3D printing is a rapidly growing manufacturing technology, which produces objects with a layer-by-layer material accumulation technique. This technology is effectively applied in automotive, aerospace, and medical industries. However, the existing research on monitoring, control, and optimization of the 3D printing process is far from enough and further study is need to seamlessly use the technology in different application domains. One promising approach is applying machine learning in the loop of 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. In Industry 4.0, the focus is on automation and data exchange in manufacturing. However, Industry 5.0 builds upon this foundation, emphasizing collaboration and coordination between humans and machines. Machine learning plays a pivotal role in this paradigm shift, allowing 3D printers to adapt, learn, and continuously improve their performance. The marriage of these technologies enables rapid innovation and dynamic response to evolving customer demands. Machine learning algorithms can analyze vast datasets and predict potential defects, optimizing the printing process in real-time. This results in reduced material waste, enhanced product quality, and lower production costs. Moreover, it allows for real-time adjustments and self-correction, ensuring consistent output.
This Research Topic welcomes research articles focused on monitoring, control, and optimization of 3D printing. Researchers from both academia and Industry are invited to contribute original research, case studies or reviews. Articles containing experimental results are highly appreciated. Simulation-based articles can also be considered for publication. The topic will highlight advances in the following areas but not limited to:
• Machine learning for monitoring, control, and optimization of 3D printing.
• Integrating 3D printing into Industry 4.0 and Industry 5.0.
• Digital twins in 3D printing.
• AI-driven design for additive manufacturing.
• Quality assurance and predictive maintenance in 3D printing.
• Human-machine collaboration of the 3D printing process in Industry 5.0.
• Investigating how 3D printing transforms supply chain dynamics and decentralized manufacturing.
• The role of 3D printing in social manufacturing.
• Bioprinting and healthcare applications.
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.
3D printing is a rapidly growing manufacturing technology, which produces objects with a layer-by-layer material accumulation technique. This technology is effectively applied in automotive, aerospace, and medical industries. However, the existing research on monitoring, control, and optimization of the 3D printing process is far from enough and further study is need to seamlessly use the technology in different application domains. One promising approach is applying machine learning in the loop of 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. In Industry 4.0, the focus is on automation and data exchange in manufacturing. However, Industry 5.0 builds upon this foundation, emphasizing collaboration and coordination between humans and machines. Machine learning plays a pivotal role in this paradigm shift, allowing 3D printers to adapt, learn, and continuously improve their performance. The marriage of these technologies enables rapid innovation and dynamic response to evolving customer demands. Machine learning algorithms can analyze vast datasets and predict potential defects, optimizing the printing process in real-time. This results in reduced material waste, enhanced product quality, and lower production costs. Moreover, it allows for real-time adjustments and self-correction, ensuring consistent output.
This Research Topic welcomes research articles focused on monitoring, control, and optimization of 3D printing. Researchers from both academia and Industry are invited to contribute original research, case studies or reviews. Articles containing experimental results are highly appreciated. Simulation-based articles can also be considered for publication. The topic will highlight advances in the following areas but not limited to:
• Machine learning for monitoring, control, and optimization of 3D printing.
• Integrating 3D printing into Industry 4.0 and Industry 5.0.
• Digital twins in 3D printing.
• AI-driven design for additive manufacturing.
• Quality assurance and predictive maintenance in 3D printing.
• Human-machine collaboration of the 3D printing process in Industry 5.0.
• Investigating how 3D printing transforms supply chain dynamics and decentralized manufacturing.
• The role of 3D printing in social manufacturing.
• Bioprinting and healthcare applications.
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.