Intelligent Parameter Decision-Making and Multi-objective Prediction for Multi-layer and Multi-pass LDED Process

Abstract:

The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height. The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology (height error and roughness). Results show that using the signal-to-noise ratio and grey relational analysis, the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows: laser power of 800 W, powder feeding rate of 0.3 r/min, step distance of 1.6 mm, and scanning speed of 20 mm/s. Subsequently, a Genetic Bayesian-back propagation (GB-BP) network is constructed to predict multi-objective responses. Compared with the traditional back propagation network, the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14% and 71.43%, respectively. This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.