Artificial Intelligence: a Modern Approach to Increasing Productivity and Improving Weld Quality in Tig Welding

IMPRO VING WELD QUALITY IN TIG WELDING Martin Appiah Kesse ARTIFICIAL INTELLIGENCE: A MODERN APPROACH TO INCREASING PRODUCTIVITY AND IMPROVING WELD QUALITY IN TIG WELDING ACTA UNIVERSITATIS LAPPEENRANTAENSIS 972 Martin Appiah Kesse ARTIFICIAL INTELLIGENCE: A MODERN APPROACH TO INCREASING PRODUCTIVITY AND IMPROVING WELD QUALITY IN TIG WELDING Dissertation for the degree of Doctor of Science (Technology) to be presented with due permission for public examination and criticism in room 1316 at Lappeenranta-Lahti University of Technology LUT, Lappeenranta, Finland on the 29th of July 2021, at noon. Acta Universitatis Lappeenrantaensis 972 Supervisors Associate Professor Huapeng Wu LUT School of Energy Systems Lappeenranta-Lahti University of Technology LUT Finland DSc (Tech) Tuomas Skriko LUT School of Energy Systems Lappeenranta-Lahti University of Technology LUT Finland Reviewers Emeritus Professor Suck-Joo Na Department of Mechanical Engineering Korea Advanced Institute of Science and Technology South Korea Professor Yanhong Wei Lab of Welding technology Nanjing University of Aeronautics and Astronautics China Opponent Emeritus Professor Suck-Joo Na Department of Mechanical Engineering Korea Advanced Institute of Science and Technology South Korea ISBN 978-952-335-686-3 ISBN 978-952-335-687-0 (PDF) ISSN-L 1456-4491 ISSN 1456-4491 Lappeenranta-Lahti University of Technology LUT LUT University Press 2021 Abstract Martin Appiah Kesse Artificial intelligence: A modern approach to increasing productivity and improving weld quality in TIG welding Lappeenranta 2021 90 pages Acta Universitatis Lappeenrantaensis 972 Diss. Lappeenranta-Lahti University of Technology LUT ISBN 978-952-335-686-3, ISBN 978-952-335-687-0 (PDF), ISSN-L 1456-4491, ISSN 1456-4491 Recent years have seen the welding industry facing demands for improved productivity and efficiency together with simultaneous enhancement of the quality of welded structures. The welding industry has met these challenges by developing novel alloys, increasing the level of automation, and expanding the use of dissimilar welding. The utilization of materials with complicated chemical composition necessitates a detailed understanding of material behaviour and how the materials can be combined while ensuring structural integrity. Suitable joining methods for both thick and thin plates are required, as is effective control of joining processes and related technology. A key aspect of welding control is understanding of the dynamics and interactions of the various parameters associated with welding processes and procedures. Recent developments in artificial intelligence (AI) modelling tools have led to a vision of AI removing the element of human mechanical effort from welding operations. Various AI-based methods have been developed and applied with the aim of attaining good mechanical properties and improving weld quality. These approaches include design of experiment (DoE) techniques and algorithms, conventional regression analysis and the use of computational networks, including neural networks and fuzzy logic. In welding technology, these methods have primarily been used to optimise different welding parameters. Although researchers have found neural networks to be a better approach for optimisation than other available alternatives, it is, however, a black box approach. Consequently, it is difficult to ascertain how the algorithm arrives at a decision, which is knowledge of importance for human welders and future development of welding techniques and technology. The question then becomes: Can an AI model be developed that overcomes this deficiency? This PhD dissertation aims to contribute to the state-of-the-art in terms of knowledge of the applicability of AI in welding technology by developing an AI framework using an ANFIS and fuzzy deep neural network from which it is possible to ascertain the underlying decision-making logic as an alternative method to predict welding parameters for optimisation of the welding process. To meet the objective of the work, an in-depth understanding of different welding and optimisation processes is first required. Methodologically, a comprehensive literature review approach and experimental work are used as the basis for suggesting the proposed AI framework. The AI framework for welding technology was designed using a fuzzy deep neural network, which is a combination of fuzzy logic and a deep neural network. The fuzzy logic and deep neural network are incorporated into the framework with a Likert scaling strategy. In normal practice, AI decision-making tools using deep learning techniques require big data from which to learn. For welding applications, obtaining this big data is challenging, because of the laborious and costly nature of welding experiments, and limited experimental data is thus available. The added value of the work in this study is that the AI approach used overcomes the limitation of the big data requirement. Where big data is not available for the algorithm to learn from, the system can mathematically manipulate the small data using its inference engine and extract its own big data from the available small data using the technique of data augmentation. The AI framework was developed, validated and tested with the TIG welding process to predict weld bead geometry. The results showed a predictive accuracy of 92.59% when compared to results from a real experimental welding data set. It is expected in the future that this created model will help the welder to bypass trial and error during the selection of welding parameters when welding. This model can be part of the standard welding procedure document to help the welder when performing welding works. This tool will also be useful for industries in the welding sector and can be used for educational purposes. Keywords: TIG welding, artificial intelligence, deep neural network, structural Integrity, data augmentation Acknowledgements It is a great joy to say that at long last the challenging journey of doctoral study is approaching its end. When considering what has been achieved so far, many people come to mind without their prayers and support I could not have gotten this far. Firstly, I would like to express my profound gratitude to the lecturers and laboratory technicians who contributed to the completion of this research work. I gratefully acknowledge the efforts of Professor Paul Kah for his input in the form of valuable advice and comments on the articles at the heart of this dissertation. My appreciation goes to my supervisors Associate Professor Huapeng Wu and Dr Tuomas Skriko for their time and efforts in bringing this work to a successful conclusion. I would like to thank Professor Heikki Handroos and Harri Eskelinen for their support throughout this journey. I thankfully acknowledge the efforts of Esa Hiltunen and his team for carrying out the laboratory welding experiments. I would also like to thank Peter Jones for his valuable input in reviewing my articles and commenting on aspects of the dissertation. A special thanks go to Sari Damsten-Puustinen and Saara Merritt at the LUT Doctoral School Office for their immense support and encouragement. My heartfelt thanks to Junior Researcher Eric Buah for his great support and encouragement; you have proven to me that a true friend is the one who will stick by you not only with your positives but your negatives as well. I would also like to express my deepest gratitude to Dr Muyiwa Olabode and family for all their support and prayers. Your time and energy encouraging me in my work cannot be overlooked. A special thanks go to Dr Emmanuel Afrane Gyasi for all the support he has given me throughout this journey. I would also like to acknowledge Dr. Godwin Ayetor and Engr Justice Hatsu may God bless you all for your support. My heartfelt appreciation to Dr Emma Kwegyir-Afful and family for their support. Special appreciation to Josephine Naa Kai Klufio for her enormous support and prayers God richly bless you. I wish to acknowledge the encouragement of family and friends. My special thanks are extended to my brothers, Alexander Kesse, Michael Adu Kesse, Emmanuel Kwame Kesse and Daniel Kesse for their encouragement and support throughout this journey. Also, my appreciation goes to Dr Eric Martial Mvola Belinga, Dr Pavel Layus, Dr Francois Miterand Njock Bayock, Paulina Dufie, Ing Appiah Osei Agyemang, Doris Ampong, Pearl Sharon, and others that have in one way or the other supported me on my journey. Finally, my sincere appreciation is expressed to my immediate family, my treasured wife, Zita Appiah Kesse, for all the encouragement, forbearance and understanding exercised during this research. I feel blessed, thank you all. Martin Appiah Kesse July 2021 Lappeenranta, Finland To Dedication Dedicated to God almighty and to my mother Salome Appiah and my late father Daniel Kesse for teaching me love, respect, honesty, the value of hard work and belief in God, and in recognition of their devotion and efforts keeping the family on the right track. Contents Abstract Acknowledgements Contents List of publications 11 Nomenclature 13 1 Introduction 17 1.1 Background ............................................................................................. 17 1.2 Research problem .................................................................................... 19 1.3 Research objectives and motivation ........................................................ 19 1.4 Research questions .................................................................................

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