| 000 | 02586nam a2200229 4500 | ||
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| 003 | OSt | ||
| 005 | 20250122094133.0 | ||
| 008 | 240912b |||||||| |||| 00| 0 eng d | ||
| 040 | _aAlliance University | ||
| 082 | _a621 CHA | ||
| 100 | _aChacko, Mathew | ||
| 245 | _aDigital Twin-Based Cyber-Physical Quality System for Autonomous Manufacturing | ||
| 260 |
_aBengaluru _bAlliance University _c2024 |
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| 300 | _a175 | ||
| 502 |
_aAlliance University _bPh. D, _cAlliance College of Engineering and Design, Alliance University _d2024 _gGuide: Atul _hAlliance College of Engineering and Design |
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| 520 | _aThe 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 | _aEngineering | ||
| 650 | _aEngineering and Technology | ||
| 650 | _aEngineering Mechanical | ||
| 856 | _uhttps://shodhganga.inflibnet.ac.in/handle/10603/589343 | ||
| 942 | _cDTD | ||
| 999 |
_c48179 _d48179 |
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