Description
For 50 years, robotic systems have automated injection moulding. While rotational moulding is less advanced, recent innovations like ROBOMOULD® have improved its process control. This technology uses a 6-axis robot arm to manipulate the mould, enabling precise control over thickness distribution.
This dataset is the measurement results from a full factorial study to study the three control parameters (rocking angle, rotations per rock, and robot feed rate or speed) of this robotic rotational moulding system. The moulded parts were produced using an industrial sized fuel tank mould on the robotic moulding system. This study compares experimental and simulated thickness distributions in robotic rotational moulding. A MATLAB-based finite element model predicts thickness distribution based on powder contact time. Results show that robot feeding rate and the interaction between rocking angle and rotations per movement significantly influence thickness distribution. The simplified simulation model accurately predicts trends at higher feeding rates.
These measurement results can be used to inform future experiments, calibrate simulation models, or validate existing simulations.
This dataset is the measurement results from a full factorial study to study the three control parameters (rocking angle, rotations per rock, and robot feed rate or speed) of this robotic rotational moulding system. The moulded parts were produced using an industrial sized fuel tank mould on the robotic moulding system. This study compares experimental and simulated thickness distributions in robotic rotational moulding. A MATLAB-based finite element model predicts thickness distribution based on powder contact time. Results show that robot feeding rate and the interaction between rocking angle and rotations per movement significantly influence thickness distribution. The simplified simulation model accurately predicts trends at higher feeding rates.
These measurement results can be used to inform future experiments, calibrate simulation models, or validate existing simulations.
| Date made available | 08 Sept 2025 |
|---|---|
| Publisher | Queen's University Belfast |
| Date of data production | Jan 2022 - Dec 2022 |
Student theses
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Investigation of parameters controlling the robotic rotational moulding process
Zhou, H. (Author), Martin, P. (Supervisor) & Garcia-Palacios, E. (Supervisor), Dec 2025Student thesis: Doctoral Thesis › Doctor of Philosophy
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