机构

• 合肥物质科学研究院 [32]
• 西安光学精密机械研究... [4]
• 上海硅酸盐研究所 [4]
• 生态环境研究中心 [3]
• 成都山地灾害与环境研... [2]
• 地质与地球物理研究所 [1]
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采集方式

• OAI收割 [53]

内容类型

• 期刊论文 [50]
• 会议论文 [3]

发表日期

• 2025 [1]
• 2024 [2]
• 2023 [2]
• 2022 [7]
• 2021 [5]
• 2020 [9]
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学科主题

• Chemistry,... [1]
• Energy & F... [1]
• Materials ... [1]
• Plant Scie... [1]

筛选

浏览/检索结果: 共53条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Application of physics-guided deep learning model in tool wear monitoring of high-speed milling 期刊论文  OAI收割 MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2025, 卷号: 224 作者:  Li, Shenshen;  Li, Jun;  Zhu, Kunpeng   |  收藏  |  浏览/下载：4/0  |  提交时间：2024/11/20 Formation mechanism of pore defects and surface ripples under different process parameters via laser powder bed fusion by numerical simulation and experimental verification 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2024 作者:  Duan, Xianyin;  Long, Tao;  Zhu, Kunpeng;  Li, Wei;  Fuh, Ying Hsi Jerry   |  收藏  |  浏览/下载：90/0  |  提交时间：2024/11/20 Pore defects  Melt pool evolution  Surface ripples  Laser powder bed fusion  Process parameters   Relationship between energy efficiency and surface morphologies in micro-milling of SLM Inconel 718 期刊论文  OAI收割 JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, 2024, 卷号: 31 作者:  Duan, Xianyin;  Su, Fengxiao;  Gao, Shuaishuai;  Zhu, Kunpeng;  Deng, Ben   |  收藏  |  浏览/下载：11/0  |  提交时间：2024/11/22 Energy efficiency  Chip morphology  Surface morphology  Selective laser melting (SLM)  Specific cutting energy   Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System 期刊论文  OAI收割 IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2023 作者:  Li, Shenshen;  Lin, Xin;  Shi, Hu;  Shi, Yungao;  Zhu, Kunpeng   |  收藏  |  浏览/下载：15/0  |  提交时间：2023/11/17 Deep learning  physics-guided data model  tool condition monitoring   Online tool wear monitoring by super-resolution based machine vision 期刊论文  OAI收割 COMPUTERS IN INDUSTRY, 2023, 卷号: 144 作者:  Zhu, Kunpeng;  Guo, Hao;  Li, Si;  Lin, Xin   |  收藏  |  浏览/下载：43/0  |  提交时间：2022/12/22 Single image super -resolution  Sparse decomposition  Micro machining  Tool monitoring   Generic Cutting Force Modeling with Comprehensively Considering Tool Edge Radius, Tool Flank Wear and Tool Runout in Micro-End Milling 期刊论文  OAI收割 MICROMACHINES, 2022, 卷号: 13 作者:  Gao, Shuaishuai;  Duan, Xianyin;  Zhu, Kunpeng;  Zhang, Yu   |  收藏  |  浏览/下载：38/0  |  提交时间：2022/12/23 cutting force  mechanical modeling  micro-end milling  tool edge radius  tool flank wear  tool runout   Interpretable deep learning approach for tool wear monitoring in high-speed milling 期刊论文  OAI收割 COMPUTERS IN INDUSTRY, 2022, 卷号: 138 作者:  Guo, Hao;  Zhang, Yu;  Zhu, Kunpeng   |  收藏  |  浏览/下载：73/0  |  提交时间：2022/05/16 Tool wear monitoring  Interpretability  Deep learning  Attention   Motion feature based melt pool monitoring for selective laser melting process 期刊论文  OAI收割 JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2022, 卷号: 303 作者:  Lin, Xin;  Wang, Qisheng;  Fuh, Jerry Ying Hsi;  Zhu, Kunpeng   |  收藏  |  浏览/下载：46/0  |  提交时间：2022/03/28 Selective laser melting  Motion feature of melt pool  Intelligent online monitoring  Connected component analysis  K-means clustering   A Novel Optical Frequency-Hopping Scheme Using Dual Drive Mach-Zehnder Modulator 期刊论文  OAI收割 IEEE PHOTONICS JOURNAL, 2022, 卷号: 14, 期号: 1 作者:  Jin, Ya;  Chen, Shaokang;  Xie, Zhuang;  Zhai, Kunpeng;  Xu, Changda   |  收藏  |  浏览/下载：67/0  |  提交时间：2022/02/07 Optical fiber dispersion  Optical fibers  Frequency shift keying  Optical fiber networks  Amplitude modulation  Optical modulation  Fiber gratings  Dual drive Mach-Zehnder modulator  optical frequency hopping  physical layer security  secure transmission   Metal-based additive manufacturing condition monitoring methods: From measurement to control 期刊论文  OAI收割 ISA TRANSACTIONS, 2022, 卷号: 120 作者:  Lin, Xin;  Zhu, Kunpeng;  Fuh, Jerry Ying Hsi;  Duan, Xianyin   |  收藏  |  浏览/下载：50/0  |  提交时间：2022/05/16 Metal-based additive manufacturing  Condition monitoring  Measurement and control  Machine learning