In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
Tóm tắt
In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.
Tài liệu tham khảo
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, e00938–e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Amirzadeh, A., Raessi, M., & Chandra, S. (2013). Producing molten metal droplets smaller than the nozzle diameter using a pneumatic drop-on-demand generator. Experimental Thermal and Fluid Science, 47, 26–33. https://doi.org/10.1016/j.expthermflusci.2012.12.006
Beck, V. A., Watkins, N. N., Ashby, A. S., Martin, A. A., Paul, P. H., Jeffries, J. R., & Pascall, A. J. (2020). A combined numerical and experimental study to elucidate primary breakup dynamics in liquid metal droplet-on-demand printing. Physics of Fluids, 32, 112020. https://doi.org/10.1063/5.0029438
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. Springer.
Castrejón-Pita, J. R., Martin, G. D., Hoath, S. D., & Hutchings, I. M. (2008). A simple large-scale droplet generator for studies of inkjet printing. Review of Scientific Instruments, 79, 075108. https://doi.org/10.1063/1.2957744
Chang, T., Mukherjee, S., Watkins, N. N., Stobbe, D. M., Mays, O., Baluyot, E. V., Pascall, A. J., & Tringe, J. W. (2020). In-situ monitoring for liquid metal jetting using a millimeter-wave impedance diagnostic. Scientific Reports, 10, 22325. https://doi.org/10.1038/s41598-020-79266-2
Chang, T., Mukherjee, S., Watkins, N. N., Benavidez, E., Gilmore, A. M., Pascall, A. J., & Stobbe, D. M. (2021). Millimeter-wave electromagnetic monitoring for liquid metal droplet-on-demand printing. Journal of Applied Physics, 130, 144502. https://doi.org/10.1063/5.0065989
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017). Deep learning: A primer for radiologists. Radiographics, 37, 2113–2131. https://doi.org/10.1148/rg.2017170077
Cheng, S. X., Li, T., & Chandra, S. (2005). Producing molten metal droplets with a pneumatic droplet-on-demand generator. Journal of Materials Processing Technology, 159, 295–302. https://doi.org/10.1016/j.jmatprotec.2004.05.016
Dobson, A. J., & Barnett, A. G. (2018). An introduction to generalized linear models. CRC Press.
Gaikwad, A., Giera, B., Guss, G. M., Forien, J.-B., Matthews, M. J., & Rao, P. (2020). Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion—a single-track study. Additive Manufacturing, 36, 101659. https://doi.org/10.1016/j.addma.2020.101659
Gerdes, B., Zengerle, R., Koltay, P., & Riegger, L. (2018). Direct printing of miniscule aluminum alloy droplets and 3D structures by StarJet technology. Journal of Micromechanics and Microengineering, 28, 074003. https://doi.org/10.1088/1361-6439/aab928
Han, Y., & Dong, J. (2017a). High-resolution direct printing of molten-metal using electrohydrodynamic jet plotting. Manufacturing Letters, 12, 6–9. https://doi.org/10.1016/j.mfglet.2017.04.001
Han, Y., & Dong, J. (2017b). High-resolution electrohydrodynamic (EHD) direct printing of molten metal. Procedia Manufacturing, 10, 845–850. https://doi.org/10.1016/j.promfg.2017.07.070
Idell, Y., Watkins, N., Pascall, A., Jeffries, J., & Blobaum, K. (2019). Microstructural characterization of pure tin produced by the drop-on-demand technique of liquid metal jetting. Metallurgical and Materials Transactions A, 50, 4000–4005. https://doi.org/10.1007/s11661-019-05357-z
Imani, F., Gaikwad, A., Montazeri, M., Rao, P., Yang, H., & Reutzel, E. (2018). Process mapping and in-process monitoring of porosity in laser powder bed fusion using layerwise optical imaging. Journal of Manufacturing Science and Engineering, 10(1115/1), 4040615.
Jin, Z., Zhang, Z., Demir, K., & Gu, G. X. (2020). Machine learning for advanced additive manufacturing. Matter, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023
Kumar, A., & Maji, K. (2020). Selection of process parameters for near-net shape deposition in wire arc additive manufacturing by genetic algorithm. Journal of Materials Engineering and Performance, 29, 3334–3352. https://doi.org/10.1007/s11665-020-04847-1
Larsen, S., & Hooper, P. A. (2022). Deep semi-supervised learning of dynamics for anomaly detection in laser powder bed fusion. Journal of Intelligent Manufacturing, 33, 457–471. https://doi.org/10.1007/s10845-021-01842-8
Lee, T.-M., Kang, T. G., Yang, J. S., Jo, J., Kim, K.-Y., Choi, B.-O., & Kim, D.-S. (2008a). Gap adjustable molten metal DoD inkjet system with cone-shaped piston head. Journal of Manufacturing Science and Engineering, 10(1115/1), 2917367.
Lee, T., Kang, T. G., Yang, J., Jo, J., Kim, K., Choi, B., & Kim, D. (2008b). Drop-on-demand solder droplet jetting system for fabricating microstructure. IEEE Transactions on Electronics Packaging Manufacturing, 31, 202–210. https://doi.org/10.1109/TEPM.2008.926285
Lee, X. Y., Saha, S. K., Sarkar, S., & Giera, B. (2020). Automated detection of part quality during two-photon lithography via deep learning. Additive Manufacturing, 36, 101444. https://doi.org/10.1016/j.addma.2020.101444
Li, H., Mei, S., Wang, L., Gao, Y., & Liu, J. (2014). Splashing phenomena of room temperature liquid metal droplet striking on the pool of the same liquid under ambient air environment. International Journal of Heat and Fluid Flow, 47, 1–8. https://doi.org/10.1016/j.ijheatfluidflow.2014.02.002
Luo, J., Qi, L.-H., Zhou, J.-M., Hou, X.-H., & Li, H.-J. (2012). Modeling and characterization of metal droplets generation by using a pneumatic drop-on-demand generator. Journal of Materials Processing Technology, 212, 718–726. https://doi.org/10.1016/j.jmatprotec.2011.04.014
Luo, J., Qi, L., Tao, Y., Ma, Q., & Visser, C. W. (2016a). Impact-driven ejection of micro metal droplets on-demand. International Journal of Machine Tools and Manufacture, 106, 67–74. https://doi.org/10.1016/j.ijmachtools.2016.04.002
Luo, Z., Wang, X., Lingyun, W., Sun, D., & Li, Z. (2016b). Drop-on-demand electromagnetic printing of metallic droplets. Materials Letters. https://doi.org/10.1016/j.matlet.2016.11.021
Medsker, L. R., & Jain, L. (2001). Recurrent neural networks. Design and Applications, 5, 64–67. https://doi.org/10.1201/9781003040620
Meng, L., McWilliams, B., Jarosinski, W., Park, H.-Y., Jung, Y.-G., Lee, J., & Zhang, J. (2020). Machine learning in additive manufacturing: A review. JOM Journal of the Minerals Metals and Materials Society, 72, 2363–2377. https://doi.org/10.1007/s11837-020-04155-y
Montazeri, M., Nassar, A. R., Stutzman, C. B., & Rao, P. (2019). Heterogeneous sensor-based condition monitoring in directed energy deposition. Additive Manufacturing, 30, 100916. https://doi.org/10.1016/j.addma.2019.100916
Nussbaumer, H. J. (1981). The fast Fourier transform. Fast Fourier Transform and Convolution Algorithms. https://doi.org/10.1007/978-3-662-00551-4_4
Pasandideh-Fard, M., Bhola, R., Chandra, S., & Mostaghimi, J. (1998). Deposition of tin droplets on a steel plate: Simulations and experiments. International Journal of Heat and Mass Transfer, 41, 2929–2945. https://doi.org/10.1016/S0017-9310(98)00023-4
Poozesh, S., Saito, K., Akafuah, N. K., & Graña-Otero, J. (2016). Comprehensive examination of a new mechanism to produce small droplets in drop-on-demand inkjet technology. Applied Physics A, 122, 110. https://doi.org/10.1007/s00339-016-9630-9
Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., Simpson, T. W., Lu, Y., & Tang, Q. (2022). Research and application of machine learning for additive manufacturing. Additive Manufacturing, 52, 102691. https://doi.org/10.1016/j.addma.2022.102691
Radovic, M., Ghalwash, M., Filipovic, N., & Obradovic, Z. (2017). Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics, 18, 9. https://doi.org/10.1186/s12859-016-1423-9
Rao, P., Bukkapatnam, S., Beyca, O., Kong, Z. J., & Komanduri, R. (2014). Real-time identification of incipient surface morphology variations in ultraprecision machining process. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4026210
Rifkin, R., & Lippert, R., (2007). Notes on regularized least squares. Retrieved from http://hdl.handle.net/1721.1/37318.
Rosenfeld, A. (1970). Connectivity in digital pictures. Journal of the Association for Computing Machinery, 17(1), 146–160. https://doi.org/10.1145/321556.321570
Sanaat, A., Shiri, I., Ferdowsi, S., Arabi, H., & Zaidi, H. (2022). Robust-deep: A method for increasing brain imaging datasets to improve deep learning models’ performance and robustness. Journal of Digital Imaging. https://doi.org/10.1007/s10278-021-00536-0
Simonelli, M., Aboulkhair, N., Rasa, M., East, M., Tuck, C., Wildman, R., Salomons, O., & Hague, R. (2019). Towards digital metal additive manufacturing via high-temperature drop-on-demand jetting. Additive Manufacturing, 30, 100930. https://doi.org/10.1016/j.addma.2019.100930
Sohn, H., & Yang, D. Y. (2005). Drop-on-demand deposition of superheated metal droplets for selective infiltration manufacturing. Materials Science and Engineering: A, 392, 415–421. https://doi.org/10.1016/j.msea.2004.09.049
Song, M., Kartawira, K., Hillaire Keith, D., Li, C., Eaker Collin, B., Kiani, A., Daniels Karen, E., & Dickey Michael, D. (2020). Overcoming Rayleigh-Plateau instabilities: Stabilizing and destabilizing liquid-metal streams via electrochemical oxidation. Proceedings of the National Academy of Sciences, 117, 19026–19032. https://doi.org/10.1073/pnas.2006122117
Stein, S., Zhao, W., Hentschel, O., Bickmann, C., Roth, S., Frick, T., & Schmidt, M. (2018). Flight trajectory analysis of CuSn-droplets generated by laser drop on demand jetting, using stereoscopic high-speed imaging. Optics Express, 26, 10968–10980. https://doi.org/10.1364/oe.26.010968
Sukhotskiy, V., Karampelas, I., Garg, G., Verma, A., Tong, M., Vader, S., Vader, Z., & Furlani, E. (2017). Magnetohydrodynamic drop-on-demand liquid metal 3D printing. Proceedings of the Solid Freeform Fabrication. https://doi.org/10.26153/tsw/16905
Sukhotskiy, V., Tawil, K., & Einarsson, E. (2021). Printability regimes of pure metals using contactless magnetohydrodynamic drop-on-demand actuation. Physics of Fluids, 33, 053303. https://doi.org/10.1063/5.0050354
Vaissier, B., Pernot, J.-P., Chougrani, L., & Véron, P. (2019). Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing. Computer-Aided Design, 110, 11–23. https://doi.org/10.1016/j.cad.2018.12.007
Wang, C.-H., Tsai, H.-L., Wu, Y.-C., & Hwang, W.-S. (2016). Investigation of molten metal droplet deposition and solidification for 3D printing techniques. Journal of Micromechanics and Microengineering, 26, 095012. https://doi.org/10.1088/0960-1317/26/9/095012
Wang, T., Kwok, T.-H., & Zhou, C. (2017). In-situ droplet inspection and control system for liquid metal Jet 3D printing process. Procedia Manufacturing, 10, 968–981. https://doi.org/10.1016/j.promfg.2017.07.088
Wang, T., Kwok, T.-H., Zhou, C., & Vader, S. (2018). In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of Manufacturing Systems, 47, 83–92. https://doi.org/10.1016/j.jmsy.2018.04.003
Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https://doi.org/10.1016/j.addma.2020.101538
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., & Chen, S. (2021). Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01725-4
Yuan, B., Guss, G. M., Wilson, A. C., Hau-Riege, S. P., DePond, P. J., McMains, S., Matthews, M. J., & Giera, B. (2018). Machine-learning-based monitoring of laser powder bed fusion. Advanced Materials Technologies, 3, 1800136. https://doi.org/10.1002/admt.201800136
Zhong, S.-Y., Qi, L.-H., Xiong, W., Luo, J., & Xu, Q.-X. (2017). Research on mechanism of generating aluminum droplets smaller than the nozzle diameter by pneumatic drop-on-demand technology. The International Journal of Advanced Manufacturing Technology, 93, 1771–1780. https://doi.org/10.1007/s00170-017-0484-x