Researching Additive Manufacturing
The aims of the Brokering Additive Manufacturing project are to develop an agent-based distributed manufacturing system to meet big demand.
Author: James Gopsill
BAM Promotional Video and Blog
We have been working with our marketing team at the University to provide some new content and material to dessiminate the work and vision of a Brokered Additive Manufactured future.
The Machines are Talking!
The team have been in full flow following the Brokering Additive Manufacturing meeting and created the base code that turns AM machines into agents that can talk with another.
DETI EC5.1 – Digital Thread: Phase Two Report Released
The second of three reports as part of EC5.1 has been released by DETI and concerns the reported practice in the Digitial Thread.
BAM Kicks-Off!
Brokering Additive Manufacturing officially started today (1st October 2021) with the team meeting physically! at Queen’s Building to set out the research agenda and plans of action.
BAM @ TCT 3SIXTY
Jennifer Johns is attending TCT this week to discuss AM and the opportunities for a networking and co-ordination of the UK’s AM […]
BAM @ SDM21
Brokering Additive Manufacturing has published and presented their second paper entitled “Achieving responsive and sustainable manufacturing through a brokered agent-based production paradigm” at the International Conference on Sustainable Design and Manufacturing (SDM).
Quantum Computing @ ICED21
The Design Manufacturing Futures Lab has published and presented its first paper entitled “Quantum Combinatoral Design” exploring the potential of applying Quantum Computing to Engineering Design problems. This was published and presented at the International Conference on Engineering Design 2021 (ICED21)
Capturing Perceptions of Shape and Form using Machine Learning @ ICED21
The Design Manufacturing Futures Lab has published and presented a paper entitled “Capturing Mathematical and Human Perceptions of Shape and Form through Machine Learning” that extends the groups knowledge, capability and understanding of how ML could be applied to support Engineering Design. This was published and presented at the International Conference on Engineering Design 2021 (ICED21)