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Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

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Published on: June 27, 2014

Development of an Open-Source Injection Mold Monitoring System.

Tiago E P Gomes¹, Mylene S Cadete¹, Jorge A F Ferreira¹

¹TEMA-Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

A new, affordable, open-source monitoring system for injection molding was developed. This system collects real-time sensor data for Industry 4.0 applications and predictive maintenance, distinguishing normal from abnormal operations.

Keywords:

data acquisition graphical user interface information technology injection molding tool monitoring low cost open source software real time user-computer interface

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Area of Science:

Manufacturing Engineering
Industry 4.0
Sensor Technology

Background:

Effective data collection from injection molding sensors is crucial for Industry 4.0.
Existing commercial monitoring systems can be expensive and complex.
There is a need for accessible, real-time data acquisition solutions.

Purpose of the Study:

To develop and test an affordable, open-source alternative to commercial injection molding monitoring systems.
To enable real-time data acquisition and display for Industry 4.0 applications.
To lay the groundwork for a predictive maintenance platform.

Main Methods:

An Arduino microcontroller-based data acquisition module was utilized.
Open-source firmware and software, including a Graphical User Interface (GUI), were developed.
The system was tested in real-world conditions for monitoring an injection mold.

Main Results:

The system provides affordable, simple, real-time data acquisition and display.
It can visualize and record data from up to six sensors at 10 Hz.
Successful monitoring of mold temperature, cavity pressure, acceleration, and extraction force was demonstrated.

Conclusions:

The developed system successfully monitors injection molds in real-time.
It can effectively distinguish between normal and abnormal operating patterns.
This provides a foundation for implementing predictive maintenance in the injection molding industry.