Background:

1. Hierarchical Clustering for Tool Condition Monitoring Background: Tool condition monitoring is of increasing importance for the industry to prevent lost of valuable materials and improve efficiency. Hierarchical Clustering can be used to group and summarize large data to produce association rules according to the items contained in each rule, to be used for tool condition monitoring purpose. Objectives: The purpose of this project is to investigate the capabilities and area of usage of Hierarchical Clustering, and its suitability and performance for use in Tool Condition Monitoring system. Methodology: Extensive research and testing is need on Hierarchical clustering method in order to construct the system, which includes: Understand the different types of Hierarchical Clustering method available and the actual working principle behind it Research on different kind of algorithms available for determination of optimal cluster size and its suitability on Hierarchical clustering Compare results obtained against different sources(i.e. MATLAB) to ascertain the accuracy of the final product. Findings & Achievements: The Prototype system for Hierarchical clustering system to be used for tool wear prediction was built. Experimental setup ProtoType System Determine Optimal Cluster Size Team Members: Woo Meng Chew (FYP) Dr. Li Xiang (MEC) Dr. Goh Kiah Mok (MEC) Miss. Zhou Jun hong (MEC) For enquiries, please contact: Dr Gan Oon Peen Group Manager Manufacturing execution & Control Group Tel: 65 67938406 | Fax: 65 6791 6377 Email: opgan@SIMTech.a-star.edu.sg