, R, Kurfess, T., & Guzzo, J. (2017). A Fog Computing-Based Framework for Process Monitoring and Prognosis in Cyber-Manufacturing, Transactions of the SME, Journal of Manufacturing Systems, 43(1), 25-34. 
61. Wu, D., Liu, X., Hebert, S., Gentzsch, W., & Terpenny, J. (2017). Democratizing Digital Design and Manufacturing Using High Performance Cloud Computing: Performance Evaluation and Benchmarking, Transactions of the SME, Journal of Manufacturing Systems, 43(1), 316-326.
62. Jennings, C., Wu, D., & Terpenny, J. (2016). Forecasting Obsolescence Risk and Product Life Cycle with Machine Learning, IEEE Transactions on Components, Packaging, and Manufacturing Technology, 6(9), 1428-1439. 
63. Wu, D., Rosen, D.W., Panchal, J., & Schaefer, D. (2016). Understanding Communication and Collaboration in Social Product Development through Social Network Analysis, Transactions of the ASME, Journal of Computing and Information Science in Engineering, 16(1), 011001. 
64. Wu, D., Terpenny, J., & Gentzsch, W. (2015). Economic Benefit Analysis of Cloud-Based Design, Engineering Analysis, and Manufacturing, Transactions of the ASME, Journal of Manufacturing Science and Engineering, 137(4), 040903. 
65. Wu, D., Rosen, D.W., & Schaefer, D. (2015). Scalability Planning for Cloud-Based Manufacturing Systems, Transactions of the ASME, Journal of Manufacturing Science and Engineering, 137(4), 040911. 
66. Wu, D., Rosen, D.W., Wang, L., & Schaefer, D. (2015). Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation, Computer-Aided Design, 59(1), 1-14. 
67. Wu, D., Thames, J.L., Rosen, D.W., & Schaefer, D. (2013). Enhancing the Product Realization Process with Cloud-Based Design and Manufacturing Systems, Transactions of the ASME, Journal of Computing and Information Science in Engineering, 13(4), 041004-041004-14. 
68. Wu, D., Greer, M.J., Rosen, D.W., & Schaefer, D. (2013). Cloud Manufacturing: Strategic Vision and State-of-the-Art, Transactions of the SME, Journal of Manufacturing Systems, 32(4), 564-579. 
69. Wu, D., Zhang, L., Jiao, J., & Lu, R. (2013). SysML-based Design Chain Information Modeling for Variety Management in Production Reconfiguration, Journal of Intelligent Manufacturing, 24(3), 575-596.