Fatima Aurora SaizVicomtech · Industry and Advanced Manufacturing
Fatima Aurora Saiz
PhD. Artificial Intelligence researcher for the manufacturing industry.
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Citations since 2017
9 Research Items
This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribu...
This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of two deep learning-based models’ performa...
This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional d...
The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before. The virus causes a respiratory illness like the flu with various symptoms such as cough or fever that, in severe cases, may cause pneumonia. The COVID-19 spreads so quickly between people, affecting to 1,200,000 people wor...
This work presents an optical inspection-guiding system for electronic board manufacturing. The system monitors in real time the mounting process of electronic components performed by an operator. It visually guides the operator through the mounting process while checking the correctness of its actions. As a consequence, mounting errors are reduced...
Personalized production is moving the progress of industrial automation forward, and demanding new tools for improving the decision-making of the operators. This paper presents a new, projection-based augmented reality system for assisting operators during electronic component assembly processes. The paper describes both the hardware and software s...
The final product quality control is critical for any manufacturing process. In the case of steel products, there are different inspection methods that are able to classify the defects, but they usually require human intervention. In this context, a deep learning-based automatic defect classifier method for steel surfaces is proposed. The method co...