KE22 FINAL YEAR PROJECT PHASE 2 Modeling and Simulation of Milling Forces SIMTech Project Ryan Soon, Henry Woo, Yong Boon April 9, 2011 Confidential – Internal Only

2| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only 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| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only 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)  Too much features –Use correlation coefficient method and cut down on the features

4| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only DATA CORRELATION ANALYSIS – 2  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 AEFxFyFz

5| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only FUZZY SYSTEM IDENTIFICATION

6| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only 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| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only OVERVIEW OF ANFIS  ANFIS architecture  Premise ANFIS MF(Bell)  Consequence Linear Sugeno  Learning Algorithms FWBW PremiseFixedGradient Descent ConsequenceLSEFixed

8| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only 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| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only 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| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only OPTIMAL HIERARCHICAL CLUSTERING # OF LINGUISTIC VARIABLES  Example on SRE variables, opt # of cluster = 3  Perform HC on selected features on FX Variables Name# of Clusters p2p4 std_fea4 sre3 fstd4

11| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only ANFIS ARCHITECTURES # OF LINGUISTIC VARIABLES  ANFIS with 4 inputs variables contains 3~4 linguistics variables generated 192 Rules!

12| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only ANFIS – RESULTS # OF LINGUISTIC VARIABLES  ANFIS Predict vs Actual –Train Data with Avg Error 4.84 –Test Data with Avg Error  Membership Functions –P2p –Std_fea –Sre –fstd

13| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only OPTIMAL HIERARCHICAL CLUSTERING # OF RULES  Build HC on all variables, opt # of cluster = 5

14| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only ANFIS ARCHITECTURES # OF RULES  ANFIS with 4 inputs variables contains 5 linguistics variables and 5 rules.  Each cluster centre is a fuzzy rules!

15| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only ANFIS – RESULTS # OF RULES  ANFIS Predict vs Actual –Train Data with Avg Error 5.75 –Test Data with Avg Error  Membership Functions –P2p –Std_fea –Sre –fstd

16| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only 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| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only THE END Demo

18| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only THE END Q&A

19| KE22 FYP, Modeling and Simulation of Milling Forces | April 9, 2011 | Confidential – Internal Only BACKUP