An AWS machine learning-based indirect monitoring method for deburring in aerospace industries towards industry 4.0

• Main Authors: Wijaya, Tomi, Lee, Daryl, Tjahjowidodo, Tegoeh, Then, David, Manyar, Omey M., Caesarendra, Wahyu, Pappachan, Bobby Kaniyamkudy
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2018
• Subjects: Machine Learning; DRNTU::Engineering::Mechanical engineering; Internet of Thing
• Online Access: https://hdl.handle.net/10356/89166; http://hdl.handle.net/10220/47026
• Institution: Nanyang Technological University

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.