Boosting the Artificial Intelligence solutions training phase by means of process simulation methods
[eng] Despite the emergence of Industry 4.0 and the rise of a data-driven manufacturing paradigm, the acquisition of valuable data in a cost-efficient and sustainable manner for manufacturing processes remains a challenge for many companies. Conducting non-productive tests on the production line in...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/223482 |
| Acceso en línea: | https://hdl.handle.net/2445/223482 http://hdl.handle.net/10803/695375 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge automàtic Intel·ligència artificial Innovacions tecnològiques Estampació (Metal·listeria) Machine learning Artificial intelligence Technological innovations Metal stamping |
| Sumario: | [eng] Despite the emergence of Industry 4.0 and the rise of a data-driven manufacturing paradigm, the acquisition of valuable data in a cost-efficient and sustainable manner for manufacturing processes remains a challenge for many companies. Conducting non-productive tests on the production line in an industrial plant result in a waste of raw materials, energy, human resources, and time. Furthermore, executing high fidelity manufacturing simulations entails a significant temporal and computational burden. Consequently, these drawbacks hinder the creation of knowledge in manufacturing processes and the development of technologies that aim to enhance and influence in the process performance, such as optimization or AI-based tools. This is especially critical for tools that benefit from the availability of large volumes of data and real-time responses, like Digital Twins and Reinforcement Learning agents. Therefore, it is necessary to provide methods that facilitate data generation in industrial environments. This dissertation is devoted to present a set of general methods to companies and manufacturers to boost the data generation phase in the industrial context. Concretely, we focus on a fast and efficient way to model manufacturing processes through the development of Machine Learning-based Surrogate Models. We propose different general theoretical frameworks implementing or combining machine learning techniques for surrogate modeling applicable in distinct manufacturing process. The thesis demonstrates that the proposed methods enable significant cost and time reductions in different practical manufacturing applications while maintaining high accuracy in modeling and predicting process variables. We investigate the importance of the data chosen to construct the Surrogate Models and the transfer of the knowledge in the Surrogate Models from simulation to real plants by means of Trans fer Learning. Overall, this supposes an improvement of the presented surrogate modeling methods and it facilitates the deployment of Surrogate Models in real-world industrial plants. The developed models during the thesis are a valuable asset in other studies, acting as a virtual environment to train Reinforcement Learning agents in hot stamping or supporting a Digital Twin of the high pressure die casting process. The thesis helps to advance towards the innovation of data-driven manufacturing by providing practical and efficient solutions in the direction of a better understanding of the manufacturing processes, leading to an enhancement in their performance and sustainability. |
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