{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T02:50:00Z","timestamp":1776307800827,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007195","name":"Universit\u00e0 degli Studi di Napoli Federico II","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007195","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Nowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller.<\/jats:p>","DOI":"10.1007\/s10845-023-02307-w","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T11:43:09Z","timestamp":1705578189000},"page":"1291-1310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8686-1474","authenticated-orcid":false,"given":"Giulio","family":"Mattera","sequence":"first","affiliation":[]},{"given":"Alessandra","family":"Caggiano","sequence":"additional","affiliation":[]},{"given":"Luigi","family":"Nele","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"issue":"6","key":"2307_CR1","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":"2307_CR2","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"},{"key":"2307_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/app13053307","author":"A Caggiano","year":"2023","unstructured":"Caggiano, A., Giulio, M., & Luigi, N. (2023). Smart tool wear monitoring of CFRP\/CFRP stack drilling using autoencoders and memory-based neural networks. Applied Sciences (switzerland). https:\/\/doi.org\/10.3390\/app13053307","journal-title":"Applied Sciences (switzerland)"},{"key":"2307_CR4","first-page":"4666","volume-title":"International conference on machine learning","author":"C Dann","year":"2022","unstructured":"Dann, C., Mansour, Y., Mohri, M., Sekhari, A., & Sridharan, K. (2022). Guarantees for epsilon-greedy reinforcement learning with function approximation. International conference on machine learning (pp. 4666\u20134689). PMLR."},{"key":"2307_CR5","unstructured":"Datta, L. (2020). A survey on activation functions and their relation with Xavier and He normal initialization. Preprint retrieved from https:\/\/arxiv.org\/abs\/2004.06632"},{"key":"2307_CR6","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.rcim.2014.08.008","volume":"31","author":"D Ding","year":"2015","unstructured":"Ding, D., Pan, Z., Cuiuri, D., & Li, H. (2015). A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robotics and Computer-Integrated Manufacturing, 31, 101\u2013110.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"1","key":"2307_CR7","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s00170-014-6307-4","volume":"76","author":"T Doodman","year":"2015","unstructured":"Doodman, T., Ali, R., & Pariz, N. (2015). Improving the dynamic metal transfer model of gas metal arc welding (GMAW) process. The International Journal of Advanced Manufacturing Technology, 76(1), 657\u2013668.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2307_CR8","volume-title":"Ital-IA thematic workshops","author":"T Forni","year":"2023","unstructured":"Forni, T., Mario, V., Fabio, L. P., Andrea, L., Matteo, B., & Francesco, M. (2023). AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications. Ital-IA thematic workshops. CEUR-WS."},{"key":"2307_CR9","volume-title":"Addressing function approximation error in actor-critic methods","author":"S Fujimoto","year":"2018","unstructured":"Fujimoto, S., van Hoof, H., & Meger, D. (2018). Addressing function approximation error in actor-critic methods. PMLR."},{"key":"2307_CR10","unstructured":"Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In\u00a0Proceedings of the thirteenth international conference on artificial intelligence and statistics\u00a0(pp. 249\u2013256). JMLR Workshop and Conference Proceedings."},{"key":"2307_CR11","volume-title":"Computational welding mechanics","author":"JA Goldak","year":"2005","unstructured":"Goldak, J. A., & Akhlaghi, M. (2005). Computational welding mechanics. Springer Science & Business Media."},{"issue":"8","key":"2307_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MCOM.001.2001237","volume":"59","author":"M Groshev","year":"2021","unstructured":"Groshev, M., Guimar\u00e3es, C., Mart\u00edn-P\u00e9rez, J., & de la Oliva, A. (2021). Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence. IEEE Communications Magazine, 59(8), 14\u201320. https:\/\/doi.org\/10.1109\/MCOM.001.2001237","journal-title":"IEEE Communications Magazine"},{"key":"2307_CR13","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In\u00a0International conference on machine learning\u00a0(pp. 1861\u20131870). PMLR."},{"issue":"1","key":"2307_CR14","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1243\/095440805X33216","volume":"220","author":"AJL Harrison","year":"2006","unstructured":"Harrison, A. J. L., Lim, C. N., & Gilbertson, M. A. (2006). Modelling and control of the dynamics of bubbling fluidized beds. Proceedings of the Institution of Mechanical Engineers, Part e: Journal of Process Mechanical Engineering, 220(1), 43\u201353.","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part e: Journal of Process Mechanical Engineering"},{"key":"2307_CR15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10295","author":"V Hasselt","year":"2016","unstructured":"Hasselt, V., Hado, A. G., & Silver, D. (2016). Deep reinforcement learning with double Q-learning. Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v30i1.10295","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"11","key":"2307_CR16","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.3390\/ma13112491","volume":"13","author":"P Henckell","year":"2020","unstructured":"Henckell, P., Gierth, M., Ali, Y., Reimann, J., & Bergmann, J. P. (2020). Reduction of energy input in wire arc additive manufacturing (WAAM) with gas metal arc welding (GMAW). Materials, 13(11), 2491. https:\/\/doi.org\/10.3390\/ma13112491","journal-title":"Materials"},{"issue":"11","key":"2307_CR17","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1002\/aic.690361118","volume":"36","author":"MA Henson","year":"1990","unstructured":"Henson, M. A., & Seborg, D. E. (1990). Input-output linearization of general nonlinear processes. AIChE Journal, 36(11), 1753\u20131757.","journal-title":"AIChE Journal"},{"key":"2307_CR18","doi-asserted-by":"crossref","unstructured":"Juneja, P. K., Sharma, A., Sharma, A., Mishra, R. R., & Gill, F. S. (2020). A review on model order reduction techniques for reducing order of industrial process transfer function model. In\u00a02020 International Conference on Advances in Computing, Communication & Materials (ICACCM)\u00a0(pp. 346\u2013350). IEEE.","DOI":"10.1109\/ICACCM50413.2020.9212832"},{"key":"2307_CR19","unstructured":"Kakade, S., & Langford, J. (2002). Approximately optimal approximate reinforcement learning. In\u00a0Proceedings of the Nineteenth International Conference on Machine Learning\u00a0(pp. 267\u2013274)."},{"issue":"3","key":"2307_CR20","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1021\/ef800984v","volume":"23","author":"Y-D Lang","year":"2009","unstructured":"Lang, Y.-D., Malacina, A., Biegler, L. T., Munteanu, S., Madsen, J. I., & Zitney, S. E. (2009). Reduced order model based on principal component analysis for process simulation and optimization. Energy & Fuels, 23(3), 1695\u20131706.","journal-title":"Energy & Fuels"},{"key":"2307_CR21","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.cirpj.2022.11.003","volume":"40","author":"C Li","year":"2023","unstructured":"Li, C., Zheng, P., Yin, Y., Wang, B., & Wang, L. (2023). Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP Journal of Manufacturing Science and Technology, 40, 75\u2013101. https:\/\/doi.org\/10.1016\/j.cirpj.2022.11.003","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"key":"2307_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2022.3172762","author":"Z Li","year":"2022","unstructured":"Li, Z., Hou, Z., Pan, Z., Dan, W., & Jing, X. (2022). A non-autoregressive dynamic model based welding parameter planning method for varying geometry beads in WAAM. IEEE Transactions on Industrial Electronics. https:\/\/doi.org\/10.1109\/TIE.2022.3172762","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2307_CR23","unstructured":"Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D. & Wierstra, D. (2015). Continuous control with deep reinforcement learning. Preprint retrieved from https:\/\/arxiv.org\/abs\/1509.02971"},{"issue":"10","key":"2307_CR24","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1016\/j.jmatprotec.2012.05.010","volume":"212","author":"FR Liu","year":"2012","unstructured":"Liu, F. R., Zhang, Q., Zhou, W. P., Zhao, J. J., & Chen, J. M. (2012). Micro scale 3D FEM simulation on thermal evolution within the porous structure in selective laser sintering. Journal of Materials Processing Technology, 212(10), 2058\u20132065.","journal-title":"Journal of Materials Processing Technology"},{"issue":"8","key":"2307_CR25","doi-asserted-by":"publisher","first-page":"2819","DOI":"10.1007\/s10845-018-1399-6","volume":"30","author":"Lu Liu","year":"2019","unstructured":"Liu, Lu., Tian, S., Xue, D., Zhang, T., & Chen, Y. Q. (2019). Industrial feedforward control technology: A review. Journal of Intelligent Manufacturing, 30(8), 2819\u20132833.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2307_CR26","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1007\/11840817_87","volume-title":"Artificial neural networks\u2014ICANN 2006","author":"L Matignon","year":"2006","unstructured":"Matignon, L., Laurent, G. J., & Le Fort-Piat, N. (2006). Reward function and initial values: Better choices for accelerated goal-directed reinforcement learning. In D. K. Stefanos, S. Andreas, D. W\u0142odzis\u0142aw, & O. Erkki (Eds.), Artificial neural networks\u2014ICANN 2006 (pp. 840\u2013849). Springer."},{"key":"2307_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s40194-023-01641-0","author":"G Mattera","year":"2022","unstructured":"Mattera, G., Caggiano, A., & Nele, L. (2022). Reinforcement learning as data-driven optimization technique for GMAW process. Welding in the World. https:\/\/doi.org\/10.1007\/s40194-023-01641-0","journal-title":"Welding in the World"},{"key":"2307_CR28","doi-asserted-by":"publisher","first-page":"200181","DOI":"10.1016\/j.iswa.2023.200181","volume":"17","author":"G Mattera","year":"2023","unstructured":"Mattera, G., & Mattera, R. (2023). Shrinkage estimation with reinforcement learning of large variance matrices for portfolio selection. Intelligent Systems with Applications, 17, 200181. https:\/\/doi.org\/10.1016\/j.iswa.2023.200181","journal-title":"Intelligent Systems with Applications"},{"key":"2307_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-023-02085-5","author":"G Mattera","year":"2023","unstructured":"Mattera, G., Paolela, D., & Nele, L. (2023). Monitoring and control the wire arc additive manufacturing process using artificial intelligence techniques: a review. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-023-02085-5","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2307_CR30","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-47766-4_3","volume-title":"Markov decision processes in practice","author":"MRK Mes","year":"2017","unstructured":"Mes, M. R. K., & Arturo, P. R. (2017). Approximate dynamic programming by practical examples. Markov decision processes in practice (pp. 63\u2013101). Springer."},{"issue":"7540","key":"2307_CR31","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529\u2013533.","journal-title":"Nature"},{"key":"2307_CR32","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1007\/978-3-319-56991-8_32","volume-title":"Proceedings of SAI intelligent systems conference (IntelliSys) 2016","author":"SS Mousavi","year":"2018","unstructured":"Mousavi, S. S., Michael, S., & Enda, H. (2018). Deep reinforcement learning: An overview. In B. Yaxin, K. Supriya, & B. Rahul (Eds.), Proceedings of SAI intelligent systems conference (IntelliSys) 2016 (pp. 426\u2013440). Spriner."},{"issue":"2","key":"2307_CR33","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1080\/18811248.1998.9733829","volume":"35","author":"K Nabeshima","year":"1998","unstructured":"Nabeshima, K., Suzudo, T., Suzuki, K., & Erdin\u00e7, T. \u00dc. R. K. C. A. N. (1998). Real-time nuclear power plant monitoring with neural network. Journal of Nuclear Science and Technology, 35(2), 93\u2013100.","journal-title":"Journal of Nuclear Science and Technology"},{"issue":"7","key":"2307_CR34","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.3390\/app12073615","volume":"12","author":"L Nele","year":"2022","unstructured":"Nele, L., Mattera, G., & Vozza, M. (2022). Deep neural networks for defects detection in gas metal arc welding. Applied Sciences, 12(7), 3615.","journal-title":"Applied Sciences"},{"key":"2307_CR35","doi-asserted-by":"publisher","first-page":"100032","DOI":"10.1016\/j.addlet.2022.100032","volume":"2","author":"JP Oliveira","year":"2022","unstructured":"Oliveira, J. P., Gouveia, F. M., & Santos, T. G. (2022). Micro wire and arc additive manufacturing (\u00b5-WAAM). Additive Manufacturing Letters, 2, 100032. https:\/\/doi.org\/10.1016\/j.addlet.2022.100032","journal-title":"Additive Manufacturing Letters"},{"issue":"124","key":"2307_CR36","first-page":"1","volume":"20","author":"I Osband","year":"2019","unstructured":"Osband, I., Van Roy, B., Russo, D. J., & Wen, Z. (2019). Deep exploration via randomized value functions. Journal of Machine Learning Research, 20(124), 1\u201362.","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"2307_CR37","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/37.845038","volume":"20","author":"S Piche","year":"2000","unstructured":"Piche, S., Sayyar-Rodsari, B., Johnson, D., & Gerules, M. (2000). Nonlinear model predictive control using neural networks. IEEE Control Systems Magazine, 20(3), 53\u201362. https:\/\/doi.org\/10.1109\/37.845038","journal-title":"IEEE Control Systems Magazine"},{"issue":"2","key":"2307_CR38","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s10846-017-0468-y","volume":"86","author":"AS Polydoros","year":"2017","unstructured":"Polydoros, A. S., & Nalpantidis, L. (2017). Survey of model-based reinforcement learning: applications on robotics. Journal of Intelligent & Robotic Systems, 86(2), 153\u2013173.","journal-title":"Journal of Intelligent & Robotic Systems"},{"issue":"1","key":"2307_CR39","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cirp.2020.04.010","volume":"69","author":"PC Priarone","year":"2020","unstructured":"Priarone, P. C., Pagone, E., Martina, F., Catalano, A. R., & Settineri, L. (2020). Multi-criteria environmental and economic impact assessment of wire arc additive manufacturing. CIRP Annals, 69(1), 37\u201340.","journal-title":"CIRP Annals"},{"key":"2307_CR40","unstructured":"Qin, S. J. & Thomas A. B. (1997). An overview of industrial model predictive control technology. In AIche symposium series (pp. 232\u2013256)."},{"issue":"1","key":"2307_CR41","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1146\/annurev-control-053018-023825","volume":"2","author":"B Recht","year":"2019","unstructured":"Recht, B. (2019). A tour of reinforcement learning: the view from continuous control. Annual Review of Control, Robotics, and Autonomous Systems, 2(1), 253\u2013279. https:\/\/doi.org\/10.1146\/annurev-control-053018-023825","journal-title":"Annual Review of Control, Robotics, and Autonomous Systems"},{"issue":"1","key":"2307_CR42","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1021\/i200032a041","volume":"25","author":"DE Rivera","year":"1986","unstructured":"Rivera, D. E., Morari, M., & Skogestad, S. (1986). Internal model control: PID controller design. Industrial & Engineering Chemistry Process Design and Development, 25(1), 252\u2013265.","journal-title":"Industrial & Engineering Chemistry Process Design and Development"},{"key":"2307_CR43","unstructured":"Schulman, J., Sergey L., Pieter A., Michael J., & Philipp M. (2015). Trust region policy optimization. In International conference on machine learning (pp. 1889\u20131897)."},{"key":"2307_CR44","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. Preprint retrieved from https:\/\/arxiv.org\/abs\/1707.06347"},{"issue":"5","key":"2307_CR45","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1049\/iet-cta.2019.0259","volume":"14","author":"B Sereni","year":"2020","unstructured":"Sereni, B., Assun\u00e7\u00e3o, E., & Teixeira, M. C. M. (2020). New gain-scheduled static output feedback controller design strategy for stability and transient performance of LPV systems. IET Control Theory & Applications, 14(5), 717\u2013725.","journal-title":"IET Control Theory & Applications"},{"key":"2307_CR46","unstructured":"Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014). Deterministic policy gradient algorithms. In\u00a0International conference on machine learning\u00a0(pp. 387-395). PMLR."},{"issue":"3","key":"2307_CR47","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/23.589532","volume":"44","author":"J Sola","year":"1997","unstructured":"Sola, J., & Sevilla, J. (1997). Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44(3), 1464\u20131468.","journal-title":"IEEE Transactions on Nuclear Science"},{"key":"2307_CR48","doi-asserted-by":"crossref","unstructured":"Spielberg, S. P. K., Gopaluni, R. B., & Loewen, P. D. (2017). Deep reinforcement learning approaches for process control. In 2017 6th international symposium on advanced control of industrial processes (AdCONIP) (pp. 201\u2013206).","DOI":"10.1109\/ADCONIP.2017.7983780"},{"key":"2307_CR49","doi-asserted-by":"publisher","DOI":"10.1201\/9781003065654","volume-title":"Seemingly unrelated regression equations models: Estimation and inference","author":"VK Srivastava","year":"2020","unstructured":"Srivastava, V. K., & Giles, D. E. A. (2020). Seemingly unrelated regression equations models: Estimation and inference. CRC Press."},{"key":"2307_CR50","volume-title":"Reinforcement Learning: An introduction","author":"RS Sutton","year":"1998","unstructured":"Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An introduction. MIT Press."},{"issue":"9","key":"2307_CR51","doi-asserted-by":"publisher","first-page":"2184","DOI":"10.1016\/j.engappai.2013.06.016","volume":"26","author":"YH Wang","year":"2013","unstructured":"Wang, Y. H., Li, T. H., & Lin, C. J. (2013). Backward Q-learning: The combination of Sarsa algorithm and Q-learning. Engineering Applications of Artificial Intelligence, 26(9), 2184\u20132193.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"3","key":"2307_CR52","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00992696","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3), 229\u2013256.","journal-title":"Machine Learning"},{"key":"2307_CR53","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.addma.2018.08.004","volume":"23","author":"B Wu","year":"2018","unstructured":"Wu, B., Pan, Z., Ding, D., Cuiuri, D., & Li, H. (2018a). Effects of heat accumulation on microstructure and mechanical properties of Ti6Al4V alloy deposited by wire arc additive manufacturing. Additive Manufacturing, 23, 151\u2013160.","journal-title":"Additive Manufacturing"},{"key":"2307_CR54","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.jmapro.2018.08.001","volume":"35","author":"B Wu","year":"2018","unstructured":"Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Jing, Xu., & Norrish, J. (2018b). A review of the wire arc additive manufacturing of metals: Properties, defects and quality improvement. Journal of Manufacturing Processes, 35, 127\u2013139.","journal-title":"Journal of Manufacturing Processes"},{"key":"2307_CR55","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.jmapro.2020.07.060","volume":"58","author":"C Xia","year":"2020","unstructured":"Xia, C., Pan, Z., Zhang, S., Polden, J., Wang, L., Li, H., Yanling, Xu., & Chen, S. (2020). Model predictive control of layer width in wire arc additive manufacturing. Journal of Manufacturing Processes, 58, 179\u2013186.","journal-title":"Journal of Manufacturing Processes"},{"key":"2307_CR56","doi-asserted-by":"crossref","unstructured":"Xiao, J., Liu, N., Lua, J., Saathoff, C., & Seneviratne, W. P. (2020). Data-driven and reduced-order modeling of composite drilling. In\u00a0AIAA Scitech 2020 forum\u00a0(p. 1859).","DOI":"10.2514\/6.2020-1859"},{"issue":"1","key":"2307_CR57","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s10845-012-0682-1","volume":"25","author":"J Xiong","year":"2014","unstructured":"Xiong, J., Zhang, G., Jianwen, Hu., & Lin, Wu. (2014). Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. Journal of Intelligent Manufacturing, 25(1), 157\u2013163.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"12","key":"2307_CR58","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1109\/TAC.2002.805670","volume":"47","author":"SY Xu","year":"2002","unstructured":"Xu, S. Y., & Chen, T. W. (2002). Robust H-infinity control for uncertain stochastic systems with state delay. IEEE Transactions on Automatic Control, 47(12), 2089\u20132094.","journal-title":"IEEE Transactions on Automatic Control"},{"issue":"2","key":"2307_CR59","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/3516.588624","volume":"2","author":"B Yao","year":"1997","unstructured":"Yao, B., Al-Majed, M., & Tomizuka, M. (1997). High-performance robust motion control of machine tools: An adaptive robust control approach and comparative experiments. IEEE\/ASME Transactions on Mechatronics, 2(2), 63\u201376.","journal-title":"IEEE\/ASME Transactions on Mechatronics"}],"updated-by":[{"DOI":"10.1007\/s10845-024-02450-y","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T00:00:00Z","timestamp":1720051200000}}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02307-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02307-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02307-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T22:29:57Z","timestamp":1738621797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02307-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,18]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2307"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02307-w","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s10845-024-02450-y","asserted-by":"object"}]},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,18]]},"assertion":[{"value":"26 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s10845-024-02450-y","URL":"https:\/\/doi.org\/10.1007\/s10845-024-02450-y","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}