Ανάπτυξη εναζωτωμένων- ενανθρακωμένων σύνθετων υλικών μεταλλικής μήτρας και επικαλύψεων: μελέτη των δομικών, μηχανικών ιδιοτήτων και της αντιδιαβρωτικής συμπεριφοράς των τελικών προϊόντων

This Ph.D dissertation was occupied with the development of composite materials and coatings by applying nitriding and carburizing methods on metal matrices and then το study the microstructural and mechanical properties of the new products as well as their anticorrosion behavior. Firstly presents a new alternative method of carburization by using glucose as carburizing medium. It demonstrates how the α-Fe and fcc Ni plates after pack glucose carburizing regarding their obtainable morphology can affect their corrosion resistance into 3.5% wt. NaCl solution. Remarkable result derives from the case of carburized α-Fe sample at 6500C in which corrosion rate found thirteen times lower than pure α-iron can reach due to a solid solution formation of carbon into iron’s lattice. The second study was focused on the comparison between pack boronizing and pack glucose carburizing process that had been conducted on Iron- Based Austenitic Steels (IBAS) at the same temperature treatment and time, in order to exhibit by which method is achieved the better hardness and corrosion resistance. It was found that carburized IBAS show approximately three times lower Vickers hardness value and four times smaller corrosion rate compared to borided ones by making them more resistant to corrosion phenomena but more weakness to wear resistance. Furthermore, in the third study, Pack Nitriding efforts were made to decorate α- Iron substrate by priori interaction of solid NH4HCO3 in order to influence the base metal reactivity towards boron and its ability to react and form stable compounds with boron by inducing subsequent Pack Boriding using mixture of amorphous boron. A correlation between the substrate structure and the phase formation was observed. It was found that A1-type structure of the γ-Fe enhances the formation of the Fe2B phase. This confirms the assumption of commensurability of the structures of Fe2B and γ-Fe. Additionally, through this study it can be drawn that involving previous decoration of a metal surface by pack nitriding using ammonium biocarbonate and applying subsequent procedure of boron coating, it can be achieved boriding at a relatively low temperature than is usually performed. At the final study, the aim of the research was firstly to figure out the reason which leads Al-Si alloys to mechanical degradation at elevated temperatures and then to induce gas Chemical Vapor Deposition (CVD) nitriding in order to optimize their mechanical properties. Gas CVD nitriding was achieved chiefly by manufacturing a Thermal CVD apparatus in where two different experiments were conducted by using two different gas ammonia mixtures in the same growth temperature but in distinct time depositions. Both experiments revealed the existence of AlN (N-O bond) layer with the second one that occurs in bigger concentration of ammonia and higher time growth to show bigger layer thickness and higher atomic concentration of nitrogen at surface. Accomplishing micro-hardness measurements, nitrided Al-Si alloys exhibit 58.5% higher HK hardness than untreated ones can achieve, fact that leads Al-Si alloys after gas nitriding to show better wear resistance and machinability.Σκοπός της παρούσας διατριβής ήταν η ανάπτυξη σύνθετων μεταλλικών υλικών και επικαλύψεων με ανάπτυξη μεθόδων ενανθράκωσης και εναζώτωσης και μετέπειτα χαρακτηρισμός των δομικών και μηχανικών ιδιοτήτων τους καθώς και της αντιδιαβρωτικής τους συμπεριφοράς. Αρχικά, μια νέα εναλλακτική μέθοδος ενανθράκωσης παρουσιάσθηκε με τη χρήση γλυκόζης ως ενανθρακωτικό μέσο. Η μελέτη αυτή στηρίχθηκε στην ενανθράκωση δειγμάτων α-φάσης σιδήρου και γ-φάσης νικελίου με Χημική Εναπόθεση πάκτωσης (pack cementation CVD) μέσα σε γλυκόζη. Αξιοσημείωτα αποτελέσματα φανέρωσε η μελέτη αυτή αφού η διαφορετική μικροδομή που ανέπτυσσε κάθε ενανθρακωμένο δείγμα στις διάφορες θερμοκρασίες ανάπτυξης επηρέαζε θεμελιωδώς την αντίστασή του έναντι στη διάβρωση με εντυπωσιακή την περίπτωση σχηματισμού φερριτικού στερεού διαλύματος μέσα στην μεταλλική μήτρα του σιδήρου από τον άνθρακα, για ενανθράκωσή α-Fe δειγμάτων στους 6500C που οδήγησε σε δεκατρείς φορές μικρότερο ρυθμό διάβρωσης συγκριτικά με τα ακατέργαστα. Στη συνέχεια παρουσιάσθηκε μια μελέτη που εξηγεί αναλυτικά τη διαφορά ανάμεσα στη σκληρότητα και την αντιδιαβρωτική συμπεριφορά που αναδεικνύουν δείγματα Ωστενιτικού χάλυβα που υποβλήθηκαν σε Χημική Εναπόθεση πάκτωσης (pack cementation CVD) μέσα σε σκόνη Ekabor II και πάκτωσης μέσα σε γλυκόζη στις ίδιες συνθήκες ανάπτυξης. Από τα αποτελέσματα αυτής της μελέτης προέκυψε ότι τα βοριωμένα δείγματα Ωστενιτικού χάλυβα συγκριτικά με τα ενανθρακωμένα που κατεργάστηκαν στις ίδιες συνθήκες ανάπτυξης, παρουσίασαν περίπου τρείς φορές υψηλότερο βαθμό σκληρότητας (HV) και τέσσερις φορές μεγαλύτερο ρυθμό διάβρωσης από αυτό που παρουσίασαν τα ενανθρακωμένα, γεγονός που αναδεικνύει τα ενανθρακωμένα δείγματα να κερδίζουν την υπεροχή ως προς την αντιδιαβρωτική προστασία και να χάνουν ως προς την επιφανειακή σκληρότητα. Ακόμη μια σημαντική μελέτη που επετεύχθη στα πλαίσια των επιφανειακών κατεργασιών βασίστηκε στην εκ των προτέρων εναζώτωση της επιφάνειας του α-σιδήρου με Χημική Εναπόθεση πάκτωσης (pack cementation CVD) κόνεως ανθρακικής αμμωνίας και κατόπιν βορίωσής της με Χημική Εναπόθεση πάκτωσης (pack cementation CVD) κόνεως μείγματος άμορφου άνυδρου βορίου. Ο χαρακτηρισμός της μικροδομής των κατεργασμένων αυτών δειγμάτων ανέδειξε ότι υπήρξε συσχέτιση της δομής του εναζωτωμένου υποστρώματος με το σχηματισμό της φάσης που προέκυψε από τη μετέπειτα βορίωση τους και επιπρόσθετα μέσω αυτής της μελέτης προέκυψε ότι διενεργώντας μια εκ των προτέρων επιφανειακή διαμόρφωση σε μια μεταλλική επιφάνεια σιδήρου με εναζώτωση επιτυγχάνεται ακόλουθη βορίωση σε πολύ μικρότερη θερμοκρασία από ότι συνήθως υλοποιείται. Τέλος, ένα άλλο θέμα που θίχτηκε στη παρούσα διατριβή ήταν η εύρεση της αιτίας που οδηγεί σε μηχανική υποβάθμιση των ιδιοτήτων των κραμάτων Al-Si έπειτα από έκθεση τους σε υψηλές θερμοκρασίες. Για τη προστασία και την αποφυγή των κραμάτων Al-Si από φαινόμενα επιφανειακής φθοράς, κρίθηκε αναγκαία η εφαρμογή τους σε επιφανειακή κατεργασία. Η μέθοδος που χρησιμοποιήθηκε ήταν Χημική Εναπόθεση με Αέρια Εναζώτωση (Gas Nitriding CVD) με τη χρήση δυο διαφορετικών μειγμάτων αέριας αμμωνίας σε διαφορετικούς χρόνους εναπόθεσης υπό την ίδια θερμοκρασία ανάπτυξης. Η αέρια εναζώτωση πραγματοποιήθηκε μέσα σε σύστημα θερμικής χημικής εναπόθεσης (Thermal CVD apparatus) το οποίο σχεδιάστηκε και κατασκευάστηκε στα πλαίσια της παρούσας διατριβής. Σύμφωνα με τα πειραματικά αποτελέσματα που προέκυψαν και από τις δύο δοκιμές αέριας εναζώτωσης, στρώμα νιτριδίου του αλουμινίου AlN (N-O δεσμού) σχηματίστηκε πάνω στην επιφάνεια του κράματος Al-Si με μεγαλύτερο πάχος και μεγαλύτερο ποσοστό ατομικής συγκέντρωσης αζώτου να επιτυγχάνεται στη δοκιμή με χρήση αέριας αμμωνίας υψηλότερης συγκέντρωσης και μεγαλύτερου χρόνου εναπόθεσης. Ακολουθώντας μικροσκληρομετρήσεις στα βέλτιστα εναζωτωμένα δείγματα, βρέθηκε ότι η επιφανειακή τους σκληρότητα προσέγγισε αύξηση 58,5% HK συγκριτικά με τα ακατέργαστα, γεγονός που οδηγεί τα Al-Si κράματα σε καλύτερες μηχανικές ιδιότητες προστατεύοντάς τα έναντι στην επιφανειακή φθορά

Computer-Aided Design of 3D-Printed Clay-Based Composite Mortars Reinforced with Bioinspired Lattice Structures

Towards a sustainable future in construction, worldwide efforts aim to reduce cement use as a binder core material in concrete, addressing production costs, environmental concerns, and circular economy criteria. In the last decade, numerous studies have explored cement substitutes (e.g., fly ash, silica fume, clay-based materials, etc.) and methods to mimic the mechanical performance of cement by integrating polymeric meshes into their matrix. In this study, a systemic approach incorporating computer aid and biomimetics is utilized for the development of 3D-printed clay-based composite mortar reinforced with advanced polymeric bioinspired lattice structures, such as honeycombs and Voronoi patterns. These natural lattices were designed and integrated into the 3D-printed clay-based prisms. Then, these configurations were numerically examined as bioinspired lattice applications under three-point bending and realistic loading conditions, and proper Finite Element Models (FEMs) were developed. The extracted mechanical responses were observed, and a conceptual redesign of the bioinspired lattice structures was conducted to mitigate high-stress concentration regions and optimize the structures’ overall mechanical performance. The optimized bioinspired lattice structures were also examined under the same conditions to verify their mechanical superiority. The results showed that the clay-based prism with honeycomb reinforcement revealed superior mechanical performance compared to the other and is a suitable candidate for further research. The outcomes of this study intend to further research into non-cementitious materials suitable for industrial and civil applications

Generate-Paste-Blend-Detect: Synthetic dataset for object detection in the agriculture domain

Object detection is a challenging task, hindered by the scarcity of large annotated datasets. In agriculture, the lack of annotated insect datasets often results in domain-specific models that lack generalization. Data collection and annotation can be expensive and time-consuming. This paper proposes a simple approach to generate synthetic datasets for object detection that requires only a small dataset of target objects and a larger background dataset that fits the desired environment. The approach named Generate-Paste-Blend-Detect uses Denoising Diffusion Probabilistic Models (DDPM) to artificially “generate” objects, “paste” them on a background image, “blend” them with the environment to avoid pixel artifacts which result in poor performance for trained models, and finally use an object detection model to “detect” the artificially added object instances. The proposed methodology is demonstrated in the agricultural domain to detect whiteflies achieving a mean average precision (mAP50) of 0.66 with the state-of-the-art YOLOv8 object detection model. This approach enables domain-specific detection with minimal labor and cost

Mechanical Performance of Recycled 3D Printed Sustainable Polymer-Based Composites: A Literature Review

The development of efficient waste valorization strategies has emerged as an important field in the overall efforts for alignment with the environmental goals that have been set by the European Union (EU) Green Deal regarding the development of sustainable circular economy models. Additive manufacturing has emerged as a sustainable method for secondary life product development with the main advantages of it being a form of net-zero waste production and having the ability to successfully transport complex design to actual products finding applications in the industry for rapid prototyping or for tailored products. The insertion of eco-friendly sustainable materials in these processes can lead to significant reduction in material footprints and lower energy demands for the manufacturing process, helping achieve Sustainable Development Goal 12 (SDG12) set by the EU for responsible production and consumption. The aim of this comprehensive review is to state the existing progress regarding the incorporation of sustainable polymeric composite materials in additive manufacturing (AM) processes and identify possible gaps for further research. In this context, a comprehensive presentation of the reacquired materials coming from urban and industrial waste valorization processes and that are used to produce sustainable composites is made. Then, an assessment of the printability and the mechanical response of the constructed composites is made, by taking into consideration some key thermal, rheological and mechanical properties (e.g., viscosity, melting and degradation temperature, tensile and impact strength). Finally, existing life cycle analysis results are presented regarding overall energy demands and environmental footprint during the waste-to-feedstock and the manufacturing processes. A lack of scientific research was observed, regarding the manifestation of novel evaluation techniques such as dynamic mechanical analysis and impact testing. Assessing the dynamic response is vital for evaluating whether these types of composites are adequate for upscaling and use in real life applications

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications

Deep learning-based multi-spectral identification of grey mould

Early detection of economically important plant diseases, such as grey mould caused by Botrytis cinerea, is of major importance for the timely application of disease management strategies and the reduction of impacts on crop production and the environment. In this study, artificial inoculation of leaves of cucumber plants with B. cinerea under controlled environment was performed. Multi-spectral imaging was used to capture the fungal spectrum response at 460, 540, 640, 700, 775 and 875 nm, leveraging both RGB and Near Infrared (NIR) channels. Two annotated image datasets were created from the collected multi-spectral images named Botrytis-detection and Botrytis-classification. Several deep learning-based classification and object detection experiments were conducted on both datasets. Classification results indicated that deep learning models can separate the two classes with accuracy 0.93 (F1-score 0.89), while object detection achieved a mean average precision (mAP50) of 0.88, paving the way for future early detection of grey mould caused by B. cinerea

Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence

In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices

Fabrication and Optimization of 3D-Printed Silica Scaffolds for Neural Precursor Cell Cultivation

The latest developments in tissue engineering scaffolds have sparked a growing interest in the creation of controlled 3D cellular structures that emulate the intricate biophysical and biochemical elements found within versatile in vivo microenvironments. The objective of this study was to 3D-print a monolithic silica scaffold specifically designed for the cultivation of neural precursor cells. Initially, a preliminary investigation was conducted to identify the critical parameters pertaining to calcination. This investigation aimed to produce sturdy and uniform scaffolds with a minimal wall-thickness of 0.5 mm in order to mitigate the formation of cracks. Four cubic specimens, with different wall-thicknesses of 0.5, 1, 2, and 4 mm, were 3D-printed and subjected to two distinct calcination profiles. Thermogravimetric analysis was employed to examine the freshly printed material, revealing critical temperatures associated with increased mass loss. Isothermal steps were subsequently introduced to facilitate controlled phase transitions and reduce crack formation even at the minimum wall thickness of 0.5 mm. The optimized structure stability was obtained for the slow calcination profile (160 min) then the fast calcination profile (60 min) for temperatures up to 900 °C. In situ X-ray diffraction analysis was also employed to assess the crystal phases of the silicate based material throughout various temperature profiles up to 1200 °C, while scanning electron microscopy was utilized to observe micro-scale crack formation. Then, ceramic scaffolds were 3D-printed, adopting a hexagonal and spherical channel structures with channel opening of 2 mm, and subsequently calcined using the optimized slow profile. Finally, the scaffolds were evaluated in terms of biocompatibility, cell proliferation, and differentiation using neural precursor cells (NPCs). These experiments indicated proliferation of NPCs (for 13 days) and differentiation into neurons which remained viable (up to 50 days in culture). In parallel, functionality was verified by expression of pre- (SYN1) and post-synaptic (GRIP1) markers, suggesting that 3D-printed scaffolds are a promising system for biotechnological applications using NPCs

Metal 3D-Printed Bioinspired Lattice Elevator Braking Pads for Enhanced Dynamic Friction Performance

The elevator industry is constantly expanding creating an increased demand for the integration of high technological tools to increase elevator efficiency and safety. Towards this direction, Additive Manufacturing (AM), and especially metal AM, is one of the technologies that could offer numerous competitive advantages in the production of industrial parts, such as integration of complex geometry, high manufacturability of high-strength metal alloys, etc. In this context, the present study has 3D designed, 3D printing manufactured, and evaluated novel bioinspired structures for elevator safety gear friction pads with the aim of enhancing their dynamic friction performance and eliminating the undesired behavior properties observed in conventional pads. Four different friction pads with embedded bioinspired surface lattice structures were formed on the template of the friction surface of the conventional pads and 3D printed by the Selective Laser Melting (SLM) process utilizing tool steel H13 powder as feedstock material. Each safety gear friction pad underwent tribological tests to evaluate its dynamic coefficient of friction (CoF). The results indicated that pads with a high contact surface area, such as those with car-tire-like and extended honeycomb structures, exhibit high CoF of 0.549 and 0.459, respectively. Based on the acquired CoFs, Finite Element Models (FEM) were developed to access the performance of braking pads under realistic operation conditions, highlighting the lower stress concentration for the aforementioned designs. The 3D-printed safety gear friction pads were assembled in an existing emergency progressive safety gear system of KLEEMANN Group, providing sufficient functionality