Xuebing Li , Jing Ni , Xianli Liu , Caixu Yue , Shuming Yang , Xia Ji , Steven Y. Liang , Lihui Wang
{"title":"航空航天薄壁件的无颤振铣削","authors":"Xuebing Li , Jing Ni , Xianli Liu , Caixu Yue , Shuming Yang , Xia Ji , Steven Y. Liang , Lihui Wang","doi":"10.1016/j.jmatprotec.2025.118903","DOIUrl":null,"url":null,"abstract":"<div><div>The milling process of aerospace thin-walled parts requires extremely high geometric precision and surface quality, as these factors significantly influence aircraft performance and operational reliability. Milling chatter not only severely compromises machined surface integrity and accelerates tool wear, but also induces catastrophic production failures and significant economic losses. Over recent decades, the machining community has dedicated substantial efforts to investigating milling chatter mechanisms and developing corresponding control strategies. Remarkable progress has been made in terms of chatter stability prediction, online condition monitoring, and active/passive suppression techniques, with the ultimate objective of achieving chatter-free milling operations. However, compared with conventional milling processes,</div><div>thin-walled part machining presents distinctive challenges due to their inherent characteristics such as low structural rigidity, poor machinability, and complex dynamics involved during milling operations (including time-varying behaviors, modal coupling, and position-dependent effects). These combined factors pose significant obstacles to effective chatter control. This paper consequently concentrates on recent advancements in milling chatter research for aerospace thin-walled parts: (i) Establishing dynamic models that accurately characterize actual milling processes by incorporating force-induced deformation and tool wear effects; integrating dynamic parameter updating techniques with probabilistic stability lobe diagram (SLD) solution approaches to provide risk-aware chatter prediction results. (ii) Leveraging multi-signal fusion and statistical analysis/artificial intelligence (AI) to realize real-time chatter condition monitoring; exploring effective measures to improve monitoring model generalization capabilities under limited sample sizes and variable operational conditions. (iii) Evaluating passive and active chatter suppression strategies systematically, combined with digital twin technology to enable seamless integration of chatter monitoring, suppression, and process optimization. (iv) Discussing milling chatter-induced part surface/sub-surface defects, with related indexes to quantify the effect of chatter marks on surface integrity. Through critical analysis of cutting-edge research and industrial applications, we further evaluate current research limitations and present promising future directions. These include innovations in chatter mechanism modeling, uncertainty quantification, physics-AI hybrid methodologies, edge-cloud-fog monitoring systems, novel materials development, metaverse-enabled human-computer interfaces, and collaborative control technologies of shape accuracy-surface integrity.</div></div>","PeriodicalId":367,"journal":{"name":"Journal of Materials Processing Technology","volume":"341 ","pages":"Article 118903"},"PeriodicalIF":6.7000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Chatter-free milling of aerospace thin-walled parts\",\"authors\":\"Xuebing Li , Jing Ni , Xianli Liu , Caixu Yue , Shuming Yang , Xia Ji , Steven Y. Liang , Lihui Wang\",\"doi\":\"10.1016/j.jmatprotec.2025.118903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The milling process of aerospace thin-walled parts requires extremely high geometric precision and surface quality, as these factors significantly influence aircraft performance and operational reliability. Milling chatter not only severely compromises machined surface integrity and accelerates tool wear, but also induces catastrophic production failures and significant economic losses. Over recent decades, the machining community has dedicated substantial efforts to investigating milling chatter mechanisms and developing corresponding control strategies. Remarkable progress has been made in terms of chatter stability prediction, online condition monitoring, and active/passive suppression techniques, with the ultimate objective of achieving chatter-free milling operations. However, compared with conventional milling processes,</div><div>thin-walled part machining presents distinctive challenges due to their inherent characteristics such as low structural rigidity, poor machinability, and complex dynamics involved during milling operations (including time-varying behaviors, modal coupling, and position-dependent effects). These combined factors pose significant obstacles to effective chatter control. This paper consequently concentrates on recent advancements in milling chatter research for aerospace thin-walled parts: (i) Establishing dynamic models that accurately characterize actual milling processes by incorporating force-induced deformation and tool wear effects; integrating dynamic parameter updating techniques with probabilistic stability lobe diagram (SLD) solution approaches to provide risk-aware chatter prediction results. (ii) Leveraging multi-signal fusion and statistical analysis/artificial intelligence (AI) to realize real-time chatter condition monitoring; exploring effective measures to improve monitoring model generalization capabilities under limited sample sizes and variable operational conditions. (iii) Evaluating passive and active chatter suppression strategies systematically, combined with digital twin technology to enable seamless integration of chatter monitoring, suppression, and process optimization. (iv) Discussing milling chatter-induced part surface/sub-surface defects, with related indexes to quantify the effect of chatter marks on surface integrity. Through critical analysis of cutting-edge research and industrial applications, we further evaluate current research limitations and present promising future directions. These include innovations in chatter mechanism modeling, uncertainty quantification, physics-AI hybrid methodologies, edge-cloud-fog monitoring systems, novel materials development, metaverse-enabled human-computer interfaces, and collaborative control technologies of shape accuracy-surface integrity.</div></div>\",\"PeriodicalId\":367,\"journal\":{\"name\":\"Journal of Materials Processing Technology\",\"volume\":\"341 \",\"pages\":\"Article 118903\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Materials Processing Technology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924013625001931\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Materials Processing Technology","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924013625001931","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Chatter-free milling of aerospace thin-walled parts
The milling process of aerospace thin-walled parts requires extremely high geometric precision and surface quality, as these factors significantly influence aircraft performance and operational reliability. Milling chatter not only severely compromises machined surface integrity and accelerates tool wear, but also induces catastrophic production failures and significant economic losses. Over recent decades, the machining community has dedicated substantial efforts to investigating milling chatter mechanisms and developing corresponding control strategies. Remarkable progress has been made in terms of chatter stability prediction, online condition monitoring, and active/passive suppression techniques, with the ultimate objective of achieving chatter-free milling operations. However, compared with conventional milling processes,
thin-walled part machining presents distinctive challenges due to their inherent characteristics such as low structural rigidity, poor machinability, and complex dynamics involved during milling operations (including time-varying behaviors, modal coupling, and position-dependent effects). These combined factors pose significant obstacles to effective chatter control. This paper consequently concentrates on recent advancements in milling chatter research for aerospace thin-walled parts: (i) Establishing dynamic models that accurately characterize actual milling processes by incorporating force-induced deformation and tool wear effects; integrating dynamic parameter updating techniques with probabilistic stability lobe diagram (SLD) solution approaches to provide risk-aware chatter prediction results. (ii) Leveraging multi-signal fusion and statistical analysis/artificial intelligence (AI) to realize real-time chatter condition monitoring; exploring effective measures to improve monitoring model generalization capabilities under limited sample sizes and variable operational conditions. (iii) Evaluating passive and active chatter suppression strategies systematically, combined with digital twin technology to enable seamless integration of chatter monitoring, suppression, and process optimization. (iv) Discussing milling chatter-induced part surface/sub-surface defects, with related indexes to quantify the effect of chatter marks on surface integrity. Through critical analysis of cutting-edge research and industrial applications, we further evaluate current research limitations and present promising future directions. These include innovations in chatter mechanism modeling, uncertainty quantification, physics-AI hybrid methodologies, edge-cloud-fog monitoring systems, novel materials development, metaverse-enabled human-computer interfaces, and collaborative control technologies of shape accuracy-surface integrity.
期刊介绍:
The Journal of Materials Processing Technology covers the processing techniques used in manufacturing components from metals and other materials. The journal aims to publish full research papers of original, significant and rigorous work and so to contribute to increased production efficiency and improved component performance.
Areas of interest to the journal include:
• Casting, forming and machining
• Additive processing and joining technologies
• The evolution of material properties under the specific conditions met in manufacturing processes
• Surface engineering when it relates specifically to a manufacturing process
• Design and behavior of equipment and tools.