Sensing & IoT

Skills Developed

• Mechatronics - Repurposed an Ender 3 3D printer to function as an automated testing platform.

• Cloud - Used MQTT protocol to communicate sensor data from 3 ESP32 modules to a Raspberry Pi. The Pi communicated with AWS where a server had been setup to allow for remote monitoring and control.  

Solo Project, Grade A*

Read the paper linked below for full details.

Owain Pill - SIOT Project

Abstract

Liquid Silicone Rubber (LSR) is an incredibly versatile material that is widely used in the medical, transportation, robotics, and renewable energy sectors [1]. LSR is used because of its thermal stability, bio-compatibility, flexible nature, chemical inertness, and more [2].

The main manufacturing methods for LSR are injection molding and casting. Injection molding is used in mass manufacture and has a high upfront cost, while casting is used in creating a small run of items [3]. The casting process for LSR is highly manual: it requires manually measuring the mass of Parts A and B of silicone, mixing thoroughly, degassing, pouring, and demolding [4]. Another technique, known as "brush-on molding", requires the aforementioned steps to be completed many times in sequence to build up thin layers of silicone that will each stick to the previous layer and not droop down with gravity [5].

This process appears similar to many Additive Manufacturing (AM) techniques, where each layer is added incrementally to build up the shape [6]. Current AM techniques for silicone mainly focus on processes similar to Stereolithography (SLA) or Fused Deposition Modelling (FDM) [7]. The end goal of this project is to implement an AM technique for LSR that takes inspiration from brush-on molding with its layer-by-layer approach, but instead of painting layers onto the outside of a part, layers are cast into the inside of a part. This would enable far more automation in the molding process and enable new shapes to be manufactured that would have been impossible with standard casting.

This paper explores the beginnings of a smart casting system through the use of an Internet of Things (IoT) system that can process data about the silicone casting process and automate testing of the silicone. This testing is used to show that the curing process of LSR can be modelled by a logistic function, which is useful to the wider goal of creating a smart casting machine as it shows when the silicone will no longer "droop" and a new layer of silicone can be applied.