Mrs GIOVANNA MARTINEZ ARELLANO's Outputs (38)

Trustworthy, Responsible and Ethical Artificial Intelligence in Manufacturing and Supply Chains: Synthesis and Emerging Research Questions (2025)
Journal Article
Brintrup, A., Baryannis, G., Tiwari, A., Ratchev, S., Martinez-Arellano, G., & Singh, J. (in press). Trustworthy, Responsible and Ethical Artificial Intelligence in Manufacturing and Supply Chains: Synthesis and Emerging Research Questions. Data-Centric Engineering.

In recent years, the manufacturing sector has seen an influx of Artificial Intelligence applications, seeking to harness its capabilities to improve productivity. However, manufacturing organisations have limited understanding of risks that are posed... Read More about Trustworthy, Responsible and Ethical Artificial Intelligence in Manufacturing and Supply Chains: Synthesis and Emerging Research Questions.

6D Grasp Pose Estimation using Machine Learning with Synthetic Data: Explainable Grasping with Point Cloud Networks (2025)
Presentation / Conference Contribution
Steikunas, A., & Martínez-Arellano, G. (Eds.). (n.d.). 6D Grasp Pose Estimation using Machine Learning with Synthetic Data: Explainable Grasping with Point Cloud Networks [Edited Proceedings]. AI 2025: Forty-fifth SGAI International Conference on Artificial Intelligence, Cambridge, UK. https://www.bcs-sgai.org/ai2025/

Deep learning-based robotic grasping systems rely on vast quantities of labelled data for training, driving the need for synthetically generated data for training models. However, due to their black-box nature, understanding why grasp predictions suc... Read More about 6D Grasp Pose Estimation using Machine Learning with Synthetic Data: Explainable Grasping with Point Cloud Networks.

A Continual Learning Approach for Adaptive Error Detection in 3D Printing (2025)
Presentation / Conference Contribution
Martínez-Arellano, G., & George, S. (Eds.). (n.d.). A Continual Learning Approach for Adaptive Error Detection in 3D Printing [Edited Proceedings]. 3rd European Symposium on Artificial Intelligence in Manufacturing: ESAIM'25, San Sebastian, Spain. https://aim-net.eu/esaim2025/

Despite its wide use, material extrusion additive manufacturing still presents accuracy and speed challenges, which translate into a range of different defects, and ultimately scrap parts. To address this, data-driven approaches based on machine lear... Read More about A Continual Learning Approach for Adaptive Error Detection in 3D Printing.

Force Estimation in a 6-DOF UR5 Robot Using Machine Learning Algorithms for Precise Force Control (2025)
Presentation / Conference Contribution
Karaca, A., Khanesar, M. A., Martínez-Arellano, G., & Branson, D. T. (2025, August 3-6). Force Estimation in a 6-DOF UR5 Robot Using Machine Learning Algorithms for Precise Force Control [Paper presentation]. 2025 IEEE International Conference on Mechatronics and Automation, Beijing, China. https://nottingham-repository.worktribe.com/output/52838703

Robot arms inherently exhibit nonlinear dynamics, which makes force control a challenging task. In each posture of the robot arm, the end-effector deflection varies depending on the applied force. This means that even under the same force input, the... Read More about Force Estimation in a 6-DOF UR5 Robot Using Machine Learning Algorithms for Precise Force Control.

Reinforcement Learning-Based Sealant Process Control (2025)
Presentation / Conference Contribution
Narvato, R., Kendall, P., Sanderson, D., Ratchev, S., & Martínez Arellano, G. (2025). Reinforcement Learning-Based Sealant Process Control. Procedia CIRP.

Sealant deposition is a fundamental process in aerospace assembly ensuring the longevity of aerostructures by preventing contaminant transfer and leakage. This process is typically done manually which can lead to inconsistent, time-consuming, and som... Read More about Reinforcement Learning-Based Sealant Process Control.

A facile methodology to identify microstructural grains on etched surfaces using panoptic segmentation (2025)
Journal Article
Girerd, T., Martínez-Arellano, G., Clare, A. T., & Speidel, A. (2025). A facile methodology to identify microstructural grains on etched surfaces using panoptic segmentation. Materials Characterization, 227, 115224. https://doi.org/10.1016/j.matchar.2025.115224

The advancement of manufacturing processes demands the deployment of new innovative solutions to control polycrystalline material microstructures in cheap, safe and rapid manner. Analysing polycrystalline microstructures requires grain segmentation,... Read More about A facile methodology to identify microstructural grains on etched surfaces using panoptic segmentation.

Semantic Knowledge Representation in Asset Administration Shells: Empowering Manufacturing Utilization (2024)
Presentation / Conference Contribution
Elshafei, B., Martínez-Arellano, G., Chaplin, J. C., Sanderson, D., & Ratchev, S. (Eds.). (2024). Semantic Knowledge Representation in Asset Administration Shells: Empowering Manufacturing Utilization [Edited Proceedings]. 33rd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2024), Taiwan, Taichung. https://doi.org/10.1007/978-3-031-74482-2_32

Within the context of Industry 4.0 and the Reference Architecture Model Industrie 4.0, the Asset Administration Shell (AAS) framework has emerged as a critical component for implementing Digital Twins that facilitate a seamless data exchange within a... Read More about Semantic Knowledge Representation in Asset Administration Shells: Empowering Manufacturing Utilization.

A Tool for Generating and Labelling Domain Randomised Synthetic Images for Object Recognition in Manufacturing (2024)
Presentation / Conference Contribution
Martínez-Arellano, G., & Buck, M. G. (2024, October 16). A Tool for Generating and Labelling Domain Randomised Synthetic Images for Object Recognition in Manufacturing [Paper presentation]. ESAIM 2024 – 2nd European Symposium on Artificial Intelligence in Manufacturing, Athens. Greece. https://nottingham-repository.worktribe.com/output/39990821

Reconfigurable manufacturing systems are becoming the only viable option to respond to changing product volumes and product specification , which are currently major challenges for the manufacturing industry. Part of this adaptation requires vision s... Read More about A Tool for Generating and Labelling Domain Randomised Synthetic Images for Object Recognition in Manufacturing.

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. (2025). 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, 138, 169-191. 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.

Improving the Development and Reusability of Industrial AI Through Semantic Models (2024)
Presentation / Conference Contribution
Martínez-Arellano, G., & Ratchev, S. (Eds.). (2024). Improving the Development and Reusability of Industrial AI Through Semantic Models [Edited Proceedings]. Conference on Learning Factories 2024, University of Twente, The Netherlands. https://doi.org/10.1007/978-3-031-65411-4_22

Despite some of the success of AI, particularly machine learning, in industrial applications such as condition monitoring, quality inspection and asset control , solutions are typically bespoke and not robust in the long term. There is a considerable... Read More about Improving the Development and Reusability of Industrial AI Through Semantic Models.

Towards Frugal Industrial AI : A Framework for the Development of Scalable and Robust Machine Learning Models in the Shop Floor (2024)
Presentation / Conference Contribution
MARTINEZ ARELLANO, G. (2024, June 23-26). Towards Frugal Industrial AI : A Framework for the Development of Scalable and Robust Machine Learning Models in the Shop Floor [Paper presentation]. FAIM 2024, Taichung, Taiwan.

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.

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.

Optimal Manufacturing Configuration Selection: Sequential Decision Making and Optimization using Reinforcement Learning (2023)
Presentation / Conference Contribution
Torayev, A., Abadia, J. J. P., Martínez-Arellano, G., Cuesta, M., Chaplin, J. C., Larrinaga, F., Sanderson, D., Arrazola, P. J., & Ratchev, S. (2023). Optimal Manufacturing Configuration Selection: Sequential Decision Making and Optimization using Reinforcement Learning. Procedia CIRP, 120, 986-991. https://doi.org/10.1016/j.procir.2023.09.112

In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minim... Read More about Optimal Manufacturing Configuration Selection: Sequential Decision Making and Optimization using Reinforcement Learning.

Enabling Coordinated Elastic Responses of Manufacturing Systems through Semantic Modelling (2023)
Presentation / Conference Contribution
Martínez-Arellano, G., Niewiadomski, K., Mo, F., Elshafei, B., Chaplin, J. C., Mcfarlane, D., & Ratchev, S. (2023). Enabling Coordinated Elastic Responses of Manufacturing Systems through Semantic Modelling. IFAC-PapersOnLine, 56(2), 7402-7407. https://doi.org/10.1016/j.ifacol.2023.10.617

Resilience to supply chain disruptions and to changing product volumes and specifications are currently major challenges for the manufacturing sector. To maintain quality and productivity, manufacturers need to be able to respond to disruption using... Read More about Enabling Coordinated Elastic Responses of Manufacturing Systems through Semantic Modelling.

Investigating multi-level ontology to support manufacturing during demand fluctuation (2023)
Presentation / Conference Contribution
Kazantsev, N., Niewiadomski, K., Martínez-Arellano, G., Elshafei, B., Mo, F., & Murthy, S. R. (Eds.). (2023). Investigating multi-level ontology to support manufacturing during demand fluctuation [Edited Proceedings]. Low-Cost Digital Solutions for Industrial Automation (LoDiSA 2023), Cambridge, UK. https://doi.org/10.1049/icp.2023.1752

Responding to demand fluctuations represents a challenge for manufacturers, as they often face resource limitations and cannot assess all potential solutions. This paper presents a low-cost semantic engineering solution (ontology) to coordinate poten... Read More about Investigating multi-level ontology to support manufacturing during demand fluctuation.

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, 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.

Methodology for Digital Transformation: A Continuous Improvement Approach (2023)
Presentation / Conference Contribution
Hernández Oliver, K., Martínez-Arellano, G., & Segal, J. (Eds.). (2023). Methodology for Digital Transformation: A Continuous Improvement Approach [Edited Proceedings]. 1st Workshop on Low-Cost Digital Solutions for Industrial Automation, Cambridge, UK.

Digital technologies have the potential to significantly transform the manufacturing industry by achieving improvements in productivity, quality and sustainability. Despite this, the uptake is relatively low as companies face a number of challenges i... Read More about Methodology for Digital Transformation: A Continuous Improvement Approach.

Low-cost System for Visual Inspection of Corrosion: An Industrial Case Study (2023)
Presentation / Conference Contribution
Hernández Oliver, K. H., Martínez-Arellano, G., & Segal, J. (Eds.). (2023). Low-cost System for Visual Inspection of Corrosion: An Industrial Case Study [Edited Proceedings]. 1st Workshop on Low-Cost Digital Solutions for Industrial Automation, Cambridge, UK.

The use of digital technologies around the world has increased considerably, modifying the way in which daily activities are conducted. The manufacturing sector is no exception. Over the last decade, digital technologies have become a key element for... Read More about Low-cost System for Visual Inspection of Corrosion: An Industrial Case Study.