[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121845-en":3,"doc-seo-121845-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},121845,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Framework for automatic production simulation tuning with machine learning - Paper","Production system simulation supports planning and control by enabling resource optimization, but manual model creation and tuning remains tedious, error-prone, and costly. Current automation often depends on expert knowledge, limiting flexibility across use cases. With Industry 4.0, abundant data enables virtual models and closer integration into digital twins. This work proposes a machine-learning-driven real2sim method that learns simulation parameters from observed system behavior to minimize the reality gap and forecast via a digital shadow, validated through simulations.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 121 (2024) 49–54  \n11th CIRP Global Web Conference (CIRPe 2023)  \nFramework for automatic production simulation tuning with machine  \nlearning  \nMarvin Carl Maya,, Alexander Finkea , Katharina Theunera , Gisela Lanzaa,b  \nawbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany b Global Advanced Manufacturing Institute (GAMI), KIT China Branch, Suzhou 215123, China  \n* Corresponding author. Tel.: +49 1523-950 2624; fax: +49 721 608-45005 . [E-mail address: marvin.may@kit.edu](E-mail address: marvin.may@kit.edu) [marvin.may@kit.edu](marvin.may@kit.edu)  \nAbstract  \nProduction system simulation is a powerful tool for optimizing the use of resources on both the planning and control level. However, creating and tuning such models manually is a tedious and error-prone task. Despite some approaches to automate this process, the state-of-the-art relies on the generation of models, by incorporating the knowledge of experts. Nevertheless, effectively creating and tuning such production simulations is, thus, a key driver for reducing costs, carbon footprint, and tardiness and therefore an essential factor in today´s production. Beneficial would be automated and flexible frameworks, since these are applicable to different use cases requiring less effort. Yet, in the age of Industry 4.0, data is ubiquitous and easily available and can serve as a basis for virtual models representing reality. Increasingly, these virtual models shall be interlinked with the current state of real-world systems to form so-called digital twins. As automated and flexible frameworks are missing, this paper proposes a novel approach where observed real system behavior is used and fed into a large-scale machine learning model trained on a plethora of possible parameter sets. The main target is to train this machine learning model to minimize the reality gap between the behavior of the simulated and real system by selecting corresponding simulation system parameters. By estimating those parameters an enhancement of the simulation will emerge. An interlink to real systems can be derived resulting in a digital shadow which is capable to forecast the future similarly to reality. The approach to overcoming the gap between reality and simulation (real2sim) is validated in simulations.  \n© 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0)) Peer-review under responsibility of the scientific committee of the 11th CIRP Global Web Conference  \nKeywords: Production Simulation; Machine Learning; Simulation Tuning; Simulation Generation  \n1. Introduction  \nWorldwide, the pressure for flexible yet efficient production systems is growing rapidly. To optimize those, multidimensional optimization issues have to be tackled and complex dependencies need to be designed [8] . Due to the unchanged mental capacity of human beings, supportive techniques for the planning of these production systems have to be applied.  \nIncreased attention is paid to simulations, serving as tools for decision-making in production control [2] . Panzer et al. [19] provide a review of research regarding neural networks in production planning and control whereby a large number of research was implemented in simulations. As the application of simulation programs, is referblack to as a common tool to improve the efficiency and control of manufacturing systems, they help to fine-tune the production systems and, thus, to foresee the near term behavior of the regarded production system [15] . By applying carefully selected suitable concepts on their appli-  \ncation, this can drastically reduce material and resource usage [18] . However, in order to derive meaningful information,","cbCaifu4NaBJ1W8E","https://ap.wps.com/l/cbCaifu4NaBJ1W8E","pdf",660494,1,6,"English","en",105,"# Introduction\n## Simulation for production planning and control\n## Need for digital-twin-aligned models\n## Machine-learning-assisted automation\n# Proposed framework\n## Real2Sim concept and learning target\n## Parameter selection to reduce reality gap\n# Case study and evaluation\n## Building, refining and testing\n## Results and discussion\n# Conclusion","[{\"question\":\"Why is automatic tuning of production simulation important?\",\"answer\":\"Manual simulation creation and tuning are time-consuming and prone to errors. Automatic tuning improves efficiency and supports better decision-making in production planning and control.\"},{\"question\":\"How does the proposed approach reduce the reality gap?\",\"answer\":\"Observed real system behavior is used to train a large-scale machine learning model. The model selects simulation parameters that minimize differences between simulated and real behavior.\"},{\"question\":\"What is the relationship between this method and digital twins?\",\"answer\":\"By interlinking the simulation with real systems, the approach derives a digital shadow capable of forecasting future behavior similarly to reality, aligning with digital-twin concepts.\"}]","Framework for automatic production simulation tuning with machine learning - Paper | PDF",1785807188,15,{"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},"framework-for-automatic-production-simulation-tuning-with-machine-learning-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/framework-for-automatic-production-simulation-tuning-with-machine-learning-paper/121845/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is automatic tuning of production simulation important?","Question",{"text":75,"@type":76},"Manual simulation creation and tuning are time-consuming and prone to errors. Automatic tuning improves efficiency and supports better decision-making in production planning and control.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach reduce the reality gap?",{"text":80,"@type":76},"Observed real system behavior is used to train a large-scale machine learning model. The model selects simulation parameters that minimize differences between simulated and real behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the relationship between this method and digital twins?",{"text":84,"@type":76},"By interlinking the simulation with real systems, the approach derives a digital shadow capable of forecasting future behavior similarly to reality, aligning with digital-twin concepts.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"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":53,"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]