Additive manufacturing (AM) is gaining widespread adoption in the manufacturing industry due to its capability to fabricate intricate and high-performance components. In parallel, the increasing emphasis on functional materials in AM has highlighted the critical need for accurate prediction of the mechanical behavior of composite systems. This study experimentally investigates the tensile strength and surface quality of carbon fiber-reinforced nylon composites (PA12-CF) fabricated via fused deposition modeling (FDM) and models their behavior using artificial neural networks (ANNs). A Taguchi L27 orthogonal array was employed to design experiments involving five critical printing parameters: layer thickness (100, 200, and 300 µm), infill pattern (gyroid, honeycomb, and triangles), nozzle temperature (250, 270, and 290 °C), printing speed (50, 100, and 150 mm/s), and infill density (30, 60, and 90%). An analysis of variance (ANOVA) revealed that the infill density had the most significant influence on the resulting tensile strength, contributing 53.47% of the variation, with the strength increasing substantially at higher densities. In contrast, the layer thickness was the dominant factor in determining surface roughness, accounting for 53.84% of the variation, with thinner layers yielding smoother surfaces. Mechanistically, a higher infill density enhances the internal structural integrity of the parts, leading to an improved load-bearing capacity, while thinner layers improve the interlayer adhesion and surface finish. The highest tensile strength achieved was 69.65 MPa, and the lowest surface roughness recorded was 9.18 µm. An ANN model was developed to predict both the tensile strength and surface roughness based on the input parameters. Its performance was compared with that of three other machine learning (ML) algorithms: support vector regression (SVR), random forest regression (RFR), and XGBoost. The ANN model exhibited superior predictive accuracy, with a coefficient of determination (R² > 0.9912) and a mean validation error below 0.41% for both outputs. These findings demonstrate the effectiveness of ANNs in modeling the complex relationships between FDM parameters and composite properties and highlight the significant potential of integrating ML and statistical analysis to optimize the design and manufacturing of high-performance AM fiber-reinforced composites.

Declared a pandemic disease, COVID-19 has affected the lives of millions of people and had significant effects on public health. Despite the development of effective vaccines against COVID-19, cases continue to increase worldwide. According to studies in the literature, artificial intelligence methods are used effectively for the detection of COVID-19. In particular, deep-learning-based approaches have achieved very good results in clinical diagnostic studies and other fields. In this study, a new approach using x-ray images is proposed to detect COVID-19. In the proposed method, the angle transform (AT) method is first applied to the x-ray images. The AT method proposed in this study is an important novelty in the literature, as there is no such approach in previous studies. This transformation uses the angle information created by each pixel on the image with the surrounding pixels. Using the AT approach, eight different images are obtained for each image in the dataset. These images are trained with a hybrid deep learning model, which combines GoogleNet and long short-term memory (LSTM) models, and COVID-19 disease detection is carried out. A dataset from the Mendeley database is used to test the proposed approach. A high classification accuracy of 98.97% is achieved with the AT + GoogleNet + LSTM approach. The results obtained were also compared with other studies in the literature. The presented results reveal that the proposed method is successful for COVID-19 detection using chest x-ray images. Direct transfer methods were also applied to the data set used in the study. However, worse results were observed according to the proposed approach. The proposed approach has the flexibility to be applied effectively to different medical images.

Son zamanlarda kimlik tanıma başta olmak üzere, yaş tanıma ve cinsiyet tanıma gibi konular hem akademik hem de diğer alanlarda (sanayi, bilişim, sağlık vb.) yaygın olarak üzerinde araştırma yapılan konulardandır. Cinsiyet tanıma (CT) erkek ve kadın arasında ayrım yapan özelliklere dayalı olarak bireyin cinsiyetini belirlemektir. Yapay zeka alanında, CT örüntü tanıma yönteminin en önemli uygulamalarından biri olarak kabul edilmektedir. Bu çalışmada kişilerin 5 farklı bölgesine takılmış olan ivmeölçer, manyetometre ve jiroskop sensörlerinden elde edilen işaretler kullanılarak cinsiyet tanıma (CT) için üç (3) farklı öznitelik çıkarım metodu önerilmiştir. İşaretlerden öznitelik çıkarımı CT’nın en önemli aşamalarından biridir. Çünkü CT’nin başarısı çıkarılan özniteliklere bağlıdır. Ancak CT için uygun özniteliklerin çıkarım zor bir problemdir. Sensörlerden elde edilen işaretlere Bir Boyutlu Yerel İkili Örüntüler (1B-YİÖ), Bir Boyutlu Sağlam Yerel İkili Örüntüler (1B-SYİÖ) ve Ağırlıklandırılmış Bir Boyutlu Sağlam Yerel İkili Örüntüler (A-1B-SYİÖ) olmak üzere farklı dönüşüm yöntemleri uygulanmıştır. Dönüşüm işlemlerinden sonra yeni oluşan işaretlerde istatistiksel öznitelikler elde edilmiştir. Bu öznitelikler kullanılarak farklı makine öğrenmesi yöntemler (SVM, RF, YSA, Knn) ile sınıflandırma işlemleri gerçekleştirilmiştir. Elde edilen sonuçlara göre 1B-YİÖ (%96.04), 1B-SYİÖ (%96.72) ve A-1B-SYİÖ (%97.28) yöntemlerin CT için etkin öznitelikler sağladığı görülmüştür. Bu çalışmada önerilen yeni yaklaşımlar sayesinde sensör işaretleri kullanılarak CT işleminin yüksek bir başarı oranı ile gerçekleştirildiği belirlenmiştir.

Purpose: The study aims to predict and optimize two critical parameters, surface roughness and energy consumption, in additive manufacturing (AM) processes using carbon fiber-reinforced polylactic acid (PLA) material. These parameters are essential for enhancing the efficiency and quality of AM-produced components.Design/methodology/approach: A mechanical connector was fabricated using the AM process, employing the Box–Behnken experimental design method with four input parameters: layer thickness (LT) (150–200–300 µm), infill density (ID) (40%–80%–100%), nozzle temperature (NT) (200–210–220 °C) and printing speed (PS) (40–80–120 mm/s). Predictive models were developed using four machine learning (ML) algorithms: Gaussian process regression (GPR), extreme gradient boosting (XGBoost), artificial neural network (ANN) and random forest regression (RFR). Model performance was evaluated using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE) and R-squared (R²). Additionally, ANOVA was conducted to identify the most influential parameters on surface roughness and energy consumption.Findings: The RFR model demonstrated superior accuracy with low error values and high R² scores in estimating both surface roughness and energy consumption. ANOVA results indicated that LT (43.96%) and PS (40.01%) were the most significant factors affecting surface roughness, while LT (50.45%) and ID (27.58%) significantly influenced energy consumption.Originality/value: This study underscores the effectiveness of ML algorithms and statistical analysis in modeling and optimizing AM processes. The findings provide valuable insights into improving the efficiency and quality of 3D-printed components, particularly through the integration of carbon fiber-reinforced materials and advanced predictive modeling techniques.

Delta Robot is taking an important place in industry 4.0. Its ability to work fast and with precision is the reason why this robot now is used worldwide in the industrial field. Delta robots are mostly used for packaging, 3D printing, pick and place, etc. This robot can be controlled using programmable logic controller (PLC) devices. In recent years, image processing methods have been widely used in many robot applications. In this study, it is aimed to ensure that the robot successfully moves to the determined points by using image processing methods. Images for this study were obtained by attaching a camera to the robot system. In this study, the aim is to set the robot to move based on the visual data that is taken by the camera. The coordinates of each object are defined after several steps in image processing. To be able to move the robot to the desired coordinates, inverse and forward kinematic analysis is proposed. The obtained values are transferred to PLC using OPC. The necessary coding in TIA Portal is done to control the motor drivers so that the three stepper motors can ensure the delta robot moves to the desired coordinate position.