Please use this identifier to cite or link to this item:
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/873
Title: | Cyber-Physical Quality Systems in Manufacturing |
Authors: | Chacko, Mathew Atul |
Keywords: | Cyber-Physical Systems Machine Learning Internet ofThings (IoT) Image Processing |
Issue Date: | 5-Apr-2021 |
Publisher: | Science Research Society |
Abstract: | Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysingsensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. Theproposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of thetools we can detect quality issues and develop an automated quality system using machine learning techniques. |
URI: | https://doi.org/10.17762/turcomat.v12i2.1805 http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/873 |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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1805-Article Text-3382-1-10-20210408.pdf Restricted Access | 1.63 MB | Adobe PDF | View/Open Request a copy |
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