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来源:机电工程学院          点击:
报告人 钟润阳 时间 6月3日19:30
地点 腾讯会议直播 报告时间


会议地点:腾讯会议直播(ID:613 481 705 链接:https://meeting.tencent.com/s/ZZKgYDD9syNA




钟润阳,香港大学工业与制造系统工程系的助理教授。2016年6月至2019年1月,在新西兰的奥克兰大学机械工程系担任讲师。分别在广东工业大学和香港大学获得硕士和博士学位。研究领域包括物联网制造,制造业大数据和之智能建筑供应链管理。目前已经发表了160多篇论文(其中大约80篇SCI和80篇会议论文)。截至2021年5月10日,谷歌学术的引用次数超过6400次,H指数为37,i10指数为84。在web of science上有9篇高被引论文。此外,担任多家国际知名期刊的编辑委员和客座编辑:International Journal of Production Economics(客座编辑),Computers & Industrial Engineering: An International Journal(地区编辑,2019至今),International Journal of Computer-Integrated Manufacturing (副编辑2019至今)等。参与国家自然亚美游体育官方app基金、国家研发部、香港科技基金会和香港大学的一系列项目。目前为CIRP RA(2017-2020)、ASME(美国)、HKIE(香港)、IET(英国)、IEEE(美国)和LSCM(香港)的成员。在第15届IFAC/IEEE/IFIP/IFORS制造业信息控制问题研讨会上获得青年作者奖、新西兰中国亚美游体育官方app家协会(获奖者)颁发的青年亚美游体育官方app家奖(2017年)以及在著名IEEE会议上的获得多篇最佳会议论文。

报告1:Big Data Analytics for Internet of Things (IoT)-enabled Manufacturing



Internet of Things (IoT) technologies like RFID (Radio Frequency Identification) are used for manufacturing shopfloors where typical manufacturing resources are converted into smart manufacturing objects (SMOs). SMOs are equipped with appropriate level of intelligence, allowing them to intelligently predict and react to physical world events, and make decisions autonomously with and without human intervention. Along with the enormous interactions among large number of SMOs in production sites, great myriad of data will be generated. Such Big Data is able to greatly upgrade the IoT-enabled manufacturing and create better decision-making mechanisms.

This talk introduces an IoT-enabled manufacturing by systematically deploying RFID devices and wireless network to create a smart environment where SMOs are able to interact and interconnect with each other so that vast number of data will be generated. A Big Data Analytics approach is presented to make full use the data for various production decision-makings. Firstly, an innovative RFID-Cuboid model is proposed to organize the abstract and complex sensed data. Secondly, a Big Data Analytics framework is presented to process the organized RFID Big Data. Thirdly, data-driven production decisions like planning and scheduling, logistics and performance evaluation models are presented. Finally, some practical cases will be demonstrated for examining the feasibility and practicality of the proposed approaches.

报告2:IoT-enabled AGV System using Digital Twin for Smart Manufacturing



Automated Guided Vehicles (AGVs) are used to deliver materials and goods around a factory autonomously. These robots may follow a predetermined path and thus the flexibility is confined. To traverse a factory, some form of information regarding the mapping of the factory is required. Radio Frequency Identification (RFID) technology shows a promising capability in this field as well as its ability to assist in decision-making. Using a scenario of a testbed factory, several AGVs are presented using a laid-out path based on RFID technology. RFID tags embedded into the path are used for making decisions and to identify the location to achieve automatic logistics. These robots are controlled using a Digital Twin enabled solution. From the testing scenario, it is showed that RFID technology which is important in Industry 4.0 is able to capture the real-time data for automatic logistics in a smart factory.











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