論壇名稱：智能制造發(fā)展介紹

會議地點(diǎn)：騰訊會議直播（ID:613 481 705 鏈接:https://meeting.tencent.com/s/ZZKgYDD9syNA）

主辦單位：機(jī)電工程學(xué)院

主持人：劉永奎

講座人介紹：

鐘潤陽，香港大學(xué)工業(yè)與制造系統(tǒng)工程系的助理教授。2016年6月至2019年1月，在新西蘭的奧克蘭大學(xué)機(jī)械工程系擔(dān)任講師。分別在廣東工業(yè)大學(xué)和香港大學(xué)獲得碩士和博士學(xué)位。研究領(lǐng)域包括物聯(lián)網(wǎng)制造，制造業(yè)大數(shù)據(jù)和之智能建筑供應(yīng)鏈管理。目前已經(jīng)發(fā)表了160多篇論文（其中大約80篇SCI和80篇會議論文）。截至2021年5月10日，谷歌學(xué)術(shù)的引用次數(shù)超過6400次，H指數(shù)為37，i10指數(shù)為84。在web of science上有9篇高被引論文。此外，擔(dān)任多家國際知名期刊的編輯委員和客座編輯：International Journal of Production Economics（客座編輯），Computers & Industrial Engineering: An International Journal（地區(qū)編輯，2019至今），International Journal of Computer-Integrated Manufacturing （副編輯2019至今）等。參與國家自然科學(xué)基金、國家研發(fā)部、香港科技基金會和香港大學(xué)的一系列項(xiàng)目。目前為CIRP RA（2017-2020）、ASME（美國）、HKIE（香港）、IET（英國）、IEEE（美國）和LSCM（香港）的成員。在第15屆IFAC/IEEE/IFIP/IFORS制造業(yè)信息控制問題研討會上獲得青年作者獎、新西蘭中國科學(xué)家協(xié)會（獲獎?wù)撸╊C發(fā)的青年科學(xué)家獎（2017年）以及在著名IEEE會議上的獲得多篇最佳會議論文。

報(bào)告1：Big Data Analytics for Internet of Things (IoT)-enabled Manufacturing

會議時(shí)間：6月3日19:30

講座內(nèi)容：

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.

報(bào)告2：IoT-enabled AGV System using Digital Twin for Smart Manufacturing

會議時(shí)間：6月3日20:30

講座內(nèi)容：

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.