Application of Artificial Intelligence Techniques to a Sensorial Module

This paper explores the increasingly strong relationship between the concepts of zero defect manufacturing and artificial intelligence, which aims to maximise efficiency in product development. For this purpose, a physical prototype was developed that replicates a process (the heating of paper samples submerged in a solution of lemon juice), in order to validate a pipeline for data collection and processing. Therefore, a dataset associated with the process was produced and system models were developed using supervised learning algorithms (K-Nearest Neighbours, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression, Random Forest and Gradient Boosting), and it was concluded that the models developed with KNN produced satisfactory results in this task.