Mechanical properties of micro-alloyed steels studied using a evolutionary deep neural network

ABSTRACT

The existing Evolutionary Neural Net Algorithm (EvoNN) for data-driven modeling has been augmented during this study using an evolutionary deep neural net strategy to give rise to a novel algorithm named EvoDN, which has been further upgraded to an improved version named EvoDN2. This study reports an application of EvoDN2 to study vanadium and niobium based micro-alloyed steels. For this purpose, a dataset for ultimate tensile strength, elongation and Charpy impact energy at −40°C is collected and trained using aforementioned EvoNN, EvoDN2, and another in house algorithm named Bi-objective genetic programming (BioGP). This trained models are then optimized to get optimized properties using a constrained version Reference Vector Evolutionary Algorithm (cRVEA). The results are thoroughly compared with the existing correlations and prior work and found to be well within the acceptable range.

Keywords:

• Vanadium
• micro-alloyed steel
• multi-objective optimization
• evolutionary computation
• neural net
• genetic programming
• reference vector
• deep neural network
• metamodeling
• single variable response
• parallel plot