A Semantic Web Approach to Fault Tolerant Autonomous Manufacturing

Kalach, Fadi El; Wickramarachchi, Ruwan; Harik, Ramy; Sheth, Amit P.

doi:10.1109/mis.2023.3235677

Cited by 3 publications

(2 citation statements)

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“…These reasoners use rules defined by experts to query through the generated KG, identify the satisfied rules based on the information provided in the KG then add or modify the corresponding triple as necessary. This can be seen through work such as (Guo et al, 2021) (El Kalach et al, 2023) and (B. Zhou et al, 2022).…”

Section: Reasoningmentioning

confidence: 99%

“…In essence this reflects the ability to semantically model the different assets in the facility utilizing the ontologies developed. Real time analog sensor data was integrated into a manufacturing KG using RDF (El Kalach et al, 2023). A novel multi-layer KG was introduced that captures the real-time data obtained from IoT technology in CPPS environments and is continuously updated (M. Liu et al, 2022).…”

Section: Knowledge Graph Generationmentioning

confidence: 99%

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Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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Section: Reasoningmentioning

confidence: 99%

Section: Knowledge Graph Generationmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

Self Cite

View full text Add to dashboard Cite

show abstract

Leveraging computer vision towards high-efficiency autonomous industrial facilities

Yousif,

Burns,

El Kalach

et al. 2024

J Intell Manuf

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Manufacturers face two opposing challenges: the escalating demand for customized products and the pressure to reduce delivery lead times. To address these expectations, manufacturers must refine their processes, to achieve highly efficient and autonomous operations. Current manufacturing equipment deployed in several facilities, while reliable and produces quality products, often lacks the ability to utilize advancements from newer technologies. Since replacing legacy equipment may be financially infeasible for many manufacturers, implementing digital transformation practices and technologies can overcome the stated deficiencies and offer cost-affordable initiatives to improve operations, increase productivity, and reduce costs. This paper explores the implementation of computer vision, as a cutting-edge, cost-effective, open-source digital transformation technology in manufacturing facilities. As a rapidly advancing technology, computer vision has the potential to transform manufacturing operations in general, and quality control in particular. The study integrates a digital twin application at the endpoint of an assembly line, effectively performing the role of a quality officer by utilizing state-of-the-art computer vision algorithms to validate end-product assembly orientation. The proposed digital twin, featuring a novel object recognition approach, efficiently classifies objects, identifies and segments errors in assembly, and schedules the paths through the data pipeline to the corresponding robot for autonomous correction. This minimizes the need for human interaction and reduces disruptions to manufacturing operations.

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