Browsing by Author "Kartal, F."

Now showing 1 - 6 of 6

Advancements in polylactic acid research: From material properties to sustainable applications
(2024) Kaptan, A.; Kartal, F.
This review article provides a comprehensive examination of the latest advancements in the research and development of Polylactic Acid (PLA) and its composites, with a focus on enhancing material properties and exploring sustainable applications. As a biodegradable and bio-base polymer, PLA has emerged as a promising alternative to conventional petroleum-based plastics across various industries, including packaging, 3D printing, and biomedical fields. The review delves into studies investigating the effects of environmental conditions on PLA’s hydrolytic stability and structural integrity, as well as the benefits of blending PLA with other biopolymers to improve its mechanical properties. It also covers research on optimizing three dimensional printing parameters for PLA, underscoring the importance of raster orientation and print layer thickness in achieving desired mechanical strength and object durability. Additionally, the incorporation of nanofillers and copolymers is discussed as a strategy for enhancing PLA’s moisture resistance and overall performance. By summarizing key findings from a wide range of studies, this article aims to shed light on the significant progress made in PLA research, while pointing out future research directions to resolve existing limitations and fully capitalize on PLA’s potential as a green material solution. To better cater to the needs of design engineers, this review highlights how advancements in PLA research can be directly applied to improve product design and functionality. Specifically, it discusses the enhanced mechanical properties, sustainability benefits, and versatility of PLA in various industrial applications, providing engineers with a deeper understanding of how to utilize PLA in eco-friendly design solutions.
Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy
(2024.01.01) Kartal, F.; Kaptan, A.
This study aims to predict machinability and high performance optimum surface roughness (Ra) by developing multiple regression models and artificial neural network (ANN) model for abrasive water jet cutting (AWJC) of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure (3500-4000 Bar), nozzle distance (2-5 mm), abrasive flow rate (200-350 g/min), abrasive grain size (100-110 mesh), and nozzle traverse speed (240300 mm/min) were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Additionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance (ANOVA). The Artificial Neural Network (ANN) model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error (MSE) and mean absolute error (MAE). The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65% from the multiple regression models created to estimate the surface roughness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5%, 25.4% and 21.9%, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contribute to improving process planning and control.
Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy
(Yildiz Technical University, 2024) Kartal, F.; Kaptan, A.
This study aims to predict machinability and high performance optimum surface roughness (Ra) by developing multiple regression models and artificial neural network (ANN) model for abrasive water jet cutting (AWJC) of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure (3500-4000 Bar), nozzle distance (2-5 mm), abrasive flow rate (200-350 g/min), abrasive grain size (100-110 mesh), and nozzle traverse speed (240-300 mm/min) were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Additionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance (ANOVA). The Artificial Neural Network (ANN) model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error (MSE) and mean absolute error (MAE). The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65% from the multiple regression models created to estimate the surface roughness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5%, 25.4% and 21.9%, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contribute to improving process planning and control.
Experimental investigation and optimization of the effect garnet vibratory tumbling as a post-process on the surface quality of 3D printed PLA parts
(2024) Kartal, F.; Kaptan, A.
The method known as additive manufacturing causes high surface roughness between layers depending on the technique used at the end of the product development process. This can be an important problem in three-dimensional (3D) manufacturing depending on the usage area. To solve this problem, this experimental study investigated the effect of vibratory rolling (VT) on surface roughness in 3D printed Polylactic Acid (PLA) parts using garnet abrasive particles. Optimization with the best parameters was also performed and the results were analyzed. The surface roughness (Ra) values were measured at different vibration durations for each mesh size. The study involved subjecting the printed parts to vibratory tumbling using garnet abrasive particles of various mesh sizes (80, 90, 100, 120, 150, 180, and 220 mesh). Surface roughness measurements were taken at different vibration durations (2, 4, 6, 8, 10, and 12 hours) for each mesh size. A surface roughness measuring device was used to obtain the roughness values. The findings reveal that vibratory tumbling with garnet abrasive particles effectively reduces surface roughness in 3D printed parts. As the vibration duration increased, smoother surfaces were achieved. The surface roughness of the printed samples was reduced by 60% on average by using the optimum values after post-process.
Mechanical Performance of Salvadora Persical (Miswak) Reinforced Polylactic Acid Matrix Composites for Three Dimensional Printing
(2023-10-15) Kartal, F.; Kaptan, A.
This study examines the mechanical performance of polylactic acid (PLA) matrix composites reinforced with Salvadora Persica (Miswak). With the increasing use of environmentally friendly materials, researchers are focusing on the production of biodegradable materials. However, incompatibility between PLA and filler materials used in PLA composites causes mechanical problems during production. This study deals with the production and characterization of PLA composites containing lignocellulosic and inorganic fillers using maleic anhydride grafted polylactic acid (PLA/g/MA) as a matrix. The aim of the research is to examine the mechanical specifications of Miswak powder reinforced PLA composites and to evaluate their suitability for practical applications. PLA was used as the matrix material and PLA/g/MA was used as the compatibilizer. Composites containing Miswak powder at different weight concentrations (5%, 10%, 15% and 20%) were characterized using scanning electron microscopy along with tensile and bending tests. The obtained results showed that different Miswak concentrations affect the mechanical specifications of the composites. Composites at 5% concentration demonstrated excellent interlayer adhesion and high mechanical strength, demonstrating favorable mechanical specifications. The findings show that Miswak powder is a potential filling material to improve the mechanical specifications of PLA composites and provide antimicrobial benefits. The results of this study shed light on the mechanical performance of Miswak reinforced PLA matrix composites, which are promising for 3D printing applications. In addition, it is stated that the materials used, such as natural filling materials, contribute to the development of sustainable and environmentally friendly materials by reducing the environmental impact.
Sustainable Reinforcement of PLA Composites with Waste Beech Sawdust for Enhanced 3D-Printing Performance
(2024.01.01) Kartal, F.; Kaptan, A.
This study investigates the incorporation of waste beech sawdust (WBS) into polylactic acid (PLA) composites for use in additive manufacturing, with a focus on enhancing the mechanical performance and sustainability of 3D-printed components. WBS, a byproduct of industrial timber processing, was used in varying concentrations (0-20%) to produce PLA composite filaments through a single-screw extrusion process, which were subsequently used in fused filament fabrication (FFF). Mechanical properties including tensile, flexural, and impact strengths were evaluated alongside thermal stability and microstructural analysis. The results indicated that the addition of WBS led to increased stiffness and hardness of the PLA composites, with optimal mechanical performance observed at 5-10% WBS content. Beyond this concentration, significant reductions in tensile, flexural, and impact strengths were noted, likely due to poor particle dispersion and inadequate interfacial adhesion, as confirmed by scanning electron microscopy (SEM) analysis. Thermal stability analysis via thermogravimetric analysis (TGA) revealed a reduction in degradation temperature with increased WBS content, which may limit the use of these composites in high-temperature applications. Despite certain limitations, this research underscores the potential of WBS as a sustainable reinforcing agent in 3D-printed PLA composites. The incorporation of WBS contributes to environmental sustainability by utilizing waste materials while also reducing the dependence on non-renewable resources. The findings suggest that PLA-WBS composites, particularly at lower WBS concentrations, offer an effective balance between improved sustainability and mechanical performance, making them suitable for a range of 3D printing applications. Future studies should focus on enhancing filler dispersion and interfacial bonding to further optimize the properties of PLA-WBS composites.