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Neural Network Control of Autoclave Curing of Composite Materials NEURAL NETWORK CONTROL OF AUTOCLAVE CURING OF COMPOSITE MATERIALS Thesis Submitted to The School of Engineering UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The Degree Master of Science in Chemical Engineering by Maria Claudia Baptista UNIVERSITY OF DAYTON Dayton, Ohio December, 1994 ABSTRACT NEURAL NETWORK CONTROL OF AUTOCLAVE CURING OF COMPOSITE MATERIALS Baptista, Maria Claudia University of Dayton, 1994 Advisor: Dr. C. William Lee A back propagation neural network has been developed to determine the temperature cure cycle of a fiber reinforced epoxy matrix composite material in an autoclave. The self-directed neural network controller performed temperature control by adjusting the set-point of the autoclave based on five sensor inputs. The curing process was simulated by a one-dimensional heat transfer model that included heat source and convective boundary conditions. These two programs, the simulator and the controller, were installed on two separate computers. The neural network controller was developed using NeuroWindows™ in the Visual Basic™ environment. The neural network controller used for testing consists of an input layer with five neurons that represent the process and material states, one hidden layer with six neurons, and an output layer with a single neuron for temperature set point adjustment. The neural network controller, when trained by a cure cycle for a given thickness panel, was able to 111 THESIS 35 08892 NEURAL NETWORK CONTROL OF AUTOCLAVE CURING OF COMPOSITE MATERIALS Approved by: C. William Lee, Ph.D. Professor Committee Chairperson Tony E. Saliba, Ph.D. Kevif{J. Myers, D.Sc., P.E. Associate Professor Associate Professor Committee Member Committee Member Donald L. Moon, Ph.D. Joseph Lestingi, D.Eng.^B.E. Associate Dean Dean Graduate Engineering Programs School of Engineering & Research School of Engineering 11 cure the same thickness panel. This was verified by two cases. When a network was trained by a cycle for a thin panel, it was unable to satisfactorily control the cure of a thick panel, and vice versa. When the two training sets are combined to train a network, the resulting controller was unable to interpolate to control the cure of a panel of an intermediate thickness. It is surmised that the back propagation neural network is capable of capturing an operator's decision mechanism on temperature control of a heat transfer process as it was able to carry out a cure cycle having been given only randomized instances of the process state. The five process variables chosen as the input vector can only represent a particular case of heat transfer process and are not enough for the network to generalize. IV ACKNOWLEDGMENTS I wish to acknowledge and express my appreciation to the members of the committee, Dr. Kevin J. Myers and Dr. Tony E. Saliba. My gratitude goes also to Dr. Ronald A. Servais and Dr. Sarwan Sandhu for their encouragement. In addition, special thanks are due to my advisor, Dr. C. William Lee for his precious guidance and patience during this work. I thank Fulbright Commission, Encyclopedia Britannica, American Chamber of Commerce in Sao Paulo, University of Dayton Department of Chemical and Materials Engineering, and Rhodia S.A. for the financial support provided during my master’s program at the University of Dayton. I also would like to thank my family and my many friends for their help and encouragement. And finally to my grandparents, Adelia and Benedicto Gomes, to whom this work is dedicated. v PREFACE Artificial Intelligence has stimulated the interest of researchers in various fields of knowledge. Some are very interested in finding the analogy with the physiology of physical systems, in understanding how human brain processes information, how learning occurs, where the information is stored and how memory works. There are so many questions surrounding the field and very few answers. The development of simplified mathematical models also interested engineers looking for new tools to solve practical problems. The interest in applying Artificial Intelligence to solve Chemical Engineering problems has increased substantially lately. Some problems in the Chemical Process Industries cannot be solved by standard mathematical techniques or their solutions can be complicated, costly, and time consuming. In order to solve such problems, alternative methods have been developed. One of the proposed methods is Artificial Neural Networks. It can be applied in problems where the knowledge about the process is available but there is no mathematical model to represent it. An application of neural networks to process control is presented in this work. vi TABLE OF CONTENTS ABSTRACT ....................................................................................................................... iii ACKNOWLEDGMENTS ............................................................................................. v PREFACE .......................................................................................................................... vi TABLE OF CONTENTS ............................................................................................. vii LIST OF ILLUSTRATIONS .......................................................................................... ix LIST OF TABLES ........................................................................................................... xi LIST OF SYMBOLS/ABBREVIATIONS .................................................................... xii CHAPTER I. INTRODUCTION .................................................................................. 1 II. LITERATURE REVIEW ...................................................................... 3 Autoclave Curing of Composite Materials .......................................... 3 Artificial Neural Networks ..................................................................... 7 Back Propagation Neural Network Learning Algorithm ................... 11 Applicationsof Neural Networks in Chemical Engineering Processes 14 III. NEURAL NETWORK CONTROLLER DEVELOPMENT ........... 16 Neural Network Controller .................................................................... 16 Autoclave Process Simulator ................................................................. 20 Process Monitoring ................................................................................. 21 vii IV. TRAINING AND TESTING OF THE NEURAL NETWORK CONTROLLER ...................................................................................... 25 Neural Network Controller Training .................................................... 25 Neural Network Controller Testing ..................................................... 29 Case 1 Training ....................................................................................... 30 Case 1 Testing ......................................................................................... 38 Case 2 Training ....................................................................................... 40 Case 2 Testing ......................................................................................... 43 Case 3 Training ....................................................................................... 43 Case 3 Testing ......................................................................................... 49 V. SUMMARY AND CONCLUSIONS ................................................... 54 REFERENCES ................................................................................................................. 58 APPENDICES .................................................................................................................. 62 APPENDIX A Source Code Listing of Neural Network Controller (NNC) ............................. 63 APPENDIX B Thin Panel (32 plies) Training Set ..................................................................... 95 Thick Panel (256 plies) Training Set .................................................................. 100 APPENDIX C Physical Properties Used ..................................................................................... 110 Panel Thicknesses Used ...................................................................................... Ill viii LIST OF ILLUSTRATIONS 2.1. Schematic of the Prepreg Lay-up ....................................................................... 4 2.2. Conventional, Rule-Based/Expert System, and Neural Network Controller 7 2.3. A Neuron-Like Structure .................................................................................... 9 2.4. Sigmoidal Activation Function ........................................................................... 10 2.5. Back Propagation Neural Network Architecture ............................................. 11 3.1. NNC Graphical User Interface ........................................................................... 19 3.2 Lay-Up and Temperature Sensor Locations ....................................................... 22 3.3. Closed-Loop Self-Directed Neural Network Control System ......................... 24 4.1 Neural Network Controller Development Procedure ....................................... 26 4.2. Case 1 Training Using Cure Cycle for 32-Ply Laminate ................................. 31 4.3. Case 1 RMS Error Variation During Training ................................................... 33 4.4. Back Propagation Neural Network Architecture Used ................................... 35 4.5. Weight Variations During Training
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