2024 Summer Undergraduate Research Program (SURP) Symposium
Location
John Deere Auditorium, Curris Business Building, University of Nothern Iowa
Presentation Type
Poster Presentation (UNI Access Only)
Document Type
poster
Abstract
This study presents a preliminary method to assess and predict the quality of chemically bonded sand molds through the use of embedded sensors. The foundry process heavily relies on the quality of molds, which can be affected by environmental factors such as VOCs, pressure, humidity, and temperature. By integrating sensors with the ESP8266 microcontroller, this project aims to monitor these parameters in real-time, providing valuable data for improving mold quality.
Start Date
26-7-2024 11:00 AM
End Date
26-7-2024 1:30 PM
Event Host
Summer Undergraduate Research Program, University of Northern Iowa
Faculty Advisor
Lisa Riedle
Department
Department of Applied Engineering and Technical Management
Copyright
©2024 Nathaniel A. Addo, Maurice Kuubata
File Format
application/pdf
Recommended Citation
Addo, Nathaniel A. and Kuubata, Maurice, "Development of a Multi-Sensor Circuit Board Integrating ESP8266, BME680, and SGP40 for Environmental Monitoring (VOCs, Pressure, Temperature, Humidity) to Produce Stronger Molds in a Metal Casting Foundry Process" (2024). Summer Undergraduate Research Program (SURP) Symposium. 28.
https://scholarworks.uni.edu/surp/2024/all/28
Development of a Multi-Sensor Circuit Board Integrating ESP8266, BME680, and SGP40 for Environmental Monitoring (VOCs, Pressure, Temperature, Humidity) to Produce Stronger Molds in a Metal Casting Foundry Process
John Deere Auditorium, Curris Business Building, University of Nothern Iowa
This study presents a preliminary method to assess and predict the quality of chemically bonded sand molds through the use of embedded sensors. The foundry process heavily relies on the quality of molds, which can be affected by environmental factors such as VOCs, pressure, humidity, and temperature. By integrating sensors with the ESP8266 microcontroller, this project aims to monitor these parameters in real-time, providing valuable data for improving mold quality.
