Investigation of parameters controlling the robotic rotational moulding process

Abstract

Robotic rotational moulding represents an innovative advancement in polymer processing, integrating robotic motion control with direct electric heating to deliver unprecedented flexibility and efficiency. This research aimed to investigate the distinctive control capabilities offered by robotic arm systems and electrically heated moulds, examining their impact on final product characteristics. The primary objective was to determine how product wall thickness distribution correlates with robotic motion control parameters and advanced zonal heating techniques.

This study constitutes the first comprehensive investigation of robotic rotational moulding on an industrial-ready platform and the first detailed account of developing an electric-heated mould for such systems. Three key motion control parameters— Feed (percentage of robot maximum motion speed), Angle (mould rocking angle) and Rotation (mould rotations based on rocking)—were identified as primary determinants of wall-thickness uniformity, with recommended operating ranges of higher Feed (60–100%), larger Angle (60°–76°) and reduced Rotation (2–5) for optimal thickness distribution. Optimisation within the recommended ranges delivered superior uniformity, while three-axis robotic motion reduced thickness variation by 38.2%. Targeted zonal heating further improved uniformity by 35.2%, and internal air cooling shortened cycle time by 45.5%, minimising distortion and shrinkage. DEM simulation in Ansys Rocky, despite using a limited particle count, yielded a promising 0.61 correlation with experimental thickness profiles.

By systematically exploring robotic control, mould heating and cooling within a single application-ready platform, this work advances scientific understanding of rotational moulding and provides a validated blueprint for modern polymer manufacturing.

Thesis embargoed until 31st December 2026

Date of Award	Dec 2025
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	CITI-GENS, Horizon 2020
Supervisor	Peter Martin (Supervisor) & Emi Garcia-Palacios (Supervisor)