Browsing by Author "EMEL, ERDAL"

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Deep learning-based detection of aluminum casting defects and their types
(Elsevier, 2022-11-28) Parlak, İsmail Enes; Emel, Erdal; EMEL, ERDAL; Bursa Uludağ Üniversitesi/Endüstri Mühendisliği Bölümü; 0000-0002-9220-7353; N-8691-2014
Due to its unique properties, high-pressure aluminum die-casting parts are used quite often, especially in the automotive industry. However, die-casting is a process which requires non-destructive testing of the critical components using technologies such as X-ray to examine the internal defects that are not otherwise visible. Such a timeconsuming visual inspection requires well-trained human specialists with the utmost attention. In this study, state-of-the-art deep learning-based object detection methods were trained using an X-ray image dataset of aluminum parts to detect internal defects and predict their types without human attention. The Al-Cast image dataset used in this study contains 3466 images of parts produced in high-pressure die casting machines. It is shared as an open-access original database for the nondestructive testing (NDT) community. ASTM standard definitions for aluminum casting defects are used in determining their types, and to the best of our knowledge, this novel approach is the first in the deep learning literature. Among the 12 deep learning-based object detection methods used for comparison, YOLOv5 versions yielded the highest detection accuracy (0.956 mAP) with the shortest training time (0.75 h). In addition, tests were performed for both original and contrast enhanced images on 348 test images. YOLOv5m performed an accurate detection performance of 95.9%. Additionally, YOLOv5n can process 132 images per second. This study can be considered the first step of an artificial intelligence product that can detect internal defects of aluminum casting parts with industrial standards and explain the relationship between highpressure injection die casting parameters and these defects.
Mixed-model assembly line balancing with smoothing approach based on tabu search algorithm
(Gazi Üniversitesi, 2015-01-01) Yağmahan, Betül; Emel, Erdal; YAĞMAHAN, BETÜL; EMEL, ERDAL; Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü; 0000-0003-1744-3062; 0000-0002-9220-7353; N-8691-2014; B-5557-2017
Mixed-model assembly lines are needed for the assembly of products with a variety of models at comparatively lower costs. The design of such lines requires the work to be done at stations well balanced, satisfying the constraints such as time, space and location for optimal productivity and efficiency. This paper presents a heuristic algorithm for the mixed-model assembly line balancing problem to minimize the number of stations for a given cycle time. The proposed algorithm further reduces time discrepancies among stations due to differences in times for common operations of different models by using a smoothing approach which is based on the tabu search algorithm.
Remaining useful life estimation of turbofan engines with deep learning using change-point detection based labeling and feature engineering
(Mdpi, 2023-11-01) Ensarioğlu, Kıymet; Emel, Erdal; EMEL, ERDAL; İnkaya, Tülin; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi.; 0000-0002-6260-0162; 0000-0002-9220-7353; JNT-1214-2023; N-8691-2014
Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional-convolutional neural network-long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics.