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Machine learning to predict rework time for CNC router
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-30) González Giacoman, Daniel Alejandro; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; puemcuervo; Orta Castañón, Pedro Antonio; Ahuett Garza, Horacio; School of Engineering and Sciences; Campus Monterrey
The industry is always in constant change and looking for ways to gain an advantage over its competitors. The fourth industrial revolution has brought massive change to the way things are done in the industry. The fourth industrial revolution brought Big Data, the Internet of things and Artificial intelligence, which gives us new ways to gather a lot of information from different sources and use it for our benefit. The present work develops a methodology to create a new machine learning algorithm to predict rework time for pieces that come out of a CNC router, using python and prove that for this case the created algorithm is better than a statistical model. To validate the methodology and prove the hypothesis of the thesis an experiment will be made to obtain 2 results: the best set of cutting parameters for the selected material and which is the best machine learning algorithm for this problem. To make the experiment the parameters must be set, a database needs to be created to train and test the ML algorithms and the code and libraries to be used should be created to fit the problem to be solved. This will be done by giving a background into databases, artificial intelligence, and how to know by the given results which type of artificial intelligence method is the best for the proposed problem.

Ciencias Exactas y Ciencias de la Salud

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