ACOUSTIC EMISSION MONITORING OF THE POWDER BED FUSION PROCESS WITH MACHINE LEARNING APPROACH

Abstract

Laser powder bed fusion (L-PBF) is an additive manufacturing process where a heat source (such as a laser) consolidates material in powder form to build three-dimensional parts. For quality control purposes, this thesis uses real-time monitoring in L-PBF. Defects such as pores and cracks can be detected using Acoustic Emission (AE) during the powder bed selective laser melting process via the machine learning approach. This thesis investigates the performance of several Machine Learning (ML) techniques for online defect detection within the Laser Powder Bed Fusion (L- PBF) process. The goal is to improve the consistency in product quality and process reliability. The application of acoustic emission (AE) sensors to receive elastic waves during the printing process is a cost-effective way of meeting such a goal. For the first step, stainless steel 316L was produced via eight parameters. The acoustic emission signals received during the printing and data collection steps are analyzed using an AE sensor under various process parameters. Several time and frequency-domain features were extracted from data during the mining process from the AE signals. K-means clustering is employed during unsupervised learning, and a neural network approach was used for the supervised machine learning on the dataset. Data labelling is conducted for different laser powers, clustering results, and signal time durations. The results showed the potential of real-time quality monitoring using AE in the L-PBF process. Some process parameters within this project were intentionally adjusted to create three various levels of defects in H13 tool steel samples. First classes were printed with minimum defects, second classes with intentional cracks, and last classes with intentional cracks and porosities. AE signals were acquired during the samples' manufacturing process. Three different machine learning (ML) techniques were applied to analyze and interpret the data. First, using a hierarchical K-means clustering method, the data was labelled. This was followed by a supervised deep learning neural network (DL) to match acoustic signals with defect type. Second, a principal component analysis (PCA) was used to reduce the dimensionality of the data. A Gaussian Mixture Model (GMM) enabled the fast detection of defects, which is suitable for online monitoring. Third, a variational auto-encoder (VAE) approach was used to obtain a general feature of the signal, which could be used as an input for the classifier. Quality trends in AE signals collected from 316L samples were successfully detected using a supervised DL trained on the H13 tool steel dataset. The VAE approach shows a new method for detecting defects within the L-PBF processes, which would eliminate the need for model training in different materials.