Year: 2025
Type: dissertation
Abstract: In current engineering applications, it is common to encounter a wide range of devices driven by voltage signals. Among these, many generate outputs that can be interpreted as geometrical components, whose morphology is strongly influenced by the characteristics of the voltage signal controlling the actuator. Throughout this thesis, this scenario is examined by applying methodologies aimed at facilitating the optimization of voltage signal inputs to achieve a desired output. This objective is pursued through several specific goals, including acquiring a solid understanding of modern predictive modelling, comprehending the fundamentals of data-driven approaches and developing an initial model capable of predicting shape-related characteristics. The specific case study of this project focuses on a publicly available repository database that links a parameterized voltage signal-composed of three main pulses, each defined by its duration, amplitude, and inter-pulse delay-as the input, with the resulting geometric parameters derived from the characterization of droplets produced by a piezoelectric dispenser driven by that voltage signal as the output. First, the database was prepared through a cleaning process in which cluster analysis was applied to refine the data for subsequent use in constructing predictive models based on neural networks. Using the processed database, a neural network was then trained to predict two output parameters-defining a geometrical object simplified as an ellipse-from eight input parameters that characterize the voltage signal. Subsequently, the most and least influential input variables in the output formation were identified through correlation analysis. Based on this information, two additional predictive models were developed to estimate the geometrical outputs describing the ellipse once again: one trained using the three most influential inputs, and another trained with six inputs after removing the two least influential variables. This project made it possible to explore whether predictive models based on voltage signals could be effectively constructed using a reduced number of inputs. The comparison between the reduced-input models and the full-input model demonstrated that the predictive capability remained comparable in terms of precision, validating the efficiency of the reduced-input approach.
Author: Castillo López, Adrian
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7111566432