Publicação
Product Quality Prediction based on Raw Matter Composition in Industry 4.0
| Resumo: | Industry 4.0 came to revolutionize the industry with automation, decentralization and modulation. With these concepts, the industry can be more efficient with the resources and produce more cost-efficient products. Namely in the manufacturing domain, companies are facing challenges to redesign and adjust their manufacturing systems and processes to guarantee high quality products with limited resources to reduce the costs. To achieve this, it is important to acknowledge the challenges in the manufacturing processes and tackle them to reduce the number of defective products. In this dissertation, the current scenario of RiaStone's stoneware factory, along with the challenges in its manufacturing process in terms of its Overall Production Efficiency, and present a possible solution based in Artificial Intelligence, more specifically, Machine Learning. The objective is to build a prediction model capable of predicting the final product quality based on the chemical composition of the raw matter through the application of machine learning techniques to analyse, clean and integrate the available datasets and to develop the model based on such datasets. |
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| Autores principais: | Ferreira, Pedro Pinto Guedes Nave |
| Assunto: | Industry 4.0 Final Product Quality Artificial Intelligence Machine Learning |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Industry 4.0 came to revolutionize the industry with automation, decentralization and modulation. With these concepts, the industry can be more efficient with the resources and produce more cost-efficient products. Namely in the manufacturing domain, companies are facing challenges to redesign and adjust their manufacturing systems and processes to guarantee high quality products with limited resources to reduce the costs. To achieve this, it is important to acknowledge the challenges in the manufacturing processes and tackle them to reduce the number of defective products. In this dissertation, the current scenario of RiaStone's stoneware factory, along with the challenges in its manufacturing process in terms of its Overall Production Efficiency, and present a possible solution based in Artificial Intelligence, more specifically, Machine Learning. The objective is to build a prediction model capable of predicting the final product quality based on the chemical composition of the raw matter through the application of machine learning techniques to analyse, clean and integrate the available datasets and to develop the model based on such datasets. |
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