About this Research Topic
Frontiers in Mechanical Engineering is delighted to present the 'Methods and Applications In' series of article collections.
Methods and Applications in Digital Manufacturing will publish high-quality methodical studies on key topics in the field, and discuss their applications. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries.
It is of great significance to explore new online detection methods for the manufacturing process under continuous production conditions for improving the supply quality of products. For example, high cutting temperature and load changes during the digital manufacturing process will inevitably influence machines' surface integrity and product performance. Additionally, artificial intelligence has recently revolutionized modern industry and is widely used for the purpose of intelligent condition monitoring. Artificial intelligence assists in extracting useful knowledge and making appropriate decisions from the measured signal in manufacturing systems.
The Methods in Digital Manufacturing collection aims to highlight these latest experimental techniques and methods used to investigate fundamental questions in Digital Manufacturing. Original Research, Review Articles or Opinion Articles on methodologies or applications including the advantages and limitations of each are welcome. This Research Topic includes technologies and up-to-date methods which help aim to help advance science.
Potential topics include but are not limited to the following:
-> Feature extraction in condition monitoring
-> Dynamics analysis
-> Knowledge-based fault diagnosis
-> Knowledge-based product quality surveying
-> Remaining useful life prediction
-> Few-shot learning in condition monitoring
-> AI-based quality prediction in digital manufacturing
-> Statistical inference method in condition monitoring
-> Semi-supervised and unsupervised learning in condition monitoring
-> Multi-sensor fused condition monitoring
Methods and Applications in Digital Manufacturing will publish high-quality methodical studies on key topics in the field, and discuss their applications. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries.
It is of great significance to explore new online detection methods for the manufacturing process under continuous production conditions for improving the supply quality of products. For example, high cutting temperature and load changes during the digital manufacturing process will inevitably influence machines' surface integrity and product performance. Additionally, artificial intelligence has recently revolutionized modern industry and is widely used for the purpose of intelligent condition monitoring. Artificial intelligence assists in extracting useful knowledge and making appropriate decisions from the measured signal in manufacturing systems.
The Methods in Digital Manufacturing collection aims to highlight these latest experimental techniques and methods used to investigate fundamental questions in Digital Manufacturing. Original Research, Review Articles or Opinion Articles on methodologies or applications including the advantages and limitations of each are welcome. This Research Topic includes technologies and up-to-date methods which help aim to help advance science.
Potential topics include but are not limited to the following:
-> Feature extraction in condition monitoring
-> Dynamics analysis
-> Knowledge-based fault diagnosis
-> Knowledge-based product quality surveying
-> Remaining useful life prediction
-> Few-shot learning in condition monitoring
-> AI-based quality prediction in digital manufacturing
-> Statistical inference method in condition monitoring
-> Semi-supervised and unsupervised learning in condition monitoring
-> Multi-sensor fused condition monitoring
Keywords: Digital Manufacturing, Methods, Applications, #CollectionSeries
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.
