Advances in Manufacturing ›› 2026, Vol. 14 ›› Issue (1): 43-102.doi: 10.1007/s40436-025-00567-8
• • 上一篇
Tai-Min Luo1,2, Jin Zhang1,2, Chen-Jie Deng1,2, Dai-Xin Luo1,2, Gui-Bao Tao1,2, Hua-Jun Cao1,2
收稿日期:2024-06-14
修回日期:2024-08-15
发布日期:2026-03-23
通讯作者:
Hua-Jun Cao Email:E-mail:hjcao@cqu.edu.cn
E-mail:hjcao@cqu.edu.cn
作者简介:Tai-Min Luo is a master degree candidate at the State Key Laboratory of Mechanical Transmissions, Chongqing University, China. His research interest is intelligent machining technology for difficult-to-machine materials.基金资助:Tai-Min Luo1,2, Jin Zhang1,2, Chen-Jie Deng1,2, Dai-Xin Luo1,2, Gui-Bao Tao1,2, Hua-Jun Cao1,2
Received:2024-06-14
Revised:2024-08-15
Published:2026-03-23
Contact:
Hua-Jun Cao Email:E-mail:hjcao@cqu.edu.cn
E-mail:hjcao@cqu.edu.cn
Supported by:摘要： With the continuous advancement of science and technology, alongside the increasing significant attention within the manufacturing industry, high-performance demands are placed on advanced equipment and components because of extreme temperatures, heavy impact loads, and other challenging operating conditions. The importance of resource conservation and environmental preservation is becoming more widely recognized. This paper reviews green machining technology, driven by digital intelligence. Initially, the background of green machining powered by digital technologies is introduced, focusing on digitalization, intelligence, and sustainability as key factors for improving machining efficiency, enhancing product performance, and minimizing both energy consumption and environmental pollution. Subsequently, the paper elaborates on the current research and development in digital intelligence-driven green machining technologies, highlighting four critical areas: smart toolholders, minimal quantity lubrication (MQL), machine tool compensation, machine tool energy consumption monitoring, and intelligent carbon emission control. Lastly, the future trends and challenges in these technologies are discussed, with an outlook on the growing importance of green machining in response to technological advancements and evolving market demands.
The full text can be downloaded at https://doi.org/10.1007/s40436-025-00567-8
Tai-Min Luo, Jin Zhang, Chen-Jie Deng, Dai-Xin Luo, Gui-Bao Tao, Hua-Jun Cao. Green machining technology and application driven by digital intelligence: a review[J]. Advances in Manufacturing, 2026, 14(1): 43-102.
Tai-Min Luo, Jin Zhang, Chen-Jie Deng, Dai-Xin Luo, Gui-Bao Tao, Hua-Jun Cao. Green machining technology and application driven by digital intelligence: a review[J]. Advances in Manufacturing, 2026, 14(1): 43-102.
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