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 • KEY IDEA  Optimizing manufacturing asset and predictive maintenance • What is Milling?  customized material of different shapes and features • What to Optimize  Predict remaining lifespan of cutter • How to Optimize  Implementing a Hybrid KE Model using • Hierarchical Clustering (HC) • Adaptive Neural Fuzzy Inferences System (ANFIS) • Resulting in an optimal HC-ANFIS hybrid • Why Optimal  determine optimal cluster size and automatically produce optimal ANFIS structure

5. System Description • Machine sensors attached to the milling process • Cutting force sensor in x, y, z dimension • Acoustic emission sensor that measure high frequency stress wave

6. System Description • 6 cutter tools’ data given • Over 300+ samples given for each cutter • At specific interval • Measure sensors’ readings • Measure tool wear using electronic microscope

7. System Description • ANFIS by itself can solve the prediction problem • But required expert knowledge on rules determination and membership function • Use HC to determine rules and membership function • By providing optimal cluster size, membership function parameters • How to determine the optimal cluster size of HC • By using cluster balance method • Improve overall learning and application performance • Coded HC module in .NET C# • Coded ANFIS module in Python

8. Grid Partition with HC approach

9. Grid Partition with HC Issue • Complexity of the ANFIS structure is based on the product of each input’s cluster size • Given that p, q, r, s represented the cluster size of the 4 force features • ANFIS would generate (p * q * r * s) number of inferences rules • For E.g. if p = q = r = s = 10, then number of inferences rules = 10,000! • This is computationally intensive and infeasible to implement

10. HC-ANFIS Approach

11. HC-ANFIS Approach findings • Less rules produced hence a more feasible solution than the previous approach • As the features were combined, much lesser ANFIS inferences rules were created thus resulting in a much lesser intensive computation and a feasible solution to implement

12. Results

13. Benefits by organization • HC System • Fast and customizable input selection for different application needs • Customized output, to facilitate future seamless integration between HC and other system • Novel cluster balance implementation to determine optimal HC cluster size • HC-ANFIS System • Provide an alternative automated tool wear prediction method for SimTech sponsor

14. Benefits by students • Enforce what student learned in course • Knowledge Modeling and Management • Use different techniques (i.e. interview, UML diagrams) and CommonKADS to gather and capture user requirements • Utilize the knowledge learned in class (i.e. Clustering, Fuzzy Inferences System and Neural Network) to come up with a Hybrid system design and final product • Product Development • Understand the underlying principle and math of how Clustering, Fuzzy Inferences System and Neural Network works • Explore and innovate new KE techniques • Understand the importance and usage of the HC and ANFIS application in real world situation • Learned from users on the proper result testing technique • Result must be repeatable and reliable

15. DEMO • Show capability of • Grid Partition with HC • HC-ANFIS • Subtractive Clustering (MATLAB)

16. The End Q&A

17. BACKUP

18. 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

19. 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

20. 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

21. Fuzzy System Identification

22. 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)

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

24. 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.

25. 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

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

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

28. 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

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

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

31. 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

32. 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