Background:

1. Adaptive Neuro-Fuzzy Inference System for Tool Condition Monitoring • Background: • Tool condition monitoring is of increasing importance for the industry to prevent lost of valuable materials and improve efficiency. Adaptive Neuro-Fuzzy is potentially one of the many methods possible for tool wear prediction. • Objectives: • The purpose of this project is to investigate the capabilities and performance of Adaptive Neuro-Fuzzy algorithm for tool wear prediction in a case study and compare against that of traditional ANFIS algorithm for accuracy and efficency. • Methodology: • The work necessary to start from the test bed and obtain a correlation model between the sensors’ signal and the tool wear is done by: • Optimization of parameters used for prediction models by Hierarchical Clustering. • Build up correlation between features and tool wear using Adaptive Neuro-Fuzzy model • Compare the result against ANFIS • Findings & Achievements: • The accuracy and efficiency of Adaptive Neuro-Fuzzy model against ANFIS. Experimental setup Prediction results Prototype system Team Members: Lim Yong Boon (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