Electron Beam Powder Bed Fusion (EB-PBF) is a powerful additive manufacturing technique capable of producing high-performance metal parts with complex geometries. However, inherent process instabilities can lead to defects that compromise part quality and structural integrity. Traditional non-destructive testing (NDT) methods are often costly and time-consuming, and typically require specialized equipment and/or expertise. As a promising alternative, this study explores the feasibility of using active infrared thermography (IRT), coupled with deep learning, for the automated quality assessment of EB-PBF fabricated parts. We present a case study using parts with artificially induced subsurface defects, captured through active thermo-graphic imaging. A comprehensive dataset of thermal images is generated and used to train and evaluate a customized deep learning framework based on the You Only Look Once (YOLO) architecture for automated defect detection and categorization. Our results demonstrate the potential of combining IRT with data-driven analysis to offer a fast, contactless, and scalable solution for inspecting EB-PBF parts, while also highlighting the current limitations and future directions for this emerging approach.

Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.

With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE'24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced Manufacturing Execution System (MES), which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300k€ academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach.

Additive Manufacturing (AM), a cornerstone of Industry 4.0, is expected to revolutionise production in practically all industries. However, multiple production challenges still exist, preventing its diffusion. In recent years, Machine Learning algorithms have been employed to overcome these hurdles. Nonetheless, the usage of these algorithms is constrained by the scarcity of data together with the challenges associated with accessing and integrating the information generated during the AM pipeline. In this work, we present a vendor-agnostic platform for AM that enables collecting, storing, analysing and linking the heterogeneous data of the complete AM process. We conducted an extensive analysis of the different AM datatypes and identified the most suitable technologies for storing them. Furthermore, we performed an in-depth study of the requirements of different AM stakeholders to develop a rich and intuitive Graphical User Interface. We showcased the specific usage of the platform for Powder Bed Fusion, one of the most popular AM processes, in a real industrial scenario, integrating specific existing modules for in-situ monitoring and real-time defect detection.

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.

The quality of products has become a key factor for success in the current manufacturing industry. This is especially true in the aviation field where components for aircraft engines called honeycombs are produced. Due to their small dimension and peculiar shape, such components typically undergo a severe and cumbersome visual inspection by specialized operators. However, this process is highly prone to human error, requires a lot of time and a high number of undetected defects have been reported. In order to reduce the whole inspection time, ensure higher quality and guarantee standardization and process control, this paper presents an innovative strategy for the fully-automated inspection of honeycomb engine parts. The proposed solution is a two-phase process fully controlled by a robot, leveraging a camera as well as a purposely designed optic fibers sensor, coupled with Artificial Intelligence (AI) algorithms for the detection of different types of defects. To assess the functionality and validity of the proposed solution, a fully functioning prototype is described and characterized.

Recent developments have resulted in multiple techniques trying to explain how deep neural networks achieve their predictions. The explainability maps provided by such techniques are useful to understand what the network has learned and increase user confidence in critical applications such as the medical field or autonomous driving. Nonetheless, they typically have very low resolutions, severely limiting their capability of identifying finer details or multiple subjects. In this paper we employ an encoder-decoder architecture with skip connection known as U-Net, originally developed for segmenting medical images, as an image classifier and we show that state of the art explainable techniques applied to U-Net can generate pixel level explanation maps for images of any resolution.

MOTIVATION: Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. RESULTS: We show that Wasserstein Generative Adversarial Networks enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in two different cell models: the primary human renal cortical epithelial cells (HRCE) and the African green monkey kidney epithelial cells (VERO). In contrast to previous methods, our deep learning-based approach does not require any annotation, and can also be used to solve subtle tasks it was not specifically trained on, in a self-supervised manner. For example, it can effectively derive a dose-response curve for the tested treatments. AVAILABILITY AND IMPLEMENTATION: Our code and embeddings are available at https://gitlab.com/AlesioRFM/gan-dl StyleGAN2 is available at https://github.com/NVlabs/stylegan2 .

In the context of Industry 4.0, metal Additive Manufacturing (AM) is considered a promising technology for medical, aerospace and automotive fields. However, the lack of assurance of the quality of the printed parts can be an obstacle for a larger diffusion in industry. To this date, AM is most of the times a trial-and-error process, where the faulty artefacts are detected only after the end of part production. This impacts on the processing time and overall costs of the process. A possible solution to this problem is the in-situ monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build. In this paper, we describe a system for in-situ defects monitoring and detection for metal Powder Bed Fusion (PBF), that leverages an off-axis camera mounted on top of the machine. A set of fully automated algorithms based on Computer Vision and Machine Learning allow the timely detection of a number of powder bed defects and the monitoring of the object's profile for the entire duration of the build.

Incremental learning involves Machine Learning paradigms that dynamically adjust their previous knowledge whenever new training samples emerge. To address the problem of multi-task incremental learning without storing any samples of the previous tasks, the so-called Expert Gate paradigm was proposed, which consists of a Gate and a downstream network of task-specific CNNs, a.k.a. the Experts. The gate forwards the input to a certain expert, based on the decision made by a set of autoencoders. Unfortunately, as a CNN is intrinsically incapable of dealing with inputs of a class it was not specifically trained on, the activation of the wrong expert will invariably end into a classification error. To address this issue, we propose a probabilistic extension of the classic Expert Gate paradigm. Exploiting the prediction uncertainty estimations provided by Bayesian Convolutional Neural Networks (B-CNNs), the proposed paradigm is able to either reduce, or correct at a later stage, wrong decisions of the gate. The goodness of our approach is shown by experimental comparisons with state-of-the-art incremental learning methods.

The first three industrial revolutions came about as a result of mechanization, electricity, and information technology (IT), respectively. Now, the introduction of the Internet of Things and Services into the manufacturing environment is fostering a 4th industrial revolution (Industry 4.0), where the smart optimization and computerization of all the actors and phases of the manufacturing process, including the conceptualization and design of a product, as well as its production and transaction, are taking a leading role.

One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, typically reducing both material waste and time to market. Nonetheless, the lack of repeatability of the manufacturing process, which typically translates into a lack of reproducibility and reliability of the quality of the final products compared to traditional subtractive technologies, is currently one of the major barriers to the widespread adoption of AM in mass production. To overcome this limitation, there are growing efforts in recent years toward better integration of advanced information technologies into AM, exploiting the layer-by-layer nature of the build. The consequence of these efforts is twofold: 1) the integration of advanced sensing technologies into the AM systems, making possible the in situ monitoring of huge amounts of data at multiple time scales and resolutions and 2) the ever-increasing role of data-driven approaches [especially machine learning (ML)] in the analysis of such data to provide real-time quality monitoring and process optimization. This article introduces and reviews the key technological developments of this phenomenon, with a special focus on metal powder bed fusion (PBF) technologies that are attracting the highest attention by the industrial AM community. After introducing the main manufacturing quality issues and needs that have to be developed and optimized, we provide a wide overview of the latest progress of in situ monitoring and control in metal PBF, with special regards to sensing technologies and ML approaches. Finally, we identify the open challenges and future research directions in this field.

SUMMARY: In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. AVAILABILITY AND IMPLEMENTATION: Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: High-throughput next-generation sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically increases with coverage, collapsing such files into compact sets of non-redundant reads has the 2-fold advantage of reducing file size and speeding-up the alignment, avoiding to map the same sequence multiple times. METHOD: BioSeqZip generates compact and sorted lists of alignment-ready non-redundant sequences, keeping track of their occurrences in the raw files as well as of their quality score information. By exploiting a memory-constrained external sorting algorithm, it can be executed on either single- or multi-sample datasets even on computers with medium computational capabilities. On request, it can even re-expand the compacted files to their original state. RESULTS: Our extensive experiments on RNA-Seq data show that BioSeqZip considerably brings down the computational costs of a standard sequence analysis pipeline, with particular benefits for the alignment procedures that typically have the highest requirements in terms of memory and execution time. In our tests, BioSeqZip was able to compact 2.7 billion of reads into 963 million of unique tags reducing the size of sequence files up to 70% and speeding-up the alignment by 50% at least. AVAILABILITY AND IMPLEMENTATION: BioSeqZip is available at https://github.com/bioinformatics-polito/BioSeqZip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Arterial Spin Labelling (ASL) is a perfusion MRI technique with tremendous applications in the study of biological markers and prognostic factors of brain tumors and in the assessment of neural diseases, moreover, it is completely non-invasive as it uses the magnetically inverted blood of the patient as an endogenous tracer. Unfortunately this powerful method is only viable in very limited conditions due to its extreme sensitivity to artifacts originated by head motion, that are not effectively addressed by the current software solutions. This paper presents a motion correction procedure that addresses this issue and provides improved solutions to enhance ASL images of the brain in presence of severe head motion. Experimental results run on a motion-affected pCASL dataset show the concept and demonstrate the superiority of our proposed procedure compared to standard 3D registration.

In this paper we present a fully automated morphology-based technique for segmentation of nuclei in cancer tissue images and we compare it with a common technique for biomedical image processing, namely active contours. We discuss the limitations of active contours in the processing of immunohistochemical images characterized by heterogeneously stained nuclear region and noise caused by the presence of multiple tissue layers in the sample. We describe the integration of the proposed approach in a fully automated protein activity quantification tool. Finally, we demonstrate and motivate through extensive experiments that our fully automated morphology-based approach provides better accuracy compared to various active contours implementations.

An accurate and standardized technique for tumor tissue segmentation is a critical step for monitoring and quantifying the activity of specific families of pro- teins involved in multi-factorial genetic pathologies. However, fully automated tissue and cell segmentation in clinical images presents many challenges related to the characteristics of the images that make traditional approaches substantially ineffective or incomplete. In this paper we present a fully-automated algorithm that is able to perform accurate and fast segmentation of tissue images. Experimental results on several real-life datasets demonstrate the high level of accuracy achievable thanks to our approach.