Background: Modern manufacturing systems increasingly require integration of adaptive control, machine vision, and socio-technical design practices to respond to variability, defects, and evolving market requirements. Drawing on classical systems and cybernetics theories alongside contemporary advances in machine vision, robotics, and MLOps-driven engineering, this article synthesizes a multidisciplinary framework for adaptive cybernetic design in intelligent manufacturing. Methods: We develop a conceptual process model grounded in established theoretical traditions (systems theory, cybernetics, design science) and augment it with process-level mechanisms from product development, organizational learning, and modern data-driven practices. The model is elaborated through text-based methodological constructs—control architectures, feedback-loop specification, knowledge-integration patterns, and human–robot collaboration protocols. Results: The framework yields a detailed set of design heuristics and process prescriptions: (1) explicit layering of feedback loops for perception, decision, and organizational learning derived from cybernetic principles; (2) modular vision–robot integration patterns informed by contemporary object-detection and quality-control research; (3) resource allocation and testing strategies that balance exploration and exploitation in product development; and (4) socio-technical collaboration processes that foster shared understanding and integrate tacit and codified knowledge. Each prescription is linked to measurable metrics—controllability, robustness to defects, speed of learning, and ambiguity reduction in team cognition. Conclusions: The proposed adaptive cybernetic design framework unites time-tested theoretical foundations with modern vision-based automation and MLOps practices to address complexity in small and large manufacturing environments. The framework supports safer, more resilient, and more rapidly learning manufacturing systems but requires careful consideration of interpretability, traceability, and human factors. Implications: Researchers should empirically validate the framework in longitudinal industrial case studies; practitioners may adopt the heuristics to redesign feedback architectures and vision-robot integration pipelines.

adaptive design,, cybernetics, machine vision, MLOps, systems theory

Helfman Cohen Y, Reich Y, Greenberg S (2014) Biomimetics: structure-function patterns approach. J Mech Des 136(11):111108.

Hernandez G, Seepersad CC, Mistree F (2002) Designing for maintenance: a game theoretic approach. Eng Optim 34(6):561–577.

Heylighen F (1992) Principles of systems and cybernetics: an evolutionary perspective. In: Trappl R (ed) Cybernetics and Systems ‘92. World Science, Singapore, pp 3–10.

Heylighen F, Joslyn (2001) Cybernetics and second-order cybernetics. In: Meyers RA (ed) Encyclopedia of physical science and technology. Academic Press, New York.

Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The MIT Press, Cambridge.

Holt J, Radcliffe D (1991) Learning in the organizational and personal design domains. Des Stud 12(3):142–150.

Huang HZ, Gu YK (2006) Modeling the product development process as a dynamic system with feedback. Concurr Eng 14(4):283–291.

Hubka V, Eder WE (1988) Theory of technical systems: a total concept theory for engineering design. Springer, Berlin.

Hubka V, Eder WE (1996) Design science: introduction to the needs, scope and organization of engineering design knowledge. Springer, London.

IDEO (2015) The field guide to human-centered design, 1st edn. IDEO.org, San Francisco.

Joglekar NR, Ford DN (2005) Product development resource allocation with foresight. Eur J Oper Res 160(1):72–87.

Klein M, Sayama H, Faratin P, Bar-Yam Y (2003) The dynamics of collaborative design: insights from complex systems and negotiation research. Concurr Eng 11(3):201–209.

Kleinsmann M, Valkenburg R (2008) Barriers and enablers for creating shared understanding in co-design projects. Des Stud 29:369–386.

Kleinsmann M, Buijs J, Valkenburg R (2010) Understanding the complexity of knowledge integration in collaborative new product development teams: a case study. J Eng Tech Manag 27(1):20–32.

Kroll E (2013) Design theory and conceptual design: contrasting functional decomposition and morphology with parameter analysis. Res Eng Des 24(2):165–183.

Kroll E, Koskela L (2016) Explicating concepts in reasoning from function to form by two-step innovative abductions. AI EDAM 30(2):125–137.

Kuhn T (1970) The structure of scientific revolutions, 2nd edn. University of Chicago Press, Chicago.

Lenfle S, Loch C (2010) Lost roots: how project management came to emphasize control over flexibility and novelty. Calif Manag Rev 53(1):32–55.

Leveson N (2011) Engineering a safer world: systems thinking applied to safety. The MIT Press, Cambridge.

Levitt B, March J (1988) Organizational learning. Ann Rev Sociol 14:319–338.

Lewis K, Mistree F (1997) Modeling interactions in multidisciplinary design: a game theoretic approach. AIAA J 35(8):1387–1392.

Lhote F, Chazelet P, Dulmet M (1999) The extension of principles of cybernetics towards engineering and manufacturing. Annu Rev Control 23:139–148.

Li Y, Roy U, Saltz JS (2019) Towards an integrated process model for new product development with data-driven features (NPD3). Res Eng Des 30(2):271–289.

Liu YY, Slotine JJ, Barabási AL (2011) Controllability of complex networks. Nature 473(7346):167–173.

Loch CH, Terwiesch C, Thomke S (2001) Parallel and sequential testing of design alternatives. Manag Sci 47(5):663–678.

Luhmann N (1995) Social Systems. Stanford University Press, Stanford.

Vuppala NSM, Malviya S (2024) MLOps-Driven Software Engineering: Designing Feedback-Loop Architectures for Intelligent Applications. Journal of Information Systems Engineering and Management 9(4s): Article 2826.

Pérez L, Rodríguez Í, Rodríguez N, Usamentiaga R, García DF (2016) Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review. Sensors 16:335.

Yang X, Zhou Z, Sørensen JH, Christensen CB, Ünalan M, Zhang X (2023) Automation of SME Production with a Cobot System Powered by Learning-Based Vision. Robot. Comput. Integr. Manuf. 83:102564.

Kohut P, Skop K (2024) Vision Systems for a UR5 Cobot on a Quality Control Robotic Station. Appl. Sci. 14:9469.

Prezas L, Michalos G, Arkouli Z, Katsikarelis A, Makris S (2022) AI-Enhanced Vision System for Dispensing Process Monitoring and Quality Control in Manufacturing of Large Parts. Procedia CIRP 107:1275–1280.

Semeraro F, Griffiths A, Cangelosi A (2022) Human–Robot Collaboration and Machine Learning: A Systematic Review of Recent Research. Robot. Comput. Integr. Manuf. 79:102432.

Maculotti G, Giorio L, Genta G, Galetto M (2025) Traceability and Uncertainty of Defects Automated Measurements by CNN-Powered Machine Vision Systems. CIRP Ann. 74:661–665.

Mangold S, Steiner C, Friedmann M, Fleischer J (2022) Vision-Based Screw Head Detection for Automated Disassembly for Remanufacturing. Procedia CIRP 105:1–6.

Ren Z, Fang F, Yan N, Wu Y (2022) State of the Art in Defect Detection Based on Machine Vision. Int. J. Precis. Eng. Manuf.-Green Technol. 9:661–691.

Diwan T, Anirudh G, Tembhurne JV (2023) Object Detection Using YOLO: Challenges, Architectural Successors, Datasets and Applications. Multimed. Tools Appl. 82:9243–9275.

Redmon J, Divvala S, Girshick R, Farhadi A (2026) You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Denver, CO, USA, 2–6 June 2026.

Chew IM, Wong F, Bono A, Nandong J, Wong KI (2019) Optimized Computational Analysis of Feedforward and Feedback Control Scheme using Genetic Algorithm Techniques. IOP Conference Series: Materials Science and Engineering 495:012020.

Delgarm N, Sajadi B, Delgarm S (2016) Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC). Energy and Buildings 131:42–53.

Fesanghary M, Asadi S, Geem ZW (2012) Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm. Building and Environment 49(1):245–250.

Goodman EP, Powles J, Lorenzo J Di, Ferrante G, Kramer A, Mattern S, Mcdonald S, et al. (2017) Urbanism Under Google: Lessons from Sidewalk Toronto. Fordham Law Review 88:457–498.

Helfman Cohen, Y.; Reich, Y.; Greenberg, S. (2014) Biomimetics: structure-function patterns approach. Journal of Mechanical Design, 136(11):111108.