Closed-loop digital twin for the product lifecycle.

• Ano de defesa: 2020
• Autor(a) principal: Durão, Luiz Fernando Cardoso dos Santos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Digital twin; Indústria 4.0; Industry 4.0; Internet das coisas; Internet of things; Manufatura inteligente; PLM; Smart manufacturing
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3136/tde-05032021-101637/

As a result of Industry 4.0 developments, the amount of product data collected over the entire product lifecycle has been growing. Advances in digitalization resulted in the intensive use of data in the manufacturing environment. Different manufacturing systems store data across the product lifecycle - PLM (Product Lifecycle Management), ERP (Enterprise Resource Planning), and MES (Manufacturing Execution Systems), among other specialized IT systems. In many cases, there is already a connection between these systems. However, the integration between company internal manufacturing data with real-time customers\' usage data is still initial. Therefore, the Internet of Things (IoT) has become an important research agenda. Information and communication technologies have been employed to digitally mirror the lifecycle of a corresponding physical product in Digital Twin (DT) applications. However, Digital Twin implementation has been focused on the beginning of life and manufacturing machines data, leaving space for developing a DT model that encompasses and connects different phases of the product lifecycle. Besides, implementing such a model in a multiplatform environment - connecting various systems - is also an open issue. This thesis proposes the definition of a Closed-loop Digital Twin implemented as a middleware software that connects the PLM, ERP, and MES systems with customers\' usage data. The proposed concept was implemented at a learning factory based on industry standard software. The implement DT processes product data to provide analysis results. This research also proposes a DT definition and typology model to support its understanding and implementation and an IoT selection model. Results demonstrated the concept potential to consolidate product data, support data analyses based on algorithms, and provide insights for different phases and stakeholders of the product lifecycle.