Vol 8 - Issue 2

Uncertainties and Reliability of Multiphysical Systems

List of Articles

4th edition of the Moroccan Workshop on 3D Printing hosted by Hassan II University Library of Mohamed SEKKAT, Casablanca, 2024

The Moroccan Association of Additive Manufacturing and 3D Printing is an association dedicated among other things to the promotion of scientific research in the promising field of additive manufacturing and 3D printing in Morocco. The organization of workshops is one of the key strategies to achieve this goal.

This study investigates the effect of support infill angle and its density, transverse at 90°, inclined at 45°, axial direction at 0° and crossed filament by (0°/90°), (45°/-45°) and (0°/45°) on surface quality and mechanical properties using three different strategies. The surface roughness and flexural properties of the specimens are analysed and compared as well as the material waste and printing time. According to results of this study, the variations in the support infill angle resulted in diverse flexural strength and surface quality.

Over the last decade, there has been a large interest in the use of 3D printing to manufacture microfluidic devices, since it has the ability to circumvent traditional fabrication techniques limitations. These include being unable to really make complex three-dimensional architectures, expensive and time-consuming processes to change device designs, and difficulty transitioning from prototyping to mass production. In this literature review, we will look at the current trends in 3D printed microfluidics, as well as recent advances and new developments in fabrication techniques, materials, and applications. Integration of 3D printing in microfluidics research has helped in the rapid prototyping of fluidic channels and structures with high complexity at an effective cost. Applications of 3D printed microfluidics are described in the areas of healthcare, diagnostics, chemical synthesis, and biotechnology. This paper also delineates the challenges and future prospects of 3D printed microfluidics, giving insight into potential research directions and technological developments.

Particularly focusing on 4D printing, a technology enabling objects to transform over time. We explore smart materials, emphasizing moisture-responsive variants crucial for 4D printing. Notably, cellulose emerges as key, offering renewable and sustainable bio-based filaments. We detail the meticulous preparation of cellulose from sugarcane bagasse, obtaining high-purity fibers essential for 4D printing. These filaments exhibit versatile stiffness and moisture responsiveness, crucial for hygromorphic structures. Our proposed method integrates a codesign approach tailored for 4D printing, utilizing fused filament fabrication and cellulose-filled filaments. Through this investigation, we uncover cellulose’s potential in sensor technology and additive manufacturing, marking significant progress in responsive materials and 4D printing.

Laser powder bed fusion (LPBF) is an additive manufacturing technique whose efficiency and quality depend largely on a consistent and precise powder spreading procedure. This article examines the crucial role of powder spreading in influencing the quality of 3D-printed parts. Through case studies and experimental results, the article demonstrates in detail the impact of parameters such as: powder flowability, spreading speed, layer thickness, and recoater type on powder uniformity during spreading. In addition, the paper presents a comparison between types of recoaters in order to obtain optimum surface finish, mechanical properties, and reduced defects. This paper reviews the most appropriate powder spreading techniques to maintain the flowability and uniformity of the powder. Therefore, the primary objective of this work is to present an in-depth review of the impact of powder spreading dynamics in LPBF. In addition, it aims to demonstrate to the reader the various factors influencing powder spreading and the methodologies employed to optimize this crucial process.

The rapid advancements in 3D printing technology and materials are revolutionizing the field of physics. While industries are reaping the benefits of these innovations, physics students are also poised to gain from this technological shift. To understand the current state of 3D printing in physics education, we conducted a case study involving 100 students from FABLAB-FSK. This research included an implementation phase and a survey to gauge student perspectives on the intersection of 3D printing and physics education. Our research aimed to assess the extent of students’ familiarity with 3D printing technology, exploring their knowledge of its capabilities and its potential applications in physics projects. The survey focused on understanding their awareness of 3D printing’s role in developing new materials and its effectiveness as a teaching tool in physical sciences.

Additive manufacturing, has shown great promises, offering unexpectable design freedom and manufacturing flexibility. but traditional design methodologies cannot fully exploit the functionality of am. this study explores the impact of generative design and topology optimization in the design and production of parts in additive production. by using these advanced design tools, we aim to optimize the case study performance, minimize material usage, and unlock design opportunities. The case study is a gripper arm which is part of a robot deputed to the handle meteors is presented to demonstrate the application of these methods. additionally, it offers a useful description of Autodesk’s generative design, a software, used in our case, that uses generative design to generate a series of potential solutions to a static structural design problem. the finding reveal how generative design and topology optimization can significantly enhance component performance, reduce mass, and enable complex geometries that are difficult or impossible when using traditional methods. additionally, the paper highlights the importance of taking manufacturability constraints into account during the design process.

An analysis of topology optimization employing deep learning, namely Generative Adversarial Networks (GANs), and topology optimization utilizing the Solid Isotropic Material with Penalization (SIMP) method is presented in this research. We describe the theoretical foundations of GANs and the SIMP technique. A cantilever beam with predetermined boundary conditions was the topic of a static study to show the practical efficacy of these methods. The structural performance parameters, such as maximal directional displacement, maximal Von Mises stress, and deformation energy. The findings show that deep learning-based topology optimization, as demonstrated by TopologyGAN, provides considerable benefits in terms of improved design correctness and computing performance.