Module | Implement Manufacturing Data Mining Techniques
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- Designed to specially cater to local industry demand
- Highly practical and intensive
- Latest knowledge and up-to-date technology
- Case studies highlighting industrial applications
- Expert trainers in the field with industrial experience
This course is designed for operation directors and managers, production/process engineers, R&D Engineers and IT support staff working on processes, production and quality improvement in manufacturing industries such as precision engineering, aerospace, automotive electronics, semiconductor, oil and gas, pharmaceutical and medtech.
Fundamentals of Data Mining
- Introduction to data mining concept and applications in manufacturing
- Process correlation modelling and data pattern analyses through statistical methods
- Advanced data clustering technologies for anomaly detection and classification
- Process performance prediction using artificial intelligence (neural networks, etc)
Case Studies by Grouping Projects Using Real Production Data
- Data preparation
- Problem statement
- Technical challenges
- Project objectives
- Data collection and pre-processing
- Data analysis
- Major factor identification by correlation efficient analysis
- Correlation modeling by multiple regression method and root cause analysis
- K-means clustering and pattern based regression modeling
- Correlation modeling by fuzzy neural networks method for quality estimation
- What-if predictive analysis for process improvement & DOE design
- Root cause analysis for major factor identification
- Project conclusion
- Improving plan
- Identifying yield improvement areas
- Production/process improvement plan
||Dr Li Xiang is a Senior Scientist and Team Lead in SIMTech. She has more than 20 years of experience in research on computational intelligence, data mining, and statistical analyses such as neural networks, fuzzy logic systems, unsupervised data clustering, and regression modelling. Her research expertise includes data mining and knowledge discovery, decision support systems, in-situ process monitoring and quality control. She is a member of the IEEE.
View Dr Li Xiang's researcher portfolio here.
Click here to view the above video on YouTube.