Integrating Sales, Design and Production: A Configuration System for Automation in Mass Customization

Wehlin, C.; Vidner, Olle; Poot, Leon; Tarkian, Mehdi

doi:10.1115/detc2021-68426

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…In the pursuit of enhancing the design, Research and Development serves as the driving force, and MDO emerges as the vehicle to automate this crucial stage. While MDO frameworks commonly incorporate influences from neighboring stages such as finance [24,25] and production planning [21,25], it is noteworthy that traditional MDO frameworks often overlook the invaluable insights from previous iterations, including manufacturing, quality control, and customer feedback. In addressing this gap, ML emerges as a fitting approach to extract and seamlessly embed this wealth of knowledge into the design process [26].…”

Section: Design Automation In the Manufacturing Systemmentioning

confidence: 99%

Machine Learning In Design Engineering and Manufacturing

Villena Toro

2023

Linköping Studies in Science and Technology. Licentiate Thesis

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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.

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Section: Design Automation In the Manufacturing Systemmentioning

confidence: 99%

Machine Learning In Design Engineering and Manufacturing

Villena Toro

2023

Linköping Studies in Science and Technology. Licentiate Thesis

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“…The system can both scan through an existing stock of components and evaluate the manufacturing of new ones during a company-wide optimization process. The PCS is further presented by Wehlin et al (2021) and Vidner et al (2021b).…”

Section: Spiral Staircasementioning

confidence: 99%

Design Automation Systems for the Product Development Process: Reflections from Five Industrial Case Studies

2022

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This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

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