KE22 Final Year Project Phase 2

1. KE22 Final Year ProjectPhase 2 Modeling and Simulation of Milling Forces SIMTech Project Ryan Soon, Henry Woo, Yong Boon April 9, 2011 Confidential – Internal Only

2. Problem Description • 3 set of cutter tool data were given • 07, 31, T12 • Belong to the same family type but with differences in drill bit shape and knife edges • Problem domain requires us to build a hybrid KE system to predict the cutter tool wear • Full Microsoft .NET C# implementation of Hybrid KE system • Hierarchical Clustering • Derive number of Fuzzy linguistic values for each variable • Derive number of Fuzzy rules • ANFIS (Neural Fuzzy System) to learn and predict the tool wear • Generic tool wear prediction model

3. Data Correlation analysis – 1 • And within each cutter tool data • 3 sets of individual tool head data  F1, F2, F3 • Within each “F” data (315 records) • Acoustic emission data (16 features) • Force (x dimension) data (16 features) • Force (y dimension) data (16 features) • Force (y dimension) data (16 features) • Too much features • Use correlation coefficient method and cut down on the features

4. Data Correlation analysis – 2 AE Fx Fy Fz • By using Pearson Correlation Coefficients, the linear dependence between the measured features values and the tool wear values can be calculated • AE data is not influencing the tool wear strongly • The top influencing features are consistent between the 3 forces

5. Fuzzy System Identification

6. Overview of Hierarchical Clustering • Agglomerative HC starts with each object describing a cluster, and then combines them into more inclusive clusters until only one cluster remains. • 4 Main Steps • Construct the finest partition • Compute the distance matrix • DO • Find the clusters with the closest distance • Put those two clusters into one cluster • Compute the distances between the new groups and the remaining groups by recalculated distance to obtain a reduced distance matrix • UNTIL all clusters are agglomerated into one group. • Ward Methods, minimize ESS (Error Sum-Of-Square)

7. Overview of ANFIS • ANFIS architecture • Premise ANFIS MF(Bell) • Consequence Linear Sugeno • Learning Algorithms

8. Optimal Hierarchical Clustering • Determine the numbers of clustering using RSS with penalty. Where, is the penalty factor for addition # of cluster. K’ and K = number of clusters RSS = Residual Sum of Squares • Borrow concept from K-means using RSS as goal function.

9. Hierarchical Clustering + ANFIS • Two Different Approaches for HC + ANFIS • Use HC to determine # of linguistic values for each input features • Use HC to determine # of rules

10. Optimal Hierarchical Clustering# of Linguistic Variables • Example on SRE variables, opt # of cluster = 3 • Perform HC on selected features on FX

11. ANFIS Architectures# of Linguistic Variables • ANFIS with 4 inputs variables contains 3~4 linguistics variables generated 192 Rules!

12. ANFIS – Results# of Linguistic Variables • ANFIS Predict vs Actual • Train Data with Avg Error 4.84 • Test Data with Avg Error 15.00 • Membership Functions • P2p • Std_fea • Sre • fstd

13. Optimal Hierarchical Clustering# of Rules • Build HC on all variables, opt # of cluster = 5

14. ANFIS Architectures # of Rules • ANFIS with 4 inputs variables contains 5 linguistics variables and 5 rules. • Each cluster centre is a fuzzy rules!

15. ANFIS – Results # of Rules • ANFIS Predict vs Actual • Train Data with Avg Error 5.75 • Test Data with Avg Error 15.218 • Membership Functions • P2p • Std_fea • Sre • fstd

16. What’s next? • Full .NET C# Implementation • Development of Hierarchical Clustering toolset with frontend GUI • Manual range input of number cluster by user • Optimal clustering suggesting the optimal number of cluster • Make use of ANFIS model to evaluate • GUI engine for cluster center drawing • Development of ANFIS toolset with frontend GUI • Develop the ANFIS Engine which will do the optimization • Develop User Interface for: • Display predicted tool-wear result • Evaluation of error

17. The End Demo

18. The End Q&A

19. BACKUP