EVOLUTIONARY QUALITY FUNCTION DEPLOYMENT WITH INTEGRATED AI AND SYSTEM EVOLUTION PLANNING FOR INDUSTRY 5.0

Stelian BRAD, Diana ŢICUDEAN, Vasile-Dragoș BARTOȘ, Emilia BRAD, Ionuț Chiș ADRIAN

Abstract


This paper proposes an evolutionary form of the Quality Function Deployment (QFD) framework that integrates artificial intelligence, TRIZ-based evolution planning, and digital twin technologies to meet the adaptive demands of Industry 5.0. The model ensures continuous alignment with evolving requirements by introducing a temporalized matrix and AI-driven interpretation of stakeholder input. A case study on an urban delivery robot demonstrates the framework’s capacity to translate real-time feedback into actionable design adaptations, reducing retrofit costs and enhancing system responsiveness.


Full Text:

PDF

References


T. B. Wu, M. H. Wang, "Using quality function deployment and analytic network process to improve the design of an e-learning system," Expert Systems with Applications, vol. 38, no. 7, pp. 7914–7922, 2011.

M. Panetto, J. Cecil, "Industry 5.0 and beyond: Towards a human-centric and sustainable industrial system," Computers in Industry, vol. 138, 103642, 2022.

C. Xie, X. Wu, "A survey on fuzzy QFD methodologies and applications," Expert Systems with Applications, vol. 36, no. 4, pp. 8277–8284, 2009.

H. K. Chan, "Artificial intelligence in quality function deployment: A review," International Journal of Production Research, vol. 40, no. 18, pp. 5183–5206, 2002.

F. Salimi, J. M. Ruiz, "Application of TRIZ in product design: A systematic review," Journal of Engineering Design, vol. 32, no. 3, pp. 151–173, 2021.

C. R. Bag, "The Industry 5.0 framework: Viability-based integration of the resilience, sustainability, and human-centricity perspectives," Sustainability, vol. 14, no. 18, 11567, 2022.

A. Ghosh, M. Bose, "AI-supported quality function deployment: A review," International Journal of Computer Integrated Manufacturing, vol. 35, no. 3, pp. 347–365, 2022.

M. J. Kim, S. B. Park, "A machine learning-based approach to improve QFD in product development," International Journal of Advanced Manufacturing Technology, vol. 114, no. 5, pp. 1603–1617, 2021.

P. M. Asadi, H. M. Hashemipour, "Integration of TRIZ and QFD for innovative product development: A case study," International Journal of Industrial Engineering Computations, vol. 9, no. 4, pp. 493–508, 2018.

Q. Tao, X. Liu, "A review of digital twin in product design and development," International Journal of Production Research, vol. 59, no. 14, pp. 4200–4217, 2021.

"Digital Twin-Driven Product Design Framework," International Journal of Production Research, vol. 56, no. 7, pp. 2458–2475, 2019.

"Digital Twin for Product Design Collaboration: A Systematic Literature Review," International Journal of Advanced Manufacturing Technology, vol. 128, pp. 4209–4235, 2025.

F. Wu et al., "Knowledge-empowered, collaborative, and co-evolving AI models: The post-LLM roadmap," Engineering, 2024.

M. Dhanda et al., "Reviewing human-robot collaboration in manufacturing: Opportunities and challenges in Industry 5.0," Robotics and Computer-Integrated Manufacturing, vol. 93, 102937, 2025.

I. Beltagy, K. Lo, A. Cohan, "SciBERT: A pretrained language model for scientific text," EMNLP-IJCNLP Proceedings, pp. 3615–3620, 2019.

R. Miikkulainen et al., "Creative AI through evolutionary computation: Principles and examples," AI Magazine, vol. 40, no. 1, pp. 16–28, 2019.

R. Roy, R. Warren, "Designing product-service systems using QFD and TRIZ," International Journal of Production Research, vol. 50, no. 6, pp. 1702–1717, 2012.

"Digital Twin and Generative AI for Product Development," Procedia CIRP, vol. 126, pp. 512–517, 2024.


Refbacks

  • There are currently no refbacks.


JOURNAL INDEXED IN :