Publications

1.Zhang Y. (2019), "Three-Dimensional-Printing for Microfluidics or the Other Way Round?" International Journal of Bioprinting 5(2), 192. doi: 10.18063/ijbv5i2.192​ 
2.Liu D., & Tuan T. (2019), "The ejecting lamella of impacting compound droplets". Applied Physics Letters, 115(7), 073702. doi: 10.1063/1.5097370
3.Ng W. L., & Yeong W. Y. (2019), "The future of skin toxicology testing - 3D bioprinting meets microfluidics". International Journal of Bioprinting 5(2.1). doi:10.18063/ijb.v5i2.1.237
4.Choong Y. Y. C., Maleksaeedi S., Eng H., Yu S., Wei J., & Su P. C. (2019), "High speed 4D printing of shape memory polymers with nanosilica". Applied Materials Today, 18, 100515. doi:10.1016/j.apmt.2019.100515
5.Ng W. L., Lee J. M., Zhou M., Chen Y.-W., Lee K.-X. A., Yeong W. Y., & Shen Y.-F. (2020), "Vat polymerization-based bioprinting – process, materials, applications and regulatory challenges" Biofabrication, 12(2), 022001. doi:10.1088/1758-5090/ab6034
6.Ng W.L., Chan A., Ong Y.S., & Chua C.K. (2020), "Deep learning for fabrication and maturation of 3D bioprinted tissues and organs". Virtual and Physical Prototyping, 15(3), 340-358. doi: 10.1080/17452759.2020.1771741
7.Zhang W., Ma Y., Zheng J., & Allen W. J. (2020), "Tetrahedral Mesh Deformation with Positional Constraints". ​Computer Aided Geometric Design, 81, 101909. doi: 10.1016/j.cagd.2020.101909
8.Chin S.Y., Dikshit V., Priyadarshini B.M., & Zhang, Y. (2020), "Powder-based 3D Printing for the Fabrication of Device with Micro and Mesoscale Features". Micromachines, 11(7), 658. doi: 10.3390/mi11070658
9.Choong Y.Y.C., Tan H.W., Patel D.C., Choong W.T.N., Chen C.-H., Low H.Y., Tan M.J., Patel C.D., & Chua C.K. (2020), "The global rise of 3D printing during the COVID-19 pandemic". Nature Review Materials, 5(9), 637-639. doi: 10.1038/s41578-020-00234-3
10.Priyadarshini B.M., Dikshit V., & Zhang Y. (2020), "3D printed bioreactors for in vitro modeling and analysis". International Journal of Bioprinting, 6(4), 269. doi: 10.18063/ijb.v6i4.267
11.Cai C., Tey W​. S., Chen J., Zhu W., Liu X., Liu T., Zhao L., & Zhou K. (2020), "Comparative study on 3D printing of polyamide 12 by selective laser sintering and multi jet fusion". Journal of Materials Processing Technology, 288, 116882. doi:10.1016/j.jmatprotec.2020.116882
12.Liu D., & Tran T. (2020), "Size-dependent spontaneous oscillations of Leidenfrost droplets". Journal of Fluid Mechanics, 902, A21. doi: 10.1016/j.jmatprotec.2020.116882
13.Xu D., Zheng M., Jiang L., Gu C., Tan R., & Cheng P. (2020), "Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference". IEEE Internet of Things Journal, 7(10). 9540-9551. doi: 10.1109/JIOT.2020.2983278
14.Kumar P., Jayaraj R., Suryawanshi J., Satwik U.R., McKinnell J., & Ramamurty U. (2020), "Fatigue strength of additively manufactured 316L austenitic stainless steel". Acta Materialia, 199, 225-239. doi: 10.1016/j.actamat.2020.08.033
15.Liu D., Nguyen T.B., Nguyen N.V., & Tran T. (2020), "Sailing Droplets in Superheated Granular Layer". Physical Review Letters, 125(16), 168002. doi: 10.1103/PhysRevLett.125.168002
16.Lee J.M., Suen S.K.Q., Ng W.L., Ma W.C., & Yeong W.Y. (2020), "Bioprinting of Collagen: Considerations, Potentials and Applications". Macromolecular Bioscience, 21(1), 2000280. doi: 10.1002/mabi.202000280
17.Breier, J., Baldwin, A., Balinsky, H., & Liu, Y. (2020). "Risk Management Framework for Machine Learning Security". arXiv preprint arXiv:2012.04884.
 

Book Chapters

1.Choong Y.Y.C., Chua K.H.G., & Wong C.H. (2020, October) "Control of process parameters for directed energy deposition of PH15-5 stainless steel parts". In Industry 4.0-Shaping the Future of the Digital World: Proceedings of the 2nd International Conference on Sustainable Smart Manufacturing (S2M 2019), 9-11 April 2019, Manchester, UK (p.142). CRC Press. doi:10.1201/9780367823085-26

 

18.Ng, W. L., Ayi, T. C., Liu, Y.-C., Sing, S. L., Yeong, W. Y., & Tan, B.-H. (2021), "Fabrication and Characterization of 3D Bioprinted Triple-layered Human Alveolar Lung Models". International Journal of Bioprinting, 7(2). doi:10.18063/ijb.v7i2.332
19.Pengfei Tan, Fei Shen, Wei Shian Tey, Lihua Zhao, Kun Zhou, (2021). "A numerical study on the packing quality of fibre/polymer composite powder for powder bed fusion additive manufacturing". Virtual and Physical Prototyping, 16(S1-S18).doi:10.1080/17452759.2021.1922965
20.Kaijuan Chen, How Wei Benjamin Teo, Wei Rao, Guozheng Kang, Kun Zhou, Jun Zeng, Hejun Du, (2021). "Experimental and modelling investigation on the viscoelastic-viscoplastic deformation behavior of polyamide 12 printed by Multi Jet Fusion", International Journal of Plasticity, 143(1-27). doi.org/10.1016/j.ijplas.2021.103029
21.Joyce Xin-Yan Lim & Quang-Cuong Pham (2021). "Automated post-processing of 3D-printed parts: artificial powdering for deep classification and localisation", Virtual and Physical Prototyping, 16(3). doi: 10.1080/17452759.2021.1927762
22.Tey, W. S., Cai, C., & Zhou, K. (2021). A Comprehensive Investigation on 3D Printing of Polyamide 11 and Thermoplastic Polyurethane via Multi Jet Fusion, Polymers, 13(13), 2139. doi:10.3390/polym13132139
23.H. Kong et al., "EDLAB: A Benchmark for Edge Deep Learning Accelerators," in IEEE Design & Test, doi:10.1109/MDAT.2021.3095215.
24.Vo, Q., & Tran, T. (2021). Mediation of lubricated air films using spatially periodic dielectrophoretic effect. Nature Communications, 12(1). doi:10.1038/s41467-021-24534-6
25.Liu, X., Tey, W. S., Choo, J. Y. C., Chen, J., Tan, P., Cai, C., … Zhou, K. (2021). Enhancing the mechanical strength of Multi Jet Fusion–printed polyamide 12 and its glass fiber-reinforced composite via high-temperature annealing, Additive Manufacturing, 46, 102205. doi:10.1016/j.addma.2021.102205
26.Chen, J., Liu, X., Tian, Y., Zhu, W., Yan, C., Shi, Y., … Zhou, K. (2021). "3D-Printed Anisotropic Polymer Materials for Functional Applications". Advanced Materials, 34(5), 2102877. doi:10.1002/adma.202102877
27.Ng WL, Huang X, Shkolnikov V, et al., (2022). "Controlling Droplet Impact Velocity and Droplet Volume: Key Factors to Achieving High Cell Viability in Sub-Nanoliter Droplet-based Bioprinting". Int J Bioprint. http://doi.org/10.18063/ijb. v8i1.424
28.
Di Liu, Hao Kong, Xiangzhong Luo, Weichen Liu, Ravi Subramaniam. (2021). "Bringing AI to edge: From deep learning’s perspective". Neurocomputing, ISSN 0925-2312. doi:https://doi.org/10.1016/j.neucom.2021.04.141.
29.Benjamin Teo, H. W., Chen, K., Tran, V. T., Du, H., Zeng, J., & Zhou, K. (2021). Non-isothermal crystallization behaviour of polyamide 12 analogous to multi-jet fusion additive manufacturing process. Polymer, 124256. doi:10.1016/j.polymer.2021.124256
30.Chua, P. C., Moon, S. K., Ng, Y. T., and Ng, H. Y. (2021). A Surrogate Model to Predict Production Performance in Digital Twin-Based Smart Manufacturing. ASME. J. Comput. Inf. Sci. Eng. June 2022; 22(3): 031007. doi: 10.1115/1.4053038


Conference Proceedings

1.Yang, J. Q., Zhou, S., Van Le, D., Ho, D., & Tan, R. (2021). Improving Quality Control with Industrial AIoT at HP Factories: Experiences and Learned Lessons. 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). https://doi.org/10.1109/secon52354.2021.9491592
2.S. Huai, L. Zhang, D. Liu, W. Liu and R. Subramaniam, "ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems," 2021 58th ACM/IEEE Design Automation Conference (DAC), 2021, pp. 151-156, doi: 10.1109/DAC18074.2021.9586309

 

31.Chen, J., Zhao, L., Zhou, K. (2022). Improvement in the mechanical performance of Multi Jet Fusion-printed aramid fiber/polyamide 12 composites by fiber surface modification. Additive Manufacturing, 51, 102576. doi:10.1016/j.addma.2021.102576
32.Chen, J., Tan, P., Liu, X., Tey, W. S., Ong, A., Zhao, L., & Zhou, K. (2022). High-strength light-weight aramid fibre/polyamide 12 composites printed by Multi Jet Fusion. Virtual and Physical Prototyping, 17(2), 295–307. doi:10.1080/17452759.2022.2036931
33.Chen, K., Koh, Z. H., Le, K. Q., Teo, H. W. B., Zheng, H., Zeng, J., … Du, H. (2022). Effects of build positions on the thermal history, crystallization, and mechanical properties of polyamide 12 parts printed by Multi Jet Fusion. Virtual and Physical Prototyping, 1–18. doi:10.1080/17452759.2022.2046478
34.Liu, X., Tey, W. S., Tan, P., Leong, K. K., Chen, J., Tian, Y., … Zhou, K. (2022). Effect of the fibre length on the mechanical anisotropy of glass fibre–reinforced polymer composites printed by Multi Jet Fusion. Virtual and Physical Prototyping, 17(3), 734–748. doi:10.1080/17452759.2022.2059638
35.S. Zhou, D. V. Le, R. Tan, J. Q. Yang and D. Ho, "Configuration-Adaptive Wireless Visual Sensing System with Deep Reinforcement Learning," in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2022.3175182.
36.Hou, Yanbei and Gao, Ming and An, Ran and Tey, Wei Shian and Li, Boyuan and Chen, Jiayao and Zhao, Lihua and Zhou, Kun, Surface Modification of Oriented Glass Fibers for Improving Mechanical Properties and Flame Retardancy of Powder Bed Fusion–Printed Polyamide 12 Composites.
37.Suntornnond, Ratima and Ng, Wei Long and Huang, Xi and Yeow, Chuen Herh Ethan and Yeong, Wai Yee, "Improving printability of hydrogel-based bio-inks for thermal inkjet bioprinting applications via saponification and heat treatment processes," in J. Mater. Chem. B, doi:10.1039/D2TB00442A.
38.Chen, J., Zhao, L., & Zhou, K. (2022). Multi‐Jet Fusion 3D Voxel Printing of Conductive Elastomers. Advanced Materials, 34(47), 2205909. Portico. https://doi.org/10.1002/adma.202205909
39.Priyadarshini, B. M., Kok, W. K., Dikshit, V., Feng, S., Li, K. H. H., & Zhang, Y. (2022). 3D printing biocompatible materials with Multi Jet Fusion for bioreactor applications. International Journal of Bioprinting, 9(1). https://doi.org/10.18063/ijb.v9i1.623
40.Teo, H. W. B., Tran, V. T., Chen, K., Le, K. Q., Du, H., Zeng, J., & Zhou, K. (2022). Investigation of polyamide 12 isothermal crystallization through the application of the phase‐field model. Polymers for Advanced Technologies, 34(2), 748–757. Portico. https://doi.org/10.1002/pat.5926
41.
Goh, G. D., Lee, J. M., Goh, G. L., Huang, X., Lee, S., & Yeong, W. Y. (2022). Machine Learning for Bioelectronics on Wearable and Implantable Devices: Challenges and Potential. Tissue Engineering Part A, 29(1–2), 20–46. https://doi.org/10.1089/ten.tea.2022.0119
42.Radhakrishnan, J., Kumar, P., Gan, S. S., Bryl, A., McKinnell, J., & Ramamurty, U. (2022). Fatigue resistance of the binder jet printed 17-4 precipitation hardened martensitic stainless steel. Materials Science and Engineering: A, 865, 144451. https://doi.org/10.1016/j.msea.2022.144451
43.Radhakrishnan, J., Kumar, P., Gan, S. S., Bryl, A., McKinnell, J., & Ramamurty, U. (2022). Microstructure and tensile properties of binder jet printed 17–4 precipitation hardened martensitic stainless steel. Materials Science and Engineering: A, 860, 144270. https://doi.org/10.1016/j.msea.2022.144270

Conference Proceedings

1.H. Kong, D. Liu, X. Luo, W. Liu and R. Subramaniam, "HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 2022, pp. 708-713, doi: 10.1109/ASP-DAC52403.2022.9712593
2.Ting Song, X., Chen, C.-H., Kuo, J.-Y., & Patel, C. D. (2022). Parametric design workflow of periodic lattice structures for additive manufacturing: A case study. Materials Today: Proceedings, 70, 554–559. https://doi.org/10.1016/j.matpr.2022.09.557
3.N. Adrian, V. -T. Do and Q. -C. Pham, "DFBVS: Deep Feature-Based Visual Servo," 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico City, Mexico, 2022, pp. 1783-1789, doi: 10.1109/CASE49997.2022.9926560.
4.Pružinec, J., Nguyen, Q. A., Baldwin, A., Griffin, J., & Liu, Y. (2022). KUBO: a framework for automated efficacy testing of anti-virus behavioral detection with procedure-based malware emulation. Proceedings of the 13th International Workshop on Automating Test Case Design, Selection and Evaluation. https://doi.org/10.1145/3548659.3561307
5.Huai, S., Liu, D., Kong, H., Luo, X., Liu, W., Subramaniam, R., Makaya, C., & Lin, Q. (2022). Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems. 2022 IEEE 40th International Conference on Computer Design (ICCD). https://doi.org/10.1109/iccd56317.2022.00097

44.Huai, S., Liu, D., Kong, H., Liu, W., Subramaniam, R., Makaya, C., & Lin, Q. (2022). Latency-Constrained Dnn Architecture Learning for Edge Systems Using Zerorized Batch Normalization. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4186455
45.Lu, N., Tay, H. M., Petchakup, C., He, L., Gong, L., Maw, K. K., Leong, S. Y., Lok, W. W., Ong, H. B., Guo, R., Li, K. H. H., & Hou, H. W. (2023). Label-free microfluidic cell sorting and detection for rapid blood analysis. Lab on a Chip, 23(5), 1226–1257. https://doi.org/10.1039/d2lc00904h
46. Chen, K., Teo, H. W. B., Tian, Y., Wu, S., Kang, G., Zhou, K., Zeng, J., & Du, H. (2023). Effect of build direction on tension–tension low cycle fatigue behavior of polyamide 12 parts printed by Multi Jet fusion. International Journal of Fatigue, 170, 107514. https://doi.org/10.1016/j.ijfatigue.2023.107514
47.Chen, J., Van Le, D., Tan, R., & Ho, D. (2023). NNFacet: Splitting Neural Network for Concurrent Smart Sensors. IEEE Transactions on Mobile Computing, 1–14. https://doi.org/10.1109/tmc.2023.3238342
48.Li, W., Teo, H. W. B., Chen, K., Zeng, J., Zhou, K., & Du, H. (2023). Mesoscale simulations of spherulite growth during isothermal crystallization of polymer melts via an enhanced 3D phase-field model. Applied Mathematics and Computation, 446, 127873. https://doi.org/10.1016/j.amc.2023.127873
49.Hou, Y., Gao, M., Chen, J., Tey, W. S., Chen, M., Zheng, H., Li, B., Zhao, L., & Zhou, K. (2023). Preparation of iron oxide–coated aramid fibres for improving the mechanical performance and flame retardancy of multi jet fusion–printed polyamide 12 composites. Virtual and Physical Prototyping, 18(1). https://doi.org/10.1080/17452759.2023.2171892
50.Kumar, P., Radhakrishnan, J., Gan, S. S., Bryl, A., McKinnell, J., & Ramamurty, U. (2023). Tensile and fatigue properties of the binder jet printed and hot isostatically pressed 316L austenitic stainless steel. Materials Science and Engineering: A, 868, 144766. https://doi.org/10.1016/j.msea.2023.144766
51.Tan, P., Liu, X., Shian Tey, W., Huang, J., & Zhou, K. (2023). Numerical investigation of fiber orientations and homogeneity in powder bed fusion of fiber/polymer composites. Virtual and Physical Prototyping. https://doi.org/10.1080/17452759.2022.2162928
52.Chua, G. A., Ravindran, A., Senga, J. R. L., & Viswanathan, S. (2023). Job scheduling for maximum revenue on uniform, parallel machines with major and minor setups and job splitting. Computers & Industrial Engineering, 178, 109147. https://doi.org/10.1016/j.cie.2023.109147
53.Pootheri, S., Ellam, D., Grübl, T., & Liu, Y. (2023). A Two-Stage Automatic Color Thresholding Technique. Sensors, 23(6), 3361. https://doi.org/10.3390/s23063361
54.Tian, Y., Chen, K., Zheng, H., Kripalani, D. R., Zeng, Z., Jarlöv, A., Chen, J., Bai, L., Ong, A., Du, H., Kang, G., Fang, Q., Zhao, L., Qi, H. J., Wang, Y., & Zhou, K. (2023). Additively Manufactured Dual‐Faced Structured Fabric for Shape‐Adaptive Protection. Advanced Science. Portico. https://doi.org/10.1002/advs.202301567
55.Le, K. Q., Tran, V. T., Chen, K., Teo, H. W. B., Zeng, J., Zhou, K., & Du, H. (2023). Predicting crystallinity of polyamide 12 in multi jet fusion process. Journal of Manufacturing Processes, 99, 1–11. https://doi.org/10.1016/j.jmapro.2023.05.043
56.Shi, H., Jiang, L., Zheng, J., & Zeng, J. (2023). Self-Parameterization Based Multi-Resolution Mesh Convolution Networks. Computer-Aided Design, 162, 103550. https://doi.org/10.1016/j.cad.2023.103550
57.
Kong, H., Liu, D., Huai, S., Luo, X., Subramaniam, R., Makaya, C., Lin, Q., & Liu, W. (2023). EdgeCompress: Coupling Multi-Dimensional Model Compression and Dynamic Inference for EdgeAI. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1–1. https://doi.org/10.1109/tcad.2023.3276938
58.Huang, X., Ng, W. L., & Yeong, W. Y. (2023). Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-023-02167-4
59.Chen, J., An, R., Tey, W. S., Zeng, Q., Zhao, L., & Zhou, K. (2023). In Situ Filler Addition for Homogeneous Dispersion of Carbon Nanotubes in Multi Jet Fusion–Printed Elastomer Composites. Advanced Science, 10(25). Portico. https://doi.org/10.1002/advs.202300593
60.Do, V.-T., & Pham, Q.-C. (2023). Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces. IEEE Robotics and Automation Letters, 8(9), 5552–5559. https://doi.org/10.1109/lra.2023.3296943
61.Lee, J. M., Huang, X., Goh, G. L., Tran, T., & Yeong, W. Y. (2023). Understanding droplet jetting on varying substrate for biological applications. International Journal of Bioprinting, 9(5), 758. https://doi.org/10.18063/ijb.758
62.Chen, M., Hou, Y., An, R., Tey, W. S., Gao, M., Chen, J., Zhao, L., & Zhou, K. (2023). Investigation of the mechanical properties of polyimide fiber/polyamide 12 composites printed by Multi Jet Fusion. Virtual and Physical Prototyping, 18(1). https://doi.org/10.1080/17452759.2023.2246032
63.Ng, W.L., Huang, X., Shkolnikov, V. et al. Polyvinylpyrrolidone-based bioink: influence of bioink properties on printing performance and cell proliferation during inkjet-based bioprinting. Bio-des. Manuf. (2023). https://doi.org/10.1007/s42242-023-00245-3
64.Van Le, D., Yang, J. Q., Zhou, S., Ho, D., & Tan, R. (2023). Design, Deployment, and Evaluation of an Industrial AIoT System for Quality Control at HP Factories. ACM Transactions on Sensor Networks. https://doi.org/10.1145/3618300
65.Huai, S., Kong, H., Luo, X., Liu, D., Subramaniam, R., Makaya, C., Lin, Q., & Liu, W. (2023). On Hardware-Aware Design and Optimization of Edge Intelligence. IEEE Design & Test, 40(6), 149–162. https://doi.org/10.1109/mdat.2023.3307558
66.Huai, S., Kong, H., Luo, X., Li, S., Subramaniam, R., Makaya, C., Lin, Q., & Liu, W. (2023). CRIMP: Compact & Reliable DNN Inference on In-Memory Processing via Crossbar-Aligned Compression and Non-ideality Adaptation. ACM Transactions on Embedded Computing Systems, 22(5s), 1–25. https://doi.org/10.1145/3609115
67.Chua, P. C., Moon, S. K., Ng, Y. T., & Lopez, M. (2023). Strategic Production Process Design With Additive Manufacturing in a Make-to-Stock Environment. Journal of Manufacturing Science and Engineering, 145(11). https://doi.org/10.1115/1.4063285
68.Tan, P., Zhou, M., Tang, C., Su, Y., Qi, H. J., & Zhou, K. (2023). Multiphysics modelling of powder bed fusion for polymers. Virtual and Physical Prototyping, 18(1). https://doi.org/10.1080/17452759.2023.2257191
69.Huai, S., Kong, H., Li, S., Luo, X., Subramaniam, R., Makaya, C., Lin, Q., & Liu, W. (2023). EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems. IEEE Embedded Systems Letters, 1–1. https://doi.org/10.1109/les.2023.3321599
70.Chiang, P.-J., Peter Davidson, K., Wheeler, J. M., Ong, A., Erickson, K., & Seita, M. (2023). Site-specific alloying through binder jet 3D printing. Materials & Design, 112384. https://doi.org/10.1016/j.matdes.2023.112384

Conference Proceedings

1.Huai, S., Liu, D., Luo, X., Chen, H., Liu, W., & Subramaniam, R. (2023). Crossbar-Aligned & Integer-Only Neural Network Compression for Efficient in-Memory Acceleration. Proceedings of the 28th Asia and South Pacific Design Automation Conference. https://doi.org/10.1145/3566097.3567856