Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

Abstract

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at $$650\,^\circ $$ 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of $$1.40\%$$ 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

“…If the data (X,Y) is non-linear, the above formulation of SVR will not work. To overcome the issue of non-linearity for SVR, the data are mapped to a higher dimension using a kernel function [35].…”

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

“…Process monitoring [40] Root cause analysis of process failure [41], Process modelling [42] Process fault prediction [43], Process characteristics prediction [44] Self-optimizing process planning [45], Adaptive process control [46] Machine Machine tool monitoring [47] Fault diagnosis [48], Downtime prediction [49] RUL prediction [50], Tool wear prediction [51] Adaptive compensation of errors [52,53],…”

On the Thermomechanical Aging of Lpbf Alloy 718

“…Spherical pores come from gas entrapment and irregular pores form as a result of poor inter track/layer fusion or pores resulting from keyhole closure [21]. Porosity has always been a determining factor for creep performance and has recently been shown to be the most influential factor affecting the creep rate, more so than other LPBF process factors such as build orientation and scan strategy [22]. Another interrupted creep study also found that the main crack initiation points were pores [23], more specifically irregular pores [24].…”