Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/54682
Title: Online waviness and material removal prediction in aerospace MRO robotic polishing
Authors: Wang, Runfeng.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
Issue Date: 2012
Abstract: Polishing is a common task in aerospace industry, providing better surface quality, and therefore increasing the structure intensity and system working efficiency. Manual polishing often gives low repeatability and it is a tedious task for the workers. On the other hand, traditional robotized polishing has a problem of ineligible deviation between actual and desired robot path. Therefore, force control technologies which provide large tolerances of positioning errors are introduced to the polishing process. However, the problem is that, offline measurement of surface quality reduces the efficiency significantly. Thus, there is an urgent need of certain techniques that can estimate the surface quality online, and help with more robust process control, which is also the objective of this project. The basic idea is to build a data-driven model, which reveals the relationship between online monitoring signals and surface quality parameters. The model is trained offline based on the data obtained from a series of experiments, but through validation, it could be used for online prediction of surface waviness and material removal. Inputs of the model are the features extracted from the raw signals of dynamometer, current sensor and AE sensor, and outputs are the surface quality parameters, including surface waviness, and material removal. To train the model, these output parameters are measured offline after each experiment. The key research issues involved in this project include sensing techniques, signal processing techniques, feature extraction and correlation modelling study.
URI: http://hdl.handle.net/10356/54682
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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