1 / 40

Applications of Computational Intelligence Techniques in Engineering

Applications of Computational Intelligence Techniques in Engineering. B Samanta International Visiting Professor Robert Morris University. Presentation Summary. Motivation Computational Intelligence Different CI techniques Applications of CI techniques Recent Work Work done at RMU

gabi
Download Presentation

Applications of Computational Intelligence Techniques in Engineering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Applications of Computational Intelligence Techniques in Engineering B Samanta International Visiting Professor Robert Morris University RMU_Summer2005_Samanta

  2. Presentation Summary • Motivation • Computational Intelligence • Different CI techniques • Applications of CI techniques • Recent Work • Work done at RMU • Way forward • Conclusions RMU_Summer2005_Samanta

  3. 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_Samanta

  4. Computational Intelligence (CI) • Intelligence built in computer programs • Covers • Evolutionary computing • Fuzzy computing • Neuro-computing • Also known as • Soft computing RMU_Summer2005_Samanta

  5. 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_Samanta

  6. 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_Samanta

  7. 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_Samanta

  8. ANN- a typical example Input layer Hidden layer x1 u1 y1 u2 y2 x2 . . . . . . uQ xN yM RMU_Summer2005_Samanta

  9. Fuzzy Logic Steps involved • Fuzzification using membership functions (MFs)-input • Generation of rule base • Aggregation • Defuzzification using MFs -output RMU_Summer2005_Samanta

  10. Fuzzy Logic (contd.) • Input and output MFs • Number • Type • Parameters • Rule base (experience guided) RMU_Summer2005_Samanta

  11. 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_Samanta

  12. Fuzzy Logic – An Example ANFIS structure for an example system with 2 inputs and 1 output. RMU_Summer2005_Samanta

  13. Snapshot of rule base for an example system with 2 inputs and 1 output. RMU_Summer2005_Samanta

  14. 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_Samanta

  15. 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_Samanta

  16. Rotating Machine with Sensors Signal Conditioning and Data Acquisition Feature Extraction Training Data Set Test Data Set GA based selection of features and parameters Training of ANN/ SVM No No Is ANN/ SVM Training Complete ? Yes Is GA based selection over? Yes Trained ANN/ SVM with selected features ANN / SVM Output Machine Condition Diagnosis RMU_Summer2005_Samanta Fig. 1. Flow chart of diagnostic procedure

  17. 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_Samanta

  18. (+ (* (X1 X2))(exp(3)) plus exp times X1 X2 3 GP output –An Example • Terminals (leaves): inputs x1, x2 and constant 3 • Nodes: Math functions *,+, exp • Output: x1*x2+exp(3) RMU_Summer2005_Samanta

  19. 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_Samanta

  20. 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_Samanta

  21. 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_Samanta

  22. 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_Samanta

  23. 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_Samanta

  24. 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_Samanta

  25. 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_Samanta

  26. RMU_Summer2005_Samanta

  27. RMU_Summer2005_Samanta

  28. 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 263.2 s RMU_Summer2005_Samanta

  29. RMU_Summer2005_Samanta

  30. RMU_Summer2005_Samanta

  31. RMU_Summer2005_Samanta

  32. 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: 463.7 s RMU_Summer2005_Samanta

  33. GP output tree for Roughness model RMU_Summer2005_Samanta

  34. 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_Samanta

  35. 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_Samanta

  36. 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_Samanta

  37. 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_Samanta

  38. 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_Samanta

  39. 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_Samanta

  40. 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 RMU_Summer2005_Samanta

More Related