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Mrs GIOVANNA MARTINEZ ARELLANO's Outputs (32)

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 Modular and Plug-and-Produce Manufacturing Apps (2022)
Presentation / Conference Contribution
Torayev, A., Martínez-Arellano, G., Chaplin, J. C., Sanderson, D., & Ratchev, S. (2022, June). Towards Modular and Plug-and-Produce Manufacturing Apps. Presented at 55th CIRP Conference on Manufacturing Systems “Leading Manufacturing Systems Transformation”, Lugano, Switzerland

Industry 4.0 redefines manufacturing systems as smart and connected systems where software solutions provide additional capabilities to the manufacturing equipment. However, the connection of manufacturing equipment with software solutions is challen... Read More about Towards Modular and Plug-and-Produce Manufacturing Apps.

A Graphical Environment to Support the Development of Affordable Digital Manufacturing Solutions (2021)
Presentation / Conference Contribution
Ling, Z., de Silva, L., Hawkridge, G., McFarlane, D., Martínez-Arellano, G., Schönfuß, B., & Thorne, A. (2021, November). A Graphical Environment to Support the Development of Affordable Digital Manufacturing Solutions. Presented at 11th International Workshop on Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA’21), Cluny, France

Digital solutions have the potential to drastically transform manufacturing operations, but smaller manufacturing businesses (SMEs) have been reluctant to adopt digital solutions due to perceived investment and upskilling costs. The Digital Manufactu... Read More about A Graphical Environment to Support the Development of Affordable Digital Manufacturing Solutions.

Visualisation on a Shoestring: a low-cost approach for building visualisation components of industrial digital solutions (2021)
Presentation / Conference Contribution
Martínez-Arellano, G., McNally, M. J., Chaplin, J. C., Ling, Z., McFarlane, D., & Ratchev, S. (2021, November). Visualisation on a Shoestring: a low-cost approach for building visualisation components of industrial digital solutions. Presented at 11th International Workshop on Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA’21), Cluny, France

The rate of adoption of digital solutions in manufacturing environments remains low despite the benefits these can bring. This is particularly acute among industrial small and medium enterprises (SMEs), who typically do not have the confidence to ado... Read More about Visualisation on a Shoestring: a low-cost approach for building visualisation components of industrial digital solutions.

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.

Towards an active learning approach to tool condition monitoring with Bayesian deep learning (2019)
Presentation / Conference Contribution
Martinez Arellano, G., & Ratchev, S. (2019, June). Towards an active learning approach to tool condition monitoring with Bayesian deep learning. Presented at ECMS 2019: 33rd International ECMS Conference on Modelling and Simulation

With the current advances in the Internet of Things (IoT), smart sensors and Artificial Intelligence (AI), a new generation of condition monitoring solutions for smart manufacturing is starting to emerge. Computer Numerical Control (CNC) machines can... Read More about Towards an active learning approach to tool condition monitoring with Bayesian 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.