Mrs GIOVANNA MARTINEZ ARELLANO's Outputs (16)

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. (2024, June). Semantic Knowledge Representation in Asset Administration Shells: Empowering Manufacturing Utilization. Presented at 33rd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2024), Taiwan, Taichung

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). A Tool for Generating and Labelling Domain Randomised Synthetic Images for Object Recognition in Manufacturing. Paper presented at ESAIM 2024 – 2nd European Symposium on Artificial Intelligence in Manufacturing, Athens. Greece

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.

Improving the Development and Reusability of Industrial AI Through Semantic Models (2024)
Presentation / Conference Contribution
Martínez-Arellano, G., & Ratchev, S. (2024, April). Improving the Development and Reusability of Industrial AI Through Semantic Models. Presented at Conference on Learning Factories 2024, University of Twente, The Netherlands

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). Towards Frugal Industrial AI : A Framework for the Development of Scalable and Robust Machine Learning Models in the Shop Floor. Paper presented at 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.

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, October). Optimal Manufacturing Configuration Selection: Sequential Decision Making and Optimization using Reinforcement Learning. Presented at 56th CIRP Conference onManufacturing Systems, CIRP CMS ‘23, Cape Town

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, July). Enabling Coordinated Elastic Responses of Manufacturing Systems through Semantic Modelling. Presented at IFAC World Congress 2023: The 22nd World Congress of the International Federation of Automatic Control, Yokohama, Japan

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. (2023, September). Investigating multi-level ontology to support manufacturing during demand fluctuation. Presented at Low-Cost Digital Solutions for Industrial Automation (LoDiSA 2023), Cambridge, UK

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.

Methodology for Digital Transformation: A Continuous Improvement Approach (2023)
Presentation / Conference Contribution
Hernández Oliver, K., Martínez-Arellano, G., & Segal, J. (2023, September). Methodology for Digital Transformation: A Continuous Improvement Approach. Presented at 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. (2023, September). Low-cost System for Visual Inspection of Corrosion: An Industrial Case Study. Presented at 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.

Elastic manufacturing systems: A system view on operations, firm, and supply chain resilience (2023)
Presentation / Conference Contribution
Kazantsev, N., Niewiadomski, K., El-Shafei, B., Murthy, S. R., Chaplin, J., Martínez-Arellano, G., Ratchev, S., Velu, C., Mcfarlane, D., & Minshall, T. (2023, July). Elastic manufacturing systems: A system view on operations, firm, and supply chain resilience. Presented at 30th EurOMA Conference, Leuven, Belgium

'Black swan' events-such as military conflicts, pandemics, and semiconductor crises-drastically change product volume and mix patterns and shorten how long manufacturers have to cost-effectively respond to them. This is a particular challenge for hig... Read More about Elastic manufacturing systems: A system view on operations, firm, and supply chain resilience.

Capacity Modelling and Measurement for Smart Elastic Manufacturing Systems (2023)
Presentation / Conference Contribution
Elshafei, B., Mo, F., Chaplin, J. C., Arellano, G. M., & Ratchev, S. (2023, March). Capacity Modelling and Measurement for Smart Elastic Manufacturing Systems. Presented at Aerotech 2023, Fort Worth, Texas, USA

Aerospace manufacturing is improving its productivity and growth by expanding its capacity for production by investing in new tools and more equipment to provide additional capacity and flexibility in the face of widespread supply disruptions and unp... Read More about Capacity Modelling and Measurement for Smart Elastic Manufacturing Systems.

UX- for Smart-PSS: Towards a Context-aware Framework (2022)
Presentation / Conference Contribution
Carrera-Rivera, A., Larrinaga, F., Lasa, G., & Martinez-Arellano, G. (2022, October). UX- for Smart-PSS: Towards a Context-aware Framework. Presented at 6th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2022), Valletta, Malta

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 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.