An Innovative Method to Monitor and Control an Injection Molding Process Condition using Artificial Intelligence based Edge Computing System

Analysis of Wind Speed Prediction using Artificial Neural Network and Multiple Linear Regression Model using Tinyml on Esp32

“…For this paper, ESP32 microcontrollers are programmed with Arduino IDE; thus, research on TinyML implementations for ESP32 will rely on the TensorFlow Lite Micro and Eloquent TinyML libraries. The possibility of implementing machine learning models on the ESP32 microcontroller by implementing the TFLM library is demonstrated by implementing an Artificial Neural Network (ANN) model to predict the specific volume of moulded product parts from pressure and temperature data from sensors [14]. Eloquent TinyML is an Arduino library that simplifies the deployment of TensorFlow Lite models on compatible microcontrollers [13,14].…”

“…The possibility of implementing machine learning models on the ESP32 microcontroller by implementing the TFLM library is demonstrated by implementing an Artificial Neural Network (ANN) model to predict the specific volume of moulded product parts from pressure and temperature data from sensors [14]. Eloquent TinyML is an Arduino library that simplifies the deployment of TensorFlow Lite models on compatible microcontrollers [13,14]. As of current, no research has been found on the use of AI models with Eloquent TinyML on ESP32 microcontrollers in the real world.…”