Predictive Manufacturing: Enabling Technologies, Frameworks and Applications

Pulikottil, Terrin; Estrada-Jimenez, Luis Alberto; Nikghadam-Hojjati, Sanaz; Barata, José

doi:10.1007/978-3-030-78288-7_5

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The opportunities and difficulties of quality engineering for additive manufacturing methods have been identified by Colosimo et al ( 2018) [21]. The enabling methods, concepts, and potential uses of predictive manufacturing were given by Pulikottil et al ( 2021) [22]. The essential role of Quality 4.0 in improving the industrial sector completely was highlighted by Javaid et al ( 2021) [23].…”

Section: Figure 2 Predictive Quality Analysismentioning

confidence: 99%

Intelligent and Automated Product Quality Assurance System using Machine Learning

Aashish Arora

2024

jes

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The paper discusses the use of advanced technologies like the Internet of Things (IoT) and cyber-physical systems (CPS) to enhance product quality assurance in production industries. It proposes an intelligent approach using machine learning and termed the model as ETLViT, which integrates transfer learning models and vision transformers (ViT). The proposed model is a three-layered architecture integrated with a cyber-physical system (CPS). The first layer collects images of the processed products using a CPS, while the second layer transmits the data to a cloud server. The third layer analyzes the input image and predicts any faults in the product, with results also stored in the cloud server. The learning process involves noise removal, feature extraction, and classification, which are performed simultaneously by the ensemble model called ETLViT, designed using transfer learning and vision transformers. The result was presented with transfer learning models, ensemble model with machine learning and ETLViT. Among all of them, ETLViT outperforms the best.

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Section: Figure 2 Predictive Quality Analysismentioning

confidence: 99%