Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89166
Title: An AWS machine learning-based indirect monitoring method for deburring in aerospace industries towards industry 4.0
Authors: Wijaya, Tomi
Lee, Daryl
Tjahjowidodo, Tegoeh
Then, David
Manyar, Omey M.
Caesarendra, Wahyu
Pappachan, Bobby Kaniyamkudy
Keywords: Machine Learning
DRNTU::Engineering::Mechanical engineering
Internet of Thing
Issue Date: 2018
Source: Caesarendra, W., Pappachan, B. K., Wijaya, T., Lee, D., Tjahjowidodo, T., Then, D., & Manyar, O. M. (2018). An AWS machine learning-based indirect monitoring method for deburring in aerospace industries towards industry 4.0. Applied Sciences, 8(11), 2165-. doi: 10.3390/app8112165
Series/Report no.: Applied Sciences
Abstract: The number of studies on the Internet of Things (IoT) has grown significantly in the past decade and has been applied in various fields. The IoT term sounds like it is specifically for computer science but it has actually been widely applied in the engineering field, especially in industrial applications, e.g., manufacturing processes. The number of published papers in the IoT has also increased significantly, addressing various applications. A particular application of the IoT in these industries has brought in a new term, the so-called Industrial IoT (IIoT). This paper concisely reviews the IoT from the perspective of industrial applications, in particular, the major pillars in order to build an IoT application, i.e., architectural and cloud computing. This enabled readers to understand the concept of the IIoT and to identify the starting point. A case study of the Amazon Web Services Machine Learning (AML) platform for the chamfer length prediction of deburring processes is presented. An experimental setup of the deburring process and steps that must be taken to apply AML practically are also presented.
URI: https://hdl.handle.net/10356/89166
http://hdl.handle.net/10220/47026
ISSN: 2076-3417
DOI: http://dx.doi.org/10.3390/app8112165
Rights: © 2018 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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