
The primary objective of this paper is to introduce a new holistic approach to the design of alloy steels based on a biologically inspired multi-objective immune optimisation algorithm. To this aim, a modified population adaptive based immune algorithm (PAIA2) and a multi-stage optimisation procedure are introduced, which facilitate a systematic and integrated fuzzy knowledge extraction process. The extracted (interpretable) fuzzy models are able to fully describe the mechanical properties of the investigated alloy steels. With such knowledge in hand, locating the ‘best’ processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels is possible. The research has also enabled to unravel the power of multi-objective optimisation (MOP) for automating and simplifying the design of the heat treated alloy steels and hence to achieve ‘right-first-time’ production.
bmjgoldcheck, H650 Systems Engineering, G700 Artificial Intelligence, NotOAChecked, 620, I400 - Artificial intelligence, H660 - Control systems, Multi-objective optimisation, 'Right-First-Time' Production of Alloy Steels, Artificial Immune Algorithm, H650 - Systems engineering, Fuzzy modelling, H660 Control Systems
bmjgoldcheck, H650 Systems Engineering, G700 Artificial Intelligence, NotOAChecked, 620, I400 - Artificial intelligence, H660 - Control systems, Multi-objective optimisation, 'Right-First-Time' Production of Alloy Steels, Artificial Immune Algorithm, H650 - Systems engineering, Fuzzy modelling, H660 Control Systems
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| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
