[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84793-en":3,"doc-seo-84793-105":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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84793,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Model-Guided Local Bayesian Optimization for Tuning of Interpretable Controllers in Injection Molding","Advanced control methods effectively regulate cavity pressure in plastics injection molding, a key driver of part quality. Yet the resulting control laws are often too abstract to interpret on the factory floor, restricting industrial uptake. Controller tuning is further complicated by diverse mold geometries and materials. A method is presented for cycle-efficient, risk-aware automatic optimization of interpretable controllers during manufacturing using a physics-inspired neural mixture-of-local-experts model and a residual Gaussian Process for local Bayesian optimization.","Model-Guided Local Bayesian Optimization for Tuning of Interpretable  \nControllers in Injection Molding  \nJens Ahlers 1 , Robert Gllinger1 , Xu Chen 1 , Heike Vallery 1 ,2 , and Sebastian Stemmler1  \narXiv :2607 .05159v1 [ ee ss . SY] 6 Jul 2026  \nAbstract—Advanced control methods have proven effective for controlling cavity pressure, a key determinant of partquality attributes, in the plastics injection molding process. However, the abstract nature of the resulting control laws makes them difficult to interpret in a production environment, thereby limiting adoption in industrial applications. Additionally, controller optimization poses a severe challenge due to the diversity of mold geometries and materials.  \nWe propose a method to automatically optimize interpretable controllers during manufacturing while being cycle-efficient and risk-aware. The approach uses a Physics-Inspired Neural Mixture-of-Local-Experts model of the injection molding dynamics and augments its simulated closed-loop costs with a residual Gaussian Process, enabling Local Bayesian Optimization of controller parameters.  \nWe benchmark the algorithm against Vanilla Bayesian Optimization (BO) in simulation, using three controllers with parameter counts ranging from 1 to 30. Using the local method, we identify controller parameters that yield costs comparable to or lower than those of global BO over 20 optimization iterations, while mitigating high-cost excursions during tuning.  \nI. INTRODUCTION  \nInjection molding (IM) is one of the most vital processes in the plastics processing industry, enabling the large-scale production of almost arbitrarily complex plastic products [1] . With worldwide annual production exceeding 400 million tons and continuing to rise, the efficiency and quality of this process are critical [2] .  \nUsually in industrial IM, control schemes switch between screw-velocity control during the injection phase and screwpressure control during the packing phase [3] . Determining the switch-over point between the two control strategies remains a topic of current research [4] .  \nAlthough in-mold cavity-pressure sensors are not considered an industrial standard in IM, the relationships between cavity pressure and part-quality attributes have been widely studied in the literature [5] . Due to strong correlations between the cavity pressure and part-quality attributes, cavity pressure is frequently used as a feature in quality-prediction models [6] . Some approaches even treat cavity pressure asthe controlled variable [7] .  \nTreating cavity pressure as the controlled variable during the injection and packing phases circumvents the need to  \n*The presented research was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under the funding code 378417139 (Phasen¨ubergreifende Prozessf¨uhrungskonzepte beim Spritzgießen unter Nutzung moderner Regelungsstrategien) .  \n1Institute of Automatic Control, RWTH Aachen University, Aachen, Germany;  \n2Faculty of Mechanical Engineering, TU Delft, Delft, The Netherlands {j .ahlers, r .goellinger, x .chen, h .vallery, [s.stemmler](s.stemmler}@irt.rwth-aachen.de)[}](s.stemmler}@irt.rwth-aachen.de)[@irt.rwth-aachen.de](s.stemmler}@irt.rwth-aachen.de)  \ndetermine a switch-over point. The approaches reported in the literature predominantly rely on dynamic models to perform online or in-between-cycle optimizations to determine a suitable controller output [8] .  \nHowever, model-based controllers face two challenges to industrial adoption:  \n1) Sophisticated model-based control algorithms almost behave like a black box. This makes it impossible for machine operators to assess and verify controller outputs, leading them to mistrust such controllers [9] .  \n2) Obtaining accurate and reliable process models is difficult since cavity-pressure dynamics are highly sensitive to mold geometry, sensor location, and material properties [10] . Due to the individuality and almost arbitrary complexity of m","cbCaipJMDyp34gg2","https://ap.wps.com/l/cbCaipJMDyp34gg2","pdf",624902,2,1,7,"English","en",105,"# Introduction\n## Injection molding background and control phases\n## Motivation: interpretability and tuning challenges\n## Bayesian Optimization and high-dimensional tuning\n## Related work and local optimization approaches","[{\"question\":\"Why are interpretable controllers important for injection molding in practice?\",\"answer\":\"Interpretable control laws enable machine operators to assess and verify controller behavior. Their transparent structure reduces mistrust and supports adoption in production environments.\"},{\"question\":\"What makes controller optimization challenging across different injection molding setups?\",\"answer\":\"Mold geometries and material properties vary widely, which makes process dynamics difficult to model consistently and increases uncertainty during controller tuning.\"},{\"question\":\"How does the proposed method enable cycle-efficient and risk-aware tuning?\",\"answer\":\"It combines a physics-inspired neural mixture-of-local-experts model of injection molding dynamics with a residual Gaussian Process that augments simulated closed-loop costs, then applies Local Bayesian Optimization to tune controller parameters efficiently.\"}]",1784198274,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"model-guided-local-bayesian-optimization-for-tuning-of-interpretable-controllers-in-injection-molding","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/model-guided-local-bayesian-optimization-for-tuning-of-interpretable-controllers-in-injection-molding/84793/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"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","2026-07-23","2026-07-16",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},"Why are interpretable controllers important for injection molding in practice?","Question",{"text":75,"@type":76},"Interpretable control laws enable machine operators to assess and verify controller behavior. Their transparent structure reduces mistrust and supports adoption in production environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes controller optimization challenging across different injection molding setups?",{"text":80,"@type":76},"Mold geometries and material properties vary widely, which makes process dynamics difficult to model consistently and increases uncertainty during controller tuning.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method enable cycle-efficient and risk-aware tuning?",{"text":84,"@type":76},"It combines a physics-inspired neural mixture-of-local-experts model of injection molding dynamics with a residual Gaussian Process that augments simulated closed-loop costs, then applies Local Bayesian Optimization to tune controller parameters efficiently.","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":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"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"]