Publication
Application of Predictive Quality Systems in Multi-Stage Systems
| Summary: | The evolution of technology in recent years has had a significant impact on various sectors of society. One of the most affected sectors is the industry sector, which has greatly benefited from this technological advancement. A new paradigm, known as Industry 4.0, has emerged, making industrial processes more intelligent, autonomous, and flexible. For any company to thrive in this competitive landscape, adaptation to this new reality is essential. The increase in competitiveness has required the adherence to strict standards of excellence. Among these, product quality stands out as a critical factor that not only determines the economic success of a company but also influences the broader economic landscape of society. One of the key applications of Industry 4.0 is product quality control. This field is responsible for ensuring that the final product’s quality is maximized through the integration of cutting-edge technologies. To guarantee this quality, predictive models are employed to estimate the final product’s characteristics based on his intermediate conditions and the surrounding production environment. However, this predictive process becomes increasingly complex in manufacturing systems, where the final product is a result of various operations in multiple stages. Identifying the source of errors is crucial for improving the accuracy of quality prediction models. Multi-Stage Manufacturing Systems pose a significant challenge in error detection due to the vast number of variables involved in the production process. In this project, a predictive quality system was developed and validated for a multi- stage process. The system was tested in a laboratory 3D printing scenario. |
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| Main Authors: | Cardoso, João Pedro Ceia da Silva Rosa |
| Subject: | Multi-Stage Quality Prediction Machine Learning Artificial Intelligence Industry 4.0 Muvu Technologies |
| Year: | 2025 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | The evolution of technology in recent years has had a significant impact on various sectors of society. One of the most affected sectors is the industry sector, which has greatly benefited from this technological advancement. A new paradigm, known as Industry 4.0, has emerged, making industrial processes more intelligent, autonomous, and flexible. For any company to thrive in this competitive landscape, adaptation to this new reality is essential. The increase in competitiveness has required the adherence to strict standards of excellence. Among these, product quality stands out as a critical factor that not only determines the economic success of a company but also influences the broader economic landscape of society. One of the key applications of Industry 4.0 is product quality control. This field is responsible for ensuring that the final product’s quality is maximized through the integration of cutting-edge technologies. To guarantee this quality, predictive models are employed to estimate the final product’s characteristics based on his intermediate conditions and the surrounding production environment. However, this predictive process becomes increasingly complex in manufacturing systems, where the final product is a result of various operations in multiple stages. Identifying the source of errors is crucial for improving the accuracy of quality prediction models. Multi-Stage Manufacturing Systems pose a significant challenge in error detection due to the vast number of variables involved in the production process. In this project, a predictive quality system was developed and validated for a multi- stage process. The system was tested in a laboratory 3D printing scenario. |
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