All Outputs (7)

Hierarchical ensemble deep learning for data-driven lead time prediction (2023)
Journal Article
Aslan, A., Vasantha, G., El-Raoui, H., Quigley, J., Hanson, J., Corney, J., & Sherlock, A. (2023). Hierarchical ensemble deep learning for data-driven lead time prediction. International Journal of Advanced Manufacturing Technology, 128(9-10), 4169-4188.

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manu... Read More about Hierarchical ensemble deep learning for data-driven lead time prediction.

A Knowledge Graph Approach for State-of-the-Art Implementation of Industrial Factory Movement Tracking System (2023)
Presentation / Conference Contribution
Vasantha, G., Aslan, A., Hanson, J., El-Raoui, H., Corney, J., & Quigley, J. (2024). A Knowledge Graph Approach for State-of-the-Art Implementation of Industrial Factory Movement Tracking System. In Flexible Automation and Intelligent Manufacturing: Esta

Digital sensing technologies are essential for realizing Industry 4.0, as they enhance productivity, assist with real-time decision-making, and provide flexibility and agility in manufacturing factories. However, implementing these technologies can b... Read More about A Knowledge Graph Approach for State-of-the-Art Implementation of Industrial Factory Movement Tracking System.

Movement Tracking-Based In-Situ Monitoring System for Additive Manufacturing (2023)
Presentation / Conference Contribution
Vasantha, G., Aslan, A., Lapok, P., Lawson, A., & Thomas, S. (2023, June). Movement Tracking-Based In-Situ Monitoring System for Additive Manufacturing. Presented at FAIM 2023: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for Mo

Monitoring and identification of defects during additive manufacturing is mostly done by bespoke optical or acoustic measurement systems. These in-situ monitoring technologies are either intrusive or sensitive to noisy manufacturing environments. We... Read More about Movement Tracking-Based In-Situ Monitoring System for Additive Manufacturing.

Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation (2023)
Presentation / Conference Contribution
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., & Corney, J. (2023, June). Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation. Presented at 32nd International Conference on Flexible Automation and Int

In complex manufacturing industries that are not fully automated and involve human workers it is important to identify deviations from the planned production schedule and locate bottlenecks for improved efficiency. This is not an easy task as it requ... Read More about Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation.

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing (2023)
Journal Article
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing. Sensors, 23(10), Article 4928. https://doi.org/10.3390/s2310

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at impro... Read More about Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing.

Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing (2023)
Data
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing. [Dataset]. https://doi.org/10.17869/enu.2023.3100035

This data contains the worker position datasets (including the event logs) and the source codes of the discrete event simulation that are used in the research article titled "Using Worker Position Data for Human-Driven Decision Support in Labour-inte... Read More about Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing.

Agent based simulation of workers’ behaviours around hazard areas in manufacturing sites (2023)
Presentation / Conference Contribution
El Raoui, H., Quigley, J., Aslan, A., Vasantha, G., Hanson, J., Corney, J., & Sherlock, A. (2023). Agent based simulation of workers’ behaviours around hazard areas in manufacturing sites. In C. Currie, & L. Rhodes-Leader (Eds.), Proceedings of the Oper

Rewards for risk taking behaviour by workers (if accidents do not occur) can be realised in the form of increased productivity or worker idle time. However, frequent unsafe behaviours of workers inevitably results in accidents and an associated loss... Read More about Agent based simulation of workers’ behaviours around hazard areas in manufacturing sites.