Jul 2026· Mechanical Theory and Systems· Vol 2, pp. 1-24· 0 citations· 137 references
Abstract
The rapid evolution of manufacturing technologies has intensified the need to align machining practices with sustainability and digital intelligence. Conventional machining processes, while productive, often result in high energy consumption, material waste, and limited adaptability to dynamic industrial demands. This review addresses the growing research gap in integrating sustainability principles with intelligent manufacturing systems for advanced materials. The primary objective of this study is to analyze and synthesize emerging trends, technologies, and frameworks that enable sustainable and intelligent machining. The review systematically examines recent advancements in Artificial Intelligence (AI), the Internet of Things (IoT), Additive and Hybrid Manufacturing, Digital Twins, and Cyber-Physical Systems (CPS). The methodology involves a comprehensive literature analysis of more than 130 peer-reviewed studies published between 2000 and 2025, emphasizing quantifiable sustainability metrics such as energy efficiency, CO2 emission reduction, and waste minimization.
Key findings reveal that AI-driven predictive analytics, IoT-enabled monitoring, and additive-hybrid manufacturing platforms significantly enhance operational efficiency and resource utilization, while digital twin frameworks support real-time process optimization and lifecycle-based sustainability evaluation. A comparative evaluation highlights that these technologies collectively contribute to energy savings of up to 40%, CO2 reductions of 25%-35%, and waste minimization of 50% in selected industrial applications.
The paper concludes with strategic recommendations for future research, including the standardization of sustainability metrics, transparent and explainable AI integration, and the development of unified digital ecosystems that combine data-driven intelligence with circular economy principles. This review thus provides a consolidated roadmap for achieving environmentally responsible, intelligent, and future-ready machining systems.
The findings indicate that effective Decision Intelligence requires the seamless integration of artificial intelligence, optimization, digital twins, knowledge graphs, explainable AI, and Human-in-the-Loop AI within a unified decision ecosystem that combines computational intelligence with human judgment.
H. Mural, Mahbub E. Khoda, Abdul Halim et al.· Journal of Sustainable Smart...· 0 citations
The rapid evolution of digital technologies has significantly transformed modern manufacturing systems, creating new opportunities for improving productivity, sustainability, and industrial resilience. In this context, Industry 5.0 has emerged as a new industrial paradigm that extends the technological achievements of Industry 4.0 by emphasizing human-centric manufacturing, sustainable production, and resilient industrial systems. Simultaneously, digital transformation has become a key enabler for integrating intelligent technologies into both textile and manufacturing industries, supporting resource-efficient production, process optimization, and environmentally responsible manufacturing practices. This paper presents a comprehensive review of the relationship between Industry 5.0, digital transformation, and sustainable textile and manufacturing systems. The study examines the fundamental principles of Industry 5.0 and discusses the role of enabling technologies, including Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, Big Data analytics, Cloud Computing, and Cyber-Physical Systems (CPS), in supporting intelligent manufacturing environments. Particular attention is devoted to the application of these technologies in textile manufacturing, where digitalization contributes to automated quality control, smart textile production, resource efficiency, digital product traceability, and circular production strategies. Based on the findings of the literature review, the authors propose a conceptual model of Industry 5.0-driven digital transformation that integrates three complementary dimensions: Digital Technologies, Human-Centred Manufacturing, and Sustainable Manufacturing. The proposed model illustrates how the interaction among these dimensions contributes to improved manufacturing performance through enhanced productivity, flexibility, product quality, environmental responsibility, and industrial resilience. Furthermore, the model demonstrates the importance of integrating technological innovation with human expertise and sustainability principles to support the future development of both textile and manufacturing systems. The proposed conceptual model may serve as a foundation for future research focused on intelligent manufacturing, smart textile production, Artificial Intelligence, Digital Twins, sustainable industrial development, and Industry 5.0 implementation strategies.
S. Srebrenkoska, D. Krstev, S. Dimitrov et al.· Zbornik radova· 0 citations
Ore blending is essential for stabilizing production, reducing costs, and improving resource utilization in mineral processing. This review systematically traces its evolution through four stages: empirical, experimental, mathematical model-based, and systematic integration. Linear programming provides fast, interpretable solutions for clear grade constraints. Nonlinear intelligent algorithms (e.g., particle swarm optimization, genetic algorithms) achieve higher accuracy for multi objective, polymetallic ores but require large datasets and sacrifice interpretability. Systematic blending, powered by 5G, AI, and digital twins, enables full chain coordination with performance gains of>30%, yet requires significant investment. Four interconnected barriers hinder industrial adoption: multi source data integration difficulties, low computational efficiency for real time control, poor model adaptability across ore types (prediction errors up to 10%), and a shortage of interdisciplinary talent. Future directions include ore property based experimental research with open access global databases, hybrid intelligent models with real time dynamic adjustment, and integrated medium to long term blending systems using digital twins and IoT. By explicitly linking experimental data, model calibration, and system deployment, this review provides a structured reference for transitioning from experience driven to data intelligent, full chain synergistic ore blending.
Industrial diversification, localization and sustainability have become central themes of Saudi Vision 2030 and industrial transformation initiatives. Within this setting, smart manufacturing is not just an issue of technological enhancement but a business model for running plants in which machines, people, materials and decision making connect with trustworthy data. This review explores how IIoT architectural design contributes to productivity, quality and sustainability of Saudi plants. Instead of traditional reviews of technology in which sensors, edge computing, cloud platform, artificial intelligence and digital twin are considered separately, this review treats all of these as components of one path from data signals to factory performance. The structured narrative review technique was used for literature and policy analysis between 2020 and 2025. The findings show that productivity comes from real-time data visualization, overall equipment effectiveness tracking, identification of bottlenecks, scheduling adaptation and predictive maintenance. Quality gains come from traceability, machine vision, statistical process control, process capability analysis and fast root-cause analysis. Sustainability is realized by means of operational measurement of energy, water, waste and carbon footprint indicators and their linking to processes of continuous improvement. At the same time, a set of barriers is found such as legacy equipment, cybersecurity risks, weak data governance, fragmented vendors, workforce skill gaps and different levels of digital maturity of small and medium-sized factories. Finally, this paper suggests phased roadmap for implementation of IIoT architecture in Saudi Arabia starting with readiness assessment and pilot projects. The key value of this paper is in the developed review framework for linking IIoT architecture with productivity, quality and sustainability of Saudi Arabia plants.
Muhammed Asad· Veredas do Direito· 0 citations
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