01. School of Engineering and Digital Sciences

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Browsing 01. School of Engineering and Digital Sciences by Subject "3D printing"

Now showing 1 - 6 of 6
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    3D PRINTING OF GELATIN/OXIDIZED CARBOXYMETHYL CELLULOSE SCAFFOLDS WITH GRADIENT POROSITY FOR BONE TISSUE REGENERATION APPLICATIONS
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-04-23) Dyussenbinov, Aibek
    This master thesis investigates the development and evaluation of 3D-printed gelatin/oxidized carboxymethyl cellulose (OxCMC) scaffolds with gradient porosity for applications in bone tissue regeneration. Recognizing the limitations of current bone repair methodologies, this research aims to mimic the natural extracellular matrix of bone through advanced scaffold engineering techniques. The thesis explores the synthesis and optimization of bioinks from gelatin and OxCMC, chosen for their biocompatibility, biodegradability, and mechanical properties conducive to 3D printing. Through extensive experimentation, including rheological tests, Fourier-transform infrared spectroscopy (FTIR) analysis, and scanning electron microscopy (SEM) imaging, scaffold formulations were tailored to achieve desired porosity gradients and mechanical strength. The novel approach of utilizing a complex 3D printing model with different pinheads for varying ink compositions is highlighted as a key innovation. This method allowed for the creation of scaffolds that not only support cell adhesion and proliferation but also replicate the porosity gradient inherent to natural bone, thereby addressing a critical aspect of scaffold design in bone tissue engineering. Results indicated a direct correlation between the polymer content in the scaffolds and their swelling ability, degradation rates, and mechanical properties. Scaffolds with higher polymer content showed less swelling but greater mechanical strength, aligning with the requirements for supporting bone tissue regeneration. The gradient scaffold, in particular, demonstrated a balance between swelling behavior and mechanical integrity, suggesting its suitability for bone tissue engineering applications. This research contributes to the field of regenerative medicine by offering a promising scaffold design strategy for bone tissue regeneration. By closely mimicking the structural and mechanical properties of natural bone, the developed scaffolds hold potential for improving the outcomes of bone repair and regeneration procedures, paving the way for future clinical applications.
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    DEVELOPMENT OF MIDDLEWARE FOR DATA-CENTRIC DIGITAL TWIN OF ADDITIVE MANUFACTURING
    (School of Engineering and Digital Sciences, 2023) Shaimergenova, Karina
    Additive Manufacturing (AM) is a process of the production, by using layer-by-layer method and different metals/alloys and polymers. Despite the various technological and other opportunities of production, one of the challenges is a Big Data issue in data management of monitoring and controlling systems. As one of solutions the data-centric Digital Twin (DT) was suggested. Data-centric Digital Twin (DT) is one of the types of digital twin and part of the Industry 4.0 technologies, which is focusing on optimization input/output data of the given current systems, processes, or services. To improve the data analysis and accuracy of data-centric DT in AM, the development of middleware was recommended. Middleware is a software system, which main role is data management and communication between simulation and physical modules of the DT. Moreover, the development of middleware for AM gives an opportunity to integrate the DT in real-time process. This thesis research introduces the implementation of the middleware in data-centric Digital Twin of Additive Manufacturing. The aim of the work is to design and develop a data-centric middleware model to enable the creation of DT for AM. The first part of research is investigation of current development of AM and data-centric DT. Then in research gap analysis part research gap and hypothesis were identified. The current and future progress are written in the methodology part of this work.
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    ENHANCING KAOLIN CLAY EXTRUSION FOR HIGH-PRECISION 3D PRINTING: MATERIALS, PROCESS OPTIMIZATION, AND RHEOLOGY
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-04-29) Konysbekov, Alisher
    The present research aims to find the best kaolin clay extrusion conditions, suitable for this technology, which would result in an increase in efficiency and quality of the products. Kaolin clay, which is renowned for its thermal stability and compatibility with complex forms, is the main focus of the research because of the growing use in the 3D printing processes worldwide for the manufacturing industries. Research is restricted by the fact that it relates only to kaolin clay and its rheological properties which are specific to the results. The first target is to develop a predictive model that will accurately define the extrusion pressure needed for kaolin clay, using the rheological parameters as a basis. The involved work focuses on the detailed performance of these parameters, such as yield stress, consistency index, and flow index, and their role in extrusion. The methodological approach of the study brings together the experimental determination of kaolin viscosity behavior with the FEA simulations in COMSOL Multiphysics. Sample preparation, performing the rheological measurements, and extruding with a ram extruder are the experimental methods that are used. On the other hand, FEA simulations are meant to recreate the extrusion process and verify experiment results. The results demonstrate that the physical properties of kaolin paste have a strong bearing on its extrusion behavior, and so the rheological properties should be taken into consideration during the printing process so as to optimize the outcome. The study is able to confirm the relation between the experimental results and the computer simulation, all of which confirm the predictive model's accuracy. The topic of the presentations will be the impact on the 3D printing business brought about by these revelations. There is a probability that defective printing can be avoided, and more efficient printing can be achieved through better knowledge of material properties. The study concludes the importance of the exact assessment of rheological parameters and the accurate 3D printing process simulation for bettering of kaolin clay extrusion in 3D printing. Research into the material properties and optimization of the extrusion process should be carried out.
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    INVESTIGATION OF DEVELOPING DIGITAL TWIN FOR ADDITIVE MANUFACTURING
    (School of Engineering and Digital Sciences, 2023) Jyeniskhan, Nursultan
    Additive manufacturing and digital twin concept are both important pillar technologies for fourth industrial revolution. The additive manufacturing method is being appealing by many highperformance industries owing to its ability to produce geometrically challenging parts with traditional manufacturing method. However, there are issues such as final product defects and requirement of human interventions and monitoring during printing processes to avoid time, material, and cost waste with additive manufacturing area. The main aim of this research is to investigate the developments of digital twin technology for additive manufacturing specifically in fused deposition modeling 3D printers. The main objectives are to develop digital twin architecture and creating digital twin model for FDM printers. Digital twin is virtual model or digital representation of a physical entity, process, or component. Due to the challenges in additive manufacturing field, digital twin technology is considered as one of the possible solutions to fully digitize additive manufacturing processes and solve additive manufacturing problems such as real-time monitoring and controlling, predicting the faults and errors of printers and parts to avoid further waste on time and material, and increase manufacturing efficiency. After developing digital twin framework and architecture to implement, digital twin development is conducted. The main approach used to fulfill this research is to use Raspberry Pi 3B+ to connect FDM printer to OctoPrint, open-source software, to remotely control and monitor. In addition to this, extracting important data from OctoPrint and use them in modeling digital twin of FDM printer and its processes. The developed digital twin for the FDM printer meets its functional requirements such as bidirectional communication between physical and digital models, real-time remotely control and monitoring, and integration of machine learning for leveraging FDM printers to smart manufacturing. The main key contributions of this study to knowledges are identification of benefits of digital twin implementation in different level, and implementation challenges. The development of the digital twin framework and architectural design are another important key contribution in addition to novel digital twin model of FDM printer with integration of some intelligence level.
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    Preliminary investigation of dimensional accuracy of FDM printed part
    (Nazarbayev University School of Engineering and Digital Sciences, 2019) Kopeyeva, Assel; Yerubayeva, Anel; Trubayev, Sanjar
    Additive Manufacturing (AM) technology has advanced over the last decade and becomes a feasible alternative for the traditional subtractive manufacturing technology to create complex shapes. Fused Deposition Modeling (FDM) is one the most used processes of AM, whose working principle is consisted in the setting the machine temperature necessary to melt the material and pushing by a nozzle. There are many benefits appear due to the nature of FDM process, as well as several difficulties come from the influence of processing parameters such as: infill density, infill pattern, layer thickness, print speed, platform temperature, extruder temperature etc. on the final properties of the pieces. This project is focused on 3D FDM printed shapes used as a pattern for investment casting and provides experimental studies to analyze the influence of processing parameters on the dimensional accuracy and surface finishing of FDM parts. Previous related works on optimization of FDM process parameters are considered in assessing the effect of varied configurations. The dimensions of the 3D printed patterns are measured and compared with the nominal CAD dimensions using Coordinate Measuring Machine (CMM).
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    SENSOR-BASED DIGITAL TWIN FOR FUSED DEPOSITION MODELING (FDM) 3D PRINTERS
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-04-23) Shomenov, Kemel
    The development of Digital Twin for 3D printing is crucial to optimize the printing process and achieve high-quality printed objects. It allows to improve the current limitations of Fused Deposition Modeling (FDM) 3D printing such as long printing time, need for monitoring, and defects of printed parts. There are many studies on Digital Twin development for FDM 3D printing, including IoT-based monitoring, machine learning, and image processing. However, sensor-based approaches with proper sensor selection, data transfer, and visualization have not been fully explored yet. The aim of this work is the development of a Digital Twin for FDM 3D printing with improved accuracy, resulting in better control and optimization of the printing process. The main approach to building the proposed DT system consists of several important steps such as data collection, data transfer, data storage, data analysis, and a graphical user interface (GUI) that allows monitoring and control of the printing process. The system has two types of data which are data from a 3D printer and data from embedded sensors. Data from the printer were retrieved using Python, while sensor data were collected via Arduino modules and stored in a real-time database. Different sensors were compared for parameters like filament flow rate and nozzle/bed position. The Firebase database was chosen after comparison, and Unity 3D was selected as the GUI. The controller sends GCode commands to the printer line by line, enabling real-time editing and automatic defect detection. Key research results include successful integration of sensor data with printer data, selection of appropriate database and GUI platforms, and implementation of real-time control, monitoring and autonomous defect detection capabilities. The novelty of this research is that it proposes the application of affordable and accurate sensors that have not been suggested before. Furthermore, it does not use third-party hosts to control the 3D printer but instead employs Python, which allows full flexibility for defect detection and print optimization.