Ertuğrul, Şeniz

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/6718
Name Variants
Ertugrul, Seniz
Ertugrul, S.
Ertugrul, S
Ertuğrul, Şeniz
Job Title
Email Address
seniz.ertugrul@ieu.edu.tr
Main Affiliation
05.11. Mechatronics Engineering
Status
Current Staff
Website
ORCID ID
0000-0003-1766-1676
Scopus Author ID
6602271436
Turkish CoHE Profile ID
E60FF2AFC5E4A822
Google Scholar ID
9EwnOyQAAAAJ
WoS Researcher ID
LVS-6386-2024
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Seniz_Ertugrul.jpg (13.08 KB)

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
2
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents

39

Citations

545

h-index

11

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Documents

32

Citations

442

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Scholarly Output

21

Articles

8

Views / Downloads

63/207

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

41

Scopus Citation Count

50

Patents

0

Projects

4

WoS Citations per Publication

1.95

Scopus Citations per Publication

2.38

Open Access Source

10

Supervised Theses

0

JournalCount
2022 8Th Internatıonal Conference on Control, Decısıon And Informatıon Technologıes (Codıt'22)2
2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022 -- 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022 -- 17 May 2022 through 20 May 2022 -- Istanbul -- 1805912
2025 Symposium on Computational Intelligence on Engineering/Cyber Physical Systems-CIES -- Mar 17-20, 2025 -- Trondheim, Norway1
7th IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) -- Dec 07-09, 2022 -- Kuala Lumpur, Malaysia1
7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA -- MAY 23-24, 2025 -- Ankara, TURKIYE1
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Now showing 1 - 10 of 20
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Integrated Drive Train and Structural Optimization for a Dynamic System: an Evolving Conceptual Design Algorithm
    (IEEE, 2022-05-17) Gulec, Musa Ozgun; Ertugrul, Seniz
    Selecting the most suitable motor sizes, gear boxes and structure under certain constraints or desired values such as payload, speed, deflections, total weight, etc. for a dynamic system is an exhaustive and time-consuming iterative process. To overcome this problem, a newevolving conceptual design algorithm is developed. The suggested algorithm can be used for the conceptual design of any dynamic system including drive-train and structural optimization. To illustrate the suggested methodology, a robot manipulator, having 3 degrees of freedom, is selected as a case study. The objective function is minimizing the robot mass while satisfying the desired dynamic requirements and constraints of link deflections. A dynamic simulation environment for flexible body motion, containing 3 DOF robot manipulator drive-trains and flexible links, is developed in an evolving optimization loop. The lumped parameter estimation method is used to model the flexibility of uniform links in Simmechanics by allowing the estimation of deflections caused by the dynamic motion. Thus, both dynamic and structural simulations are made simultaneously in Simmechanics with no additional software. Hence, drive-trains and thickness of all links are simultaneously optimized by using the suggested evolving conceptual design algorithm.
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    Article
    Citation - WoS: 7
    Citation - Scopus: 7
    The Series Elastic Gripper Design, Object Detection, and Recognition by Touch
    (Asme, 2021-07-06) Kaya, Ozan; Taglioglu, Gokce Burak; Ertugrul, Seniz
    In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with a series elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects, is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A deep neural network (DNN) model is trained by synthetic data extracted from standard tessellation language (STL) file of selected objects. For experimental setup, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation, and the DNN model. The best DNN model is capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence, the proposed design of the gripper and the algorithm achieved the recognition of selected objects without the need for additional force/torque or tactile sensors.
  • Thumbnail Image
    Conference Object
    Integrated Drive Train and Structural Optimization for a Dynamic System: An Evolving Conceptual Design Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2022-05-17) Gulec, Musa Ozgun; Ertugrul, Seniz
    Selecting the most suitable motor sizes, gear boxes and structure under certain constraints or desired values such as payload, speed, deflections, total weight, etc. for a dynamic system is an exhaustive and time-consuming iterative process. To overcome this problem, a newevolving conceptual design algorithm is developed. The suggested algorithm can be used for the conceptual design of any dynamic system including drive-train and structural optimization. To illustrate the suggested methodology, a robot manipulator, having 3 degrees of freedom, is selected as a case study. The objective function is minimizing the robot mass while satisfying the desired dynamic requirements and constraints of link deflections. A dynamic simulation environment for flexible body motion, containing 3 DOF robot manipulator drive-trains and flexible links, is developed in an evolving optimization loop. The lumped parameter estimation method is used to model the flexibility of uniform links in Simmechanics by allowing the estimation of deflections caused by the dynamic motion. Thus, both dynamic and structural simulations are made simultaneously in Simmechanics with no additional software. Hence, drive-trains and thickness of all links are simultaneously optimized by using the suggested evolving conceptual design algorithm.
  • Thumbnail Image
    Conference Object
    Experimental Evaluation of the Success of Peg-in-Hole Tasks Learned from Demonstration
    (Institute of Electrical and Electronics Engineers Inc., 2022-05-17) Arguz, Serdar Hakan; Altun, Kerem; Ertugrul, Seniz
    Industrial robots are traditionally programmed by hard-coding the desired motion into them. That approach, however, costs significant time and effort and shows little to no promise in transferring human skills to robots. Programming by demonstration (PbD) is an alternative approach that allows robots to learn tasks from demonstrations. Because of its several advantages over the traditional method, PbD is particularly suited for tasks encountered in assembly operations, the most typical of which is the peg-in-hole task. A successful PbD implementation for a peg-in-hole task requires that the peg should still be inserted into the hole even under situations that are not encountered during the demonstrations. Previous research in the field shows that the success rate of a peg-in-hole task under such cases varies greatly. In this study, we use a UR5 manipulator to experimentally investigate how the success rate of a peg-in-hole task changes with respect to the novelty of the task, quantified in terms of the distance of the hole to its original position. It is found that the success ratio decreases as the novelty of the task increases. To increase the performance, the use of strategies that alter the robot's motion dynamically in the run time is suggested for future work.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 3
    Citation - Scopus: 3
    Experimental Evaluation of the Success of Peg-In Tasks Learned From Demonstration
    (IEEE, 2022-05-17) Arguz, Serdar Hakan; Ertugrul, Seniz; Altun, Kerem
    Industrial robots are traditionally programmed by hard-coding the desired motion into them. That approach, however, costs significant time and effort and shows little to no promise in transferring human skills to robots. Programming by demonstration (PbD) is an alternative approach that allows robots to learn tasks from demonstrations. Because of its several advantages over the traditional method, PbD is particularly suited for tasks encountered in assembly operations, the most typical of which is the peg-in-hole task. A successful PbD implementation for a peg-in-hole task requires that the peg should still be inserted into the hole even under situations that are not encountered during the demonstrations. Previous research in the field shows that the success rate of a peg-in-hole task under such cases varies greatly. In this study, we use a UR5 manipulator to experimentally investigate how the success rate of a peg-in-hole task changes with respect to the novelty of the task, quantified in terms of the distance of the hole to its original position. It is found that the success ratio decreases as the novelty of the task increases. To increase the performance, the use of strategies that alter the robot's motion dynamically in the run time is suggested for future work.
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    Article
    Citation - WoS: 4
    Citation - Scopus: 6
    Pareto Front Generation for Integrated Drive-Train and Structural Optimisation of a Robot Manipulator Conceptual Design Via Nsga-Ii
    (Sage Publications Ltd, 2023-03) Güleç, Musa Özgün; Ertugrul, Şeniz
    Due to the complexity of the process, there is no single solution for determining the motors, gearboxes and structures of a robot manipulator according to the desired dynamic performance while minimising both the deflections in the structure during the dynamic motion and total robot weight. The solution of this integrated drive-train and dynamic structural optimisation problem is generalised for three degrees of freedom (DOF) robot manipulator via Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to obtain the Pareto front of any desired robot manipulator overall conceptual design, including motors, gearboxes and thicknesses of the links. A flexible body dynamic simulation model was created in the MATLAB Simmechanics environment. The flexible bodies were defined via lumped parameter estimation method, which allows observation of the deflections in links during the dynamic motion. A library containing technical data related to motors and gearboxes was created to be utilised in the optimisation algorithm. The method accelerates the time-consuming iterative process for obtaining optimum conceptual design solutions for a dynamic system and allows for easy modification of design parameters and constraints. It also makes the algorithm suitable for different types of dynamic system designs.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Comparative Study of Identification Using Nonlinear Least Squares Errors and Particle Swarm Optimization Algorithms for a Nonlinear Dc Motor Model
    (Springer Science and Business Media Deutschland GmbH, 2021-08-24) Abedinifar M.; Ertugrul S.; Abedinifar, Masoud; Ertugrul, Seniz
    For an accurate dynamic analysis of the real-world systems, there is an extensive demand for developing the mathematical models. An accurate mathematical model can be used for optimization, fault diagnosis, controller design, etc. Many studies have been performed for developing the mathematical models of the real-world systems. They commonly utilize linear models while ignoring the possible existing nonlinearities in the model. However, having a general mathematical model including nonlinearities has great significance in performance analysis and proper control of the systems. In this paper, two algorithms including Nonlinear Least Squares Errors (NLSE) and Particle Swarm Optimization (PSO) are utilized for model identification. For this aim, the nonlinear model of a Direct Current (DC) motor is used as a case study to compare the performance of the two algorithms. In the first step, a white-box mathematical model of the DC motor including the nonlinear friction terms is developed. Then, the artificial data is generated through the developed model with the real parameters of a DC motor. Finally, NLSE and PSO algorithms are carried out to determine the unknown parameters of the nonlinear model through generated artificial data. All unknown parameters of the model are identified at the same time. The results of the two algorithms are evaluated and compared. It is shown that the PSO algorithm determines the model parameters more accurately compared to the NLSE algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 4
    Citation - Scopus: 6
    Model Based Diagnosis of Oxygen Sensors
    (Elsevier, 2019) Ekinci, Kubra; Ertugrul, Seniz
    Automotive industry targets such as complying with emission legislations and increasing fuel economy, require the improvement of air-fuel ratio control systems. Oxygen sensors are a crucial part of these control systems and regulations oblige monitoring of their performance and detecting sensor-related faults. The primary purpose of this paper is to develop a methodology for precise and accurate monitoring and diagnosis of oxygen sensors to meet legislations and performance targets while the required calibration effort is reduced. Input parameters with the highest correlation factors were selected to be utilized in different system identification methodologies to statistically determine the most fitting model. In the end, a NARX model with two hidden layers and eight neurons in each hidden layer with standard deviation and mean threshold values was determined to be the optimum design to detect if the oxygen sensor was functioning or faulty. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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    Article
    Citation - WoS: 7
    Citation - Scopus: 7
    System Identification and Artificial Intelligent (ai) Modelling of the Molten Salt Electrolysis Process for Prediction of the Anode Effect
    (Elsevier B.V., 2023-10) Kaya, O.; Abedinifar, M.; Feldhaus, D.; Diaz, F.; Ertuğrul, Şeniz; Friedrich, B.
    NdFeB magnets are widely used in various applications including electric and hybrid vehicles, wind turbines, and computer hard drives. They contain approximately 31–32 wt% Rare Earth Elements (REEs), mainly neodymium (Nd) and praseodymium (Pr), and are produced by molten salt electrolysis using fluoride electrolytes. However, anode passivation or anode effect may occur, generating greenhouse gases if insufficient amounts of metal oxides are available in the system. Therefore, in this study, a dynamic model of the electrochemical process was developed to estimate the system variables and predict the anode effect using several system identification methods. The Transfer Function (TF) estimation, Auto-Regressive with Extra inputs (ARX), Hammerstein-Weiner (HW), and Artificial Neural Network (ANN) models were used, and their results were compared based on the occurrence of the anode effect. The best model achieved an average accuracy of 96% in predicting the process output. © 2023 The Authors
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    Article
    Citation - WoS: 7
    Citation - Scopus: 6
    Nonlinear Model Identification and Statistical Verification Using Experimental Data With a Case Study of the Ur5 Manipulator Joint Parameters
    (Cambridge Univ Press, 2022-12-23) Abedinifar, Masoud; Ertugrul, Seniz; Arguz, Serdar Hakan
    The identification of nonlinear terms existing in the dynamic model of real-world mechanical systems such as robotic manipulators is a challenging modeling problem. The main aim of this research is not only to identify the unknown parameters of the nonlinear terms but also to verify their existence in the model. Generally, if the structure of the model is provided, the parameters of the nonlinear terms can be identified using different numerical approaches or evolutionary algorithms. However, finding a non-zero coefficient does not guarantee the existence of the nonlinear term or vice versa. Therefore, in this study, a meticulous investigation and statistical verification are carried out to ensure the reliability of the identification process. First, the simulation data are generated using the white-box model of a direct current motor that includes some of the nonlinear terms. Second, the particle swarm optimization (PSO) algorithm is applied to identify the unknown parameters of the model among many possible configurations. Then, to evaluate the results of the algorithm, statistical hypothesis and confidence interval tests are implemented. Finally, the reliability of the PSO algorithm is investigated using experimental data acquired from the UR5 manipulator. To compare the results of the PSO algorithm, the nonlinear least squares errors (NLSE) estimation algorithm is applied to identify the unknown parameters of the nonlinear models. The result shows that the PSO algorithm has higher identification accuracy than the NLSE estimation algorithm, and the model with identified parameters using the PSO algorithm accurately calculates the output torques of the joints of the manipulator.