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|Title:||An approach to indirect real-time predictions with amazon machine learning||Authors:||Lee, Daryl Wei Qiang||Keywords:||DRNTU::Engineering::Mechanical engineering||Issue Date:||2018||Abstract:||Manufacturing all around the world is going through a phenomenon known as industry 4.0 where digital transformations are taking place across the manufacturing value chain. Concepts such as the industrial internet of things and machine learning have become a norm in the industry. The biggest challenges currently faced by organizations in achieving the goals of industry 4.0 include the issues of interoperability, analytical complications, and cyber-security risks amongst many others. In this project, we will develop an approach to create an indirect real-time prediction system using amazon machine learning. The system will be tested on a deburring (abrasive grinding) process as a case study. The proposed approach will enable users to utilize machine learning techniques provided on the AML platform to create a machine learning model and subsequently generate predictions and display them visually in a dynamic chart.||URI:||http://hdl.handle.net/10356/75522||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
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