Person:
SOLMAZ, EROL

Profile Picture

Email Address

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

SOLMAZ

First Name

EROL

Name

Filters

2021 - 20232
Reset filters

Settings

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    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-1816-2022
    Twist 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.
  • No Thumbnail Available
    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-1095-2023
    The 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.