Paper Info
Title
A Machine Learning Framework for Real-Time Identification of Successful Snap-Fit Assemblies
Abstract
Snap-fit assemblies are widely used in the manufacturing of several product types, allowing part joining, while the parts remain unprocessed. The locking mechanism of a snap-fit is usually done within the object structure, not allowing visual identification of the successful process completion. Humans consider the forces developed between the two parts or the snapping sound, as an indication of success. This is difficult to realize in robotic assembly, and the process success is usually identified at a product quality control stage. The aim of this article is to migrate the human ability to identify a successful snap assembly to autonomous robotic assembly, via a machine learning framework, enabled by human–robot collaboration for rich data collection and labeling. The proposed framework allows learning while minimizing complexity, cost, and time. A generic feature set is proposed, which can produce good identification results in different snap assembly types. A feature transformation is also introduced that is fundamental for the real-time operation of the proposed framework and the identification of successful snap-assemblies. Three different objects are used to experimentally validate the approach using a KUKA LWR4+ robotic arm, resulting in high classification and real-time identification accuracy. Finally, a comparison with a model-based method is conducted. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</italic> —This article is motivated by the need for flexible robotic assembly processes, and specifically the characterization of snap-fits, an assembly-type widely used in automated robotic processes. The snap-fit completion cannot be easily identified from a visual inspection, but the final result is usually realized by sensing the developed forces or identifying the snapping sound during a manual assembly. Training industrial robots to imitate the human snap-fit identification will allow accurate monitoring of such assemblies and the automation of the process with certainty for the final result. A quick and accurate perception of the process status can improve the overall process deployment speed and efficiency of such assemblies. The proposed framework allows the real-time characterization of the snap-fit process with very high accuracy and is easily implemented with collaborative robots. The framework can be further extended to other assembly processes using assembly forces as an indication signal.
Year
DOI
Venue
2020
10.1109/TASE.2019.2932834
IEEE Transactions on Automation Science and Engineering
Keywords
Field
DocType
Human–robot collaboration,intelligent robots,machine learning,robot learning,smart manufacturing,snap assembly
Computer science,Snap,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
17
1
1545-5955
Citations 
PageRank 
References 
2
0.36
0
Authors
3
Name
Order
Citations
PageRank
Stefanos Doltsinis1304.59
Marios Krestenitis220.36
Zoe Doulgeri333247.11