BOMP
Status:
SDGs:
Industry:
Impact:
Origin:
School:
Established:
BOMP uses machine learning to produce the optimal interface between the factory management end-user and raw data from material properties in an industrial production environment. It aims to optimize materials selection, design, and processing via two learning components: exploring and exploiting 鈥渟mall data鈥 to discover hidden patterns.
The machine Learning algorithm is based on probabilistic Bayesian reasoning, also known as Bayesian Optimization, and can be easily adapted to challenging problems in many engineering fields, including but not limited to the design of alloys, fluid mechanics, chemical reactions, and materials processing. The final proof-of-concept is an easy-to-use software interface to hard-core science where a layman can optimize material properties and process parameters without any training. This replaces the need for process/material optimization consultancy with a fraction of the cost. This research-to-business project maps the potential avenues to exploit the scientific research as commercial business.
For more information visit the
Contact
Markus Holmstr枚m
Read more about innovation services
News from innovation ecosystem
In summer 2026, 30 % discount on Rela massages at Rela-hierojat locations
The summer benefit is valid 1.6. 鈥 31.8.2026.
The potential of urban greenery as a climate solution is not being fully utilized 鈥 a new handbook offers means to address this
The Handbook for carbon-smart urban green provides concrete tools for leveraging urban green spaces more effectively in climate and nature conservation efforts.
Transition to hydrogen economy is a marathon, not a sprint
During its first few years of operation, the Hydrogen Innovation Centre has built an active community around its doctoral school and launched the Inventors programme.