Percorrer por autor "Nakashima, Alison Shigueo"
A mostrar 1 - 2 de 2
Resultados por página
Opções de ordenação
- Adaptive inspection cell for HMI consolesPublication . Nakashima, Alison Shigueo; Sousa, Vítor Manuel Ribeiro; Lima, José; Leitão, PauloThe actual control quality standards require manufacturers to increase the inspection process. Instead of a sampling method, all items should be inspected and different equipment with different characteristics in the inspection cell need an adaptive system and the control quality cells should be enhanced. The presented work describes a self-adaptable robotized inspection cell for HMI consoles. which comprises the image acquisition system with controlled illumination and a force feedback sensor manipulated by a collaborative robot. The developed robotized cell is capable of detecting different HMI consoles and adapting the inspection routines of the manipulator robot according to the specific console. Moreover, the flexibility of the collaborative robot allows to adapt the camera positioning, lighting, and distance in a way that future HMI consoles can be inspected based on learning strategies.
- Automated industrial inspection workbench for human machine interface (HMI) consolesPublication . Nakashima, Alison Shigueo; Lima, José; Leitão, Paulo; Junior Schiavon, GilsonThe actual moment of the industrial production is changing the way of production. Now the systems are adaptable to produce different items in the same production line with a very reduced time to setup the systems. In the same way, the quality control systems must be more adaptable and intelligent possible. The present work propose the creation of intelligent and adaptable inspection cell to inspect Human Machine Interface (HMI) consoles of different types. This cell is composed by an image acquisition system with controlled illumination, a force sensor installed on the robot tool to verify the buttons’ functionality. The force tests are processed and classified using decision three, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classification method. Besides, the Thin-Film Transistor (TFT) display uses Normalized Cross-Correlation (NCC) and Correlation Coefficients (CC) to check the display’s regions. To Liquid Cristal Display (LCD) is used the same method and also be used a Neural Network Classification (NNC). In the experimental tests, four different types of consoles prototypes are tested, one of them has a TFT display and buttons, others two have only buttons and one has only a LCD display. In the inspection workbench is created, all the hardware necessary to execute the inspection was installed successfully. Moreover, the inspection methods obtained a precision higher than 90% to the buttons and display inspection.