Research Topic

Data-Driven Intelligent Machining of Thin-Walled Parts

About this Research Topic

Complex thin-walled parts, such as impellers, blades, disks and shells, are widely used within aerospace, navigation, energy and power equipment. However, high quality and efficient machining of these parts is incredibly difficult, due to tight tolerances, low stiffness, complex surfaces, difficult-to-machine materials and high integrity surfaces. Data-driven intelligent machining is a comprehensive strategy and solution for the machining of thin-walled parts, enhancing the cutter-part-machine interaction relationship through data analysis and modeling. It is also a research hotspot of Manufacturing Engineering (ME). Up to now, many new methods and technologies have been and/or are being developed in the academia and the industry fields. Many new methods and technologies have recently been, and are currently being, developed in academia and industry to improve the high-performance machining of thin-walled parts; it is important to shine a light on advancements in data-driven intelligent machining.

This Research Topic focuses on recent innovations and solutions to improve machining performance of thin-walled parts. It focusses particularly on these issues: (I) integrating different types of sensors into the machining process, such as workpiece, cutters and machine tools, in order to obtain the real-time machining state by on-machine / -line means; (II) feeding the obtained data back to the machining process dynamically, to establish a closed-loop control processing; (III) in terms of machining efficiency, the influence of elements’ time-varying state and information and energy transfer in machining system optimization.

This Research Topic aims at the international frontier of advanced machining technology and automation, focusing on the breakthrough of advanced processing theory and key technology. The Topic Editors welcome full-length original research papers and commissioned reviews which substantially advance the field of data-driven intelligent machining of thin-walled parts. Topics of interest include:
• Online/machine measurement (e.g. surface topography, profile, thickness, residual stress), Deformation compensation (global deformation and local deformation)
• Multi-sensor data fusion modeling (for machining deformation, cutting vibration, tool wear, etc.)
• Machining process variables prediction and optimization (e.g. forces, temperature, residual stress)
• Digital twin technology (e.g. for machining process simulation, visualization technology, big data processing, virtual reality augmentation intervention)
• Multi-agent systems of intelligent machining (intelligent machine/fixture/robotics, man-machine integration, etc.), measurement-processing integrated manufacturing method and equipment, etc
• Measurement-processing integrated manufacturing methods and equipment


Keywords: Digital Machining, On-line/machine Measurement, Data Fusion Modeling, Process/system Simulation, Digital twin


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.

Complex thin-walled parts, such as impellers, blades, disks and shells, are widely used within aerospace, navigation, energy and power equipment. However, high quality and efficient machining of these parts is incredibly difficult, due to tight tolerances, low stiffness, complex surfaces, difficult-to-machine materials and high integrity surfaces. Data-driven intelligent machining is a comprehensive strategy and solution for the machining of thin-walled parts, enhancing the cutter-part-machine interaction relationship through data analysis and modeling. It is also a research hotspot of Manufacturing Engineering (ME). Up to now, many new methods and technologies have been and/or are being developed in the academia and the industry fields. Many new methods and technologies have recently been, and are currently being, developed in academia and industry to improve the high-performance machining of thin-walled parts; it is important to shine a light on advancements in data-driven intelligent machining.

This Research Topic focuses on recent innovations and solutions to improve machining performance of thin-walled parts. It focusses particularly on these issues: (I) integrating different types of sensors into the machining process, such as workpiece, cutters and machine tools, in order to obtain the real-time machining state by on-machine / -line means; (II) feeding the obtained data back to the machining process dynamically, to establish a closed-loop control processing; (III) in terms of machining efficiency, the influence of elements’ time-varying state and information and energy transfer in machining system optimization.

This Research Topic aims at the international frontier of advanced machining technology and automation, focusing on the breakthrough of advanced processing theory and key technology. The Topic Editors welcome full-length original research papers and commissioned reviews which substantially advance the field of data-driven intelligent machining of thin-walled parts. Topics of interest include:
• Online/machine measurement (e.g. surface topography, profile, thickness, residual stress), Deformation compensation (global deformation and local deformation)
• Multi-sensor data fusion modeling (for machining deformation, cutting vibration, tool wear, etc.)
• Machining process variables prediction and optimization (e.g. forces, temperature, residual stress)
• Digital twin technology (e.g. for machining process simulation, visualization technology, big data processing, virtual reality augmentation intervention)
• Multi-agent systems of intelligent machining (intelligent machine/fixture/robotics, man-machine integration, etc.), measurement-processing integrated manufacturing method and equipment, etc
• Measurement-processing integrated manufacturing methods and equipment


Keywords: Digital Machining, On-line/machine Measurement, Data Fusion Modeling, Process/system Simulation, Digital twin


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.

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Submission Deadlines

17 January 2022 Abstract
18 July 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

17 January 2022 Abstract
18 July 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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