Parameters prediction in additively manufactured Al-Cu alloy using back propagation neural network

Abstract

The relationship between tensile strength, wire feeding speed and travel speed is built based on Back Propagation (BP) neural network during the wire arc additive manufacturing (WAAM) process. The introduction of a genetic algorithm for optimising the BP neural network (GA-BP) and incorporation of additional parameter combinations through the forward model markedly enhance the prediction accuracy of the process parameter reverse model. The BP neural network with a genetic algorithm model exhibits excellent training results, and the sample population regression reaches 0.97. An error value of the optimised model is only 3.10% for wire feeding speed prediction, only 1.55% for travel speed prediction. The GA-BP reverse model optimises WAAM process parameters and achieves a tensile strength exceeding 230 MPa.

KEYWORDS:

• Wire arc additive manufacturing
• artificial neural network
• genetic algorithm
• mechanical property prediction
• process parameter reverse design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Defense Industrial Technology Development Program of China under grant [number JCKY2020605C006].