Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

Daiki Ikeuchi,Alejandro Vargas-Uscategui,Xiaofeng Wu,Peter King

doi:10.3390/app11041654

Abstract

Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing.

Highlights

This study focuses on the prediction of a single-track profile in Cold Spray Additive Manufacturing (CSAM), at both normal and off-normal spray angles, using a data-efficient Artificial Neural Network (ANN) (DANN) approach to demonstrate that data-driven modelling can achieve better prediction accuracy than its mathematical counterpart that has already been adopted in CSAM
The quality of the fabricated single-track profiles was validated against the cold spray and CSAM studies in our previous study [31], confirming that each process parameter’s effects were consistent with previous relevant studies for the geometry of a single-track profile
The iterative investigation of different hidden layer architectures found that the proposed DANN model, having two hidden layers with 11 and 4 hidden neurons respectively, provided the best predictive performance (i.e., [5 11 4 1])

Summary

Introduction

Due to the particles’ kinetic energy, local metallurgical bonding and mechanical interlocking are achieved without in-flight melting This characteristic provides unique advantages that are difficult to achieve otherwise, including deposition free of melting-induced microstructure changes, the ability to handle oxygen-sensitive materials without a protective atmosphere and a high deposition rate with a narrow nozzle diameter [1,2,3,4]. Cold spray has recently been recognised to possess great potential as an alternative additive manufacturing technology and in this context is referred to as Cold Spray Additive Manufacturing (CSAM) [5,6,7,8] This potential is important when high production rates, large build sizes and repair or building on an existing structure are in demand, e.g., in aerospace industries [8,9]. These benefits have resulted in several successful demonstrations of the technology at different levels of fabrication complexity, ranging from a simple tubular structure [12], pyramidal fin array [13], to more complex parts such as topologically optimised components [14]

Methods

Results

Conclusion