Person: ÖZTÜRK, FERRUH
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Publication The investigation of the effects of spray parameters on the thermal and mechanical properties of 22MnB5 steel during hybrid quenching process(Begell House, 2021-01-01) Sevilgen, Gökhan; Ertan, Rukiye; Bulut, Emre; Öztürk, Ferruh; Eşiyok, Ferdi; Abi, Tuğçe Turan; Alyay, İlhan; SEVİLGEN, GÖKHAN; ERTAN, RUKİYE; BULUT, EMRE; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0002-7746-2014; 0000-0001-9159-5000; ABG-3444-2020; KIH-2391-2024; AAG-8907-2021; JIW-7185-2023In this paper, the investigation of the effects of spray parameters on the thermal and mechanical properties of 22MnB5 steel during the hybrid quenching (HQ) process was performed. The HQ method presented in this study includes early removal of hot-stamped parts from the die and transfer to an external multinozzle spray cooling device. The proposed method was developed due to the need for improving disadvantages of the traditional hot stamping process such as complexity of controlling the cooling rate during die quenching by using cooling channels and of providing the reduced tool contact surface temperature after a certain cycle of the hot stamping process. This paper focuses on the temperature distribution and mechanical characteristics of high-strength 22MnB5 steel during the HQ process. Moreover, the methodology developed in this paper can be used to get tailored parts where the cooling rates are locally chosen to achieve structures with graded properties, i.e., to allow local modification of final mechanical properties in order to provide high energy absorption to enhance the crashworthiness of the whole component and thus to improve the vehicle safety performance. The three-dimensional numerical model of spray cooling was also developed by using the computational fluid dynamics (CFD) method to get the suitable process parameters such as spray height and initial blank temperature and to present the detailed heat transfer analysis of hot-stamped parts during the hybrid quenching process.Publication Simplified optimization model and analysis of twist beam rear suspension system(Sage Publications, 2021-04-01) Albak, Emre İsa; Solmaz, Erol; Öztürk, Ferruh; ALBAK, EMRE İSA; SOLMAZ, EROL; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü; 0000-0001-9215-0775; 0000-0001-9369-3552; I-9483-2017; HRA-1531-2023; FRD-1816-2022Twist beam rear suspension systems are frequently used in front wheel drive cars owing to their compactness, lightweight and cost-efficiency. Since the kinematic behavior of twist beam rear suspension systems are determined by the elastic properties of the twist beam, the twist beam is the most critical component of this suspension system. In the study, a simplified optimization model is presented to offer designers the most suitable beam structure in the early stage of the vehicle system development. With the optimization model, designers will be able to obtain the most suitable twist beam structure in a very short time. Opposite wheel travel analysis based on finite element modeling of twist beam is conducted to examine the kinematic performance of the twist beam rear suspension. The cross-section, position and direction of the twist beam are the most important parameters affecting the performance of the twist beam rear suspension system. In this study, optimization studies with 25 design variables including variable cross-sections, twist beam position and twist beam orientation are performed. Nine different optimization studies are carried out to investigate the effects of design variables better. In optimization studies carried out with the genetic algorithm, the objective and constraint functions are obtained with the moving least squares meta-modeling method. In the study, toe angle, camber angle and roll steer are decided as constraints, and mass as the objective function. With the optimization models, lightweight designs up to 25% have been obtained according to the initial design. It is validated that the proposed simplified model and analysis of twist beam rear suspension with connecting bushing is a quite efficient approach in terms of accuracy and to speed up the optimum design process.Publication Rubber bushing optimization by using a novel chaotic krill herd optimization algorithm(Springer, 2021-08-27) Bilal, Halil; Öztürk, Ferruh; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Otomotiv Mühendisliği Bölümü; JIW-7185-2023This study's primary purpose is to improve the original krill herd (KH) optimization algorithm by using chaos theory and propose a novel chaotic krill herd (CKH) optimization algorithm. Fourteen different chaotic map functions have been added to the several steps of the KH and CKH optimization algorithms already existing in the literature to improve their performances. Six different well-known benchmark functions have been used to test the performances of the developed algorithm. The proposed algorithm has better performance to reach the global optimum of the objective function which has many local minimums. The proposed algorithm improved the KH and CKH optimization algorithms' performances which already exist in the literature. Proposed novel CKH has been applied to rubber bushing stiffness optimization which is a real automotive industry problem. Obtained results have been compared with KH, CKH, genetic algorithm (GA), differential evaluation algorithm (DE) and particle swarm optimization (PSO). The proposed algorithm has better performance to reach the global optimum of the objective function. The performance and validity of the algorithm have been proved not only by using six different benchmark functions but also by using finite element analysis of rubber bushing. The study is also a unique optimization activity that uses the KH algorithm to optimize rubber bushing by using nonlinear finite element analysis.Publication A new approach for battery thermal management system design based on grey relational analysis and latin hypercube sampling(Elsevier, 2021-09-16) Bulut, Emre; Albak, Emre İsa; Sevilgen, Gökhan; Öztürk, Ferruh; BULUT, EMRE; ALBAK, EMRE İSA; SEVİLGEN, GÖKHAN; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü; Bursa Uludağ Üniversitesi/Gemlik Asım Kocabıyık Meslek Yüksekokulu/Hibrit ve Elektrikli Araç Teknolojisi; 0000-0001-9159-5000; 0000-0001-9215-0775; 0000-0002-7746-2014; ABG-3444-2020; AAG-8907-2021; I-9483-2017; JCO-2416-2023; FRD-1816-2022A liquid cooling system is an effective type of battery cooling system on which many studies are presented nowadays. In this research, the effects of the mass flow rate and number of channels on the maximum temperature and pressure drop are investigated for multi-channel serpentine cooling plates. A new approach with LHS and GRA is used to obtain the optimum ranges of design parameters to minimize the pressure drop, maximum temperature and to maximize the convective heat transfer coefficient. In this study, the values of the parameters for the numerical modeling are obtained by the experiments. The width and height of the serpentine channel and mass flow rate are chosen as input parameters and the pressure drop, convective heat transfer coefficient and maximum temperature are selected as output parameters. Comparing with the base design, the optimized design provided up to 40.3% decrease in the pressure drop with a penalty of 11.3% decrease in the convective heat transfer coefficient with a slight decrease in the maximum temperature. The proposed approach can be used to design better cooling plates to keep the batteries in safe temperature ranges and to reduce the power consumption by optimizing the pressure drop and maximum temperature values.Publication Correlation between objective and subjective tests for vehicle ride comfort evaluations(Sage Publications Ltd, 2022-02-23) Boke, Tevfik Ali; Bozkurt, Rasim; Ergül, Murat; Özturk, Dogan; Emiroğlu, Sinan; KAYA, NECMETTİN; Albak, Emre Isa; Öztürk, Ferruh; ALBAK, EMRE İSA; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Gemlik Asım Kocabıyık Meslek Yüksekokulu.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0001-9215-0775; 0000-0002-8297-0777; I-9483-2017One of the most important criteria that vehicle customers take into consideration when buying vehicles is ride comfort. Ride comfort is determined in two different ways, called objective and subjective methods. This research presents an approach for subjective and objective evaluations of vehicle ride comfort through road tests. In this study, first, reliable driver evaluation for subjective tests is made and tests are performed with these reliable drivers. Correlation between objective and subjective test results is achieved for different vehicle types and road groups and software is developed to evaluate subjective ride comfort by using objective test data acquired from the vehicle. This software is intended to be used instead of subjective tests in future vehicle development studies.Publication Estimation of energy management strategy using neural-network-based surrogate model for range extended vehicle(Mdpi, 2022-12-14) Türker, Erkan; Bulut, Emre; Kahraman, Arda; Çakıcı, Mehmet; Öztürk, Ferruh; Türker, Erkan; BULUT, EMRE; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0001-9159-5000; 0000-0003-0150-8052; JCO-2416-2023; AAG-8907-2021; CDI-5654-2022; JIW-7185-2023In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects.Publication A comparative study on conventional and hybrid quenching hot forming methods of 22mnb5 steel for mechanical properties and microstructure(Springer, 2022-08-01) Eşiyok, Ferdi; Ertan, Rukiye; Sevilgen, Gökhan; Bulut, Emre; Özturk, Ferruh; Alyay, İlhan; Abi, Tuğçe Turan; ERTAN, RUKİYE; SEVİLGEN, GÖKHAN; BULUT, EMRE; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0002-7746-2014; 0000-0001-9159-5000; KIH-2391-2024; JIW-7185-2023; JCO-2416-2023; ABG-3444-2020In this paper, the conventional hot forming and hybrid quenching hot forming processes of Al-Si-coated 22MnB5 steel sheet were investigated and compared at 0.5 s-15 s holding times in the press tool related to the mechanical properties, microstructure, and dimensional accuracy. The conventional hot forming method is classified as a direct method and an indirect method. Both methods have limitations due to processing time and cooling of the press tool. To speed up the process, an alternative cooling method based on spray or jet cooling was used outside of the die tool. The hybrid quenching method involves hot forming and spray cooling process. This method, using spray parameters, provides more effective control in mechanical properties and microstructure compared to the conventional method by using spray parameters. Vickers hardness tests and tensile tests were carried out to compare mechanical properties. Changes in the microstructure of the materials were investigated using an optical microscope. The results show that spray cooling can be used as part of quenching in the hot forming process by reducing the holding time in the press tool by 97%. However, the microstructure, mechanical properties, and geometry deviations of the stamped parts are still below tolerances after the hybrid quenching hot forming process. The use of the hybrid quenching method with multi-point nozzles in the hot forming process resulted in sheet hardness up to 470 HV1 and 8% elongation with tensile strength of 1500 MPa.Publication Predictions of the design decisions for vehicle alloy wheel rims using neural network(Sage Publications Ltd, 2022-08-02) Topaloğlu, Anıl; Kaya, Necmettin; Öztürk, Ferruh; Topaloğlu, Anıl; KAYA, NECMETTİN; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0002-8297-0777; GPL-5775-2022; R-4929-2018; JIW-7185-2023The weight and modal performance of the vehicle wheels are two essential factors that affect the driving comfort of a vehicle. The main objective of this study is to present an efficient approach to reduce the weight and enhance the modal performance of the wheel by reducing the design time and computational cost. The alloy wheel rim is often used for lightweight wheel design. In this study, an approach is presented for the lightweight design of alloy wheel rims. An intelligent approach based on neural networks (NNs) is introduced to predict the optimum design parameters of the wheel rim during the wheel design phase and to improve the wheel optimization process. The Latin hypercube and Hammersley designs of the experimental methods were used to obtain a training dataset with finite element analysis. The NN and multiple linear regression (MLR) models were trained to predict the weight, first-mode frequency, and displacement values. A multi-objective genetic algorithm was employed to optimize the design decisions based on the predicted values. It was used to compute the optimum results with both the NN and MLR models for a better prediction accuracy of the wheel rim design parameters. The proposed approach allows designers to optimize design decisions and evaluate design modifications during the early stages of the wheel development phase. The surrogate-based optimization method plays an important role in the wheel rim optimization process, particularly when the optimization model is established based on computationally expensive finite element simulations, testing, and prototypes. The results show the effectiveness of the NN-combined genetic optimization approach in predicting the responses and optimizing the design decisions for the alloy wheel rim design by reducing engineering time and computational cost.Publication Determination of the fleet size of agvs with agv pools using a genetic algorithm and artificial intelligence(Mdpi, 2023-07-01) Şenaras, Onur Mesut; EREN ŞENARAS, ARZU; ÖZTÜRK, NURSEL; Solmaz, Erol; SOLMAZ, EROL; ÖZTÜRK, FERRUH; Öztürk, Ferruh; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği.; AAG-9336-2021; JBJ-1095-2023The utilization of low-cost AGVs in the industry is increasing every day, but the efficiency of these systems is low due to the lack of a central management system. Low-cost AGVs' main characteristic is navigation via magnetic sensors, which they follow via magnetic tape on the ground with a low-level automation system. The disadvantages of these systems are mainly due to only one circuit assignment and the lack of system intelligence. Therefore, in this study, AGV pools were employed to determine the required AGV number. This study begins by calculating the required AGV number for each AGV circuit combination assigned to every parking station by the time window approach. Mathematical-solution-based mixed integer programming was developed to find the optimum solution. Computational difficulties were handled with the development of a genetic-algorithm-based approach to find the solutions for complex cases. If production requirements change, system parameters can be changed to adapt to the production requirements and there is a need to determine the number of AGVs. It was demonstrated that AGVs and pool combinations did not lead to any loss in production due to the lack of available AGVs. It was shown that the proposed approach provides a fleet size which requires five fewer AGVs, with a 29% reduction in the number of AGVs. The effects of system parameter changes were also investigated with artificial neural networks (ANNs) to estimate the required AGVs in the case of production requirement changes. It is necessary to determine the effect of the change in system parameters on the number of AGVs without compromising on computational cost and time, especially for complex systems. Thus, in this study, an artificial neural network (ANN), the response surface method (RSM), and multiple linear regression (MLR) techniques were used to examine the effects of the system parameter changes on the AGV number. In the present case, the ANN obtained the solution at a good rate with reduced computational costs, time, and correction errors compared to the GA, at 0.4% (ANN), 7% (RSM), and 24% (MLR). The results show that the ANN provides solutions which can be used in workshops to determine the number of AGVs and also to predict the effect of changes in system parameters.Publication Smart cooling design using dual loop cooling to increase engine efficiency and decrease fuel emissions with artificial intelligence(Elsevier, 2022-10-26) Kula, Sinan; Bulut, Emre; Altay, Esad; Sümer, Osman; Öztürk, Ferruh; Kula, Sinan; BULUT, EMRE; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0001-9159-5000; JCO-2416-2023; HGN-4395-2022; JGV-6240-2023In this study, smart cooling design and optimization, which is based on dual loop cooling system, is used to increase the efficiency of the engine and decrease the fuel emission levels with the artificial intelligence approach. Dual circuit cooling system is used to cool down the charged air and condenser for the 1.6 lt turbocharged diesel engine. The main objective is to increase the efficiency of the engine and decrease the fuel emission levels with smart cooling system design using 1D analysis, experimental tests and neural networks. Water-cooled air charger and condenser are placed separately on engine bay. Whereas, similar applications have been used for these modules integrated on the engine itself. Artificial Neural Network approach is applied in order to optimize the water cooled air charger sizing. Input data is generated by using 1D model within the correlation of experimental test results both on dyno and road conditions. Experimental and 1D analysis data comparison shows that they are very coherent. Results showed that efficiency of the engine is increased and CO2 (g/km) emission levels are decreased about 4,1% in WLTP cycle. It's obtained with efficient dual loop cooling system and optimization based on 1D model and ANN approach.