# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 4 Applications of Semi-Supervised Learning in Additive Manufacturing
An autoregressive model with additional inputs was employed to approximate the first-order Markov chain governing the evolution of the AM process. A variational autoencoder was also utilized to extract latent variables associated with the images. Principal component analysis was then applied to reduce the dimensionality of these latent variables. Subsequently, a variational RNN was developed to process sequence data from previous time steps and predict the current state. Anomaly detection was performed by computing Kullback-Leibler divergence at each time step toassess accumulated errors. The effectiveness of this approach was evaluated across various levels of porosity in AM products, achieving an impressive receiver operating characteristic area under a curve of up to 0.999. In a separate study, [PERSON] et al. proposed a semi-supervised approach, utilizing ML algorithms exclusively with data from the defect-free regime of LPBF processes to predict anomalies [165]. The experiments involved creating overlapping lines to contract a defect-free cube of nickel-based super alloy. Various combinations of laser power and scanning velocity were tested to induce different LPBF process regimes, encompassing phenomena such as balling, lack of fusion pores, conduction mode, and keyhole pores. Data acquisition was facilitated using acoustic sensors, which normalized acoustic emission signals. Two generative CNN architectures were developed in this work. One architecture utilized a variational autoencoder, a commonly employed technique for tasks such as image denoising, dimensionality reduction, feature extraction, image generation, machine translation, and anomaly detection [166; 167; 168]. Typically, the encoder and decoder networks can efficiently learn the data representation densely and reconstruct the original input. The other architecture was based on a GAN, consisting of a generative network and a discriminative network, designed to generate new distribution samples from the training set [169]. Both methods yielded impressive accuracies of 96% and 97% for anomaly detection, respectively. [PERSON] et al. employed a semi-supervised learning approach to enable real-time anomaly detection during WAAM [170]. Their study used established process parameters to guide material deposition and collected signal data from sensors monitoring welding current and voltage. To distinguish between normal and abnormal operations, they implemented a one-class classification model, a subtype of semi-supervised learning. This model was trained exclusively on \"good\" data, enabling it to learn the typical patterns of the process. Any significant deviation from these learned patterns was flagged as an anomaly, indicating potential issues in the AM process. Compared to traditional anomaly detection methods, their approach improved detection performance by 30%, demonstrating its robustness in identifying real-time irregularities during WAAM operations. In another study, [PERSON] et al. proposed a self-supervised learning model designed to inspect and assess party quality in AM processes with minimal labeled data [171]. Their approach centered around image feature extraction, focusing on capturing defect-related features within the AM process images. These extracted features were then used to generate pseudo-labels, allowing the model to undergo self-supervised learning and adapt to defect identification without extensive labeled datasets.