Browsing by Author "Apakhan, M."

Now showing 1 - 3 of 3

Citation - Scopus: 2
Cost-Effectiveness of an Automatic Lubrication System for Bearings
(Springer Science and Business Media Deutschland GmbH, 2024) Yildiz, S.; Apakhan, M.; Aksoy, M.H.
Improper lubrication is a leading cause of bearing failures, accounting for half of all instances. Lubricating bearings with grease or oil forms a protective film that prevents direct metal-to-metal contact, reducing friction and overheating, and prolonging the bearing’s lifespan. Lubrication also acts as a barrier against foreign particles and wear. In this study, an analysis of automatic lubrication in a milling plant was carried out and compared to manuel lubrication. A milling factory with 22 roller mills with a total production capacity of 450 tons/day is considered. It is calculated that the failure of eight bearings in each mill roller due to lubrication issues results in a substantial cost of $39,000 over a two-year period. The Automatic lubrication system named “SmartLub” regulates lubricant quantity, timing, and application points, ensuring optimal lubrication. The choice of the oil pump was determined by considering both the viscosity of the oil and the head loss within the piping system utilized. By adhering to calculated frequencies, the system extends bearing service life to four years. Furthermore, the manual system reduces labor costs by $400 per roller mill every two years, while unplanned downtimes caused by lubrication issues are minimized. The automatic system eliminates the need for bi-monthly one-day shutdowns in the conventional lubrication systems, saving a total of six days per year. With an estimated lifespan of 20 years, the system achieved a payback period of 1.23 years, demonstrating its cost-effectiveness and long-term benefits. Overall, automation in bearing lubrication enhances machine efficiency, reduces spare parts and maintenance costs, and ensures optimal lubrication in the grain milling sector. Its implementation leads to extended bearing lifespan, reduced downtime, and improved profitability. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - Scopus: 1
Detection of Peak Points for Wear Control of Band Saw Blades
(Institute of Electrical and Electronics Engineers Inc., 2024) Uymaz, O.; Kaya, E.; Uymaz, S.A.; Akgül, Ü.B.; Apakhan, M.
Industrial band saw cutting machines are widely used in metalworking and mass production processes due to their high precision and efficiency. These machines offer significant advantages such as reducing labor costs, increasing productivity, ensuring occupational safety, and saving energy. However, the wear or breakage of band saw blades can negatively impact production quality and machine performance. This study compares four different edge detection algorithms for detecting wear and fractures in the blades of industrial band saw cutting machines. These algorithms are LDC, HED, Sobel, and Canny. The selected four algorithms were applied to a dataset obtained from a project supported by the 1711 Artificial Intelligence Ecosystem Call of TÜBİTAK. The performance of the edge detection algorithms was evaluated using statistical metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Experimental results showed that deep learning-based algorithms (Lightweight Dense CNN (LDC) and Holistically-Nested Edge Detection (HED)) performed with higher accuracy compared to image processing-based algorithms (Sobel and Canny). In particular, the LDC algorithm demonstrated the best performance with shorter processing times and fewer parameters. These findings reveal the potential of using deep learning-based edge detection algorithms for real-time fault detection and predictive maintenance in industrial cutting machines. The results obtained in this study indicate that deep learning-based methods can be effectively utilized to enhance the efficiency and reliability of industrial cutting machines. In this context, the applicability of the cost-effective and highly efficient LDC algorithm is particularly noteworthy for resource-limited systems. © 2024 IEEE.
Effect of Scourer Screen Patterns on the Wheat Husk Removal Efficiency
(The Association of Intellectuals for the Development of Science in Serbia "The Serbian Academic Center" Novi Sad, 2022) Bal, D.; Bakırhan, M.; Apakhan, M.; Ekem, H.; Şahin, Ö.S.; Aksoy, M.H.
The scourer machines remove wheat husk contaminated with pesticides and other impurities that adversely affect product quality and shelf life. For this purpose, at least one scourer machine is used in each industrial grain flour factory, depending on the capacity. The efficiency of these machines is highly dependent on the surface texture and scourer screen patterns. In this study, the efficiency of the wheat scourer machines with various wall patterns and meshes was investigated experimentally. The investigation employed six distinct types of scourer screens. Type-4 has produced the best results in fractured grain rate, whereas Type-2 has produced the best in ash content. It was revealed that wheat's scouring efficiency and physical and chemical properties vary depending on the scourer screen pattern. © 2022 by the authors.