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基于人工智能的岩土勘察图像处理与识别技术应用研究
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作者 马中伟 王涣 《产业科技创新》 2024年第3期75-78,共4页
本文综合介绍了基于人工智能的岩土勘察图像处理与识别技术,包括图像识别与分类、特征提取、目标检测、实时监测、数据驱动方法、多模态融合和弱监督学习等方面的关键技术。通过引入卷积神经网络和深度学习模型,这些技术提高了勘察效率... 本文综合介绍了基于人工智能的岩土勘察图像处理与识别技术,包括图像识别与分类、特征提取、目标检测、实时监测、数据驱动方法、多模态融合和弱监督学习等方面的关键技术。通过引入卷积神经网络和深度学习模型,这些技术提高了勘察效率、准确性,并支持实时监测与预警。多模态数据融合和弱监督学习解决了数据不足和多源信息整合的问题。这些应用降低了人为成本,为岩土工程提供了智能化、高效、可靠的地质信息获取手段。 展开更多
关键词 人工智能 岩土勘察 图像处理 识别技术
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A general strategy for the reliable joining of Al/Ti dissimilar alloys via ultrasonic assisted friction stir welding 被引量:7
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作者 zhongwei ma Yanye Jin +3 位作者 Shude Ji Xiangchen Meng Lin ma Qinghua Li 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2019年第1期94-99,共6页
Ultrasonic assisted friction stir welding(UaFSW) was used to join 6061-T6 aluminum and Ti6Al4 V alloys.A small plunge depth endowed with the low heat input was used and the sound joints without obvious thickness reduc... Ultrasonic assisted friction stir welding(UaFSW) was used to join 6061-T6 aluminum and Ti6Al4 V alloys.A small plunge depth endowed with the low heat input was used and the sound joints without obvious thickness reduction were achieved. A diffusion-type bonding without the intermetallic compounds layer was observed at the joint interface. The ultrasonic improved the diffusion thickness and decreased the average size of grains and titanium alloy fragments. A hook-like structure was formed at the bottom interface of the UaFSW joint, which improved the bonding length and the mechanical interlocking. The microhardness of the stir zone was increased because of the further grain refinement induced by ultrasonic. The maximum tensile strength of the UaFSW joint was 236 MPa, which reached 85% of the base6061-T6 alloy. 展开更多
关键词 FRICTION STIR welding ULTRASONIC Aluminum/titanium alloys Microstructure Mechanical properties
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Improving the mechanical property of dissimilar Al/Mg hybrid friction stir welding joint by PIO-ANN 被引量:5
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作者 Wei Hu zhongwei ma +3 位作者 Shude Ji Qi Song Mingfei Chen Wenhui Jiang 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2020年第18期41-52,共12页
Ultrasonic-stationary shoulder assisted friction stir welding(U-SSFSW)is a novel hybrid welding technique,which reveals promising prospect in joining Al/Mg dissimilar alloys.A thorough understanding of U-SSFSW process... Ultrasonic-stationary shoulder assisted friction stir welding(U-SSFSW)is a novel hybrid welding technique,which reveals promising prospect in joining Al/Mg dissimilar alloys.A thorough understanding of U-SSFSW process is imperative for the further application of this technique.Pigeon-inspired optimization(PIO)is a swarm intelligent optimization algorithm and is proposed in mathematical modeling and process optimization by artificial intelligence.In this study,PIO optimized artificial neural network(PIOANN)was firstly established to acquire the relationships between the inputs and output of the Al/Mg welding process by U-SSFSW technique.A reliable PIO-ANN was achieved and the joint with a tensile strength of 161 MPa was acquired under the PIO optimized parameters.This tensile strength is higher than any ever-reported results with the similar welding condition.The joint formation,microstructure,microhardness and fracture behaviors were systemically investigated based on the reported studies and the confirmation experiment of this study to explore the enhancing mechanism of U-SSFSW Al/Mg joint. 展开更多
关键词 Hybrid friction stir welding Al/Mg dissimilar alloys Artificial neural network Pigeon-inspired optimization Ultrasonic
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