RIPTIDE: Robot Inspecting Parts to Increase Development Efficiency

Additive manufacturing introduces challenges in quality assurance due to the high variability and volume of produced components. Traditional manual inspection methods, such as measurements taken with calipers, are time consuming, labor intensive, and prone to human error. This paper presents RIPTIDE, a quality confirmation system designed to enhance inspection accuracy and efficiency. RIPTIDE integrates a robotic pick and place mechanism with a scanning procedure to generate high fidelity three dimensional models of manufactured parts. By eliminating human intervention, this system improves consistency, reduces inspection time, and streamlines the validation process for mass volume. The proposed approach demonstrates significant potential in optimizing additive manufacturing workflows by ensuring reliable and scalable quality control.

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Publication Details

Published
2025-06-30
DOI
https://doi.org/10.1109/ur65550.2025.11078063
Primary Topic
Manufacturing Process and Optimization
Type
article
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article

RIPTIDE: Robot Inspecting Parts to Increase Development Efficiency

Angela Estela Duron, Bradley Alexander Cougar Stiles, David Anthony Torck
Manufacturing Process and Optimization
article

RIPTIDE: Robot Inspecting Parts to Increase Development Efficiency

Angela Estela Duron, Bradley Alexander Cougar Stiles, David Anthony Torck
article en

Abstract

Additive manufacturing introduces challenges in quality assurance due to the high variability and volume of produced components. Traditional manual inspection methods, such as measurements taken with calipers, are time consuming, labor intensive, and prone to human error. This paper presents RIPTIDE, a quality confirmation system designed to enhance inspection accuracy and efficiency. RIPTIDE integrates a robotic pick and place mechanism with a scanning procedure to generate high fidelity three dimensional models of manufactured parts. By eliminating human intervention, this system improves consistency, reduces inspection time, and streamlines the validation process for mass volume. The proposed approach demonstrates significant potential in optimizing additive manufacturing workflows by ensuring reliable and scalable quality control.

Texas A&M University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 73%
Manufacturing Process and Optimization
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