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    Development of Temperature Control in Climatic Test Chambers with LSTM-based Deep Neural Network Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2023) Cakiroglu, Abdullah; Bayrak, Gökay; Nurel, Ayberk
    Environmentally conditioned climatic test chambers are test devices that can simulate temperature and relative humidity conditions in a wide range as standard and can be produced in various volumes. PID is the most used control method in climatic chambers. Since the parameters in the PID controller are determined for wide ranges, the system performance is insufficient since the parameters at intermediate values and different volumes need to be optimized, long waiting times and high energy consumption to reach the target temperature value may cause the PID controller to not fully meet the requirements under some conditions. This study presents an innovative method for the control of interior space heating system with LSTM (Long-Short Time Memory)-based deep neural network model in accordance with the created test recipe. The model is trained with the dataset created with the outputs of the PID controller. As a result of the method used, an error value of 0.0014 was obtained. The presented results show that the trained model successfully predicts the outputs of the heating system according to the target temperatures. © 2023 IEEE.
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    Otomotiv Üretim Hatlarindaki 3D Parçalarin Kalite Kontrolü Için Endüstri 4.0 Ile Uyumlu Yapay Görme Sisteminin Geliştirilmesi
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bayrak, Gökay; Cakiroglu, Abdullah; Yilmaz, Imren Ozturk; Bilici, Abdullah Yasin; Candemir, Yasin Atalay
    Image processing technology is a technology that is increasingly used in almost all sectors and continues to develop today. When its usage areas are examined, it is frequently used in mold design and manufacturing, metal part production, and quality control applications after sheet metal forming. The most important advantages of image processing technology are that it reduces human labor, has a low error rate, and accelerates the processing time in the area where it is used. In this study, a machine vision system compatible with Industry 4.0 has been developed for the quality control of 3D parts in automotive production lines. An image processing-based quality control system, which is compatible with Industry 4.0, is designed with a high control sensitivity compared to existing methods, low processing time, and error detection of post-production parts, various metric measurements. The designed system consists of two main parts, the control software, and the image acquisition cabinet. First, the raw image taken in the image acquisition cabinet with appropriate lighting was transferred to the image processing software and then all the measurement results were obtained. Image processing software was performed with LabVIEW Vision Builder AI (Automated Inspection) program. A suitable software infrastructure was created for the parts by performing pixel-based measurements and morphological operations on the image. When the results obtained are examined, it is seen that error detection and measurement processes can be performed with an error rate of less than 0.04%. © 2022 IEEE.