This paper focuses on optimizing the laser engraving of acrylic plastics to reduce energy consumption and CO₂ gas emissions, without hindering the production and material removal rates. In this context, the role of laser engraving parameters on energy consumption, CO₂ gas emissions, production rate, and material removal rate was first experimentally investigated. Grey–Taguchi approach was then used to identify an optimal set of process parameters meeting the goal. The scan gap was the most significant factor affecting energy consumption, CO₂ gas emissions, and production rate, whereas, compared to other factors, its impact on material removal rate (MRR) was relatively lower. Moreover, the defocal length had a negligible impact on the response variables taken into consideration. With this laser-process-material combination, to achieve the desired goal, the laser must be focused on the surface, and laser power, scanning speed, and scan gap must be set at 44 W, 300 mm/s, and 0.065 mm, respectively.

This review examines the relationship between Human Factors (HF) integration in production system design and quality performance. While traditional technical-centric approaches often neglect the human element, thereby creating a quality ceiling, the research reveals a complex relationship between HF and quality performance. This study synthesises 33 empirical studies, and the findings show that HF interventions result in quality improvements of 2-86% through reduced defect rates, lower rework, and enhanced process capability. The findings reveal that these gains stem from optimised cognitive ergonomics, physical biomechanics, and improved human-machine interfaces, along with a robust socio-technical organizational structure, which collectively reduce human error propagation and enhance system resilience. However, implementation gaps and tradeoffs exist, especially in cost, contextual trade-offs, and diminishing returns in complex Industry 4.0 and 5.0 environments. Consequently, the study proposes the Integrated Multi-Level Human Factors Framework (IMHF-QOPS) that operates across strategic, tactical, and operational levels with feedback loops. This framework, grounded in extended socio-technical theory and cognitive load principle, provides a practical approach for quality optimization in production systems. We also developed a simulation-based validation model to bridge theory and practice, providing an actionable pathway for quality excellence. This study ultimately empowers industry leaders with an actionable roadmap to transcend the quality ceiling, promote human-centred systems to boost performance, as well as facilitate worker well-being and long-term sustainability.

The integration of Artificial Intelligence (AI), the Internet of Things (IoT), and digital twin technologies is transforming modern manufacturing by enabling real-time monitoring, predictive analytics, and intelligent decisionmaking. While existing studies often investigate these technologies individually, limited research explores their combined potential within the emerging paradigm of Industry 5.0, which emphasizes sustainability, human-centric innovation, and resilient manufacturing systems. This study addresses this gap by conducting a systematic literature review of 25 peer-reviewed studies published between 2020 and 2025, following the PRISMA framework. The review synthesizes findings across four key application domains: predictive maintenance, quality control, process optimization, and supply chain management. The analysis shows that machine learning and deep learning techniques dominate predictive maintenance and defect detection, while reinforcement learning and hybrid AI-IoT-digital twin architectures enable adaptive scheduling, energy optimization, and intelligent logistics coordination. Empirical evidence from the reviewed studies indicates that integrated smart manufacturing systems can reduce energy consumption by 30-40%, unplanned downtime by 15-25%, and defective outputs by 10-20%, contributing significantly to resource efficiency and carbon-emission reduction. By combining thematic synthesis with a conceptual integration framework, this study provides a strategic roadmap for the transition from Industry 4.0 to sustainable Industry 5.0 manufacturing ecosystems. The findings highlight how the convergence of AI, IoT, and digital twin technologies supports circular economy principles, cleaner production practices, and resilient industrial systems, positioning these technologies as key enablers of next-generation sustainable manufacturing.