Digital Twin-Based Cyber-Physical Quality System for Autonomous Manufacturing (Record no. 48179)

MARC details
000 -LEADER
fixed length control field 02586nam a2200229 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250122094133.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240912b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency Alliance University
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621 CHA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Chacko, Mathew
245 ## - TITLE STATEMENT
Title Digital Twin-Based Cyber-Physical Quality System for Autonomous Manufacturing
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Bengaluru
Name of publisher, distributor, etc Alliance University
Date of publication, distribution, etc 2024
300 ## - PHYSICAL DESCRIPTION
Extent 175
502 ## - DISSERTATION NOTE
Dissertation note Alliance University
Degree type Ph. D,
Name of granting institution Alliance College of Engineering and Design, Alliance University
Year degree granted 2024
Guide Guide: Atul
Dept Alliance College of Engineering and Design
520 ## - SUMMARY, ETC.
Summary, etc The paradigm of Manufacturing built on Cyber-Physical Systems (CPS) embodies a dynamic and transformative realm of knowledge, empowering the creation of intricately designed components through the precision of Computer Numerical Controlled (CNC) machines. Within this domain, the fusion of technology and production holds immense promise, boasting the capacity to analyze vast datasets. Yet, amid this promise lies a formidable challenge: ensuring the seamless maintenance of product quality and consistency throughout the CNC manufacturing process, a task rendered complex by the intricate dynamics inherent in such endeavours. newlineIn acknowledgement of this pivotal gap, the author of this thesis has discerned a critical imperative for industrial manufacturers: the adoption and strategic utilization of machine learning (ML) and deep learning (DL) technologies. Their integration stands poised to deliver real-time prognostications of manufacturing part quality with an astonishing accuracy rate of 96.58%. Prior frameworks, whether grounded in machine data, sensor data, or image data, have faltered in unifying the realms of manufacturing and ML into a cohesive entity capable of accurate quality prediction. The central objective of this thesis thus crystallizes into the development of a domain-specific framework for Cyber-Physical Quality Surveillance (CPQS), nestled at the confluence of ML, DL, and manufacturing methodologies. This framework harmonizes disparate data streams from machines, sensors, and images, meticulously tailored to yield predictions of quality surpassing the 95% threshold for components forged from Advanced High-Strength Steel (AHSS) via CNC machining. To this end, three novel methodologies have been conceived and executed newline newline
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering and Technology
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering Mechanical
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://shodhganga.inflibnet.ac.in/handle/10603/589343
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Doctoral Thesis & Dissertation

No items available.