Mrs GIOVANNA MARTINEZ ARELLANO's Outputs (12)

Semantic Modelling of a Manufacturing Value Chain: Disruption Response Planning (2024)
Journal Article
Elshafei, B., Martínez-Arellano, G., Chaplin, J. C., & Ratchev, S. (2024). Semantic Modelling of a Manufacturing Value Chain: Disruption Response Planning. IFAC-PapersOnLine, 58(19), 789-794. https://doi.org/10.1016/j.ifacol.2024.09.202

Supply chains have been profoundly impacted by recent global disruptions, resulting in widespread shortages of parts, goods, and raw materials, significantly affecting the manufacturing sector to the extent of halting production lin... Read More about Semantic Modelling of a Manufacturing Value Chain: Disruption Response Planning.

Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor (2024)
Journal Article
Martínez-Arellano, G., & Ratchev, S. (2024). Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor. International Journal of Advanced Manufacturing Technology, https://doi.org/10.1007/s00170-024-14508-5

Artificial intelligence (AI) among other digital technologies promise to deliver the next level of process efficiency of manufacturing systems. Although these solutions such as machine learning (ML) based condition monitoring and quality inspection a... Read More about Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor.

AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems (2024)
Journal Article
Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024). AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems. User Modeling and User-Adapted Interaction, https://doi.org/10.1007/s11257-024-09414-0

Smart Product–Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The user experience (UX) within an S-PSS is heavily influenced by the c... Read More about AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems.

Immune system inspired smart maintenance framework: tool wear monitoring use case (2024)
Journal Article
Pulikottil, T., Martínez-Arellano, G., & Barata, J. (2024). Immune system inspired smart maintenance framework: tool wear monitoring use case. International Journal of Advanced Manufacturing Technology, 132(9-10), 4699-4721. https://doi.org/10.1007/s00170-024-13472-4

As the manufacturing industry is moving towards the fourth industrial revolution, there is an increasing need for smart maintenance systems that could provide manufacturers with a competitive advantage by predicting failures. Despite various efforts... Read More about Immune system inspired smart maintenance framework: tool wear monitoring use case.

Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning (2023)
Journal Article
Pérez-Cota, F., Martínez-Arellano, G., La Cavera III, S., Hardiman, W., Thornton, L., Fuentes-Domínguez, R., Smith, R. J., McIntyre, A., & Clark, M. (2023). Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning. Scientific Reports, 13, Article 16228. https://doi.org/10.1038/s41598-023-42793-9

There is a consensus about the strong correlation between the elasticity of cells and tissue and their normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In t... Read More about Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning.

Semantic models and knowledge graphs as manufacturing system reconfiguration enablers (2023)
Journal Article
Mo, F., Chaplin, J. C., Sanderson, D., Martínez-Arellano, G., & Ratchev, S. (2024). Semantic models and knowledge graphs as manufacturing system reconfiguration enablers. Robotics and Computer-Integrated Manufacturing, 86, Article 102625. https://doi.org/10.1016/j.rcim.2023.102625

Reconfigurable Manufacturing System (RMS) provides a cost-effective approach for manufacturers to adapt to fluctuating market demands by reconfiguring assets through automated analysis of asset utilization and resource allocation. Achieving this auto... Read More about Semantic models and knowledge graphs as manufacturing system reconfiguration enablers.

Online and Modular Energy Consumption Optimization of Industrial Robots (2023)
Journal Article
Torayev, A., Martinez-Arellano, G., Chaplin, J. C., Sanderson, D., & Ratchev, S. (2024). Online and Modular Energy Consumption Optimization of Industrial Robots. IEEE Transactions on Industrial Informatics, 20(2), 1198-1207. https://doi.org/10.1109/TII.2023.3272692

Industrial robots contribute to a considerable amount of energy consumption in manufacturing. However, modeling the energy consumption of industrial robots is a complex problem as it requires considering components such as the robot controller, fans... Read More about Online and Modular Energy Consumption Optimization of Industrial Robots.

A data analytics model for improving process control in flexible manufacturing cells (2022)
Journal Article
Martínez-Arellano, G., Nguyen, T., Hinton, C., & Ratchev, S. (2022). A data analytics model for improving process control in flexible manufacturing cells. Decision Analytics Journal, 3, Article 100075. https://doi.org/10.1016/j.dajour.2022.100075

With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types... Read More about A data analytics model for improving process control in flexible manufacturing cells.

Towards Flexible, Fault Tolerant Hardware Service Wrappers for the Digital Manufacturing on a Shoestring Project (2020)
Journal Article
McNally, M. J., Chaplin, J. C., Martinez-Arellano, G., & Ratchev, S. (2020). Towards Flexible, Fault Tolerant Hardware Service Wrappers for the Digital Manufacturing on a Shoestring Project. IFAC-PapersOnLine, 53(3), 72-77. https://doi.org/10.1016/j.ifacol.2020.11.065

The adoption of digital manufacturing in small to medium enterprises (SMEs) in the manufacturing sector in the UK is low, yet these technologies offer significant promise to boost productivity. Two major causes of this lack of uptake is the high upfr... Read More about Towards Flexible, Fault Tolerant Hardware Service Wrappers for the Digital Manufacturing on a Shoestring Project.

XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications (2020)
Journal Article
Ahmadieh Khanesar, M., Bansal, R., Martínez-Arellano, G., & Branson, D. (2020). XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications. Applied Sciences, 10(8), Article 6451. https://doi.org/10.3390/app10186451

Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and sped-up pr... Read More about XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications.

Tool Wear Classification using Time Series Imaging and Deep Learning (2019)
Journal Article
Martínez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool Wear Classification using Time Series Imaging and Deep Learning. International Journal of Advanced Manufacturing Technology, 104(9-12), 3647–3662. https://doi.org/10.1007/s00170-019-04090-6

Tool Condition Monitoring (TCM) has become essential to achieve high quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper present... Read More about Tool Wear Classification using Time Series Imaging and Deep Learning.

Creating AI Characters for Fighting Games Using Genetic Programming (2016)
Journal Article
Martinez-Arellano, G., Cant, R., & Woods, D. (2017). Creating AI Characters for Fighting Games Using Genetic Programming. IEEE Transactions on Computational Intelligence and AI in Games, 9(4), 423-434. https://doi.org/10.1109/tciaig.2016.2642158

This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a genetic programmin... Read More about Creating AI Characters for Fighting Games Using Genetic Programming.