All Outputs (4)

Aerospace Assembly Gap Measurement Using Low Cost Smart Tools with Machine Vision (2019)
Conference Proceeding
Crossley, R., & Ratchev, S. (2019). Aerospace Assembly Gap Measurement Using Low Cost Smart Tools with Machine Vision. In S. Ratchev (Ed.), Precision Assembly in the Digital Age: 8th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2018, Chamonix, France, January 14—16, 2018, Revised Selected Papers (158-168). https://doi.org/10.1007/978-3-030-05931-6_15

This paper details the conversion of mature machine vision technology from a fixed position automation line based device to a handheld technology and addresses the problems associated with maintaining a consistent camera distance and light source by... Read More about Aerospace Assembly Gap Measurement Using Low Cost Smart Tools with Machine Vision.

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)
Conference Proceeding
Martinez Arellano, G., & Ratchev, S. (2019). Towards an active learning approach to tool condition monitoring with Bayesian deep learning

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.

Demonstration of Transformable Manufacturing Systems through the Evolvable Assembly Systems Project (2019)
Journal Article
Sanderson, D., Turner, A., Shires, E., Chaplin, J., & Ratchev, S. (2019). Demonstration of Transformable Manufacturing Systems through the Evolvable Assembly Systems Project. SAE Technical Papers, Article 2019-01-1363. https://doi.org/10.4271/2019-01-1363

© 2019 SAE International. All Rights Reserved. Evolvable Assembly Systems is a five year UK research council funded project into flexible and reconfigurable manufacturing systems. The principal goal of the research programme has been to define and va... Read More about Demonstration of Transformable Manufacturing Systems through the Evolvable Assembly Systems Project.