{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:11:54Z","timestamp":1783923114818,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234400","type":"print"},{"value":"9789819234417","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3441-7_47","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:36:40Z","timestamp":1783921000000},"page":"569-581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DAFT: Distribution-Aware Fine-Tuning for Adaptive Die Casting Optimization"],"prefix":"10.1007","author":[{"given":"Enpei","family":"Niu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erwu","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiya","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"47_CR1","first-page":"112","volume":"20","author":"S Pachandrin","year":"2025","unstructured":"Pachandrin, S., et al.: Data-driven prediction of casting defects in magnesium high-pressure die casting using machine learning. Int. J. Met. 20, 112\u2013131 (2025)","journal-title":"Int. J. Met."},{"issue":"1","key":"47_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/met12010001","volume":"12","author":"S Chen","year":"2021","unstructured":"Chen, S., Kaufmann, T.: Development of data-driven machine learning models for the prediction of casting surface defects. Metals. 12(1), 1 (2021)","journal-title":"Metals"},{"issue":"1","key":"47_CR3","first-page":"414","volume":"17","author":"TC Uyan","year":"2023","unstructured":"Uyan, T.C., et al.: Industry 4.0 foundry data management and supervised machine learning in low-pressure die casting quality improvement. Int. J. Met. 17(1), 414\u2013429 (2023)","journal-title":"Int. J. Met."},{"issue":"6","key":"47_CR4","volume":"13","author":"G Andriosopoulou","year":"2023","unstructured":"Andriosopoulou, G., et al.: Defect recognition in high-pressure die-casting parts using neural networks and transfer learning. Meta. 13(6), 1104 (2023)","journal-title":"Meta"},{"issue":"14","key":"47_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11142204","volume":"11","author":"CH Lin","year":"2022","unstructured":"Lin, C.H., et al.: Press casting quality prediction and analysis based on machine learning. Electronics. 11(14), 2204 (2022)","journal-title":"Electronics"},{"issue":"3","key":"47_CR6","first-page":"159","volume":"16","author":"X Tian","year":"2024","unstructured":"Tian, X., Gong, H., Du, Y., et al.: Quality prediction algorithm for die castings based on fully connected neural network. J. Precis. Forming Eng. 16(3), 159\u2013164 (2024)","journal-title":"J. Precis. Forming Eng."},{"issue":"7","key":"47_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/app12073264","volume":"12","author":"N Du\u010di\u0107","year":"2022","unstructured":"Du\u010di\u0107, N., et al.: Casting process improvement by the application of artificial intelligence. Appl. Sci. 12(7), 3264 (2022)","journal-title":"Appl. Sci."},{"key":"47_CR8","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.jmapro.2020.01.016","volume":"51","author":"S Shahane","year":"2020","unstructured":"Shahane, S., et al.: Optimization of solidification in die casting using numerical simulations and machine learning. J. Manuf. Process. 51, 130\u2013141 (2020)","journal-title":"J. Manuf. Process."},{"key":"47_CR9","first-page":"13","volume-title":"Int. Conf. Intelligent Computing for Sustainable Energy and Environment","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Qian, F., Gao, Y.: Data driven die casting smart factory solution. In: Int. Conf. Intelligent Computing for Sustainable Energy and Environment, pp. 13\u201321. Springer, Singapore (2018)"},{"issue":"10","key":"47_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/pr11102947","volume":"11","author":"Y Zhai","year":"2023","unstructured":"Zhai, Y., Liang, Q., Zhang, W.: Data-driven intelligent monitoring of die-casting machine injection system. Processes. 11(10), 2947 (2023)","journal-title":"Processes"},{"issue":"1","key":"47_CR11","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s00170-012-4190-4","volume":"65","author":"L Zhang","year":"2013","unstructured":"Zhang, L., Wang, R.: An intelligent system for low-pressure die-cast process parameters optimization. Int. J. Adv. Manuf. Technol. 65(1), 517\u2013524 (2013)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"3","key":"47_CR12","first-page":"148","volume":"14","author":"L Yang","year":"2022","unstructured":"Yang, L., Xu, L.: Design of die casting machine injection speed control system based on neural network PID controller. J. Precis. Form. Eng. 14(3), 148\u2013153 (2022)","journal-title":"J. Precis. Form. Eng."},{"issue":"7","key":"47_CR13","first-page":"995","volume":"44","author":"J Zhong","year":"2024","unstructured":"Zhong, J., Lou, J., Feng, G., et al.: Mechanism model of die casting machine injection system based on time series data-driven approach. Spec. Cast. Nonferrous Alloys. 44(7), 995\u20131000 (2024)","journal-title":"Spec. Cast. Nonferrous Alloys"},{"key":"47_CR14","doi-asserted-by":"publisher","unstructured":"Habibpour, M., et al.: An uncertainty-aware deep learning framework for defect detection in casting products. arXiv. (2021). https:\/\/doi.org\/10.48550\/arXiv.2107.11643","DOI":"10.48550\/arXiv.2107.11643"},{"key":"47_CR15","volume-title":"Advances in Neural Information Processing Systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30,  (2017)"},{"issue":"21","key":"47_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/s23218898","volume":"23","author":"K Zarzycki","year":"2023","unstructured":"Zarzycki, K., \u0141awry\u0144czuk, M.: Long short-term memory neural networks for modeling dynamical processes and predictive control. Sensors. 23(21), 8898 (2023)","journal-title":"Sensors"},{"key":"47_CR17","doi-asserted-by":"publisher","DOI":"10.1017\/dce.2024.33","volume":"5","author":"M Haywood-Alexander","year":"2024","unstructured":"Haywood-Alexander, M., et al.: Discussing the spectrum of physics-enhanced machine learning. Data-Centric Eng. 5, e30 (2024)","journal-title":"Data-Centric Eng."},{"issue":"10","key":"47_CR18","first-page":"1366","volume":"43","author":"J Guo","year":"2023","unstructured":"Guo, J., Wu, M., Xiong, S., et al.: Research progress on numerical simulation technology of die casting process. Spec. Cast. Nonferrous Alloys. 43(10), 1366\u20131375 (2023)","journal-title":"Spec. Cast. Nonferrous Alloys"},{"key":"47_CR19","volume-title":"ICLR","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., Shen, Y., Wallis, P., et al.: LoRA: low-rank adaptation of large language models. In: ICLR (2022)"},{"key":"47_CR20","volume":"13","author":"S Park","year":"2023","unstructured":"Park, S., Youm, S.: Establish a machine learning based model for optimal casting conditions management. Sci. Rep. 13, 17223 (2023)","journal-title":"Sci. Rep."},{"key":"47_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105636","volume":"118","author":"IE Parlak","year":"2023","unstructured":"Parlak, I.E., Emel, E.: Deep learning-based detection of aluminum casting defects. Eng. Appl. Artif. Intell. 118, 105636 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"47_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2024.1330257","volume":"7","author":"F Hinder","year":"2024","unstructured":"Hinder, F., Vaquet, V., Hammer, B.: One or two things we know about concept drift. Front. Artif. Intell. 7, 1330257 (2024)","journal-title":"Front. Artif. Intell."},{"key":"47_CR23","first-page":"422","volume":"120","author":"F R\u00fcdt von Collenberg","year":"2024","unstructured":"R\u00fcdt von Collenberg, F., et al.: Handling concept drift in deep learning applications for process monitoring. Procedia CIRP. 120, 422\u2013427 (2024)","journal-title":"Procedia CIRP"},{"key":"47_CR24","volume-title":"FAIM 2024. LNME","author":"M Trat","year":"2024","unstructured":"Trat, M., Bergmann, P., Ott, A., Ovtcharova, J.: A nature-inspired concept drift adaptation method for industrial data stream regression. In: FAIM 2024. LNME. Springer (2024)"},{"key":"47_CR25","volume-title":"NeurIPS","author":"X Liang","year":"2021","unstructured":"Liang, X., Wu, L., Li, J., et al.: R-Drop: regularized dropout for neural networks. In: NeurIPS, vol. 34,  (2021)"},{"issue":"3","key":"47_CR26","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s42256-023-00626-4","volume":"5","author":"N Ding","year":"2023","unstructured":"Ding, N., Qin, Y., Yang, G., et al.: Parameter-efficient fine-tuning of large-scale pre-trained language models. Nat. Mach. Intell. 5(3), 220\u2013235 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"47_CR27","volume-title":"NeurIPS 2024 Workshop","author":"A Bhatti","year":"2024","unstructured":"Bhatti, A., et al.: Beyond LoRA: exploring efficient fine-tuning techniques for time series foundational models. In: NeurIPS 2024 Workshop (2024)"},{"key":"47_CR28","volume-title":"Constraining generative models for engineering design with negative data","author":"L Regenwetter","year":"2024","unstructured":"Regenwetter, L., et al. Constraining generative models for engineering design with negative data. TMLR (2024)"},{"key":"47_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.112078","volume":"170","author":"L Wang","year":"2026","unstructured":"Wang, L., Li, S.-L., Su, X.-R., et al.: GraCMI: multi-hop graph structural modeling for predicting circRNA-miRNA interactions. Pattern Recogn. 170, 112078 (2026)","journal-title":"Pattern Recogn."},{"issue":"13","key":"47_CR30","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. PNAS. 114(13), 3521\u20133526 (2017)","journal-title":"PNAS"},{"key":"47_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4358-3","volume":"68","author":"L Wang","year":"2025","unstructured":"Wang, L., et al.: Prediction of Budd-Chiari syndrome based on attention mechanisms. Sci. China Inf. Sci. 68, 179102 (2025)","journal-title":"Sci. China Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3441-7_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:36:43Z","timestamp":1783921003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3441-7_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819234400","9789819234417"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3441-7_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}