ARTIFICIAL INTELLIGENCE INMANUFACTURING RESEARCH -- CONTENTS -- PREFACE -- RESEARCH AND REVIEW STUDIES -- APPLICATION OF NEURAL NETWORKS AND FUZZYSETS TO MACHINING AND METAL FORMING -- ABSTRACT -- 1. INTRODUCTION -- 2. NEURAL NETWORK APPLICATIONS IN MACHINING -- 2.1. Multi-Layer Perceptron and Radial Basis Function Neural Networks -- 2.2. Prediction of Machining Performance Using Neural Networks -- 2.3. Optimization of Machining Performance Using Neural Networks -- 3. NEURAL NETWORK APPLICATIONS IN METAL FORMING -- 4. FUZZY SET APPLICATIONS IN MACHINING -- 4.1. Applications of Fuzzy Set Operations -- 4.2. Applications of Fuzzy Arithmetic -- 4.3. Applications of Fuzzy Logic -- 5. FUZZY SET APPLICATIONS IN METAL FORMING -- 5.1. Applications of Fuzzy Set Operations -- 5.2. Applications of Fuzzy Arithmetic -- 5.3. Applications of Fuzzy Logic -- CONCLUSION -- REFERENCES -- MULTI-OBJECTIVE OPTIMIZATION OF MULTI-PASSMILLING PROCESS PARAMETERS USING ARTIFICIALBEE COLONY ALGORITHM -- ABSTRACT -- INTRODUCTION -- MULTI-OBJECTIVE OPTIMIZATION MODEL OF MULTI-PASSMILLING PROCESS -- Objective Functions -- Maximization of Production Rate -- Minimization of Production Cost -- Constraints -- Arbor Strength -- Arbor Deflection -- Power -- Surface Roughness Constraint -- Process Variables -- ARTIFICIAL BEE COLONY ALGORITHM -- Food Sources -- Foragers -- Unemployed Foragers -- Employed Foragers -- Experienced Foragers -- EXAMPLE -- Step 1: Parameter Selection -- Step 2: Calculate the Nectar Amount of Each Food Source -- Step 3: Determine the Probabilities by Using the Nectar Amount -- Step 4: Calculate the Number of Onlooker Bees, Which Will Be Sent to FoodSources -- Step 5: Calculate the Fitness Value of Each Onlooker Bee -- Step 6: Evaluate the Best Solution -- Step 7: Update the Scout Bee -- CONCLUSION -- REFERENCES.

OPTIMIZATION OF ABRASIVE FLOW MACHININGPROCESS PARAMETERS USING PARTICLE SWARMOPTIMIZATION AND SIMULATED ANNEALINGALGORITHMS -- ABSTRACT -- INTRODUCTION -- OPTIMIZATION ASPECTS OF ABRASIVE FLOW MACHINING PROCESS -- REVIEW OF LITERATURE ON AFM PROCESS -- OPTIMIZATION MODEL OF AFM PROCESS -- PARTICLE SWARM OPTIMIZATION -- SIMULATED ANNEALING -- APPLICATION OF PSO AND SA ALGORITHMS TO AFM PROCESSPARAMETERS OPTIMIZATION -- Optimization Using PSO Algorithm -- Optimization Using SA Algorithm -- CONCLUSIONS -- REFERENCES -- STUDY OF EFFECTS OF PROCESS PARAMETERS ONBURR HEIGHT IN DRILLING OF AISI 316 STAINLESSSTEEL USING ARTIFICIAL NEURAL NETWORKMODEL -- ABSTRACT -- 1. INTRODUCTION -- 2. ARTIFICIAL NEURAL NETWORK MODELING -- 3. EXPERIMENTAL DETAILS -- 4. RESULTS AND DISCUSSION -- 4.1. ANN Training -- 4.2. ANN Testing -- 4.3. Parametric Analysis of Process Parameters on Burr Height -- CONCLUSION -- REFERENCES -- ARTIFICIAL NEURAL NETWORK MODELING OFSURFACE QUALITY CHARACTERISTICS IN ABRASIVEWATER JET MACHINING OF TRIP STEEL SHEET -- ABSTRACT -- INTRODUCTION -- ABRASIVE WATER JET MACHINING -- ARTIFICIAL NEURAL NETWORK -- Artificial Neural Networks Overview -- Neural Network's Architecture -- Neural Network's Training -- Improvement Techniques -- EXPERIMENTAL -- Abrasive Water Jet Machining -- Materials -- Measuring Techniques -- ANN Modeling -- RESULTS AND DISCUSSION -- Modeling of TRIP 800 Steel Grade -- Modeling of TRIP 700 Steel Grade -- CONCLUSION -- REFERENCES -- MULTI-OBJECTIVE OPTIMISATION OF CUTTINGPARAMETERS FOR DRILLING ALUMINIUM AA1050 -- ABSTRACT -- NOMENCLATURE -- 1. INTRODUCTION -- 2. EXPERIMENTAL STUDY -- 3. MODELLING -- 4. OPTIMISATION -- 4.1. Formulation of the Optimisation Problem -- 4.2. Optimisation by Numerical Method -- 4.3. Optimisation by Using Genetic Algorithm -- 5. DISCUSSION -- CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES.

APPLICATION OF FUZZY LOGIC INMANUFACTURING: A STUDY ON MODELING OFCUTTING FORCE IN TURNING GFRP COMPOSITES -- ABSTRACT -- 1. INTRODUCTION -- 2. EXPERIMENTAL DETAILS -- 3. FUZZY RULE BASED MODEL -- 3.1. Fuzzy Set Theory -- 3.2. The Fuzzy Inference System -- 4. RESULTS AND DISCUSSIONS -- CONCLUSIONS -- REFERENCES -- FLANK WEAR DETECTION WITH AE SIGNAL ANDFNN DURING TURNING OF AL/15 VOL%SIC-MMC -- ABSTRACT -- 1. INTRODUCTION -- 2. EXPERIMENTAL CONDITION -- 2.1. Experimental Setup -- 3. TOOL WEAR DETECTION WITH FNN MODEL -- 4. RESULTS AND DISCUSSIONS -- CONCLUSIONS -- REFERENCES -- INTEGRATION OF PRODUCT DEVELOPMENTPROCESS USING STEP AND PDM -- ABSTRACT -- 1. INTRODUCTION -- 2. LITERATURE REVIEW -- 2.1. Integration Based on STEP -- 2.2. Integration Based on PDM -- 3. REMAINING PROBLEMS -- 4. AN INTEGRATED PLATFORM BASED ON STEP AND PDM -- 4.1. Integrated Platform Architecture -- 4.2. Generic Product Modeling Framework -- 4.2.1. STEP-Based Modeling Environment -- (1) Modeling Language: EXPRESS and EXPRESS-G -- (2) Generic Resources and STEP AP 203 -- (3) New Modeling Resources -- 4.2.2. 'Five-Phase' Modeling Methodology -- (1) Phase One: Modularization -- (2) Phase Two: Basic Modeling Objects -- (3) Phase Three: Relationships and Attributes -- (4) Phase Four: Constraints Modeling -- (5) Phase Five: Model Integration -- 4.2.3. EDM Data Exchange and Sharing Methods -- 4.2.4. EXPRESS Data Model -- 4.3. Process Modeling and Integration -- 4.3.1. Workflow Modeling Principle -- 4.3.2. Task Correlation and Status Definition -- 4.3.3. Task Step Model -- 4.4. Integration Scenario -- 5. CASE STUDY -- (1) Scenario 1: Workflow modeling -- (2) Scenario 2: Part Design -- (3) Scenario 3: Product Modeling and Integration -- (4) Scenario 4: Proofread the Designed Product -- (5) Scenario 5: Return to Design and Revise the Product -- CONCLUSION -- REFERENCES -- INDEX.