Venue: Training Room 1, Level 3, SIMTech, Tower Block, 71 Nanyang Drive, Singapore 638075
In this workshop, SIMTech researchers will share machine monitoring techniques that will help to enhance productivity in complex and high value manufacturing operations. Machine monitoring techniques provide operation visibility, a better insight of machine and tool conditions which will eventually reduce unexpected machine breakdown, improve parts surface quality, reduce rework and scrap.
The following case studies will be highlighted at the workshop :
- shop floor machine operation visibility
- machine availability and performance monitoring
- tool condition monitoring
- machine spindle condition monitoring
- grinding condition monitoring
- machine best parameters identification
-How machine monitoring techniques can help improve productivity
-Machine Monitoring concepts and approach
9.15am Case Study 1 : Shop floor machine operation visibility
9.25am Case Study 2 : Machine availiability and performance monitoring
9.35am Case Study 3 : Tool condition monitoring (milling & grinding)
9.45am Case Study 4 : Machine spindle condition monitoring
9.55am Case Study 5 : Energy monitoring
10.05am Case Study 6 : Machine best parameters identification
10.15am Coffee Break
10.30am Round Table Discussion
About the presenters
Dr Zhou Junhong, a Principal Research Engineer, joined SIMTech in 1996. Dr Zhou has been actively involved in a number of research and industry projects in the areas of process monitoring & control, and sensing & advanced signal processing. Dr Zhou's experience includes SCADA system, sensing & measurement for machine tool condition, and intelligent systems to maintain serviceability of manufacturing equipment. She is the Initiative Lead in maximising overall equipment effectiveness.
Dr Li Xiang, a Senior Scientist, joined SIMTech in 1992. Dr Li has more than 20 years experience in research on artificial intelligence, data mining and statistical analysis, such as neural networks, fuzzy logic systems, expert systems and multiple regression models. She was involved and also led many industrial projects in intelligent forecasting system, auto mould designs using intelligent optimisation & simulation, data warehousing, customer demand discovery in new product design, intelligent modelling for equipment health prognostics and process monitoring & product control in manufacturing processes. Dr Li is a Theme Leader in the area of prognostic health management in SIMTech.
Dr Ian Chan, earned his Bachelor's Degree in Mechanical Engineering from National University of Singapore, and obtained his Masters and PhD from Department of Aeronautics & Astronautics, Stanford University. He served as a Technical Lead Engineer developing structural monitoring solutions at Acellent Technologies, Inc in Sunnyvale California. He is currently a Research Scientist at SIMTech, A*STAR focusing on data analytics to improve energy efficiency for manufacturing operations and equipment. His expertise included structural monitoring, machine condition monitoring, vibration sensing, signal processing for the main purpose of equipment diagnostics and prognostics.
Ms Zhang Danhong, is a Senior Research Engineer in SIMTech. She was involved in many industry and in-house research projects and has experience in real-time information and control system, control and monitoring material handling system, SCADA system, remote monitoring, device communication, wireless sensor networks, fault diagnostics and prognostics for equipment & system.
Who Should Attend
Senior Management, Production/Process Managers, QA Managers, R&D Researchers, Engineers, Supervisors & IT support staff from the semiconductor and PE industry
To book a seat for this non-chargeable workshop, please register online. Registration closing date : 3 September 2012
Technical enquiries, please contact : Dr Zhou Junhong. Tel : 6793 8264; email : jzhou@SIMTech.a-star.edu.sg
General enquiries, please contact : Ms Samantha Sukiyama Chan, Tel : 6793 8423; email : chanskf@SCEI.a-star.edu.sg