Currently, applications of Artificial Intelligence are used in different areas of knowledge; one of its fields, Machine Learning, which provides computational tools to analyze data, is used to generate predictions of economic, atmospheric, and social events, to name a few. Due to the relevance of the advancement and the growing applications of Artificial Intelligence, it is necessary to introduce Mechatronics and Mechanical Engineering students to its use. This work shares an activity developed for Mechatronics Engineering and Mechanical Engineering students to become familiar with using Artificial Intelligence in their disciplinary area. The activity consisted of predicting the distance traveled by a dart. Various throws are made, with different conditions, to obtain data, and then a prediction model is generated. The above was done using Machine Learning tools. Among the results, the model error was 6.25%, and the students showed interest in using Artificial Intelligence to solve problems in their disciplinary area and quickly became familiar with how to use those tools.

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Introducing Mechatronics and Mechanical Engineering Students to the Use of Artificial Intelligence Through Machine Learning Tools

  • Hector Rafael Morano Okuno

摘要

Currently, applications of Artificial Intelligence are used in different areas of knowledge; one of its fields, Machine Learning, which provides computational tools to analyze data, is used to generate predictions of economic, atmospheric, and social events, to name a few. Due to the relevance of the advancement and the growing applications of Artificial Intelligence, it is necessary to introduce Mechatronics and Mechanical Engineering students to its use. This work shares an activity developed for Mechatronics Engineering and Mechanical Engineering students to become familiar with using Artificial Intelligence in their disciplinary area. The activity consisted of predicting the distance traveled by a dart. Various throws are made, with different conditions, to obtain data, and then a prediction model is generated. The above was done using Machine Learning tools. Among the results, the model error was 6.25%, and the students showed interest in using Artificial Intelligence to solve problems in their disciplinary area and quickly became familiar with how to use those tools.