Digital Twins in Additive Manufacturing: A Systematic Review

Digital Twins (DTs) are becoming popular in Additive Manufacturing (AM) due to their ability to create virtual replicas of physical components of AM machines, which helps in real-time production monitoring. Advanced techniques such as Machine Learning (ML), Augmented Reality (AR), and simulation-based models play key roles in developing intelligent and adaptable DTs in manufacturing processes. However, questions remain regarding scalability, the integration of high-quality data, and the computational power required for real-time applications in developing DTs. Understanding the current state of DTs in AM is essential to address these challenges and fully utilize their potential in advancing AM processes. Considering this opportunity, this work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: (1) What are the key types of DTs used in AM and their specific applications? (2) What are the recent developments and implementations of DTs? (3) How are DTs employed in process improvement and hybrid manufacturing? (4) How are DTs integrated with Industry 4.0 technologies? By discussing current applications and techniques, we aim to offer a better understanding and potential future research directions for researchers and practitioners in AM and DTs.

Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review

Data privacy has become a major concern in healthcare due to the increasing digitization of medical records and data-driven medical research. Protecting sensitive patient information from breaches and unauthorized access is critical, as such incidents can have severe legal and ethical complications. Federated Learning (FL) addresses this concern by enabling multiple healthcare institutions to collaboratively learn from decentralized data without sharing it. FL's scope in healthcare covers areas such as disease prediction, treatment customization, and clinical trial research. However, implementing FL poses challenges, including model convergence in non-IID (independent and identically distributed) data environments, communication overhead, and managing multi-institutional collaborations. A systematic review of FL in healthcare is necessary to evaluate how effectively FL can provide privacy while maintaining the integrity and usability of medical data analysis. In this study, we analyze existing literature on FL applications in healthcare. We explore the current state of model security practices, identify prevalent challenges, and discuss practical applications and their implications. Additionally, the review highlights promising future research directions to refine FL implementations, enhance data security protocols, and expand FL's use to broader healthcare applications, which will benefit future researchers and practitioners.

Defect Localization Using Region of Interest and Histogram-Based Enhancement Approaches in 3D-Printing

Additive manufacturing (AM), particularly 3D printing, has revolutionized the production of complex structures across various industries. However, ensuring quality and detecting defects in 3D-printed objects remain significant challenges. This study focuses on improving defect detection in 3D-printed cylinders by integrating novel pre-processing techniques such as Region of Interest (ROI) selection, Histogram Equalization (HE), and Details Enhancer (DE) with Convolutional Neural Networks (CNNs), specifically the modified VGG16 model. The approaches, ROIN, ROIHEN, and ROIHEDEN, demonstrated promising results, with the best model achieving an accuracy of 1.00 and an F1-score of 1.00 on the test set. The study also explored the models' interpretability through Local Interpretable Model-Agnostic Explanations and Gradient-weighted Class Activation Mapping, enhancing the understanding of the decision-making process. Furthermore, the modified VGG16 model showed superior computational efficiency with 30713M FLOPs and 15M parameters, the lowest among the compared models. These findings underscore the significance of tailored pre-processing and CNNs in enhancing defect detection in AM, offering a pathway to improve manufacturing precision and efficiency. This research not only contributes to the advancement of 3D printing technology but also highlights the potential of integrating machine learning with AM for superior quality control.

Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches

Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve productivity - a key challenge. This study explored the effectiveness of machine learning (ML) approaches, specifically transfer learning (TL) models, for defect detection in 3D-printed cylinders. Images of cylinders were analyzed using models including VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2. Performance was compared across two datasets using accuracy, precision, recall, and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest performance, with an average F1-score of 0.32. Similarly, in the second study, MobileNetV2 correctly classified all instances, while ResNet50 struggled with more false positives and fewer true positives, resulting in an F1-score of 0.75. Overall, the findings suggest certain TL models like MobileNetV2 can deliver high accuracy for AM defect classification, although performance varies across algorithms. The results provide insights into model optimization and integration needs for reliable automated defect analysis during 3D printing. By identifying the top-performing TL techniques, this study aims to enhance AM product quality through robust image-based monitoring and inspection.

BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions

Class imbalanced problems (CIP) are one of the potential challenges in developing unbiased Machine Learning (ML) models for predictions. CIP occurs when data samples are not equally distributed between the two or multiple classes. Borderline-Synthetic Minority Oversampling Techniques (SMOTE) is one of the approaches that has been used to balance the imbalance data by oversampling the minor (limited) samples. One of the potential drawbacks of existing Borderline-SMOTE is that it focuses on the data samples that lay at the border point and gives more attention to the extreme observations, ultimately limiting the creation of more diverse data after oversampling, and that is the almost scenario for the most of the borderline-SMOTE based oversampling strategies. As an effect, marginalization occurs after oversampling. To address these issues, in this work, we propose a hybrid oversampling technique by combining the power of borderline SMOTE and Generative Adversarial Network to generate more diverse data that follow Gaussian distributions. We named it BSGAN and tested it on four highly imbalanced datasets: Ecoli, Wine quality, Yeast, and Abalone. Our preliminary computational results reveal that BSGAN outperformed existing borderline SMOTE and GAN-based oversampling techniques and created a more diverse dataset that follows normal distribution after oversampling effect.

Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework

Deep learning becomes an elevated context regarding disposing of many machine learning tasks and has shown a breakthrough upliftment to extract features from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. To unravel the confined data source, researchers have developed a model that can solve machine learning problems with fewer data called ``Few shot learning". Few hot learning algorithms determine to solve the data limitation problems by extracting the characteristics from a small dataset through classification and segmentation methods. In the medical sector, there is frequently a shortage of available datasets in respect of some confidential diseases. Therefore, Few shot learning gets the limelight in this data scarcity sector. In this chapter, the background and basic overview of a few shots of learning is represented. Henceforth, the classification of few-shot learning is described also. Even the paper shows a comparison of methodological approaches that are applied in medical image analysis over time. The current advancement in the implementation of few-shot learning concerning medical imaging is illustrated. The future scope of this domain in the medical imaging sector is further described.

Invariant Scattering Transform for Medical Imaging

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.