Machine learning in manufacturing and industry 4.0 applications
By Rahul Rai, Manoj Kumar Tiwari, Alexandre Dolgui. International Journal of Production Research, 2021
454 citations recorded by the source corpus.
The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems.
Subfields: Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/9eae0ca968cc877ae5e8f24874c61352a457275c
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In Industrial and Manufacturing Engineering
Citations in the corpus
- The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives (2022, background): “…learning and smart manufacturing, which can be generalised as the implementation of reconfigurable supply chains and manufacturing systems (Brintrup et al. 2020, Cavalcante et al. 2019, Dolgui et al. 2020, Dolgui and Ivanov 2022, Kosasih and Brintrup 2021, Rai et al. 2021, Yoon et al. 2020).”
- Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024, unknown): “AI’s ability to analyse large volumes of data can facilitate more informed, data-driven decisions (Rai et al. 2021), while GAI’s creative capability can introduce novel solutions and strategies that were not previously considered.”