{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T20:50:29Z","timestamp":1767905429708,"version":"3.49.0"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T00:00:00Z","timestamp":1721520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T00:00:00Z","timestamp":1721520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["16\/RC\/3872"],"award-info":[{"award-number":["16\/RC\/3872"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["16\/RC\/3872"],"award-info":[{"award-number":["16\/RC\/3872"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["17\/CDA\/4695"],"award-info":[{"award-number":["17\/CDA\/4695"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["12\/RC\/2289_P2"],"award-info":[{"award-number":["12\/RC\/2289_P2"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control for the temperatures in the extruder of a Material Extrusion AM system, specifically a Big Area Additive Manufacturing (BAAM) system. Previous approaches for temperature control in AM either require the knowledge of exact model parameters, or involve discretisation of the state and action spaces to employ traditional data-driven control techniques. On the other hand, modern algorithms that can handle continuous state and action space problems require a large number of hyperparameter tuning to ensure good performance. In this work, we circumvent the above limitations by making use of a state space temperature model while focusing on both model-based and data-driven methods. We adopt the Linear Quadratic Tracking (LQT) framework and utilise the quadratic structure of the value function in the model-based analytical solution to produce a data-driven approximation formula for the optimal controller. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a BAAM system and perform an in-depth comparison of the performance of these methods. We find that we can learn an effective controller using solely simulated input\u2013output process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the often intricate and complicated process model. We believe this result is an important step towards achieving autonomous intelligent manufacturing.<\/jats:p>","DOI":"10.1007\/s10845-024-02428-w","type":"journal-article","created":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T07:01:16Z","timestamp":1721545276000},"page":"4549-4565","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data-driven linear quadratic tracking based temperature control of a big area additive manufacturing system"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4866-5525","authenticated-orcid":false,"given":"Eleni","family":"Zavrakli","sequence":"first","affiliation":[]},{"given":"Andrew","family":"Parnell","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Dickson","sequence":"additional","affiliation":[]},{"given":"Subhrakanti","family":"Dey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,21]]},"reference":[{"key":"2428_CR1","unstructured":"(2021) Additive manufacturing \u2013 General principles \u2013 Fundamentals and vocabulary. Standard, International Organization for Standardization. https:\/\/www.iso.org\/standard\/74514.html."},{"key":"2428_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2021.105272","volume":"131","author":"M Alicastro","year":"2021","unstructured":"Alicastro, M., Ferone, D., Festa, P., Fugaro, S., & Pastore, T. (2021). A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems. Computers & Operations Research, 131, 105272.","journal-title":"Computers & Operations Research"},{"key":"2428_CR3","volume-title":"Optimal control: linear quadratic methods","author":"BD Anderson","year":"2007","unstructured":"Anderson, B. D., & Moore, J. B. (2007). Optimal control: linear quadratic methods. Massachusetts: Courier Corporation."},{"issue":"6","key":"2428_CR4","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/TAC.1971.1099818","volume":"16","author":"M Athans","year":"1971","unstructured":"Athans, M. (1971). The role and use of the stochastic linear-quadratic-gaussian problem in control system design. IEEE transactions on automatic control, 16(6), 529\u2013552.","journal-title":"IEEE transactions on automatic control"},{"issue":"3731","key":"2428_CR5","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1126\/science.153.3731.34","volume":"153","author":"R Bellman","year":"1966","unstructured":"Bellman, R. (1966). Dynamic programming. Science, 153(3731), 34\u201337.","journal-title":"Science"},{"issue":"2","key":"2428_CR6","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.bushor.2011.11.003","volume":"55","author":"B Berman","year":"2012","unstructured":"Berman, B. (2012). 3-d printing: The new industrial revolution. Business Horizons, 55(2), 155\u2013162.","journal-title":"Business Horizons"},{"key":"2428_CR7","unstructured":"Bertsekas, D. (2012). Dynamic programming and optimal control: Volume I, vol.\u00a01 (Athena scientific)"},{"key":"2428_CR8","volume-title":"Neuro-dynamic programming","author":"DP Bertsekas","year":"1996","unstructured":"Bertsekas, D. P., & Tsitsiklis, J. N. (1996). Neuro-dynamic programming. The Netherlands: Athena Scientific."},{"key":"2428_CR9","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.engstruct.2018.11.045","volume":"180","author":"C Buchanan","year":"2019","unstructured":"Buchanan, C., & Gardner, L. (2019). Metal 3d printing in construction: A review of methods, research, applications, opportunities and challenges. Engineering Structures, 180, 332\u2013348.","journal-title":"Engineering Structures"},{"key":"2428_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaerosci.2021.105765","volume":"154","author":"P Byrley","year":"2021","unstructured":"Byrley, P., Boyes, W. K., Rogers, K., & Jarabek, A. M. (2021). 3d printer particle emissions: Translation to internal dose in adults and children. Journal of aerosol science, 154, 105765.","journal-title":"Journal of aerosol science"},{"issue":"4","key":"2428_CR11","doi-asserted-by":"publisher","first-page":"3758","DOI":"10.1109\/LRA.2019.2929987","volume":"4","author":"A Carron","year":"2019","unstructured":"Carron, A., Arcari, E., Wermelinger, M., Hewing, L., Hutter, M., & Zeilinger, M. N. (2019). Data-driven model predictive control for trajectory tracking with a robotic arm. IEEE Robotics and Automation Letters, 4(4), 3758\u20133765.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2428_CR12","unstructured":"Chang, C.Y., Feng, Y.W., Rawat, T.S., Chen, S.W., & Lin, A.S. (2024). Optimization of laser annealing parameters based on bayesian reinforcement learning. Journal of Intelligent Manufacturing pp. 1\u201314"},{"key":"2428_CR13","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1016\/j.jmsy.2022.11.008","volume":"65","author":"J Chung","year":"2022","unstructured":"Chung, J., Shen, B., Law, A. C. C., & Kong, Z. J. (2022). Reinforcement learning-based defect mitigation for quality assurance of additive manufacturing. Journal of Manufacturing Systems, 65, 822\u2013835.","journal-title":"Journal of Manufacturing Systems"},{"issue":"17","key":"2428_CR14","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1002\/pen.760221707","volume":"22","author":"M Costin","year":"1982","unstructured":"Costin, M., Taylor, P., & Wright, J. (1982). On the dynamics and control of a plasticating extruder. Polymer Engineering & Science, 22(17), 1095\u20131106.","journal-title":"Polymer Engineering & Science"},{"issue":"3","key":"2428_CR15","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1109\/TAC.2019.2959924","volume":"65","author":"C De Persis","year":"2019","unstructured":"De Persis, C., & Tesi, P. (2019). Formulas for data-driven control: Stabilization, optimality, and robustness. IEEE Transactions on Automatic Control, 65(3), 909\u2013924.","journal-title":"IEEE Transactions on Automatic Control"},{"key":"2428_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2023.103556","volume":"71","author":"S Dharmadhikari","year":"2023","unstructured":"Dharmadhikari, S., Menon, N., & Basak, A. (2023). A reinforcement learning approach for process parameter optimization in additive manufacturing. Additive Manufacturing, 71, 103556.","journal-title":"Additive Manufacturing"},{"key":"2428_CR17","doi-asserted-by":"crossref","unstructured":"Dharmawan, A.G., Xiong, Y., Foong, S., & Soh, G.S. (2020). 2020 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2020), pp. 4030\u20134036","DOI":"10.1109\/ICRA40945.2020.9197222"},{"key":"2428_CR18","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.promfg.2017.07.148","volume":"11","author":"UM Dilberoglu","year":"2017","unstructured":"Dilberoglu, U. M., Gharehpapagh, B., Yaman, U., & Dolen, M. (2017). The role of additive manufacturing in the era of industry 4.0. Procedia Manufacturing, 11, 545\u2013554.","journal-title":"Procedia Manufacturing"},{"issue":"1","key":"2428_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1115\/1.1344898","volume":"123","author":"C Doumanidis","year":"2001","unstructured":"Doumanidis, C., & Kwak, Y. M. (2001). Geometry modeling and control by infrared and laser sensing in thermal manufacturing with material deposition. Journal of Manufacturing Science and Engineering, 123(1), 45\u201352.","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"2428_CR20","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1023\/A:1008922709029","volume":"9","author":"C Egresits","year":"1998","unstructured":"Egresits, C., Monostori, L., & Horny\u00e1k, J. (1998). Multistrategy learning approaches to generate and tune fuzzy control structures and their application in manufacturing. Journal of Intelligent Manufacturing, 9, 323\u2013329.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"5","key":"2428_CR21","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s00170-015-7423-5","volume":"82","author":"MH Farshidianfar","year":"2016","unstructured":"Farshidianfar, M. H., Khajepour, A., & Gerlich, A. (2016). Real-time control of microstructure in laser additive manufacturing. The International Journal of Advanced Manufacturing Technology, 82(5), 1173\u20131186.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2428_CR22","unstructured":"Fazel, M., Ge, R., Kakade, S., & Mesbahi, M. (2018). International conference on machine learning (PMLR), pp. 1467\u20131476"},{"issue":"1","key":"2428_CR23","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s41693-022-00069-0","volume":"6","author":"B Felbrich","year":"2022","unstructured":"Felbrich, B., Schork, T., & Menges, A. (2022). Autonomous robotic additive manufacturing through distributed model-free deep reinforcement learning in computational design environments. Construction Robotics, 6(1), 15\u201337.","journal-title":"Construction Robotics"},{"issue":"4","key":"2428_CR24","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1177\/009524437801000401","volume":"10","author":"D Fingerle","year":"1978","unstructured":"Fingerle, D. (1978). Autogenic melt temperature control system for plastic extrusion. Journal of Elastomers & Plastics, 10(4), 293\u2013310.","journal-title":"Journal of Elastomers & Plastics"},{"key":"2428_CR25","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1016\/j.jmapro.2021.12.061","volume":"75","author":"Y Fu","year":"2022","unstructured":"Fu, Y., Downey, A. R., Yuan, L., Zhang, T., Pratt, A., & Balogun, Y. (2022). Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, 75, 693\u2013710.","journal-title":"Journal of Manufacturing Processes"},{"key":"2428_CR26","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT press."},{"key":"2428_CR27","unstructured":"Gootjes, D. (2017). Applying feedback control to improve 3d printing quality. Master\u2019s thesis, Delf University of Technology."},{"issue":"5","key":"2428_CR28","doi-asserted-by":"publisher","first-page":"3359","DOI":"10.1137\/20M1382386","volume":"59","author":"B Hambly","year":"2021","unstructured":"Hambly, B., Xu, R., & Yang, H. (2021). Policy gradient methods for the noisy linear quadratic regulator over a finite horizon. SIAM Journal on Control and Optimization, 59(5), 3359\u20133391.","journal-title":"SIAM Journal on Control and Optimization"},{"key":"2428_CR29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ins.2012.07.014","volume":"235","author":"ZS Hou","year":"2013","unstructured":"Hou, Z. S., & Wang, Z. (2013). From model-based control to data-driven control: Survey, classification and perspective. Information Sciences, 235, 3\u201335.","journal-title":"Information Sciences"},{"issue":"1","key":"2428_CR30","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0890-6955(95)92626-A","volume":"36","author":"SJ Huang","year":"1996","unstructured":"Huang, S. J., & Chiou, K. C. (1996). The application of neural networks in self-tuning constant force control. International Journal of Machine Tools and Manufacture, 36(1), 17\u201331.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"1","key":"2428_CR31","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/S0890-6955(02)00163-3","volume":"43","author":"D Hu","year":"2003","unstructured":"Hu, D., & Kovacevic, R. (2003). Sensing, modeling and control for laser-based additive manufacturing. International Journal of Machine Tools and Manufacture, 43(1), 51\u201360.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2428_CR32","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1146\/annurev-control-042920-020021","volume":"6","author":"B Hu","year":"2023","unstructured":"Hu, B., Zhang, K., Li, N., Mesbahi, M., Fazel, M., & Ba\u015far, T. (2023). Toward a theoretical foundation of policy optimization for learning control policies. Annual Review of Control, Robotics, and Autonomous Systems, 6, 123\u2013158.","journal-title":"Annual Review of Control, Robotics, and Autonomous Systems"},{"key":"2428_CR33","doi-asserted-by":"crossref","unstructured":"Johannsmeier, L., Gerchow, M., Haddadin, S. (2019) 2019 International Conference on Robotics and Automation (ICRA) (IEEE), pp. 5844\u20135850","DOI":"10.1109\/ICRA.2019.8793542"},{"key":"2428_CR34","doi-asserted-by":"crossref","unstructured":"Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering","DOI":"10.1115\/1.3662552"},{"key":"2428_CR35","doi-asserted-by":"crossref","unstructured":"Kaven, L., Huke, P., G\u00f6ppert, A., & Schmitt, R.H. (2024). Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems. Journal of Intelligent Manufacturing pp. 1\u201320 (2024)","DOI":"10.1007\/s10845-023-02309-8"},{"issue":"4","key":"2428_CR36","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/j.automatica.2014.02.015","volume":"50","author":"B Kiumarsi","year":"2014","unstructured":"Kiumarsi, B., Lewis, F. L., Modares, H., Karimpour, A., & Naghibi-Sistani, M. B. (2014). Reinforcement q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics. Automatica, 50(4), 1167\u20131175.","journal-title":"Automatica"},{"key":"2428_CR37","unstructured":"Konda, V., &\u00a0Tsitsiklis, J. (1999). Actor-critic algorithms. Advances in neural information processing systems 12"},{"key":"2428_CR38","unstructured":"Kruth, J.P., Mercelis, P., Van\u00a0Vaerenbergh, J., & Craeghs, T. (2007). Virtual and Rapid Manufacturing (Crc Press), pp. 521\u2013528"},{"issue":"6","key":"2428_CR39","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MCS.2012.2214134","volume":"32","author":"FL Lewis","year":"2012","unstructured":"Lewis, F. L., Vrabie, D., & Vamvoudakis, K. G. (2012). Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers. IEEE Control Systems Magazine, 32(6), 76\u2013105.","journal-title":"IEEE Control Systems Magazine"},{"key":"2428_CR40","doi-asserted-by":"publisher","first-page":"50119","DOI":"10.1109\/ACCESS.2019.2907287","volume":"7","author":"H Lhachemi","year":"2019","unstructured":"Lhachemi, H., Malik, A., & Shorten, R. (2019). Augmented reality, cyber-physical systems, and feedback control for additive manufacturing: A review. IEEE Access, 7, 50119\u201350135.","journal-title":"IEEE Access"},{"key":"2428_CR41","unstructured":"Li, Y. (2017). Deep reinforcement learning: an overview. arXiv preprint arXiv:1701.07274"},{"key":"2428_CR42","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, D., Silver, Y., & Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv: 1509.02971"},{"key":"2428_CR43","doi-asserted-by":"crossref","unstructured":"Liu, S., Shi, Z., Lin, J., & Yu, H.(2024). A generalisable tool path planning strategy for free-form sheet metal stamping through deep reinforcement and supervised learning. Journal of Intelligent Manufacturing pp. 1\u201327","DOI":"10.1007\/s10845-024-02371-w"},{"key":"2428_CR44","doi-asserted-by":"crossref","unstructured":"Ljung, L. (1998). Signal analysis and prediction (Springer, 1998), pp. 163\u2013173","DOI":"10.1007\/978-1-4612-1768-8_11"},{"key":"2428_CR45","unstructured":"Loffredo, A., May, M.C., Matta, A., &\u00a0Lanza, G. (2023). Reinforcement learning for sustainability enhancement of production lines. Journal of Intelligent Manufacturing pp. 1\u201317 (2023)"},{"key":"2428_CR46","doi-asserted-by":"crossref","unstructured":"Mattera, G., Caggiano, A., & Nele, L. (2024). Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing. Journal of Intelligent Manufacturing pp. 1\u201320.","DOI":"10.1007\/s10845-024-02450-y"},{"key":"2428_CR47","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1007\/s10845-017-1317-3","volume":"30","author":"D McParland","year":"2019","unstructured":"McParland, D., Baron, S., O\u2019Rourke, S., Dowling, D., Ahearne, E., & Parnell, A. (2019). Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (astm f75) using non-parametric bayesian models. Journal of Intelligent Manufacturing, 30, 1259\u20131270.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2428_CR48","first-page":"17","volume":"109","author":"FJ Mercado Rivera","year":"2020","unstructured":"Mercado Rivera, F. J., & Rojas Arciniegas, A. J. (2020). The International Journal of Advanced Manufacturing Technology. Additive manufacturing methods: techniques, materials, and closed-loop control applications, 109, 17\u201331.","journal-title":"Additive manufacturing methods: techniques, materials, and closed-loop control applications"},{"key":"2428_CR49","unstructured":"Mozaffar, M., Ebrahimi, A., & Cao, J. (2020). Toolpath design for additive manufacturing using deep reinforcement learning. arXiv preprint arXiv:2009.14365"},{"key":"2428_CR50","doi-asserted-by":"crossref","unstructured":"Mujtaba, A., Islam, F., Kaeding, P., Lindemann, T., & Gangadhara\u00a0Prusty, B. (2023). Machine-learning based process monitoring for automated composites manufacturing. Journal of Intelligent Manufacturing pp. 1\u201316","DOI":"10.21203\/rs.3.rs-2220331\/v1"},{"issue":"9\u201310","key":"2428_CR51","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1007\/s00170-021-07325-7","volume":"115","author":"V Nasir","year":"2021","unstructured":"Nasir, V., & Sassani, F. (2021). A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges. The International Journal of Advanced Manufacturing Technology, 115(9\u201310), 2683\u20132709.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2428_CR52","volume-title":"Modern control engineering","author":"K Ogata","year":"2010","unstructured":"Ogata, K., et al. (2010). Modern control engineering (Vol. 5). NJ: Prentice hall Upper Saddle River."},{"key":"2428_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102033","volume":"46","author":"F Ogoke","year":"2021","unstructured":"Ogoke, F., & Farimani, A. B. (2021). Thermal control of laser powder bed fusion using deep reinforcement learning. Additive Manufacturing, 46, 102033.","journal-title":"Additive Manufacturing"},{"key":"2428_CR54","doi-asserted-by":"crossref","unstructured":"Pandiyan,V., Cui,D., Richter, R.A., Parrilli, A., & Leparoux, M. (2023). Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework. Journal of Intelligent Manufacturing pp. 1\u201325.","DOI":"10.1007\/s10845-023-02279-x"},{"key":"2428_CR55","doi-asserted-by":"crossref","unstructured":"Parisi, F., Sangiorgio, V., Parisi, N., Mangini, A.M., Fanti, M.P., & Adam, J.M. (2023). A new concept for large additive manufacturing in construction: tower crane-based 3d printing controlled by deep reinforcement learning. Construction Innovation.","DOI":"10.1108\/CI-10-2022-0278"},{"key":"2428_CR56","unstructured":"Patrick, S., Nycz, A., & Noakes, M. (2018) in 2018 International Solid Freeform Fabrication Symposium (University of Texas at Austin)"},{"issue":"4","key":"2428_CR57","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1109\/TCST.2017.2702118","volume":"26","author":"D Piga","year":"2017","unstructured":"Piga, D., Formentin, S., & Bemporad, A. (2017). Direct data-driven control of constrained systems. IEEE Transactions on Control Systems Technology, 26(4), 1422\u20131429.","journal-title":"IEEE Transactions on Control Systems Technology"},{"key":"2428_CR58","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.addma.2018.11.006","volume":"25","author":"A Roschli","year":"2019","unstructured":"Roschli, A., Gaul, K. T., Boulger, A. M., Post, B. K., Chesser, P. C., Love, L. J., Blue, F., & Borish, M. (2019). Designing for big area additive manufacturing. Additive Manufacturing, 25, 275\u2013285.","journal-title":"Additive Manufacturing"},{"key":"2428_CR59","doi-asserted-by":"crossref","unstructured":"Rosolia, U., & Borrelli, F. (2017). Learning model predictive control for iterative tasks. a data-driven control framework. IEEE Transactions on Automatic Control,63(7), 1883\u20131896.","DOI":"10.1109\/TAC.2017.2753460"},{"key":"2428_CR60","doi-asserted-by":"crossref","unstructured":"Ruan, J., Nooning, B., Parkes, I., Blejde, W., Chiu, G., & Jain, N. (2024). Human operator decision support for highly transient industrial processes: a reinforcement learning approach. Journal of Intelligent Manufacturing pp. 1\u201316 (2024)","DOI":"10.1007\/s10845-023-02295-x"},{"issue":"2","key":"2428_CR61","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/TCST.2017.2781653","volume":"27","author":"PM Sammons","year":"2019","unstructured":"Sammons, P. M., Gegel, M. L., Bristow, D. A., & Landers, R. G. (2019). Repetitive process control of additive manufacturing with application to laser metal deposition. IEEE Transactions on Control Systems Technology, 27(2), 566\u2013575.","journal-title":"IEEE Transactions on Control Systems Technology"},{"key":"2428_CR62","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv: 1707.06347 (2017)"},{"key":"2428_CR63","doi-asserted-by":"publisher","DOI":"10.1109\/9780470544785","volume-title":"Handbook of learning and approximate dynamic programming","author":"J Si","year":"2004","unstructured":"Si, J., Barto, A. G., Powell, W. B., & Wunsch, D. (2004). Handbook of learning and approximate dynamic programming (Vol. 2). London: John Wiley & Sons."},{"key":"2428_CR64","doi-asserted-by":"crossref","unstructured":"Stoyanov, S., & Bailey, C. (2017). 2017 40th international spring seminar on electronics technology (ISSE) (IEEE), pp. 1\u20136.","DOI":"10.1109\/ISSE.2017.8000936"},{"key":"2428_CR65","unstructured":"Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. Cambridge, MA: MIT press."},{"key":"2428_CR66","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1016\/j.jmapro.2023.05.048","volume":"99","author":"K Taherkhani","year":"2023","unstructured":"Taherkhani, K., Ero, O., Liravi, F., Toorandaz, S., & Toyserkani, E. (2023). On the application of in-situ monitoring systems and machine learning algorithms for developing quality assurance platforms in laser powder bed fusion: A review. Journal of Manufacturing Processes, 99, 848\u2013897.","journal-title":"Journal of Manufacturing Processes"},{"key":"2428_CR67","doi-asserted-by":"crossref","unstructured":"Tang, L., & Landers, R.G. (2010). Melt pool temperature control for laser metal deposition processes-part i: Online temperature control. Journal of Manufacturing Science and Engineering132","DOI":"10.1115\/1.4000882"},{"key":"2428_CR68","doi-asserted-by":"crossref","unstructured":"Tapia, G., & Elwany, A. (2014). A review on process monitoring and control in metal-based additive manufacturing. Journal of Manufacturing Science and Engineering, 136(6), 060801.","DOI":"10.1115\/1.4028540"},{"key":"2428_CR69","unstructured":"Wang, Y., Li, S., Liu, C., Wang, K., Yuan, X., Yang, C., & Gui, W. (2023). Multiscale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process. IEEE Transactions on Cybernetics pp. 1\u201314"},{"issue":"2","key":"2428_CR70","doi-asserted-by":"publisher","DOI":"10.1115\/1.4034304","volume":"139","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Li, J., Gouge, M., Nassar, A. R., Michaleris, P., & Reutzel, E. W. (2017). Physics-based multivariable modeling and feedback linearization control of melt-pool geometry and temperature in directed energy deposition. Journal of Manufacturing Science and Engineering, 139(2), 021013.","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"2428_CR71","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1007\/s11665-018-3690-2","volume":"28","author":"K Wasmer","year":"2019","unstructured":"Wasmer, K., Le-Quang, T., Meylan, B., & Shevchik, S. A. (2019). In situ quality monitoring in am using acoustic emission: A reinforcement learning approach. Journal of Materials Engineering and Performance, 28, 666\u2013672.","journal-title":"Journal of Materials Engineering and Performance"},{"key":"2428_CR72","unstructured":"Watkins, C.J.C.H. (1989). Learning from delayed rewards. Ph.D. thesis, King\u2019s College, Cambridge United Kingdom"},{"key":"2428_CR73","doi-asserted-by":"crossref","unstructured":"Wood, N., & Hoelzle, D.J. (2018). 2018 Annual American Control Conference (ACC), pp. 321\u2013328.","DOI":"10.23919\/ACC.2018.8430941"},{"issue":"4","key":"2428_CR74","doi-asserted-by":"publisher","first-page":"2792","DOI":"10.1109\/LRA.2018.2839973","volume":"3","author":"B Yao","year":"2018","unstructured":"Yao, B., Imani, F., & Yang, H. (2018). Markov decision process for image-guided additive manufacturing. IEEE Robotics and Automation Letters, 3(4), 2792\u20132798.","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"4","key":"2428_CR75","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TAC.1977.1101556","volume":"22","author":"J Yuan","year":"1977","unstructured":"Yuan, J., & Wonham, W. (1977). Probing signals for model reference identification. IEEE Transactions on Automatic Control, 22(4), 530\u2013538.","journal-title":"IEEE Transactions on Automatic Control"},{"key":"2428_CR76","doi-asserted-by":"crossref","unstructured":"Zhang, Q., & Lin, Y. (2023). Integrating multi-agent reinforcement learning and 3d a* search for facility layout problem considering connector-assembly. Journal of Intelligent Manufacturing pp. 1\u201326 (2023)","DOI":"10.1007\/s10845-023-02209-x"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02428-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02428-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02428-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:03:11Z","timestamp":1758358991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02428-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,21]]},"references-count":76,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2428"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02428-w","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,21]]},"assertion":[{"value":"17 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}