Francisco Fernandez, Soorya Saravanan, Rashen Lou Omongos, Javier F Troncoso, Diego E Galvez-Aranda, Alejandro A Franco
{"title":"与电化学能量电池组件特性相关的制造参数小数据集的迁移学习评估。","authors":"Francisco Fernandez, Soorya Saravanan, Rashen Lou Omongos, Javier F Troncoso, Diego E Galvez-Aranda, Alejandro A Franco","doi":"10.1038/s44334-025-00024-1","DOIUrl":null,"url":null,"abstract":"<p><p>The performance of electrochemical cells for energy storage and conversion can be improved by optimizing their manufacturing processes. This can be time-consuming and costly with the traditional trial-and-error approaches. Machine Learning (ML) models can help to overcome these obstacles. In academic research laboratories, manufacturing dataset sizes can be small, while ML models typically require large amounts of data. In this work, we propose a simple but still novel application of a Transfer Learning (TL) approach to address these manufacturing problems with a small amount of data. We have tested this approach with pre-existing experimental and stochastically generated datasets. These datasets consisted of component properties (e.g., electrode density) related to different manufacturing parameters (e.g., solid content, comma gap, coating speed). We have demonstrated the robustness of our TL approach for manufacturing problems by achieving excellent prediction performance for electrodes in lithium-ion batteries and gas diffusion layers in fuel cells.</p>","PeriodicalId":501702,"journal":{"name":"npj Advanced Manufacturing","volume":"2 1","pages":"14"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008025/pdf/","citationCount":"0","resultStr":"{\"title\":\"Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties.\",\"authors\":\"Francisco Fernandez, Soorya Saravanan, Rashen Lou Omongos, Javier F Troncoso, Diego E Galvez-Aranda, Alejandro A Franco\",\"doi\":\"10.1038/s44334-025-00024-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The performance of electrochemical cells for energy storage and conversion can be improved by optimizing their manufacturing processes. This can be time-consuming and costly with the traditional trial-and-error approaches. Machine Learning (ML) models can help to overcome these obstacles. In academic research laboratories, manufacturing dataset sizes can be small, while ML models typically require large amounts of data. In this work, we propose a simple but still novel application of a Transfer Learning (TL) approach to address these manufacturing problems with a small amount of data. We have tested this approach with pre-existing experimental and stochastically generated datasets. These datasets consisted of component properties (e.g., electrode density) related to different manufacturing parameters (e.g., solid content, comma gap, coating speed). We have demonstrated the robustness of our TL approach for manufacturing problems by achieving excellent prediction performance for electrodes in lithium-ion batteries and gas diffusion layers in fuel cells.</p>\",\"PeriodicalId\":501702,\"journal\":{\"name\":\"npj Advanced Manufacturing\",\"volume\":\"2 1\",\"pages\":\"14\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008025/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Advanced Manufacturing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s44334-025-00024-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Advanced Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s44334-025-00024-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/18 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties.
The performance of electrochemical cells for energy storage and conversion can be improved by optimizing their manufacturing processes. This can be time-consuming and costly with the traditional trial-and-error approaches. Machine Learning (ML) models can help to overcome these obstacles. In academic research laboratories, manufacturing dataset sizes can be small, while ML models typically require large amounts of data. In this work, we propose a simple but still novel application of a Transfer Learning (TL) approach to address these manufacturing problems with a small amount of data. We have tested this approach with pre-existing experimental and stochastically generated datasets. These datasets consisted of component properties (e.g., electrode density) related to different manufacturing parameters (e.g., solid content, comma gap, coating speed). We have demonstrated the robustness of our TL approach for manufacturing problems by achieving excellent prediction performance for electrodes in lithium-ion batteries and gas diffusion layers in fuel cells.