[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127010-en":3,"doc-seo-127010-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127010,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Machine-learning-enhanced femtosecond-laser machining - towards an efficient and deterministic process control","Femtosecond laser nanomachining enables high-precision micro- and nanopatterning, yet adoption is limited by unintended surface damage and modifications caused by complex, nonlinear laser–material interactions. Conventional optimization relies on extensive trial-and-error experiments and imperfect physical or atomistic models, reducing predictability and efficiency. This review explains how machine learning can improve monitoring, modeling, prediction, parameter optimization, and autonomous beam path planning using laser parameters and in-situ or ex-situ imaging data.","Machine-learning-enhanced femtosecond-laser machining: towards an efficient and deterministic process control  \nJian Gao 1, Wenkun Xie1*, Xichun Luo1, and Yi Qin1  \n1Centre for Precision Manufacturing, DMEM, University of Strathclyde, Glasgow, G1 1XJ, UK  \nAbstract. Femtosecond laser nanomachining represents a frontier in precision manufacturing, excelling in micro- and nanopatterning across diverse materials. However, its wider adoption is hindered by unintended surface damage or modifications stemming from complex non-linear lasermaterial interactions. Moreover, traditional effective process optimisation effort to mitigate these issues typically necessitate extensive and timeconsuming trial-and-error testing. In this scenario, machine learning (ML) has emerged as a powerful solution to address these challenges. This paper provides an overview of ML ’s contributions to making femtosecond laser machining a more deterministic and efficient technique. Leveraging data from laser parameters and both in-situ and ex-situ imaging of processing outcomes, ML techniques—spanning supervised learning, unsupervised learning, and reinforcement learning—can significantly enhance process monitoring, process modeling and prediction, parameter optimisation, and autonomous beam path planning. These developments propel femtosecond laser towards an essential tool for micro-and nanomanufacturing, enabling precise control over machining outcomes and deepening our understanding  \nof the laser machining process.  \n1 Introduction  \nFemtosecond laser, with pulse durations of 10-15 seconds, plays a pivotal role in micro-and nanomanufacturing. Through its interacting with materials, various micro- and nanostructures can be created through direct ablation or the interference of laser and laserexcited electric field. The ultra-short pulse widths and exceptionally high peak intensities of femtosecond lasers can enable high spatial resolution, minimal heat-affected zones, and noncontact processing, offering advantages over traditional techniques like nanoimprinting, ion beam processing, and electron beam lithography in terms of flexibility, speed, costeffectiveness, and environmental impact [1] . However, femtosecond laser machining faces challenges in unintended surface damage and modifications, particularly at high intensities or due to suboptimal parameters. Consequently, surfaces machined with femtosecond lasers may exhibit lower quality and structural consistency, resulting in diminished functional  \n* [Corresponding author:](Corresponding author: w.xie@strath.ac.uk)[ ](Corresponding author: w.xie@strath.ac.uk)[w.xie@strath.ac.uk](Corresponding author: w.xie@strath.ac.uk)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nperformance [2,3] . At its core, this issue stems from the highly nonlinear and complex interactions between the laser and material properties, leading to unpredictable outcomes. Conventional physical models often fall short in explaining these microscale non-linear phenomena, and atomistic simulations struggle to accurately represent the results due to the challenges of scaling and multiple interacting variables [4,5] . Consequently, extensive trialand-error experimentation and significant expertise are required for effective process monitoring, control, and optimisation, which can compromise laser machining efficiency and ease of use [6] .  \nTo counter these challenges, it is imperative to incorporate advanced process control and monitoring methods, such as real-time monitoring, process modelling, path planning, and parameter optimisation. These measures foster a quick comprehension of manufacturing conditions, interpret parametric dependence, bolster predictability, and enable active and autonomous execution with minimal need for human int","cbCaiifD9dDM723r","https://ap.wps.com/l/cbCaiifD9dDM723r","pdf",1946078,3,1,"English","en",105,"# Introduction\n# ML in laser machining","[{\"question\":\"What limits the wider adoption of femtosecond laser machining?\",\"answer\":\"Unintended surface damage and modifications arise from highly nonlinear, complex interactions between the laser and material properties, making outcomes difficult to predict.\"},{\"question\":\"Why is traditional process optimization inefficient for femtosecond laser machining?\",\"answer\":\"Effective optimization typically requires extensive, time-consuming trial-and-error testing because conventional models and simulations often fail to capture microscale nonlinear phenomena accurately.\"},{\"question\":\"How does machine learning support more deterministic femtosecond laser machining?\",\"answer\":\"Machine learning uses laser parameters and imaging data to enhance process monitoring, process modeling, prediction, parameter optimization, and autonomous beam path planning through supervised, unsupervised, and reinforcement learning.\"}]","Machine-learning-enhanced femtosecond-laser machining - towards an efficient and deterministic process control | PDF",1785936266,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-enhanced-femtosecond-laser-machining-towards-an-efficient-and-deterministic-process-control","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/technology/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/machine-learning-enhanced-femtosecond-laser-machining-towards-an-efficient-and-deterministic-process-control/127010/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What limits the wider adoption of femtosecond laser machining?","Question",{"text":75,"@type":76},"Unintended surface damage and modifications arise from highly nonlinear, complex interactions between the laser and material properties, making outcomes difficult to predict.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is traditional process optimization inefficient for femtosecond laser machining?",{"text":80,"@type":76},"Effective optimization typically requires extensive, time-consuming trial-and-error testing because conventional models and simulations often fail to capture microscale nonlinear phenomena accurately.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning support more deterministic femtosecond laser machining?",{"text":84,"@type":76},"Machine learning uses laser parameters and imaging data to enhance process monitoring, process modeling, prediction, parameter optimization, and autonomous beam path planning through supervised, unsupervised, and reinforcement learning.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]