About this Research Topic
3D printing is a rapidly growing manufacturing technology that produces objects through a layer-by-layer material accumulation technique. This technology has found effective applications in the automotive, aerospace, and medical industries. However, the existing research on monitoring, control, and optimization of the 3D printing process is insufficient, necessitating further study to seamlessly integrate the technology across various application domains. One promising approach is the application of machine learning within the 3D printing process. The convergence of 3D printing and machine learning heralds a transformative era in manufacturing, often referred to as Industry 4.0. This dynamic fusion of cutting-edge technologies is reshaping the landscape of production and design, promising unprecedented efficiency, customization, and sustainability. Machine learning plays a pivotal role in this paradigm shift, allowing 3D printers to adapt, learn, and continuously improve their performance. Despite these advancements, there remains a significant gap in the comprehensive understanding and application of these technologies, highlighting the need for further investigation.
This Research Topic aims to explore the integration of machine learning with 3D printing within the frameworks of Industry 4.0. The primary objectives include addressing specific questions related to the monitoring, control, and optimization of the 3D printing process. Additionally, the research will test hypotheses concerning the efficacy of machine learning algorithms in predicting potential defects, optimizing real-time adjustments, and ensuring consistent output. By focusing on these areas, the research seeks to reduce material waste, enhance product quality, and lower production costs, ultimately driving innovation and dynamic responses to evolving customer demands.
To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- Machine learning for monitoring, control, and optimization of 3D printing.
- Integrating 3D printing into Industry 4.0.
- Digital twins in 3D printing.
- AI-driven design for additive manufacturing.
- Quality assurance and predictive maintenance in 3D printing.
- Human-machine collaboration in the 3D printing process.
- Investigating how 3D printing transforms supply chain dynamics and decentralized manufacturing.
- The role of 3D printing in social manufacturing.
- Bioprinting and healthcare applications.
Researchers from both academia and industry are welcome to contribute original research, case studies or reviews. Articles containing experimental results and simulation-based articles will also be considered for publication.
This Research Topic aims to explore the integration of machine learning with 3D printing within the frameworks of Industry 4.0. The primary objectives include addressing specific questions related to the monitoring, control, and optimization of the 3D printing process. Additionally, the research will test hypotheses concerning the efficacy of machine learning algorithms in predicting potential defects, optimizing real-time adjustments, and ensuring consistent output. By focusing on these areas, the research seeks to reduce material waste, enhance product quality, and lower production costs, ultimately driving innovation and dynamic responses to evolving customer demands.
To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- Machine learning for monitoring, control, and optimization of 3D printing.
- Integrating 3D printing into Industry 4.0.
- Digital twins in 3D printing.
- AI-driven design for additive manufacturing.
- Quality assurance and predictive maintenance in 3D printing.
- Human-machine collaboration in the 3D printing process.
- Investigating how 3D printing transforms supply chain dynamics and decentralized manufacturing.
- The role of 3D printing in social manufacturing.
- Bioprinting and healthcare applications.
Researchers from both academia and industry are welcome to contribute original research, case studies or reviews. Articles containing experimental results and simulation-based articles will also be considered for publication.
Keywords: 3D printing, industry 5.0, machine learning, Industry 4.0
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
