Manufacturing Takes a Stride Toward Industry 4.0
Release time：March 28, 2017 /
Speaker: Ip Shing Fan
Date：March 30th , 2017 11:00- 12:00 AM
Venue：NO. 2 Conference Room, Conference Center, New Main Building
This report introduces the differences between industry 4.0 and traditional factories. Besides, it involves reforms of traditional machine engineering, and cultivations of technicians who are capable of working in the future factories.
New information technology is transforming modern economy and society, such as sensor, executor，communication, AI, and data analysis. In the field of manufacturing, products are upgraded and more intelligent. Take this example. Modern electronic passenger aircraft could inform pilot and ground control system to optimize both operating and maintenance systems.
Intelligent factories are developing intelligent products. Factory machines could take the operating condition under hand, and transmit the messages to the controlling center, which confirms the optimal utilizations of the facilities, and responds to what the product configuration and demand variable call for. The industry 4.0 that Germany advocates creates a framework in this field, with a network of factory automation, physical system, robots, and intelligent system.
Intro of the Speaker：
Fan was born and studied in Hong Kong, graduated with First Class Honors in Industrial Engineering. He completed his graduate engineer training at Qualidux Industrial Co Ltd in Hong Kong. He was awarded the Commonwealth Scholarship and completed his PhD in Computer Integrated Manufacturing in Cranfield. After returning to Hong Kong, he worked as CADCAM Manager in Qualidux Industrial Co Ltd, responsible for the introduction of CAD, CAM and CNC in plastic injection design and engineering.
In 1990, Fan started to work in The CIM Institute, endowed by IBM in Cranfield, to carry out research, education and consultancy in new applications of computers in manufacturing. He led many European and UK funded research programmes to create new tools and methods in knowledge based engineering design, business performance, quality management, supply chain, and complexity science.