Causal latent space-based models for scientific learning in industry 4.0

• Autores: Joan Borràs Ferrís
• Directores de la Tesis: Alberto José Ferrer Riquelme (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2023
• Idioma: español
• Tribunal Calificador de la Tesis: Marco Reis (presid.) , María del Carmen Aguilera Morillo (secret.) , Emanuele Tomba (voc.)
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • The present Ph.D. thesis is devoted to studying, developing, and applying data-driven methodologies, based on multivariate statistical models of latent variables, to address the scientific learning paradigm in the Industry 4.0 environment. Particular emphasis is placed on causal latent variable-based models using both data coming from a planned design of experiments and, mainly, data coming from the daily production process, namely happenstance data. The dissertation is structured in five parts.

    The first part discusses the scientific learning paradigm in the Industry 4.0 environment. The objectives of the thesis are highlighted. In addition to that, a comprehensive description of latent variable-based models is presented, on which the novel methodologies proposed in this thesis are founded.

    In the second part, the novel methodological contributions are presented. Firstly, the potential of PLS to analyze data from DOE, with or without missing runs is illustrated. Then, the potential of causal latent variable-based models is concentrated on defining the raw material design space providing assurance of quality with a certain confidence level for the critical to quality attributes, jointly with the development of a novel latent space-based multivariate capability index to rank and select suppliers for a particular raw material used in a manufacturing process.

    The third part aims to address novel applications by means of causal latent variable-based models using happenstance data. First, it concerns a health application: the Pandemic COVID-19. In this context, the use of latent variable-based models is applied to develop an alternative to placebo-controlled clinical trials. Then, latent variable-based models are used to optimize processes within the framework of industrial applications.

    The fourth part introduces a graphical user interface developed in Python code that integrates the developed methods with the aim of being self-explanatory and user-friendly.

    Finally, the last part discusses the relevance of this dissertation, including proposals that deserve further research.