A Distributed Software Platform for Additive Manufacturing

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

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Impact of Users' Beliefs in Text-Based Linguistic Interaction

Linguistic interaction between humans and machines is one of the most challenging fields in the development of next-generation User Interfaces. In this work, we investigate the role of beliefs about the interlocutor in human-computer linguistic interaction. First, we introduced an experimental setup that makes use of filtered and post-processed web content to generate a realistic, generic linguistic interaction. Then, we collected dialogues from two different sets α and β, corresponding to users being unaware or aware of the artificial nature of the interlocutor, respectively. The results thus obtained, analyzed using a standard t-test procedure (N=30), demonstrate a statistically significant difference between the two sets in some of the linguistic features selected, i.e., sentence length and the number of adjectives, providing further insights to expand some of the evidence previously found in the literature

Image analytics and machine learning for in-situ defects detection in Additive Manufacturing

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

smARTworks: A Multi-sided Context-aware Platform for the Smart Museum

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In-situ defect detection of metal Additive Manufacturing: an integrated framework

Metal Additive Manufacturing (AM) is a pillar of the Industry 4.0, with many attractive advantages compared to traditional subtractive fabrication technologies. However, there are many quality issues that can be an obstacle for mass production. The in-situ camera-based monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build, can be an effective solution to this problem. In this context, the use of Computer Vision and Machine Learning algorithms have a very important role. Nonetheless, they are up to this date limited by the scarcity of data for the training, as well as by the difficulty of accessing and integrating the AM process data throughout the fabrication. To tackle this problem, this paper proposes a system for in-situ monitoring that analyses images from an off-axis camera mounted on top of the machine to detect the arising defects in real-time, with automated generation of synthetic images based on Generative Adversarial Network (GAN) for dataset augmentation purposes. The computing functionalities are embedded into a holistic distributed AM platform allowing the collection, integration and storage of data at all stages of the AM pipeline

Coupling Routing Algorithm and Data Encoding for Low Power Networks on Chip

The routing algorithm used in a Network-on-Chip (NoC) has a strong impact on both the functional and non functional indices of the overall system. Traditionally, routing algorithms have been designed considering performance and cost as the main objectives. In this study we focus on two important non functional metrics, namely, power dissipation and energy consumption. We propose a selection policy that can be coupled with any multi-path routing function and whose primary goal is reducing power dissipation. As technology shrinks, the power dissipated by the network links represents an ever more significant fraction of the total power budget. Based on this, the proposed selection policy tries to reduce link power dissipation by selecting the output port of the router which minimises the switching activity of the output link. A set of experiments carried out on both synthetic and real traffic scenarios is presented. When the proposed selection policy is used in conjunction with a data encoding technique, on average, 31% of energy reduction and 37% of power saving is observed. An architectural implementation of the selection policy is also presented and its impact on cost (silicon area) and power dissipation of the baseline router is discussed

PROBIOTICI E TERAPIA CONVENZIONALE: NUOVE FRONTIERE NELLA GESTIONE DELLE MANIFESTAZIONI ARTICOLARI DELLE MALATTIE INFIAMMATORIE INTESTINALI (IBD)

Summary: This work reports a clinical trial performed at palermo University Hospital "paolo Giaccone". From January 2004 to December 2011, 79 patients were enrolled (40 men and 39 women). All patients suffered from Inflammatory Bowel Disease (IBD) and were subjected to orthopedic consultation at the institute of Orthopaedics, University Hospital of palermo, for arthropathy to IBD. The patients were divided into two groups (A and B) and dealt with different therapies for the resolution of the inflammatory picture of the colonic mucosa and the treatment of the extraintestinal articular manifestations. Group A was treated with drug therapy: Diclofenac (75 mg im/ day for 10 days9 and Mesalazine (800 mg gastro-resistant tabletes, one tablet twice a day in mild forms, and one tablet three times per day in moderate forms). In group B, in addition to the previous treatment protocol, two probiotic mixture were added in a time of two weeks: in the first week, twice a day, one capsule containing mixture of Enterococcus faecium and saccharomices boluard was administered, with the main purpose to mitigate the intestinal inflammation; in the second week, twice a day too, one capsule containing a mixture of lactobacillus salivarius and lactobacillus acidophilus was administered, with the main purpose to mitigate the intestinal inflammation, in the second week, twice a day too, one capsule containing a mixture of lactobacillus salivarius and lactobacillus acidophilus was administered, with the aim to promote the restoration of a normal intestinal microenvironment. The attenuation of intestinal inflammation, improved by the presence of probiotics, could have important effects on the articular manifestations, resulting in a significant improvement of the arthropathy. All patients were evaluated with the Harvey-Bradshaw Index. Both Crohn Disease and Ulcerative Cholitis diagnosis was made with clinical, laboratory, endoscopic and instrumental tests; the degree of disease activity was evaluated using the criteria of Truelove and witts. The WOMAC-Score (Western Ontario Mcmaster) was used in our study to investigate the degree of articular involvement of the patients. The data were statistically evaluated and these are shown that the B group of patients treated with conventional therapy + probiotic mixture had a better resolution of the clinical and of this post-treatment parameters: WOMAC score, ESR, CRP and white blood cells; and also the B group of patients have a better response to standard therapy compared with patients who did not receive the probiotic with a remarkable statistic significance (p>0,0001)