Artificial intelligence (AI) has made significant strides in various fields, challenging conventional notions of computer capabilities. However, while data science research primarily concentrates on refining AI models, there are numerous challenges associated with integrating AI into industrial applications.Knowledge-Based Engineering, with its potential to streamline the production cycle by reusing engineering knowledge and intent, emerges as a promising avenue for AI in the industry. When engineering knowledge is effectively processed and categorized, neural networks naturally emerge as potent tools for automation. This thesis presents three case studies that demonstrate the practicality of supervised learning, particularly in the domain of neural networks, to address manufacturing automation challenges. These case studies span various stages of the manufacturing system, encompassing engineering design, production planning, and quality control phases. The first application employs supervised learning to automate the generation of engineering drawings, while the third employs optical character recognition to expedite the quality control process for complex engineering drawings. The second application centers on the estimation of fixturing clamps for welding operations in automobile parts.In summary, this thesis strives to make a meaningful contribution to the field of design engineering and manufacturing by examining the potential of AI in enhancing processes and addressing automation hurdles. By presenting case studies that showcase the utility of machine learning models in production settings, this thesis aims to stimulate further research in this evolving field.iii Realization, Linköping University. I would like to express my gratitude to everyone involved in the research projects and the rest of the division. Special thanks go to my supervisory team: Mehdi Tarkian, for believing in me and providing constant motivation; Anton Wiberg, for valuable insights and our extensive discussions about possible future projects; Peter Hallberg and Johan Ölvander, for their senior assessment and guidance. I want to thank both current and former Ph.D. students for their inspiration and for creating such a great working environment, especially my office roommates and the Spanish speakers on the other side of the corridor.I also extend my thanks to my parents, sister, and grandparents for their constant support from abroad, as well as to Irene, my life partner, for her support on a daily basis. Finally, thanks to you, for opening this thesis and reading it, at least until the end of the acknowledgments. v
Appended PublicationsPapers I-IV presented below comprise the foundation of this thesis. The papers are referred to with bold capital romans.