Abstract

Increasing demand for modern smart materials due to changing consumers’ attitudes toward healthy lifestyles and industrial strategy in line with the principles of sustainable development are driving the research towards the progress of innovative cusom-designed 3D structures. In recent years, rapid prototyping has gained considerable attention in material applications to manufacture minimally processed products with more nutritious properties and healthier features. In this case, three-dimensional (3D) printing is a promising additive manufacturing technique that offers the rapid prototyping of complex 3D architectures with consideration of customized smart structures. The successful application of 3D printing to create desired printed objects is mostly related to the development of efficient printable ink dispersions. Commonly, the printable inks must offer well-defined pseudoplasticity to provide low flow resistance through printer nozzles but also must offer a gel-like structure, possessing sufficient yield stress, to resist compressive stresses from the capillary effects post-printing. However, the application of traditional emulsions (as printing inks) in 3D printing is hampered by physical instability during the process along with its poor self-supporting feature. The latter presents numerous challenges in attaining an acceptable level of printed shape control. To overcome these obstacles, the application of structured type printable inks (like emulsion gels, high-internal phase emulsions, Pickering emulsions, etc.) to the 3D printing process has attracted considerable attention to constructing 3D printed functional materials. This presentation aims to offer a deeper understanding of the role of innovative emulsion-based templating in the structuring of printable inks to use in 3D printing with an emphasis on pseudoplasticity, viscoelasticity, droplet size, and thixotropic behavior of the printable emulsions. Besides, this webinar attempts to show a correlation between the flow behavior of emulsion-based inks with the printing quality and shape fidelity of 3D printed objects. This correlation (printability and rheological properties of inks) can effectively contribute to differences in the textural, sensory features, and structural behaviors of emulsion-based inks under the 3D printing process.

Biography

Mahdiyar Shahbazi is a Lecturer at the University of Natural Resources and Life Sciences (BOKU University) in Vienna, Austria with over 10 years of experience in the application of sustainable biomaterials (smart biopolymer, composite, and nanoparticles) in the 3D printing process. His primary research goals are directed toward the theoretical and analytical understanding of the changes in Bio-macromolecules and Biomaterials Structures (at the molecular level) through different types of 3D printing systems. He has also worked in the bio-fabrication of materials by Printing Technology (both screening and 3D printing processes) to valorize the 3D printed objectives with enhanced functionality. Concerning 3D printing technology, he has led as principal researcher and coordinator in several successive international projects in this domain. Currently, he works on producing multifunctional 3D/4D printed smart structure. The accomplishments of creating this noteworthy list of research areas are associated with the actual coordination and academics in addition to the industry involvement of Mahdiyar, linking faculty and industry partners from numerous disciplines, and securing considerable research funds. Mahdiyar has published abundant peer-reviewed articles and conference papers and offered broadly at national and international meetings as keynote and invited talks.

Abstract

Additive manufacturing (AM) is a process that allows the production of components with a
complex geometry being created layer by layer on a special platform by computer-aided design
(CAD). Today, the tendency is to manufacture larger and complex shape components. On this
regards the strength and toughness of 3D printed components became key issues. We investigate the
fracture toughness of different polymeric componets obtained by various 3D printing technologies:
PLA and PETG obtained using Fused Depositon Modelling (FDM), PA from Selective Laser
Sintering SLS), respectively epoxy resin resulted from Digital Lighnt Processing (DLP). The
investigated materials show a brittle fracture (epoxy resins) and a quasi britlle fracture for PLA,
PETG and PA. The ASTM … standard was employed determine the fracture toughness. The
fracture toughness results show that the lower values were obtained for specimens obtained via DLP
of 1 MPa m0.5 and lower depending of printing parameters. The highest fracture toughness were
obtined for PLA and PETG 3 to 5 MPa m0.5. For each printing technology were identified the
influence of manufacturing parameters (printing orientation, layer thickness, and so on) on the
fracture toughness results.

Biography

Marsavina has completed his PhD at University Politehnica Timisoara and postdoctoral
studies at Loughborough University. He worked as Associate Resercher at University of Sheffield
and Gent University and Marie Curie More Experienced Resercher at Lublin University of
Technology. He is the Vice rector for research of University Politehnica Timisoara, Vice President
of the European Structural Integrity Society and corresponding member of Romanian Academy. He
has published more than 100 papers in high impact journals and has been serving as an editorial
board member of several journals in the field of fracture, fatigue and structural integrit.

Abstract

Additive manufacturing (AM) is a process that allows the production of components with a
complex geometry being created layer by layer on a special platform by computer-aided design
(CAD). Today, the tendency is to manufacture larger and complex shape components. On this
regards the strength and toughness of 3D printed components became key issues. We investigate the
fracture toughness of different polymeric componets obtained by various 3D printing technologies:
PLA and PETG obtained using Fused Depositon Modelling (FDM), PA from Selective Laser
Sintering SLS), respectively epoxy resin resulted from Digital Lighnt Processing (DLP). The
investigated materials show a brittle fracture (epoxy resins) and a quasi britlle fracture for PLA,
PETG and PA. The ASTM … standard was employed determine the fracture toughness. The
fracture toughness results show that the lower values were obtained for specimens obtained via DLP
of 1 MPa m0.5 and lower depending of printing parameters. The highest fracture toughness were
obtined for PLA and PETG 3 to 5 MPa m0.5. For each printing technology were identified the
influence of manufacturing parameters (printing orientation, layer thickness, and so on) on the
fracture toughness results.

Biography

Marsavina has completed his PhD at University Politehnica Timisoara and postdoctoral
studies at Loughborough University. He worked as Associate Resercher at University of Sheffield
and Gent University and Marie Curie More Experienced Resercher at Lublin University of
Technology. He is the Vice rector for research of University Politehnica Timisoara, Vice President
of the European Structural Integrity Society and corresponding member of Romanian Academy. He
has published more than 100 papers in high impact journals and has been serving as an editorial
board member of several journals in the field of fracture, fatigue and structural integrit.

Abstract

In the rapidly evolving landscape of Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), the potential for innovation is vast, yet the process is fraught with challenges that can lead to significant failures. These failures not only waste valuable materials and energy but also drive up production costs, undermining the efficiency that AM promises. In this keynote, I will explore how artificial intelligence (AI), with a focus on natural language processing (NLP), can revolutionize failure management in AM. By harnessing the wealth of user-generated data from online communities, we can predict and mitigate common failures in real-time, improving the overall reliability and efficiency of 3D printing processes. I will discuss our innovative approach to analyzing user forums using AI-driven text classification, revealing insights into the most frequent failure types and their impact on production. This keynote will also highlight the future of integrating AI with AM, where advanced language models and image recognition algorithms will further enhance failure prediction and prevention. Join me as we delve into how AI and NLP are not just tools but transformative forces in the world of additive manufacturing.

Biography

Turgut Refik Çağlar studied Industrial Engineering with a focus on Mechanical Engineering at the Technical University of Berlin (TU Berlin). Since 2020, he has been working as a Research Associate in the Quality Science Department at the Institute for Machine Tools and Factory Management (IWF) at TU Berlin. His areas of expertise include Data Science, Six Sigma, and Lean Management. Since 2023, he has served as a Chief Engineer and PhD candidate in the Quality Science Department at TU Berlin. His doctoral research focuses on the application of Artificial Intelligence methods in shopfloor management and failure management systems.