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A human-centered cognitive support framework for manual industrial assembly: integrating perception, agentic reasoning, and augmented reality guidance

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dc.contributor.authorRodríguez Gasca, Mariannys
dc.contributor.authorRodríguez Gasca, Efraín Andrés
dc.contributor.authorMacêdo Barbalho, Sanderson César
dc.contributor.researchgroupGrupo de Investigación Automatización Industrial y Control (GAICO)
dc.date.accessioned2026-07-21T21:34:12Z
dc.date.issued2026-07-13
dc.descriptionContiene ilustraciones, gráficos, fotografías a color.
dc.description.abstractManual industrial assembly remains essential in high-variety and customized production, but increasing product and process complexity places substantial cognitive demands on operators. Existing assistance systems, particularly augmented reality-based solutions, improve instruction visualization and task guidance, yet they often remain weakly connected to the real assembly state and limited in reasoning, adaptation, and decision support. This paper proposes a human-centered cognitive support framework for manual industrial assembly that integrates perception, agentic reasoning, knowledge grounding, and augmented reality guidance. The study is informed by a systematic search, bibliometric overview, and literature analysis of 129 Scopus-indexed documents. The analysis shows that augmented reality dominates current cognitive support approaches, while AI-based methods are increasingly used for object recognition, contextual interpretation, adaptive information delivery, and error detection. However, perception, reasoning, guidance, and knowledge grounding are still commonly treated as isolated functions. Based on these findings, 11 review-derived design requirements are formulated and used to develop a conceptual perception-cognition-guidance framework that represents cognitive support as a closed human-centered loop grounded in procedures, constraints, rules, and memory. The framework is then translated into a layered implementation architecture comprising physical, perception, cognitive, guidance, knowledge, and application layers. Structured data contracts clarify how sensory and interaction data can be transformed into perception evidence, structured assembly states, cognitive support decisions, and device-specific guidance commands. A toy-train assembly demonstrator illustrates how procedural state modeling, multi-camera perception, YOLO11-based object detection, projector-based guidance, and contract-based data exchange can connect physical assembly events with structured reasoning and operator-facing feedback.
dc.format.extent31 páginas
dc.format.mimetypeapplication/pdf
dc.identifier.citationRodriguez, M., Rodriguez, E., & Macêdo Barbalho, S. C. (2026). A human-centered cognitive support framework for manual industrial assembly: integrating perception, agentic reasoning, and augmented reality guidance. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-026-18707-0
dc.identifier.doi10.1007/s00170-026-18707-0
dc.identifier.urihttps://hdl.handle.net/20.500.12585/14529
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00170-026-18707-0
dc.language.isoeng
dc.publisherThe International Journal of Advanced Manufacturing Technology
dc.relation.referencesDhanda M, Rogers BA, Hall S, Dekoninck E, Dhokia V (2025) Reviewing human-robot collaboration in manufacturing: opportunities and challenges in the context of industry 5.0. Robot Comput-Integr Manuf 93:102937. https://doi.org/10.1016/j.rcim.2024 .102937
dc.relation.referencesPang J, Zheng P, Fan J, Liu T (2025) Towards cognition-augmented human-centric assembly: a visual computation perspective. Robot Comput-Integr Manuf 91:102852. https://doi.org/10. 1016/j.rcim.2024.102852
dc.relation.referencesXu X, Lu Y, Vogel-Heuser B, Wang L (2021) Industry 4.0 and industry 5.0–inception, conception and perception. J Manuf Syst 61:530–535. https://doi.org/10.1016/j.jmsy.2021.10.006
dc.relation.referencesXu X, Ji T, Zheng P, Wang L (2026) Human-centric manufacturing: re-thinking, re-justifying, and re-envisioning. J Manuf Syst 84:259–268. https://doi.org/10.1016/j.jmsy.2025.12.001
dc.relation.referencesWang L (2022) A futuristic perspective on human-centric assembly. J Manuf Syst 62:199–201. https://doi.org/10.1016/j.jmsy.202 1.11.001
dc.relation.referencesEswaran M, Gulivindala AK, Inkulu AK, Bahubalendruni MR (2023) Augmented reality-based guidance in product assembly and maintenance/repair perspective: a state of the art review on challenges and opportunities. Expert Syst Appl 213:118983
dc.relation.referencesDong Z, Wang T (2024) Artificial intelligence driving perception, cognition, decision-making and deduction in energy systems: state-of-the-art and potential directions. Energy Internet 1:27–33. https://doi.org/10.1049/ein2.12010
dc.relation.referencesLee J, Su H (2025) Agentic ai for smart manufacturing. Manuf Lett 46:92–96. https://doi.org/10.1016/j.mfglet.2025.10.013
dc.relation.referencesLampen E, Lehwald J, Pfeiffer T (2020) Virtual humans in ar: evaluation of presentation concepts in an industrial assistance use case. In: Proceedings of the 26th ACM symposium on virtual reality software and technology. https://doi.org/10.1145/3385956.3418974
dc.relation.referencesHoedt S, Claeys A, Landeghem HV, Cottyn J (2016) Evaluation framework for virtual training within mixed-mo del manual assembly. IFAC-PapersOnLine 49:261–266. https://doi.org/10.10 16/j.ifacol.2016.07.614
dc.relation.referencesBahubalendruni MR, Biswal BB (2016) A review on assembly sequence generation and its automation. Proc Inst Mech Eng Part C 230:824–838. https://doi.org/10.1177/0954406215584633
dc.relation.referencesMichalos G, Makris S, Papakostas N, Mourtzis D, Chryssolouris G (2010) Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach. CIRP J Manuf Sci Technol 2:81–91. https://doi.org/10.1016/j.cirpj.2009.12.001
dc.relation.referencesPan C (2005) Integrating CAD files and automatic assembly sequence planning. publisherIowa State University
dc.relation.referencesZhang J et al (2022) Projected augmented reality assembly assistance system supporting multi-modal interaction. Int J Adv Manuf Technol 123:1353–1367. https://doi.org/10.1007/s00170- 022-10113-6
dc.relation.referencesFu M, Fang W, Gao S, an Yizhou Chen JH (2022) Edge computing-driven scene-aware intelligent augmented reality assembly. Int J Adv Manuf Technol 119:7369–7381. https://doi.org/10.1007 /s00170-022-08758-4
dc.relation.referencesKosch T, Funk M, Schmidt A, Chuang LL (2018) Identifying cognitive assistance with mobile electroencephalography: a case study with in-situ projections for manual assembly. Proc ACM Hum-Comput Interact 2. https://doi.org/10.1145/3229093
dc.relation.referencesEswaran M et al (2026) Extended reality (xr) for industrial assembly: a state-of-the-art review toward human-centric industry 5.0. Expert Syst Appl 307:130877. https://doi.org/10.1016/j.eswa.202 5.130877
dc.relation.referencesCohen Y, Faccio M, Galizia FG, Mora C, Pilati F (2017) Assembly system configuration through industry 4.0 principles: the expected change in the actual paradigms. IFAC-PapersOnLine 50:14958–14963. https://doi.org/10.1016/j.ifacol.2017.08.2550
dc.relation.referencesZhao S et al (2025) Industrial foundation models (ifms) for intelligent manufacturing: a systematic review. J Manuf Syst 82:420– 448. https://doi.org/10.1016/j.jmsy.2025.06.011
dc.relation.referencesZhou Q et al (2026) Towards zero-shot robot tool manipulation in industrial context: a modular vlm framework enhanced by multimodal affordance representation. Robot Comput -Integr Manuf 98:103161. https://doi.org/10.1016/j.rcim.2025.103161
dc.relation.referencesLi Y et al (2026) Large language models for manufacturing. J Manuf Syst 86:516–545. https://doi.org/10.1016/j.jmsy.2026.02.014
dc.relation.referencesFan J et al (2025) Vision-language model-based human-robot collaboration for smart manufacturing: a state-of-the-art survey. Front Eng Manag 12:177–200. https://doi.org/10.1007/s42524-0 25-4136-9
dc.relation.referencesRen Y, Liu Y, Ji T, Xu X (2025) Ai agents and agentic ai-navigating a plethora of concepts for future manufacturing. J Manuf Syst 83:126–133. https://doi.org/10.1016/j.jmsy.2025.08.017
dc.relation.referencesFarahani MA, Khan MI, Wuest T (2026) Hybrid agentic ai and multi-agent systems in smart manufacturing. J Manuf Syst 86:612–623. https://doi.org/10.1016/j.jmsy.2026.04.002
dc.relation.referencesMoher D, Liberati A, Tetzlaff J, Altman DG, Group P (2010) Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Int J Surg 8:336–341. https://doi.org/10.10 16/j.ijsu.2010.02.007
dc.relation.referencesWang Z et al (2021) M-ar: a visual representation of manual operation precision in ar assembly. Int J Human–Comput Interact 37:1799–1814. https://doi.org/10.1080/10447318.2021.1909278
dc.relation.referencesSimonetto M, Peron M, Fragapane G, Sgarbossa F (2021) Digital assembly assistance system in industry 4.0 era: a case study with projected augmented reality 644–651. https://doi.org/10.1007/97 8-981-33-6318-2_80
dc.relation.referencesAgati SS, Bauer RD, Hounsell MdS, Paterno AS (2020) Augmented reality for manual assembly in industry 4.0: Gathering guidelines. In: 2020 22nd Symposium on virtual and augmented reality (SVR), pp 179–188. https://doi.org/10.1109/SVR51698.2 020.00039
dc.relation.referencesGollan B, Haslgruebler M, Ferscha A, Heftberger J (2018) Making sense: experiences with multi-sensor fusion in industrial assistance systems. In: Proceedings of the 5th international conference on physiological computing systems, pp 64–74. https://d oi.org/10.5220/0007227600640074
dc.relation.referencesVan Acker BB et al (2020) Mobile pupillometry in manual assembly: a pilot study exploring the wearability and external validity of a renowned mental workload lab measure. Int J Ind Ergon 75:102891. https://doi.org/10.1016/j.ergon.2019.102891
dc.relation.referencesLampen E et al (2019) Combining simulation and augmented reality methods for enhanced worker assistance in manual assembly. Procedia CIRP 81:588–593. https://doi.org/10.1016/j.procir. 2019.03.160
dc.relation.referencesPopper KR (1977) The logic of scientific discovery. Syst Zool 26:361
dc.relation.referencesBlasing D, Hinrichsen M, Svennd Bornewasser (2020) Reduction of cognitive load in complex assembly systems. Human Interaction, Emerging Technologies and Future Applications II 495–500. https://doi.org/10.1007/978-3-030-44267-5_75
dc.relation.referencesSochor R, Schick TS, Merkel L, Braunreuther S, Reinhart G (2020) Current knowledge management in manual assembly – further development by the analytical hierarchy process, incentive and cognitive assistance systems. In: Proceedings of the conference on production systems and logistics: CPSL 2020. https://doi.org/10.15488/9662
dc.relation.referencesPang J, Zheng P (2023) An mbd-enabled digital twin modeling method for cognition assistance in human-centric smart assembly 1–6. https://doi.org/10.1109/CASE56687.2023.10260573
dc.relation.referencesFang W, Chen L, Han L, Ding J (2025) Context-aware cognitive augmented reality assembly: past, present, and future. J Ind Inf Integr 44:100780. https://doi.org/10.1016/j.jii.2025.100780
dc.relation.referencesCaprari G, Katiraee N, Berti N, Battini D (2025) Cognitive ergonomics assessment in manual and collaborative assembly systems summer school francesco turco. Proceedings
dc.relation.referencesPelosi M, Zanchettin AM, Rocco P (2025) Combined hiddenmarkov model and siamese network approach for assembly operation recognition and error detection. International Journal of Production Research 1–23. https://doi.org/10.1080/00207543.20 25.2556484
dc.relation.referencesGiridhar MP, Panicker VV (2023) Does cognitive aspects of information and material presentation matter in worker allocation in an assembly line? a case study of a recycling unit in India. Sādhanā 48:23. https://doi.org/10.1007/s12046-023-02078-3
dc.relation.referencesMüller R, Vette-Steinkamp M, Hörauf L, Speicher C, Bashir A (2018) Worker centered cognitive assistance for dynamically created repairing jobs in rework area. Procedia CIRP 72:141–146. https://doi.org/10.1016/j.procir.2018.03.137
dc.relation.referencesLampen E, Lehwald J, Pfeiffer T (2020) A context-aware assistance framework for implicit interaction with an augmented human. Virtual, Augmented and Mixed Reality. Industrial and Everyday Life Applications 91–110. https://doi.org/10.1007/97 8-3-030-49698-2_7
dc.relation.referencesMüller R, Hörauf L, Bashir A (2019) Cognitive assistance systems for dynamic environments. In; 2019 24th IEEE international conference on emerging technologies and factory automation (ETFA), pp 649–656. https://doi.org/10.1109/ET FA.2019.8868986
dc.relation.referencesMargherita P, Marcello P (2017) An ergonomics study on manual assembly process re-design in manufacturing firms. https://doi.or g/10.3233/978-1-61499-779-5-349
dc.relation.referencesWang B, Zheng L, Wang Y, Wang L, Qi Z (2025) Context-aware ar adaptive information push for product assembly: aligning information load with human cognitive abilities. Adv Eng Inform 64:103086. https://doi.org/10.1016/j.aei.2024.103086
dc.relation.referencesFink K, Ziegler A, Härdtlein C, Berger C, Daub R (2024) Assessing the human competence and individual support level in manual assembly through cognitive assistance systems. Procedia CIRP 126:811–816. https://doi.org/10.1016/j.procir.2024.08.263
dc.relation.referencesWang Z et al (2024) Visual encoding method for mr interface operation process prompts supporting blind area assembly. In: 2024 IEEE 2nd international conference on control, electronics and computer technology (ICCECT), pp 113–117. https://doi.org /10.1109/ICCECT60629.2024.10546058
dc.relation.referencesKosch T, Abdelrahman Y, Funk M, Schmidt A (2017) One size does not fit all: challenges of providing interactive worker assistance in industrial settings. In: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers, pp 1006–1011. https://doi.org/10.1 145/3123024.3124395
dc.relation.referencesLi W, Xu A, Wei M, Zuo W, Li R (2024) Deep learning-based augmented reality work instruction assistance system for complex manual assembly. J Manuf Syst 73:307–319. https://doi.org/ 10.1016/j.jmsy.2024.02.009
dc.relation.referencesPankok C et al (2017) The effects of interruption similarity and complexity on performance in a simulated visual-manual assembly operation. Appl Ergon 59:94–103. https://doi.org/10.1016/j.a pergo.2016.08.022
dc.relation.referencesPeltokorpi J, Jaber MY (2022) Interference-adjusted power learning curve model with forgetting. Int J Ind Ergon 88:103257. https://doi.org/10.1016/j.ergon.2021.103257
dc.relation.referencesAlkan B (2019) An experimental investigation on the relationship between perceived assembly complexity and product design complexity. Int J Interact Des Manuf (IJIDeM) 13:1145–1157. ht tps://doi.org/10.1007/s12008-019-00556-9
dc.relation.referencesSolmaz S, Henderickx R, den Bergh JV, Birem M (2024) Capturing the system requirements for adopting industry 5.0 in quality inspection: an evidence-based approach. Procedia CIRP 128:369–374. https://doi.org/10.1016/j.procir.2024.03.016
dc.relation.referencesSimmen Y, Eggler T, Legath A, Agotai D, Cords H (2023) Nonoverlayed guidance in augmented reality: User study in radiopharmacy. Intelligent Human Computer Interaction 516–526. https://doi.org/10.1007/978-3-031-27199-1_52
dc.relation.referencesResearch of Task Complexity Decision System for Manual Assembly Tasks Using Fuzzy Cognitive Maps to Construct Bayesian Networks, Vol. Volume 9: Mechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters of ASME International Mechanical Engineering Congress and Exposition. https://doi.org/10.1115/IM ECE2022-93881
dc.relation.referencesAbdul Hadi M et al (2022) Towards flexible and cognitive production—addressing the production challenges. Applied Sciences 12. https://www.mdpi.com/2076-3417/12/17/8696
dc.relation.referencesBrolin A, Thorvald P, Case K (2017) Experimental study of cognitive aspects affecting human performance in manual assembly Prod Manuf Res 5:141–163. https://doi.org/10.1080/21693277.2 017.1374893
dc.relation.referencesLindblom J, Thorvald P (2017) Manufacturing in the wild - viewing human-based assembly through the lens of distributed cognition. Prod Manuf Res 5:57–80. https://doi.org/10.1080/21693277 .2017.1322540
dc.relation.referencesXiao H, Duan Y, Zhang Z, Li M (2018) Detection and estimation of mental fatigue in manual assembly process of complex products. Assem Autom 38:239–247. https://doi.org/10.1108/A A-03-2017-040
dc.relation.referencesAgati SS, da S Hounsell M, Paterno AS (2024) Graal—modeling, prototyping and assessing a gamified responsible augmented assembly line system. Int J Adv Manuf Technol 132:2735–2751. https://doi.org/10.1007/s00170-024-13460-8
dc.relation.referencesPapetti A, Ciccarelli M, Brunzini A, Germani M (2022) Investigating the application of augmented reality to support wire harness activities 116–124. https://doi.org/10.1007/978-3-030-9123 4-5_11
dc.relation.referencesYan Y et al (2023) A novel adaptive visualization method based on user intention in ar manual assembly. Int J Adv Manuf Technol 129:4705–4730. https://doi.org/10.1007/s00170-023-12557-w
dc.relation.referencesPokorni B, Popescu D, Constantinescu C (2022) Design of cognitive assistance systems in manual assembly based on quality function deployment. Applied Sciences 12. https://www.mdpi.co m/2076-3417/12/8/3887
dc.relation.referencesKolbeinsson A, Lindblom J, Thorvald P (2017) Missing mediated interruptions in manual assembly: critical aspects of breakpoint selection. Appl Ergon 61:90–101. https://doi.org/10.1016/j.aperg o.2017.01.010
dc.relation.referencesLindblom J, Gündert J (2017) Managing mediated interruptions in manufacturing: selected strategies used for coping with cognitive load 389–403. https://doi.org/10.1007/978-3-319-41691-5_33
dc.relation.referencesWollter Bergman M, Berlin C, Babapour Chafi M, Falck A-C, Örtengren R (2021) Cognitive ergonomics of assembly work from a job demands–resources perspective: three qualitative case studies. International Journal of Environmental Research and Public Health 18. https://www.mdpi.com/1660-4601/18/23/12282
dc.relation.referencesKuipers N, Kolbeinsson A, Thorvald P (2021) Appropriate assembly instruction modes: factors to consider. https://doi.org/ 10.3233/ATDE210007
dc.relation.referencesTorres Y, Nadeau S, Landau K (2021) Evaluation of fatigue and workload among workers conducting complex manual assembly in manufacturing. IISE Trans Occup Ergon Human Factors 9:49– 63. https://doi.org/10.1080/24725838.2021.1997835
dc.relation.referencesBläsing D, Bornewasser M (2021) Influence of increasing task complexity and use of informational assistance systems on mental workload. Brain Sciences 11. https://www.mdpi.com/2076-34 25/11/1/102
dc.relation.referencesAcker BBV et al (2021) Development and validation of a behavioural video coding scheme for detecting mental workload in manual assembly. Ergonomics 64:78–102. https://doi.org/10.108 0/00140139.2020.1811400
dc.relation.referencesMerkel L, Berger C, Braunreuther S, Reinhart G (2019) Determination of cognitive assistance functions for manual assembly systems 198–207
dc.relation.referencesHinrichsen S, Nikolenko A, Beckmann N, Meyer F (2022) Development of a new type of carousel-based compacted work system for mixed-model assembly in mechanical engineering 0087– 0090. https://doi.org/10.1109/IEEM55944.2022.9989731
dc.relation.referencesGan ZL, Musa SN, Yap HJ, Liu C, Xu Y (2022) A conceptual framework of adaptive ar-based digital guidance system for highmix low-volume manual assembly. In: 2022 27th International conference on automation and computing (ICAC), pp 1–5. https:/ /doi.org/10.1109/ICAC55051.2022.9911112
dc.relation.referencesKelm B, Haas PH, Jochum S, Margies L, Müller R (2025) Enhancing assembly instruction generation for cognitive assistance systems with large language models. Procedia CIRP 134:7–12. https://doi.org/10.1016/j.procir.2025.03.010
dc.relation.referencesGan ZL, Musa SN, Yap HJ (2025) Machine learning on assembly guidance system ontology for manufacturing assembly. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. https://doi.org/10.1177/095440542 51395468
dc.relation.referencesFreydank E, Kießling N, Seyfried L, Cencic MR (2026) Transforming industrial training: A comparative study of volumetric video in mixed reality and paper-based instructions 456–466. https://doi.org/10.1007/978-3-031-97763-3_33
dc.relation.referencesSridhar EP et al (2025) Enhancing manual assembly: a comparative study of mixed reality and paper-based instructions assessing user performance and cognitive load. In: IISE annual conference. Proceedings. https://doi.org/10.21872/2025IISE_5071
dc.relation.referencesPetzoldt C, Keiser D, Schöbel N, Freitag M (2022) Planung von assistenzsystemen für die industrielle montage. Zeitschrift für wirtschaftlichen Fabrikbetrieb 117:157–163
dc.relation.referencesFink K, Riemensperger T, Brugger M, Berger J, Braunreuther S (2021) Konfigurator für kognitive assistenzsysteme/configuration of cognitive assistance systems. wt Werkstattstechnik Online 111:770–774. https://doi.org/10.37544/1436-4980-2021-10-116
dc.relation.referencesBüttner S, Funk M, Sand O, Röcker C (2016) Using headmounted displays and in-situ projection for assistive systems: a comparison. In: Proceedings of the 9th ACM international conference on PErvasive technologies related to assistive environments. https://doi.org/10.1145/2910674.2910679
dc.relation.referencesGiridhar MP, Lasin, Panicker VV (2020) Experimental analysis of cognitive issues impacting manual assembly task. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), pp 1–6. https://doi.org/10.1109/ICS CAN49426.2020.9262305
dc.relation.referencesBerlin C, Bergman MW, Chafi MB, Falck A-C, Örtengren R (2021) A systemic overview of factors affecting the cognitive performance of industrial manual assembly workers 371–381. https:/ /doi.org/10.1007/978-3-030-74608-7_47
dc.relation.referencesParmentier DD, Acker BBV, Detand J, Saldien J (2020) Design for assembly meaning: a framework for designers to design products that support operator cognition during the assembly process. Cogn Technol Work 22:615–632. https://doi.org/10.1007/s10111 -019-00588-x
dc.relation.referencesPeixe LC, Agati S, Hounsell MDS (2024) Manual assembly augmented reality systems implementation: a systematic literature mapping. In: Proceedings of the 25th symposium on virtual and augmented reality, pp 17–25. https://doi.org/10.1145/3625008.36 25011
dc.relation.referencesGiridhar MP, Panicker VV (2025) Towards measuring cognitive load through personalised models in a medium-scale enterprise: a worker allocation approach. Theor Issues Ergon Sci 26:304–331. https://doi.org/10.1080/1463922X.2024.2438030
dc.relation.referencesRenu RS, Righter J, Lytch J (2022) Developing requirements for a manufacturing training platform: a three-pronged approach. Volume 2: 42nd Computers and Information in Engineering Conference (CIE)
dc.relation.referencesHasan N, Alkan B (2025) Gest-sar: a gesture-controlled spatial ar system for interactive manual assembly guidance with real-time operational feedback. Machines 13. https://doi.org/10.3390/mach ines13080658
dc.relation.referencesOestreich H, Töniges T, Wojtynek M, Wrede S (2019) Interactive learning of assembly processes using digital assistance. Proc Manuf 31:14–19. https://doi.org/10.1016/j.promfg.2019.03.003y
dc.relation.referencesHeinz-Jakobs M, Große-Coosmann A, Röcker C (2022) Promoting inclusive work with digital assistance systems: Experiences of cognitively disabled workers with in-situ assembly support 377–384. https://doi.org/10.1109/GHTC55712.2022.9910994
dc.relation.referencesClaeys A et al (2022) Methodology to integrate ergonomics information in contextualized digital work instructions. Procedia CIRP 106:168–173. https://www.sciencedirect.com/science/article/pii/ S2212827122001743
dc.relation.referencesAehnelt M, Urban B (2015) The knowledge gap: providing situation-aware information assistance on the shop floor 232–243. https://doi.org/10.1007/978-3-319-20895-4_22
dc.relation.referencesMüller R, Hörauf L, Speicher C, Bashir A (2019) Situational cognitive assistance system in rework area. Proc Manuf 38:884–891. https://doi.org/10.1016/j.promfg.2020.01.170
dc.relation.referencesBleser G et al (2015) Cognitive learning, monitoring and assistance of industrial workflows using egocentric sensor networks. PLoS ONE 10:e0127769. https://doi.org/10.1371/journal.pone.0 127769
dc.relation.referencesStrenge B, Schack T (2021) Empirical relationships between algorithmic sda-m-based memory assessments and human errors in manual assembly tasks. Sci Rep 11:9473. https://doi.org/10.10 38/s41598-021-88921-1
dc.relation.referencesRiedel A et al (2021) A deep learning-based worker assistance system for error prevention: case study in a real-world manual assembly. Adv Prod Eng Manag 16:393–404. https://doi.org/10.1 4743/apem2021.4.408
dc.relation.referencesShinde PS et al (2025) Transforming msme assembly operations: smart manual assembly table for improved productivity. Int J Basic Appl Sci 14:40–51. https://doi.org/10.14419/am35a906
dc.relation.referencesQeshmy DE, Makdisi J, Ribeiro da Silva EHD, Angelis J (2019) Managing human errors: augmented reality systems as a tool in the quality journey. Proc Manuf 28:24–30. https://doi.org/10.101 6/j.promfg.2018.12.005
dc.relation.referencesRiedel A et al (2023) Evaluating augmented reality, deep learning and paper-based assistance systems in industrial manual assembly. In: Advances in production management systems. production management systems for responsible manufacturing, service, and logistics futures, pp 417–431. https://doi.org/10.1007/978-3-031- 43662-8_30
dc.relation.referencesZigart T, Schlund S (2022) Ready for industrial use? a user study of spatial augmented reality in industrial assembly. In: 2022 IEEE international symposium on mixed and augmented reality adjunct (ISMAR-Adjunct), pp 60–65. https://doi.org/10.1109/ISMAR-A djunct57072.2022.00022
dc.relation.referencesSochor R, Kraus L, Merkel L, Braunreuther S, Reinhart G (2019) Approach to increase worker acceptance of cognitive assistance systems in manual assembly. Procedia CIRP 81:926–931. https:// doi.org/10.1016/j.procir.2019.03.229
dc.relation.referencesJeffri NFS, Rambli DRA (2020) Guidelines for the interface design of ar systems for manual assembly. In: Proceedings of the 2020 4th international conference on virtual and augmented reality simulations, pp 70–77. https://doi.org/10.1145/33 85378.3385389
dc.relation.referencesNeumann A et al (2020) Avikom: towards a mobile audiovisual cognitive assistance system for modern manufacturing and logistics. In: Proceedings of the 13th ACM international conference on PErvasive technologies related to assistive environments. https:// doi.org/10.1145/3389189.3389191
dc.relation.referencesSimões B, Amicis RD, Barandiaran I, Posada J (2019) Cross reality to enhance worker cognition in industrial assembly operations. Int J Adv Manuf Technol 105:3965–3978. https://doi.org/10.1007 /s00170-019-03939-0
dc.relation.referencesPratticò FG, Di Cosmo D, Piviotti M, La Rosa G, Lamberti F (2024) Effectiveness of computer-based e-learning and virtual reality training system for the preparatory phase of a training on maintenance procedure 1–6. https://doi.org/10.1109/ICCE59016. 2024.10444354
dc.relation.referencesGuo Z et al (2022) An evaluation method using virtual reality to optimize ergonomic design in manual assembly and maintenance scenarios. Int J Adv Manuf Technol 121:5049–5065. https://doi.o rg/10.1007/s00170-022-09657-4
dc.relation.referencesUlmer J, Braun S, Cheng C-T, Dowey S, Wollert J (2023) A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation. Int J Prod Res 61:7760–7775. https://doi.org/10.1080/00207543.2023.2166140
dc.relation.referencesMeara MO, Cheng X, Eden J, Ivanova E, Burdet E (2023) A third eye to augment environment perception 1239–1244. https://doi.o rg/10.1109/ROMAN57019.2023.10309628
dc.relation.referencesTrendov A, Rizov T, Jovanoski B, Trendova KM (2026) Enhancing industrial workflows with augmented reality (ar): Ar-based poka-yoke visual assembly guide for a smart learning factory 157–172. https://doi.org/10.1007/978-3-031-97772-5_11
dc.relation.referencesPrecup S-A, Pirvu B-C, Gellert A, Zamfirescu C-B (2025) Cognitive control units in smart manufacturing: insights from a soarbased assistance system. IEEE Access 13:196167–196180. https:/ /doi.org/10.1109/ACCESS.2025.3633609
dc.relation.referencesFang W, Teng Z, Zhang Q, Wu Z (2024) A natural bare-hand interface-enabled interactive ar assembly guidance. Int J Adv Manuf Technol 133:3193–3207. https://doi.org/10.1007/s00170- 024-13922-z
dc.relation.referencesMarino E, Barbieri L, Bruno F, Muzzupappa M (2025) A novel ar recoloring technique to enhance operator performance on inspection tasks in industry 4.0 environments. IEEE Trans Visual Comput Graphics 31:4605–4618. https://doi.org/10.1109/TVCG.2024 .3410537
dc.relation.referencesWang Z et al (2024) Visual encoding method for mr interface operation process prompts supporting blind area assembly 113– 117. https://doi.org/10.1109/ICCECT60629.2024.10546058
dc.relation.referencesLi J et al (2026) An adaptive ar guidance interface layout optimization approach for human-centered assembly training systems. Adv Eng Inform 69:103975. https://doi.org/10.1016/j.aei.2025.1 03975
dc.relation.referencesWang Z et al (2022) A comprehensive review of augmented reality-based instruction in manual assembly, training and repair. Robot Comput-Integr Manuf 78:102407. https://doi.org/10.1016/ j.rcim.2022.102407
dc.relation.referencesSingh AK (2025) Vr/ar in ergonomics and workspace design: a dual-perspective analysis of applications and implications. Appl Ergon 129:104612. https://doi.org/10.1016/j.apergo.2025.104612
dc.relation.referencesDrouot M, Le Bigot N, Bricard E, de Bougrenet J-L, Nourrit V (2022) Augmented reality on industrial assembly line: impact on effectiveness and mental workload. Appl Ergon 103:103793. https://doi.org/10.1016/j.apergo.2022.103793
dc.relation.referencesSopidis G et al (2022) Micro-activity recognition in industrial assembly process with imu data and deep learning. In: Proceedings of the 15th international conference on PErvasive technologies related to assistive environments, pp 103–112. https://doi.or g/10.1145/3529190.3529204
dc.relation.referencesNakayama K, Onari H (2024) Analysis of takt time extension in assembly lines with multiple elemental works allocated to a process 581–589. https://doi.org/10.1007/978-981-97-0194-0_57
dc.relation.referencesSochor R, Merkel L, Braunreuther S, Reinhart G, Greiter F (2020) Monetäre bewertung eines anreizsystems/economic feasibility study of an incentive system for motivating manual assembly employees. wt Werkstattstechnik online 110:125–129. https:/ /doi.org/10.37544/1436-4980-2020-03-41
dc.relation.referencesJohansson P et al (2018) Assessment based information needs in manual assembly. DEStech Transactions on Engineering and Technology Research. https://doi.org/10.12783/dtetr/icpr2017/ 17637
dc.relation.referencesWang X, Ong S, Nee A (2016) Multi-modal augmented-reality assembly guidance based on bare-hand interface. Adv Eng Inform 30:406–421. https://doi.org/10.1016/j.aei.2016.05.004
dc.relation.referencesKeiser D, Petzoldt C, Walura V, Leimbrink S, Freitag M (2023) Concept and integration of knowledge management in assembly assistance systems. Procedia CIRP 118:940–945. https://doi.org/ 10.1016/j.procir.2023.06.162
dc.relation.referencesGanlin Z, Pingfa F, Jianfu Z, Dingwen Y, Zhijun W (2021) Information integration and instruction authoring of augmented assembly systems. Int J Intell Syst 36:5028–5050. https://doi.org/ 10.1002/int.22501
dc.relation.referencesRupprecht P, Kueffner-McCauley H, Trimmel M, Schlund S (2021) Adaptive spatial augmented reality for industrial site assembly. Procedia CIRP 104:405–410. https://doi.org/10.1016/ j.procir.2021.11.068
dc.relation.referencesFunk M, Hartwig M, Wischniewski S (2019) Evaluation of assistance systems for manual assembly work 794–798. https://doi.or g/10.1109/SII.2019.8700420
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc670 - Manufactura
dc.subject.lembIndustrial assembly
dc.subject.lembManufacturing processes
dc.subject.lembComputer integrated manufacturing systems
dc.subject.lembAutomation
dc.subject.lembAugmented reality
dc.subject.lembArtificial intelligence
dc.subject.lembComputer vision
dc.subject.lembHuman-computer interaction
dc.subject.lembDecision support systems
dc.subject.lembExpert systems (Computer science)
dc.subject.lembKnowledge representation (Information theory)
dc.subject.lembMachine learning
dc.subject.lembIndustrial engineering
dc.subject.odsODS 9: Industria, innovación e infraestructura. Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación
dc.subject.odsODS 12: Producción y consumo responsables. Garantizar modalidades de consumo y producción sostenibles
dc.subject.proposalindustrial assembly
dc.subject.proposalAugmented reality
dc.subject.proposalHuman-centered manufacturing
dc.subject.proposalAgentic reasoning
dc.subject.proposalHuman-centered manufacturing
dc.subject.proposalCognitive support
dc.titleA human-centered cognitive support framework for manual industrial assembly: integrating perception, agentic reasoning, and augmented reality guidance
dc.typeArtículo de revista
dc.type.coarhttp://purl.org/coar/resource_type/c_18cf
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/article
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication64d91b05-2eec-4513-9d10-ab72ec12c865
relation.isAuthorOfPublication645833b4-d139-46af-a054-b02f41055694
relation.isAuthorOfPublication.latestForDiscovery645833b4-d139-46af-a054-b02f41055694

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