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Institution Author: Turhan, CihanLanguage: en

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    Citation - Scopus: 1
    Determination of Metabolic Rate From Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation
    (Sakarya University, 2022) Özbey, M.F.; Çeter, A.E.; Turhan, C.
    Thermal comfort depends on four environmental parameters such as air temperature, mean radiant temperature, air velocity and relative humidity and two personal parameters, including clothing insulation and metabolic rate. Environmental parameters can be measured via objective sensors. However, personal parameters can be merely estimated in most of the studies. Metabolic rate is one of the problematic personal parameters that affect the accuracy of thermal comfort models. International thermal comfort standards still use a conventional metabolic rate table which is tabulated according to different activity tasks. On the other hand, ISO 8996 underestimates metabolic rates, especially when the time of activity level is short and rest time is long. To this aim, this paper aims to determine metabolic rates from physical measurements of heart rate, mean skin temperature and carbon dioxide variation by means of nineteen sample activities. 21 male and 17 female subjects with different body mass indices, sex and age are used in the study. The occupants are subjected to different activity tasks while heart rate, skin temperature and carbon dioxide variation are measured via objective sensors. The results show that the metabolic rate can be estimated with a multivariable non-linear regression equation with high accuracy of 0.97. © 2022, Sakarya University. All rights reserved.
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    Article
    Citation - WoS: 13
    Citation - Scopus: 17
    Cutting Force Prediction in Ultrasonic-Assisted Milling of Ti-6al With Different Machining Conditions Using Artificial Neural Network
    (Cambridge University Press, 2021) Namlu,R.H.; Turhan,C.; Sadigh,B.L.; Kiliç,S.E.
    Ti-6Al-4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti-6Al-4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost-and time-effective way. Copyright © The Author(s), 2020. Published by Cambridge University Press.
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    Article
    Citation - WoS: 8
    Citation - Scopus: 8
    Modeling the Mood State on Thermal Sensation With a Data Mining Algorithm and Testing the Accuracy of Mood State Correction Factor
    (Pergamon-elsevier Science Ltd, 2025) Yerlikaya-Ozkurt, Fatma; Ozbey, Mehmet Furkan; Turhan, Cihan
    Psychology is proven as an influencing factor on thermal sensation. On the other hand, mood state is one of the significant parameters in psychology field. To this aim, in the literature, mood state correction factor on thermal sensation (Turhan and Ozbey coefficients) is derived utilizing with data-driven black-box model. However, novel models which present analytical form of the mood state correction factor should be derived based on the several descriptive variables on thermal sensation. Moreover, the result of this factor should also be checked with analytical model results. Therefore, this study investigates the modelling of mood state correction factor with a data mining algorithm, called Multivariate Adaptive Regression Splines (MARS). Additionally, the mood state is also taken as a thermal sensation parameter besides environmental parameters in this algorithm. The same data, which are collected from a university study hall in a temperate climate zone, are used and the model results are compared with the thermal sensation results based on mood state correction factor which is driven via black-box model. The results show that coefficient of correlation "r" between the MARS and black-box model is found as 0.9426 and 0.9420 for training and testing. Hence, the mood state is also modelled via a data mining algorithm with a high accuracy, besides the black-box model.