RMU_Summer2005_Samanta1 Applications of Computational Intelligence Techniques in Engineering B Samanta International Visiting Professor Robert Morris University

RMU_Summer2005_Samanta2 Presentation Summary Motivation Computational Intelligence Different CI techniques Applications of CI techniques Recent Work Work done at RMU Way forward Conclusions

RMU_Summer2005_Samanta3 Motivation Use of computers for better understanding and interpretation of process/system behavior Use of available information to obtain input-output mapping. Utilization of expert/operator knowledge Ability to use imprecise, uncertain information Integration of knowledge over multiple disciplines Automated machine learning inspired from nature (neuroscience, genetics, behavioral science) Development of models for optimizing the system performance satisfying the inherent system/process constraints.

RMU_Summer2005_Samanta4 Computational Intelligence (CI) Intelligence built in computer programs Covers Evolutionary computing Fuzzy computing Neuro-computing Also known as Soft computing

RMU_Summer2005_Samanta5 CI Techniques Artificial Intelligence (AI) Artificial Neural Networks (ANNs) Fuzzy Logic (FL) Support Vector Machines (SVM) Self Organizing Maps (SOM)- unsupervised Genetic Algorithm (GA) Genetic Programming (GP) Swarm Intelligence/Particle Swarm Optimization (PSO)

RMU_Summer2005_Samanta6 CI Techniques (contd.) ANNs Multi-layer Perceptron (MLP) Radial Basis Function (RBF) Probabilistic Neural Network (PNN) Fuzzy Logic + ANN Adaptive neuro-fuzzy inference system (ANFIS)

RMU_Summer2005_Samanta7 CI Techniques (contd.) ANN structure Input layer Hidden Layer (s) Output layer Number of nodes in each layer Functions and their parameters Mostly decided on trial and error basis

RMU_Summer2005_Samanta8 ANN- a typical example x1x1 x2x2 xNxN u1u1 u2u2 uQuQ y1y1 y2y2 yMyM Input layer Hidden layer

RMU_Summer2005_Samanta9 Fuzzy Logic Steps involved Fuzzification using membership functions (MFs)-input Generation of rule base Aggregation Defuzzification using MFs -output

RMU_Summer2005_Samanta10 Fuzzy Logic (contd.) Input and output MFs Number Type Parameters Rule base (experience guided)

RMU_Summer2005_Samanta11 Neuro-Fuzzy System Combines the advantages of fuzzy logic (FL) and ANNs Starts with an initial FL structure Uses ANN for adapting the FL (MF) parameters and the rule base to the training data

RMU_Summer2005_Samanta12 Fuzzy Logic – An Example ANFIS structure for an example system with 2 inputs and 1 output.

RMU_Summer2005_Samanta13 Snapshot of rule base for an example system with 2 inputs and 1 output.

RMU_Summer2005_Samanta14 Genetic Algorithms Construction of genome (individual) Generation of initial population (group of individuals) Evaluation of individuals Selection of individuals based on criteria Generation of new individuals Mutation Crossover Repetition of the process - generation, evaluation, selection Termination of the process based on max generation no. and/or performance criteria

RMU_Summer2005_Samanta15 Combinations Combine advantages of GA and other classifiers GA and ANN GA and ANFIS GA and SVM for automatic selection of classifier structure and parameters ANNs -Number of neurons in hidden layer ANFIS - Number of MFs and their parameters SVM – SVM parameters Selection of most important system features from a pool Selection of most important sensors (in the context of on-line condition monitoring and diagnostics)- sensor fusion.

RMU_Summer2005_Samanta16 Signal Conditioning and Data Acquisition Feature Extraction Training Data SetTest Data Set Training of ANN/ SVM Is ANN/ SVM Training Complete ? No Yes ANN / SVM Output Machine Condition Diagnosis Trained ANN/ SVM with selected features Fig. 1. Flow chart of diagnostic procedure GA based selection of features and parameters Is GA based selection over? Yes No Rotating Machine with Sensors

RMU_Summer2005_Samanta17 Genetic Programming (GP) GP – a branch of GA with a lot of similarities. Main difference of GP and GA is in the representation of the solution. In GA, the output is in form of a string of numbers representing the solution. GP produces a computer program in form of a tree-based structure relating the inputs (leaves) the mathematical functions (nodes) and the output (root node).

RMU_Summer2005_Samanta18 GP output –An Example Terminals (leaves): inputs x1, x2 and constant 3 Nodes: Math functions *,+, exp Output: x1*x2+exp(3) X1 X2 times plus exp 3 (+ (* (X1 X2))(exp(3))

RMU_Summer2005_Samanta19 Applications Computer Science Pattern Recognition (PR) Data Mining Knowledge Discovery/ Machine Learning Feature Extraction and Selection Mechanical Systems Condition monitoring and diagnostics Multiobjective optimization in design Control System Design Manufacturing Systems Development of data-driven models Multiobjective optimization of machining parameters

RMU_Summer2005_Samanta20 Applications (contd.) Engineering Management/IE Inventory management Project selection Facility layout design Scheduling Medicine Patient condition monitoring and diagnosis Social Science Business Market analysis and forecasting Credit rating

RMU_Summer2005_Samanta21 Recent Work Machine Condition Monitoring and Diagnostics using ANNs-MLP, RBF, PNN SVM ANFIS GA-ANN GA-ANFIS GA-SVM GP Involving signal processing, feature extraction, selection and sensor fusion

RMU_Summer2005_Samanta22 Recent work (contd.) Materials ANN based estimation of fatigue life Modeling of material properties in terms of heat treatment parameters Rotordynamics Control System Design

RMU_Summer2005_Samanta23 Work done at RMU Intelligent Manufacturing Systems Development of Tool Wear Model ANFIS and GA-ANFIS Genetic Programming (GP) Development of machined surface roughness model ANFIS and GA Genetic Programming (GP) Mutliobjective optimization of machining parameters Minimization of machining cost Minimization of surface roughness Minimization of production time Subject to constraints on Operating parameters –speed, feed, depth of cut Cutting Force Power consumption Tested on 5 different data sets Involves different machining operations Milling, turning and Turning of hard material (>Rc 65)

RMU_Summer2005_Samanta24 Tool Wear Model Mapping of Inputs and Outputs Inputs Tool type- geometry, material Work piece Cutting speed (V) Feed rate (f) Depth of cut (d) Vibration (Vx, Vy, Vz) Forces (Fx, Fy, Fz) Cutting Time (t) Outputs Tool wear Remaining Tool Life GA/GP based selection of characteristic inputs

RMU_Summer2005_Samanta25 ANFIS based Tool Wear Model – An Example Input pool Spindle speed (x1) Feed rate (x2) Machining time (x3) Ratio of forces in 2 directions: Fx (feed)/ Fz (tangential) (x4) Output – Tool wear level Data set Training – 25 Test - 38 Number of MFs - 2 Performance – Training Root Mean Square Error (RMSE) 1.30% Test data set RMSE : 8.52% Training time 0.34 s

RMU_Summer2005_Samanta26

RMU_Summer2005_Samanta27

RMU_Summer2005_Samanta28 GA-ANFIS based roughness model – An Example Input pool Spindle speed (x1) Feed rate (x2) Depth of cut (x3) Vibration in 3 directions x (radial) (x4) y (tangential) (x5) z (feed) (x6) Output – surface roughness Data set Training – 36 Test - 24 GA based selection of best 3 features: x2, x1, x5 Number of optimum MFs - 2 Performance – Training Root Mean Square Error (RMSE) 2.60% Test data set RMSE : 6.65% Training time s

RMU_Summer2005_Samanta29

RMU_Summer2005_Samanta30

RMU_Summer2005_Samanta31

RMU_Summer2005_Samanta32 GP model for surface roughness GP was used for same data sets Training – 36 Test set – 24 Performance Training RMSE: 3.79% Test RMSE : 6.90% Training time: s

RMU_Summer2005_Samanta33 GP output tree for Roughness model

RMU_Summer2005_Samanta34 Publications Planned Predictive modeling of tool wear in turning using adaptive neuro-fuzzy inference system Modeling and prediction of tool wear in turning using genetic programming Predictive modeling of surface roughness in turning using adaptive neuro-fuzzy inference system and genetic algorithms

RMU_Summer2005_Samanta35 Publications Planned (contd.) Modeling and prediction of surface roughness in turning using genetic programming Predictive modeling of surface roughness in milling using adaptive neuro-fuzzy inference system and genetic algorithms Multiobjective evolutionary optimization of a machining process

RMU_Summer2005_Samanta36 Conferences/Journals North American Manufacturing Research Conference (NAMRC 34 ), NAMRI/SME, May 23-26, 2006, Milawukee, WI, USA. Flexible Automation and Intelligent Manufacturing (FAIM) June 26-28, 2006, Univ of Limerick, Ireland. IFAC Symposium on Information Control in Manufacturing (INCOM) May17-19, 2006, France. Journal of Manufacturing Systems/SME International Journal of Machine Tools & Manufacture

RMU_Summer2005_Samanta37 Industry-RMU collaboration Potential Interest in RMU-EOC research collaboration in the area of Laser machining. Development of machining models using CI Multiobjective constrained optimization of machining/laser system parameters Sensor fusion Interest in RMU-ExOne research collaboration in the areas of 3D printing process system Design optimization

RMU_Summer2005_Samanta38 Way Forward Scope for further collaboration with RMU Teaching – Development of new elective or short courses in consultation with Faculty Research – Joint supervision of projects/theses at Senior, MS and PhD levels Collaborative work with Faculty Outreach- Industry and Government supported research projects/contracts

RMU_Summer2005_Samanta39 Conclusions Increasing popularity of CI techniques Integrating capability over multiple disciplines Capability of incorporating imprecision and uncertainty Suitability for hard-to-model processes /systems Better alternatives to traditional hard computing scenario

RMU_Summer2005_Samanta40 THANKS Thanks to RMU Administration Sponsor of the Program SEMS/Engineering Faculty, Staff for the support and facilitating the visit Thanks to you all (in audience) For your time and patience