KE22 Final Year Project Phase 3

1. KE22 Final Year ProjectPhase 3 Modeling and Simulation of Milling Forces SIMTech Project Ryan Soon, Henry Woo, Yong Boon April 9, 2011 Confidential – Internal Only

2. Agenda

3. Objectives Understand a prognostic problem domain that enables an Hybrid implementation of Knowledge Engineering Techniques Present research effort & implementation result of overall prognostic problem domain Highlight novel prognostic optimization concept and model Challenges and benefits

4. Problem Domain Overview • Predict remaining lifespan of milling cutter tool • By implementing a Hybrid Knowledge Engineering Model using • Hierarchical Clustering (HC) • Adaptive Neural Fuzzy Inferences System (ANFIS)

5. System Description • ANFIS by itself can solve the prediction problem • Optimize ANFIS by HC  HC-ANFIS model • Reduce number of inferences rules to improve optimize learning time • Determine the optimal cluster size of HC • By using cluster balancing method borrowed from Subtractive Clustering • Improve overall learning and application performance

6. Results

7. Benefits by organization • H.C. System • Fast and customizable input selection for user to select for H.C. clustering for different needs • Customized output, to facilitate future seamless integration between H.C. and ANFIS system • Able to use new method to deduce optimal cluster, and compare against current k-means clustering tool when clusters are fed to ANFIS used by SimTech. Result is satisfactory • ANFIS System • SimTech is able to utilize software to make accurate tool-wear prediction or any other non-linear prediction

8. Benefits by students (I) • Enforced what student learned in course • Project Management • Use different techniques(i.e. interview, survey) to gather user requirements and use CommonKads to document in proper knowledge based documentation • Utilize the knowledge learned in class(i.e. Clustering and Neural Network) to come up with the system design and final product • Product Development • Understand the underlying principle of how clustering and Neural Network works • Able to improve new techniques to overcome current problems faced by user when using commercial products (i.e. Matlab)

9. Benefits by students (II) • Understand the importance and usage of the application of H.C. and ANFIS in real world situation • Learned from users on the proper testing technique for testing of final result (i.e. Result must be repeatable and reliability

10. 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

11. 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

12. 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

13. Fuzzy System Identification

14. 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)

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

16. 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.

17. 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

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

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

20. 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

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

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

23. 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

24. 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

25. The End Demo

26. The End Q&A

27. BACKUP