The advancement of 3D-printing technology has ushered in a new era in the production of machine components, building materials, prototypes, and so on. In 3D-printing techniques, the infill reduces the amount of material used, thereby reducing the printing time and sustaining the aesthetics of the products. Infill patterns play a significant role in the property of the material. In this research, the mechanical properties of specimens are investigated for gyroid, rhombile, circular, truncated octahedron, and honeycomb infill structures (hexagonal). Additionally, the tensile properties of PLA 3D-printed objects concerning their infill pattern are demonstrated. The specimens were prepared with various infill patterns to determine the tensile properties. The fracture of the specimen was simulated and the maximum yield strengths for different infill structures and infill densities were determined. The results show the hexagonal pattern of infill holds remarkable mechanical properties compared with the other infill structures. Through the variation of infill density, the desired tensile strength of PLA can be obtained based on the applications and the optimal weight of the printed parts.

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Investigation of Tensile Properties of Different Infill Pattern Structures of 3D-Printed PLA Polymers: Analysis and Validation Using Finite Element Analysis in ANSYS

Effect of 3D printing process parameters on surface and mechanical properties of FFF-printed PEEK

Fused deposition modelling (FDM)-printed parts can be treated with various post-processes to improve their mechanical properties, dimensional accuracy and surface finish. Samples of polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) parts are treated with annealing to study a ceramic powder mould’s effectiveness in order to avoid dimensional part deformation. The variables chosen are annealing temperatures and the usage of a ceramic powder mould to avoid part deformations. A flexural strength test was carried out to evaluate the mould’s influence on the mechanical properties of the part. The effectiveness of the mould has been evaluated mainly attending to the length of the part, because this is the dimension most affected by deformation. A polynomial approximation to a deformation’s length and the effectiveness of the mould allows for their prediction. Results obtained show that effectiveness increases with the annealing temperature. Nevertheless, mould effectiveness decreases when parts are fabricated with PLA, because it is a semi-crystalline thermoplastic, and it suffers a lower shrinkage during thermal post-process than amorphous polymers such as ABS. Attending to the flexural strength test, mould has no significant influence on the mechanical properties of the treated parts in both materials studied.

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Influence of Thermal Annealing Temperatures on Powder Mould Effectiveness to Avoid Deformations in ABS and PLA 3D-Printed Parts

One of the leading causes of death is cardiovascular disease, and the most common cardiovascular disease is coronary artery disease. Percutaneous coronary intervention and vascular stents have emerged as a solution to treat coronary artery disease. Nowadays, several types of vascular stents share the same purpose: to reduce the percentage of restenosis, thrombosis, and neointimal hyperplasia and supply mechanical support to the blood vessels. Despite the numerous efforts to create an ideal stent, there is no coronary stent that simultaneously presents the appropriate cellular compatibility and mechanical properties to avoid stent collapse and failure. One of the emerging approaches to solve these problems is improving the mechanical performance of polymeric bioresorbable stents produced through additive manufacturing. Although there have been numerous studies in this field, normalized control parameters for 3D-printed polymeric vascular stents fabrication are absent. The present paper aims to present an overview of the current types of stents and the main polymeric materials used to fabricate the bioresorbable vascular stents. Furthermore, a detailed description of the printing parameters’ influence on the mechanical performance and degradation profile of polymeric bioresorbable stents is presented.

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3D Printing of Polymeric Bioresorbable Stents: A Strategy to Improve Both Cellular Compatibility and Mechanical Properties

Fused Deposition Modeling (FDM) is one of the most commonly used 3D printing technologies for creating complex parts from a Computer Aided Design (CAD) model. It is observed that mechanical strength of 3D printed polylactic acid (PLA) parts are affected by various parameters associated with part, process, material and operating conditions. One of the key parameters that influences tensile and flexural strength of 3D printed PLA parts is build orientation. Researchers have investigated the effect of a limited number of build orientations on tensile strength. Moreover, less work has been reported which studies the effect of build orientation on flexural strength. None of the studies modeled tensile load and bending load as a function of thickness and compared tensile loading capacity with flexural loading for different orientations. Therefore, an attempt is made to include a greater number of build orientations that occur during 3D printing of complex PLA parts. Build orientations considered in this study are flat, flat-support, edge, edge-45, upright and upright-45 with three thicknesses i.e., 1.2 mm, 2.0 mm and 2.8 mm. Tensile and flexural tests are performed as per American Society for Testing and Materials (ASTM) standards. Experimental results show that six orientations form two groups i.e., strong orientation group and weak orientation group. PLA appears stronger in tensile loading than bending. Edge orientation is strongest during tensile as well as bending loading whereas upright orientation is weakest in tensile loading and upright-45 orientation is weakest in bending. Force trends, it can be concluded that thickness can be minimized where build orientation belongs to the strong orientation group. Similarly, thickness can be increased where build orientation belongs to the weak orientation group.

Effect of Build Orientation on Load Capacity of 3D Printed Parts

Printing parameters and materials affecting mechanical properties of FDM-3D printed Parts: Perspective and prospects

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumonia. The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. Pneumonia has caused significant deaths worldwide, and it is a challenging task to detect many lung diseases such as like atelectasis, cardiomegaly, lung cancer, etc., often due to limited professional radiologists in hospital settings. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. Although CT scan is the gold standard, CXR are still useful because it is cheaper, faster and more widespread. Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial and it can save lots of peoples life and help stopping and curing and control for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on largescale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as featureextractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. This study aims to identify pneumonia caused from other types and also healthy lungs using only X-Ray images. The model’s performance in pneumonia detection shows that the proposed model could effectively classify normal and abnormal X-rays in practice, hence reducing the burden of radiologists Index Terms – X-Ray, CXR, COVID-19, Chest X-ray images, pneumonia detection; convolutional network (CNN), image enhance.

Ameya Mahale

Papers

Printing parameters and materials affecting mechanical properties of FDM-3D printed Parts: Perspective and prospects

A Mobile Application for Early Diagnosis of Pneumonia