• Coatanea, Eric
• Panicker, Suraj
• Dhalpe, Akshay
• Wu, Di
• Dehaghani, Mostafa Rahmani
• Tang, Yifan
• Wang, G. Gary
• Balani, Shahriar Bakrani

Abstract

<jats:title>Abstract</jats:title><jats:p>Thermal history has a great effect on the part properties in metal additive manufacturing such as tensile strength and hardness. To study and control the thermal behavior of the AM processes, various data-driven thermal history modeling methods have been developed and tested on simulation data. However, their in-situ application scenarios are rarely explored and discussed. This paper aims to provide a layer-to-layer thermal history prediction model, which enables predicting the thermal history of a yet-to-print layer based on the data measured from the printed lower layers. First, the thermal behavior is analyzed to reveal the similarities in temperature curves of two successive layers. Then four input variables are identified, including the deposition rate, the relative height of the layer, the printing time, and the dwell time of one layer. Based on the selected input variables and the temperature output, a fully connected neural network with residual connection is designed to simplify the training process. Five numerical simulations are designed to collect temperature curves (curve segments of a temperature profile) on each layer, and one experimental study on wire arc additive manufacturing is completed to record the temperature curves. Based on the collected data, three cases are proposed to test the modeling framework, such as (a) dividing all simulation data into the training set and validation set, (b) training the model based on four simulation runs and its validation with the last simulation, and (c) training the model with all simulation data and testing on the experimental data. The former two cases show great prediction accuracies with a relative error of less than 5% in most cases, which indicates its potential in online prediction when trained with data from the same input conditions or same systems. While the applicability of the model trained only with simulation data should be explored further in real experiments.</jats:p>

Topics

• Deposition
• impedance spectroscopy
• experiment
• simulation
• laser emission spectroscopy
• strength
• hardness
• tensile strength
• wire
• additive manufacturing