Energy Efficiency and Sustainability of Additive Manufacturing as a Mass-Personalized Production Mode in Industry 5.0/6.0
Open Access
Abstract
This review article examines the role of additive manufacturing (AM) in increasing energy efficiency and sustainability within the evolving framework of Industry 5.0 and 6.0. This review highlights the unique ability of additive manufacturing to deliver mass-customized products while minimizing material waste and reducing energy consumption. The integration of smart technologies such as AI and IoT is explored to optimize AM processes and support decentralized, on-demand manufacturing. Thisarticle discusses different AM techniques and materials from an environmental and life-cycle perspective, identifying key benefits and constraints. This review also examines the potential of AM to support circular economy practices through local repair, remanufacturing, and material recycling. The net energy efficiency of AM depends on the type of process, part complexity, and production scale, but the energy savings per component can be significant if implemented strategically.AM significantly improves energy efficiency in certain manufacturing contexts, often reducing energy consumption by 25–50% compared to traditional subtractive methods. The results emphasize the importance of innovation in both hardware and software to overcome current energy and sustainability challenges. This review highlights AM as a key tool in achieving a human-centric, intelligent, and ecological manufacturing paradigm.Keywords
Funding Information
- Polish Minister of Science and Higher Education (RID/SP/0048/2024/01)
This publication has 100 references indexed in Scilit:
- Advancing polymer composites in civil infrastructure through 3D printingAutomation in Construction, 2025
- Unleashing the power of innovation and sustainability: Transforming cereal production in the BRICS countriesEcological Indicators, 2024
- Assessment and prediction of mega-infrastructure projects on rural ecosystems using machine learning algorithmsEnvironment, Development and Sustainability, 2024
- Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factorsEarth Science Informatics, 2024
- Transformers in Time-Series Analysis: A TutorialCircuits, Systems, and Signal Processing, 2023
- Nanocomposite bioinks for 3D bioprintingActa Biomaterialia, 2022
- State of the art of additively manufactured electromagnetic materials for topology optimized electrical machinesAdditive Manufacturing, 2022
- The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systemsCurrent Opinion in Biotechnology, 2021
- 4D Printing of Shape Memory Materials for Textiles: Mechanism, Mathematical Modeling, and ChallengesAdvanced Functional Materials, 2021
- Do green motives influence green product innovation? The mediating role of green value co‐creationCorporate Social Responsibility and Environmental Management, 2018