Purpose
This paper aims to investigate the possibility of lowering the time taken during the aircraft design for unmanned aerial vehicles by using machine learning (ML) for the configuration selection phase. In this work, a database of unmanned aircraft is compiled and is proposed that decision tree classifiers (DTC) can understand the relations between mission and operational requirements and the resulting aircraft configuration.
Design/methodology/approach
This paper presents a ML-based approach to configuration selection of unmanned aircraft. Multiple DTC are built to predict the overall configuration. The classifiers are trained with a database of 118 unmanned aircraft with 57 characteristics, 47 of which are inputs for the classification problem, and 10 are the desired outputs, such as wing configuration or engine type.
Findings
This paper shows that DTC can be used for the configuration selection of unmanned aircraft with reasonable accuracy, understanding the connections between the different mission requirements and the culminating configuration. The framework is also capable of dealing with incomplete databases, maximizing the available knowledge.
Originality/value
This paper increases the computational usage for the aircraft design while retaining requirements’ traceability and increasing decision awareness.
Yield maximization for system-level task assignment and configuration selection of configurable multiprocessors
