เล่มประชุมกรรมการครั้งที่ 2_2564 แก้ไขเพิ่มเติม

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103 วาระที่ 5.7 พิจารณาอนุมัติทุนสนับสนุนการเผยแพร่ผลงานนักศกึ ษา แกน่ กั ศกึ ษาระดับปรญิ ญาโท สาขาวิชาวศิ วกรรมไฟฟ้า จานวน 1 คน เร่อื งเสนอพิจารณา ตามคาร้องนักศกึ ษา ได้เสนอคารอ้ งขอทุนสนับสนนุ การเผยแพรผ่ ลงานวชิ าการ นักศึกษาระดบั ปรญิ ญาโท จานวน 1 คน ตามระเบียบสถาบนั ฯวา่ ดว้ ยการให้ทนุ สนับสนนุ ขอ้ 5, ข้อ 6 ดงั รายช่ือน้ี ทนุ สนับสนุนการตีพิมพ์ผลงานวิชาการ ระดับนานาชาติ ตามระเบยี บข้อ 6.2 รหัส /วนั อนุมตั ิ อาจารย์ รายการขอทุน จานวนเงนิ ขอ ใบเสรจ็ รบั เงนิ / หัวขอ้ ท่ปี รึกษา สนบั สนนุ ผลงาน อนมุ ัติ เอกสารแทนใบเสรจ็ 1.นายศราวธุ ผศ.ดร.ประสิทธ์ิ - ค่าลงทะเบียนประชมุ วชิ าการ 3,950.00 บาท หลกั ฐานใบเสรจ็ ที่ 34 อนันต์ นางทนิ ทางวิศวกรรมไฟฟ้า คร้ังที่ 43 เลมที่ 20186 รหัส 6201021602 (EECON-43) จานวน 2,000 บาท ลงวนั ท่ี 30 ต.ค. 2563 อนุมัตหิ ัวข้อเค้าโครง - คา่ ทพี่ ัก จานวน 1,350 บาท -หลกั ฐานใบเสร็จเลขท่ี 9 เมอื่ 28 ต.ค. 2563 เล่มท่ี 092 ลงวนั ที่ 29 ต.ค. 2563 - ค่าน้ามันเชื้อมนั LPG600 บาท -หลักฐานใบเสร็จเลขท่ี 610203 เลม่ ที่ 12205 ผลงานช่อื : ลงวนั ที่ 29 ต.ค 2563 “Forecasting the Power Output of Solor Photovoltaic System using Artificial Neural Networks:A Case study of PLC Center for Energy and Environment Conservation, Pathumwan Institute of Technology” - รวมเป็นเงิน 3,950.00 บาท ทั้งนี้ตามระเบียบสถาบันฯ ว่าด้วยการให้ทุนสนับสนุนการเผยแพร่ผลงานวิชาการของ นักศึกษาระดับ บัณฑิตศึกษา พ.ศ.2560 นักศึกษาได้แนบเอกสารประกอบการพิจารณา ตามเง่ือนไขเรียบร้อยแล้ว (ดงั เอกสารประกอบแนบ) จงึ เรียนมาเพ่ือโปรดอนมุ ัติทนุ สนบั สนุน นายศราวธุ อนนั ต์ นักศกึ ษาปรญิ ญาโท สาขาวชิ าวิศวกรรมไฟฟ้า จานวน 3,950 บาท ความเหน็ คณะกรรมการบัณฑิต ............................................................................................................................. ........................................................... ............................................................................................................................. ........................................................... มติท่ีประชมุ ............................................................................................................................................................. ............................ ...................................................................................................... ................................................................................... กรรมการบัณฑิตศึกษา

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106 Forecasting the Power Output of Solar Photovoltaic System using Artificial Neural Networks: A Case study of PLC Center for Energy and Environment Conservation, Pathumwan Institute of Technology. Sarawuth Anant 1, Weeragul Pratumgul2 and Prasit Nangtin3 1Department of Electrical Engineering, Faculty of Engineering, Pathumwan Institute of Technology, Thailand, [email protected] 2•3Department of Electrical Engineering, Faculty of Engineering, Pathumwan Institute of Technology, Thailand Abstract increase or decrease the power production immediately. Forecasting the powe r output of solar photovoltaic But the backup power preparation is quite expensive, thus, it is necessary to determine the appropriate amount system at different times is necessary to ensure reliable and economical electrical system operation. In this of backup power. The presence of a solar power generation forecasting system will help determine the paper, we study and applied Artificial Ne ural etwork to appropriate amount of backup electricity demand. By forecast the power produced by solar photovoltaic applying the forecasting results in the preparation of the system of PLC Center for energy and environment power plant unit orders or scheduling, the power and the conservation at Pathumwan Institute of Technology. By amount of power demand will be less different. using statistical data of relevant parameters such as Resulting in reduced demand for backup electricity. meteorological parameters (maxi mum temperature, Therefore, the accurate forecasting of electric power lowest temperature, solar radiation, rainfall, wind speed produced from solar energy is of interest to researchers and relative humidity) and some plant specifications at the large scale. There are different approaches for parameters (amount of panels, size of each panels and forecasting the energy generated from PV system based efficiency of panels). Then, divided all data into datasets on statistical methods such as linear regression and for training and testing then be fed into the back- automatic moving averages and machine learning propagation A which consists of 2 hidden layers, methods such as Neural Networks (NNs), the nearest each layer consisting of I0 neurons with decrease the neighbor, and Support Vector Regression (SVR) [2-5] number of parameters to avoid the over-fit problem. Finally, we evaluate the performance of forecasting 2. Material and Methodology results in terms of Mean Square Error (MSE) and Mean Average Percentage Error (MAPE) compare with the 2.1 Material actual values from the system and found that proposed model gave a good forecasting accuracy. The material used in this work is the dataset, for Keywords: Forecasting, Solar Photovoltaic System, training and testing the proposed model as detailed Artificial Neural etworks, Back- propagation. below. · 1. Introduction Currently in Thailand, the trend of installing solar 2.1.1 Data set power generation systems is increasing. In accordance Dataset used for outwork are divided to 2 main with the Alternative Energy Development Plan (AEDP 20 15), which has a total installed capacity of 6,000 MW groups, which are meteorological dataset and plant in the country by 2036, together with the investment cost of installing electricity from solar photovoltaic continue specifications dataset. We use meteorological parameters to decline continuously [I] . Therefore, there are entrepreneurs interested in becoming a solar power from weather report of Thailand Meteorological producer, both Small Power Producer (SPP) and Very Small Power Producer (VSPP). However, power Department, selected by considerate that it is the most generation from sunlight still has many factors that cause fluctuations . There is a problem of production capacity, influential among meteorological parameters such as inconsistency with the power demand of the area at that time and affecting the power system in terms of stability, maximum temperature, minimum temperature, rainfall , quality and re liability. Therefore, in order to enable the power system to maintain the power level and the wind speed and relative humidity as the input of amount of power demand in accordance with. H , it is necessary to prepare a back-up generation which can meteorological parameters. In addition, solar irradiation data that is not specified in the report of Thailand Meteorological Department, was fetched from NASA's Meteorological web portal. However, even though it is considered to be the most accurate weather source, but this data may have hidden flaws. For example, if the sky is in a opaque cloud cover (OCC), such as partly cloudy, mostly clear or overcast will lead to 20% bias in it [6]. While plant specifications data are derived from our demonstration site, PLC center of energy and environment conservation at Pathumwan Institute of Technology, Bangkok, Thailand. This demonstration site designs to produced I OKw solar photovoltaic system

107 from 440 watt PV panels, total amount of 25 panels that While the error vectors for output layer are calculated by are connected in series and parallel in order to generate equation below approximately 600 Volt DC power. After that, the electricity is produced will flow through inverter kit to e1 = sJ (1- sJ) I w1kek (5) convert electricity into 3-phase electricity, 380 Volt AC, i=l to supply electricity to PLC center and connected with transmission line to on-grid system. The power output Where dkis the desired output. The weight data were extracted from existing PV monitoring system of this site for comparison with output from proposed updating for output and hidden layer are given as forecasting model. All data were collected started form 1 November 2019 until 30 April 2020. All data were w1k (new)= wkJ + r;sJek stored in excel file for import to proposed model. w!i (new) = w!i +r;s;e1 (6) e~ (new)= e~ + r;ek 2.2 Methodology This section describe our proposed model for forecasting the power output of solar photovoltaic system using A1 . We develop proposed forecasting model of A according to workflow presented in Fig.! . 2.2.1 Artificial Neural Network (ANN) ( END ) In this work, we use multi-layer perceptron (MLP) Fig I. Conventional wo rkflow of developing AN neural network, which is a form of a multi-layer artificial model. neural network that used for complex tasks. The training process is supervised (Back-propagation) together with Before we develop a proposed forecasting model , It IS Hyperbolic Tangent Sigmoid (Tansig) as activation absolutely necessary to adjust the setting of the function due to it was found in [7] that it gave the most parameters that affect the performance of our model accurate prediction when compared with another during training and testing process. All data collected function. Beside this, proposed model used the will be normalized and then divided into 3 groups, for Levenberg-Marquardt (LM) as its learning algorithm training (50%), for testing (25%) and for validating since it recommended in [8]. The architecture of (25%), which adhere to recommended in [10]. proposed forecasting model in this paper is presented in Fig. 2. The input of proposed model contains the value ormalized input meteorological parameters have values of 6 meteorological parameters and three parameter from in the range, 0.1-0 .95 , thus input and output values are in plant specifications such as number of panels, size of the same cluster range and weight. The formula used in each panel (length and breadth), efficiency of panels process of normalization meteorological parameters is (%). For middle part of proposed forecasting model presented in equation I . consist of two hidden layers that compound with I0 neurons same as [9] suggests that the number of hidden layer should be less than twice the input layer size, while output layer is a forecasting the power output from solar photovoltaic system. The input of the hidden layer are assessed by equation below ek =bk (l - bk)(dk -bk) (2) While the output of output layer are assessed by this equation below bk = Jc!w~sJ - e; ) (3) J=l Where w ihi, w i0g are the connection weights between N=(Nmax -Nmin )(O-Qmin )+N . (1) layers and 8 h, 8~ are the bias terms respectively. The ( 0 max - 0 min ) mm· 1 'vVhere 1 is the normalized value, max is value +1, error vectors for hidden layer are calculated by equation min is value -1 , 0 is original value before normalization below (4) process, O max is the maximum value of meteorological parameters while Omin is the minimum value of meteorological parameters.

108 Input 2\"' Hidden (8) Lay c 1 Where A, is actual value of power output from solar B\" photovoltaic system, while F; is forecast value from ~J proposed forecasting model. From above equation, it is 0 Output found that the MSE and MAPE values are equal to 11'· 0. I 093 and 2.23% respectively. j\" : kvW (fi t: ;s ~~ Day Fig3 . Graph showing comparison of forecasted results and actual results of I OkW plant When comparing with previous research, it can be seen that our proposed forecasting model has higher accuracy as shown in tablel . Table I Com parison of di fferent models for forecasting power output firom soIar PV system Model MSE MAPE Mathematical model [12] n/a :::,:30% Fig2. Proposed forecasting model (A ANN wi th 4 meteorological 0.024 6 19.64% parameters [2] n/a 12.88% Convolutional networks [13] 0. 1093 2.23 % Our proposed model In developing proposed model for forecasting the 4. Conclusion power output from solar photovoltaic system using This paper presents the forecasting model of ANN, we use MATLAB 9.7 (2019b) to actualize electrical energy produced form solar photovoltaic system using artificial neural networks with back- mathematical equations and design a simply graphic user propagation pattern, by considering input of interface (GUT) to test the accuracy of system . meteorological parameters and plant specifications parameters. While in middle part of A model consists 3. Results and discussion of2 hidden layers, each consisting of I0 neurons and the After the development of proposed forecasting output layer, which are the prediction of the power produced from system . This proposed model was model using A is complete, we training this model developed by MATHLAB 9.7. The accuracy test of the with 91 days of datasets (from I 'ovember 20 19 - 30 predicted values of proposed model was compared with January 2020) and then, testing and validating by 46 and actual measured values and then, calculated to finding 45 days datasets (31 January - 30 April 2020) mean square error (MSE) and mean absolute percent respectively. The result of working out the proposed error (MAPE) values. We found that MSE and MAPE model for 30 days (I2 May-IO Jun 2020), the PV 10 kW values are 0.1093 and 2.3% respectively, indicated that is shown in Fig. 3. our proposed model is capable of producing accurate prediction of solar PV energy that can be utilized by the For finding the accuracy of our proposed forecasting energy management system . Whereas, there are still model using A , we use mean square error (MSE) and errors that occur from some types of sky conditions such mean absolute percent error (MAPE),which are a as partly cloudy or overcast. Therefore, in future statistical value commonly used in determining the development should consider to adding details of the accuracy of any forecasting [ll].MSE and MAPE values type of sky conditions for more accurate results. can be obtained from equation 7 and equation 8 as below MSE =L(A,- FY (7) N

109 Acknowledgment [lO]T.Hastie, R.Tibshirani and J.Friedman, The elements The authors are deeply appreciative in wishing to of statistical learning, Vol.2, New York: Springer, thanks colleagues in department of electrical engineering 2009. (energy) Pathumwan institute of technology for their support and helpful advice. Last but not least, the authors [ll]J .Moreno, A.Palmer , A.Abad and B.Blasco. \"Using wish to thanks all reviewers of EECO -43 for their the R-MAPE index as a resistant measure of forecast helpful comment and suggestions. accuracy\" Psicothema, Vol 25 , o.4, pp .S00-506 , 2013 . References [12] D.Solanki, U.Upadhyay, S.Patel, R.Chauhan, [1] Office of energy policy and planning. Ministry of S.Desai , \"Solar energy prediction using meteorological variables\" . International confere nce energy. The alternative energy development plan on Recent Innovations in Electrical, Electronics &Communication Engineering (ICRIEECE), pp.16- (AEDP). [Internet] [cited 5 May 2020]. Available 19, 2018. from [13]P .Lezhniuk, S.Kravchuk, V. etrebskiy, V.Komar and V.Lesko . \"Forecasting hourly photovoltaic http ://www.epp .go.th/index.php/th/conservation/ generation on day ahead\" 20I9 IEEE 6'\" International Conference on Energy Smart Systems aedp . (2019 IEEE ESS) , pp. 184-187,2019 [2] S.A. Jumaat, F.Crocker, M.H.A.Wahab, .H.M .Radzi and M .F.Othman, \"Prediction of photovo ltaic (PV) power pred icti on based on Artificial eural etwork with activation function selection and feature reduction method\" 26'\" World Conference on Applied Science, Engineering and Sarawuth Anant received the B.Sc. m technical education Technology, pp. l28-135 , 2020. program m electrical engineering from Pathumwan [3] M.Ding, L.Wang, R.Bi. \" An -Based approach Institute of Technology. He work as a director of engineering for forecasting the power output of photovoltaic at great china millennium (Thailand) company limited. system .\" Procedia Environment Science, Vol. II , Currently, he studying for master degree in electrical pp.1308-1315, 2011. engineering. His research interested include renewable [4) V.Lo Brano, G.Ciulla and M.Di Falco, \"Artificial energy, smart grid and artificial intelligence. Neural 1etworks to predict the power output of a PV Panel\" International Journal of Photoenergy, Vol.2, 2014. [5] F.H.Jufri and J.Jung. \"Photovoltaic generation forecasting using Artificial Teural etwork model with input variables and model parameters selection algorithm in Korea.\" international Journal of Machine Learning and Computing, Vol.7, No.5, pp.42-49, 2017. [6] M.Alomari, J.Adeeb and O.Younis, \"Solar Weeragul Pratumgul received photovoltaic power forecasting in Jourdan using the Ph .D. degree in electrical Artificial eural etworks.\" international Journal and computer engineering from of Electrical and Computing Engineering, Vol.8 , Mahasarakham University, No.I, pp.497-504, 2018. Thailand . He is interested in [7] J. .Velasco and C.F.Osia Jr., \"Photovoltaic (PV) artificial intelligence, artificial power prediction based on Artificial Teural neural network, renewable Tetwork with activation function selection and energy, smart grid and feature reduction method\" 26117 World Confernce on biomedical engineering. Applied Science, Engineering and Technology, 2020 . [8] Q-J.Zhang, K .C. Gupta and V.K. Devabhaktuni . Prasit Nangtin received the \"Artificial neural networks for RF and microwave D.Eng.degree m electrical and design-from theory to practice\" . IEEE Transactions information engineering on microwave themy and techniques, Vol.51 , 1o.4, technology from King pp . l3 39-1350, 2003 . Mongkut's University of [9] F. Panchal and M .Panchal , \"Review on methods of Technology Thonburi . He is selecting number of hidden nodes in Artificial interested in renewable energy, eural Network\" . international Journal of energy conservation, smart grid, Computer Science and information Technology, artificial neural network and Vol.3 , Issue II , pp.455-464, 2014. artificial intelligence.

110 c~E EECON-43 E 0 5 5 F I F I T SPEAiffiR'S BIOGRAPHYN UtciJIU I l aJIOU iilt ltl4t •tc AUOCUtin (TIUIIUd) Instruction: Please type or clearly write a brief biography of the presenting author. This biography is meant to provide your session ch airperson wit h en ough informat ion to introduce you to th e audience. One form is required for each paper. The informat ion ca n be provided both in Th ai or in English. Paper ID: P00470 Paper Title: Forecasting the Power Output of Solar Photovoltaic System using Artificial Neural Networks A Case study of PLC Center for Energy and Environment Conservation ,Pathumwan Institute of Technology. Speaker 's Name (with Title) : Mr. Sarawuth Anant Affiliation: Department of Electrical Engi neering, Faculty of Engineering , Pathu mwan Institute of Technology Current Status: Student (Student I Lecturer I Researcher I et c.) Education: Master of Engineering (Electrical Engineering ) (Current or Latest on ly) Research Area : renewable energy, smart grid and artificial intelligence

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127 Stress softening characteristics of thin monoholar rubber flat slab Kulyuth BOONSENG1,*, Chatchai WAIYAPATTANAKORN2, and Prayoon SURIN1 1Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, 10330, Thailand 2 Independent Academic, Bangkok, 10210, Thailand *Corresponding author e-mail: [email protected], Phone: +66 816909286 Abstract Thin monoholar rubber flat slab gives rise to unconventional mechanical properties under tension loading not found in thin solid rubber flat slab. However there has been no proper report on its stress softening characteristics. Stress softening is an important phenomenon occurring in all elastomeric materials under cyclic tension loading. Parameters pertinent to stress softening investigated in this research are stress-strain characteristics, permanent set strain and hysteresis loss energy of the thin monoholar rubber flat slab in comparison with the thin solid rubber fat slab when undergoing 7 cycles of stretching. Results show noticeable differences in stress-strain hysteresis loops of the thin monoholar rubber flat slab and the solid rubber flat slab. However, there is no significant difference in stress softening. Only discernible differences can be observed in the 2nd to 5th loading cycles. Permanent set strain and hysteresis loss energy of the monoholar rubber flat slab specimens are clearly larger than that of the solid rubber flat slab. Therefore stress softening may not be a matter of great concern when the monoholar rubber flat slab is adopted in practical applications provided both permanent set strain and hysteresis loss energy have been properly accounted for. Keywords: Thin Monoholar; Elastomeric material; Stress softening; Permanent set strain; Hysteresis loss energy 1. Introduction When a rubber specimen is submitted to successive cycles of stretching, the subsequent load required to produce the same deformation is smaller than that required during previous loading. This phenomenon is known as stress-softening and may be thought of as a decay of elastic stiffness. Stress softening is particularly evident in specimens of filled rubber vulcanizates [1-2]. Figure 1 illustrates the idealized stress softening behaviour of a rubber specimen under

128 uniaxial tension. The phenomenon begins with an unstressed and unstrained initial state at point P0 and time t0. Subsequently, the stress-strain relation moves along path A, the primary loading path, until point P1 is reached at a time t1. At this point P1, unloading of the specimen begins immediately and the stress strain relation of the rubber moves to the new path B, which lies below A, returning to the unstressed and unstrained state at point P0. If the material is then reloaded the stress–strain relation follows path B again, rather than path A, up to point P1. If the rubber is now strained beyond point P1 then path D is activated, a continuation of the original primary loading path. If subsequent unloading occurs from the point P2, the rubber retracts along a new path C to the unstressed state at P0. The shape of this second stress–strain cycle differs significantly from the first. If the material is now reloaded the stress–strain behaviour will be along the new path C, rejoining the primary loading path at the point P2. Figure 1. The idealized behavior of stress softening in rubber Technological advancement of today’s world has fueled the demand for materials that are versatile, lightweight, load-bearing and heat resistant. Special properties are necessary for specific applications such as the aviation industry, energy industry, petrochemical industry, electronic industry, etc. [3] Natural materials cannot meet certain unusual requirements completely. Generally speaking, properties of typical materials depend on their chemical composition and microstructures [4]. To improve mechanical properties of the materials in use, various methods have been researched and developed, such as friction welding [5], friction stir welding [6], heat treatment [7], composite materials [10], or even inventing a new material (smart material) [9]. Over the past decade, researchers have been interested in developing materials by means of the cellular structure which could yield remarkable mechanical properties [3-4, 10-16]. It is an interesting challenge and a trend of future research [3, 17]. In the authors’ previous work [to be published in Engineering and Applied Science Research: EASR] it has been found that under uniaxial tension loading stress-strain hysteresis loops of

129 the monoholar rubber flat slab, a form of cellular structure, are larger than that of the solid rubber flat slab, showing a greater loss of energy resulting in greater energy absorption than the solid rubber flat slab. The monoholar rubber flat slab appears with greater stiffness to tension at moderate level of loading compared with the solid rubber flat slab. Both phenomena are likely due to elastic instability present in the monoholar rubber flat slab but not discernible in the solid rubber flat slab. This benefit of adopting repeated pattern or cellular structure is in accordance with most work reported in the literature [18-29] However reports on stress softening of elastomeric materials with cellular structure have not been found in the literature. Knowledge and understanding of stress softening of elastomeric materials with cellular structure will benefit the design of material for many applications in various fields [16, 30-34]. This research thus places emphasis on experimental investigation of stress-strain characteristics, permanent set strain and hysteresis loss energy, all of which are parameters pertinent to stress softening, of the thin monoholar rubber flat slab in comparison with the thin solid rubber flat slab when undergoing 7 cycles of stretching. In the next section description of the experimentation is presented. 2. Experimental This section describes both the specimen preparation and experimental setup. The experiments to be performed are those of cyclic uniaxial tensile loading test of both the solid rubber flat slab (SFS) (the referenced specimen) and the monoholar rubber flat slab (MFS) (the investigated specimen). Both types of specimens are made of compounded rubber. Specimen preparation is to be described first then detail of the experimental setup is given succinctly. 2.1 Specimen preparation This research has opted for a compounded rubber made of Standard Thai Rubber (STR20) 40 phr, Butadiene Rubber 60 phr, silica 50 phr and other compounds used in the shoe industry. The cellular structure of interest herein is the monoholar array. This is because of its construction simplicity [35]. Figure 2 depicts geometries of both the solid and monoholar rubber rubber flat slabs employed in the experiment. Both specimens are different in physical structure at the macro level. They are labelled as solid rubber flat slab (SFS) and monoholar rubber flat slab (MFS) as shown in Figure 2. All dimensions are in millimeter. Both types of flat slabs are 1 mm. thick. They are hence thin flat slabs. Specimens preparation has been carried out as illustrated in Figure 3.

130 (a) (b) Figure 2. Specimen geometries model (a) Solid rubber flat slab (SFS) (b) Monoholar rubber flat slab (MFS) Figure 3. Specimens preparation process 2.2 Experimental setup Tensile properties are to be tested uniaxially using a universal testing machine (UTM), Narin Universal Testing Machine Model NRI-T500-20B. Stress softening are to be observed by pulling the specimens to 100 percent elongation, then the specimen is allowed to shrink back to its initial state. This action is to be performed for 7 cycles [36]. Each specimen is pulled at

131 the rate of 500 mm/min at room temperature (25 ± 2 °C) according to ASTM D412. 5 specimens are to be tested and results are averaged to yield a reported value. Maximum stress of each cycle is recorded thus stress softening percentage can be calculated by Eq. (1). Hysteresis loss energy can also be calculated by the use of Eq. (2). Stress softening percentage (%) = Ti x100 (1) T1 where T1 (MPa) is the maximum elongated stress in the first extension cycle and Ti (MPa) is the maximum elongated stress in the 1st -7th extension cycles. Hysteresis loss energy (kJ/m2) = L - U (2) where L is the area under the graph between the stress (MPa) and the displacement (mm) during stretching (kJ/m2). U is the area under the graph between the stress (MPa) and the displacement (mm) during retraction (kJ/m2). 3. Results and discussion Both the MFS and SFS specimens have been subjected to 7 cycles of stretching in order to investigate their stress-strain characteristics, permanent set strain and Hysteresis loss energy. Detailed description of important results and discussion are as follows. 3.1 Stress-strain characteristics 3.1.1 Cyclic loading stress-strain hysteretic characteristics Figure 4 shows the cyclic loading stress-strain hysteretic characteristics of the MFS and the SFS specimens. The hysteresis loops of both types of specimens, Figures 4(a) and 4(b), become smaller for successive loading cycles at a given displacement of 50 mm. (a given constant strain). This is an indication of the possible occurrence of stress softening under cyclic tension loading. It can be seen that the MFS stress-strain hysteresis loops, Figures 4(b), are larger than that of the SFS, Figures 4(a). Consider Figure 4(b) it is apparent that differences of the first cycle maximum stress and the successive cycles are larger than that of Figure 4(a). This is an evidence that there may be stronger softening of the MSF specimens in comparison with the SFS specimens. Further details are presented and discussed in the following subsections.

132 (a) (b) Figure 4. Hysteretic characteristics under 7 cycles of cyclic tension loading (a) Solid rubber flat slab (SFS) (b) Monoholar rubber flat slab (MFS) 3.1.2 Stress-strain at 100% elongation Elongated stress-strain characteristics of the MFS and SFS in this research confirm the authors’ previous results [to be published in Engineering and Applied Science Research: EASR] that greater stress is required for the same strain level in the case of the MFS. Of interest herein is the difference of the maximum stress at a given displacement of 50 mm of the first cycle and successive loading cycles, an indication of softening phenomenon. Visual comparison of Figures 5(a) and 5(b) clearly shows that the differences of the maximum stress at a given displacement of the first cycle and successive loading cycles of the MFS are larger than that of the SFS. (a) (b) Figure 5. Stress-strain under 7 cycles of cyclic tension loading (a) Solid rubber flat slab (SFS) (b) Monoholar rubber flat slab (MFS)

133 3.1.3 Maximum Stress and Stress softening Maximum stress values of 100% elongation stretch for 7 loading cycles of both the MFS and SFS are summarized in Table 1. Graphical presentation of Table 2 is in Figure 6. It is evidence that there is a clear difference in the maximum stress between both types of specimens. It is also visibly observed that the red curve of the MFS has greater drop of maximum stress than that of the black curve of the SFS, which is a confirmation of stronger softening of the MFS. Calculation of stress softening percentage (according to Eq.(1) in subsection 2.2) plotted in Figure 7 shows discernible difference in stress softening between the MFS and SFS specimens. The softening gap of the MFS and the SFS visible in Figure 7 begins from the second loading cycle and becomes larger from the third cycle to the fifth cycle. The widest gap is at cycle number 4 with the difference of around 1.8 %. The softening of the SFS drops down to about the same level as that of the MFS from the sixth cycle onward which is typical of most softening phenomena in elastomeric materials [2, 37-38]. The greater stress softening of the MFS specimens is likely a result of the presence of spatial voids, all circular holes of the monoholar pattern. Some of the voids cause shortening or breaking of the molecular chains. Hence after being stretched a few times softening observed in the MFS is discernibly stronger than that of the SFS. This is in good agreement with Govindjee and Simo [39] whose result attributed the loss in stiffness to be a result of the breaking of the molecular chain. Table 1 Maximum stress under 7 cycles of cyclic tension loading Cycle Maximum stress (MPa) no. SFS MFS 1 2.39±0.033 3.89±0.167 2 2.26±0.010 3.67±0.124 3 2.20±0.019 3.53±0.165 4 2.19±0.038 3.50±0.153 5 2.15±0.015 3.47±0.139 6 2.12±0.031 3.46±0.132 7 2.13±0.013 3.48±0.150

134 Figure 6. Maximum stress under 7 cycles of cyclic tension loading of solid rubber flat slab (SFS) and monoholar rubber flat slab (MFS) Figure 7. Stress softening percentage under 7 cycles of cyclic tension loading of solid rubber flat slab (SFS) and monoholar rubber flat slab (MFS) 3.2 Permanent set strain The permanent set strain refer to the residual extension remaining after a material sample is stretched and released [40-41]. Figure 8 shows a convex curve of permanent set strain of the MFS whereas that of the SFS is rather flat. It is a result of greater drop in maximum stress of the subsequent loading cycles that eventually flattens off after the sixth cycle. This is in accordance with the stress softening percentage plot of Figure 7. It is evidence from Figure 8 that permanent set strain of the MFS is greater than that of the SFS. Differences of permanent set strain of both types of specimens range from 4.16-5.75 %. This is again a consequence of

135 the shortening or breaking of the molecular chains due to the presence of the spatial voids in the MFS. Shorter or broken molecular chains disable complete recovery of the original state of the MFS. Residual strain or permanent set strain of the MFS is therefore greater after being stretched. Figure 8. Permanent set strain of solid rubber flat slab (SFS) and monoholar rubber flat slab (MFS) 3.3 Hysteresis loss energy Figure 9 shows that hysteresis loss energy (according to Eq. 2) of the MFS is greater than that of the SFS. Both curves are so similar with just an approximately offset of about 6.2 kJ/m2. The greatest difference of the first cycle at about 23 kJ/m2 is not surprising since the drop of maximum stress in the subsequent cycles is noticeably stronger for the MFS, particularly the drop of the second loading cycle. The offset values of the subsequent cycles are not so different because after the second loading cycle the drop of maximum stress of both types of specimens are more or less the same. The offset of both curves is simply because the stress-strain hysteretic characteristics of the MFS yield larger hysteresis loops than that of the SFS, a consequence of elastic instability arising from the presence of the spatial voids in the MFS [to be published in Engineering and Applied Science Research: EASR].

136 Figure 9. Hysteresis loss energy of solid rubber flat slab (SFS) and monoholar rubber flat slab (MFS) 4. Conclusions Stress softening is an important phenomenon occurring in all elastomeric materials under cyclic tension loading. This research has experimentally investigated stress-strain characteristics, permanent set strain and hysteresis loss energy of the thin monoholar rubber flat slab in comparison with the thin solid rubber fat slab when undergoing 7 cycles of stretching. Results show noticeable differences in stress-strain hysteresis loops of the thin monoholar rubber flat slab and the solid rubber flat slab. However, there is no significant difference in stress softening. Only discernible differences can be observed in the 2nd to 5th loading cycles. Permanent set strain and hysteresis loss energy of the monoholar rubber flat slab specimens are clearly larger than that of the solid rubber flat slab. Therefore stress softening may not be a matter of great concern when the monoholar rubber flat slab is adopted in practical applications provided both permanent set strain and hysteresis loss energy have been properly accounted for. 5. Acknowledgements The authors are thankful to the Rubber and Polymer Technology Program, Faculty of Science and Technology Songkhla Rajabhat University for materials, tools, equipment and space for the experiment. Financial support for this research from Faculty of Engineering, Pathumwan Institute of Technology is also greatly appreciated.

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1/12/2021 141Engineering and Applied Science Research also developed by scimago: SCIMAGO INSTITUTIONS RANKINGS Scimago Journal & Country Rank Enter Journal Title, ISSN or Publisher Name Home Journal Rankings Country Rankings Viz Tools Help About Us Ads by Send feedback Why this ad? Free Grammar Checker Eliminate grammar errors instantly and enhance your writing with Grammarly Grammarly DOWNLOAD Engineering and Applied Science Research Country Thailand  -  4 Subject Area and Computer Science H Index Category Computer Science Applications Engineering Engineering (miscellaneous) Publisher Faculty of Engineering, Khon Kaen University Publication type Journals ISSN 25396218, 25396161 Coverage 2017-2020 Scope Publication of the journal started in 1974. Its original name was “KKU Engineering Journal”. English and Thai manuscripts were accepted. The journal was originally aimed at publishing research that was conducted and implemented in the northeast of Thailand. It is regarded a national journal and has been indexed in the Thai-journal Citation Index (TCI) database since 2004. The journal now accepts only English language manuscripts and became open-access in 2015 to attract more international readers. It was renamed Engineering and Applied Science Research in 2017. The editorial team agreed to publish more international papers, therefore, the new journal title is more appropriate. The journal focuses on research in the eld of engineering that not only presents highly original ideas and advanced technology, but also are practical applications of appropriate technology. Homepage How to publish in this journal Contact Join the conversation about this journal Ads by Send feedbaInckteWrhny athitsiaod?nal Conference 2021 Only at the ICIRET-21 conference in Jaka a, Indonesia. Only a fe remain, hurry ICIRET.NET Lea https://www.scimagojr.com/journalsearch.php?q=21100868216&tip=sid&clean=0 1/4

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1/12/2021 144Engineering and Applied Science Research 10D0ocuments Year Value 1r0e0views) in three year windows vs. those documents other than research articles, reviews and conference Uncited documents 2017 0 papers. Uncited documents 2018 34 Uncited documents 2019 61 ← Show this widget in your own website Just copy the code below and paste within your html code: <a href=\"https://www.scimag International Conference 2021 Only at the ICIRET-21 conference in Jaka a, Indonesia. Only a fe remain, hurry ICIRET.NET Lea Metrics based on Scopus® data as of April 2020 R ramesha k 7 months ago Dear sir/madam i am planning to publish my research article in your esteemed journal. i need one clari cation that in the home page it is mentioned that \"EASR has now been accepted for inclusion in the Scopus database\". is it sure could you please con rm me at the earliest. thank you reply SCImago Team M Melanie Ortiz 7 months ago Dear Ramesha, thank you for contacting us. We are sorry to tell you that SCImago Journal & Country Rank is not a journal. SJR is a portal with scientometric indicators of journals indexed in Elsevier/Scopus. We suggest you consult the Scopus database directly to see the current index status. Keep in mind that the SJR is a static image (the update is made one time per year, next one throughout June 2020) of a database (Scopus) which is changing every day. Best Regards, SCImago Team https://www.scimagojr.com/journalsearch.php?q=21100868216&tip=sid&clean=0 3/4

145 1 Notable mechanical behavior of thin monoholar rubber flat slab under tension loading 2 3 Kulyuth Boonseng*1), Chatchai Waiyapattanakorn2) and Prayoon Surin1) 4 1) Faculty of Engineering, Pathumwan Institute of Technology (PIT), Bangkok10330, 5 Thailand 6 2) Independent Academic, Bangkok 10210, Thailand 7 *Corresponding author. Tel.: +66-8169-09286; Email address: [email protected] 8 9 10 Abstract 11 There have been strong interests in achieving unconventional mechanical properties of 12 materials utilized for various applications in recent years. Apart from the well known 13 composite material approach, an innovative approach of employing cellular structure 14 or repeated geometric pattern has been of great interest. The simple example that has 15 been proved useful and promising is the monoholar pattern. Experimental 16 investigations of monoholar rubber flat slab have demonstrated a number of unusual 17 mechanical behaviors under compression loading unable to achieve before. It is thus 18 enticing to find out if the same is possible under tension loading. This research therefore 19 embarks on applying tension load on a thin monoholar rubber flat slab, 1/10 as thick as 20 the flat slab tested under compression loading. Results of the experiments clearly 21 demonstrate similar behaviors as observed in the case of compression loading. However 22 stress plateau is not observable in the stress-strain curve obtained under tension loading. 23 This is because the test specimen reaches rupture point after undergoing a certain level 24 of stress. Ligament thickness has been observed to effect noticeable drop in stress level 25 before gradually increasing towards stress level that causes full blown rupture. This 26 may lead to some promising applications related to tension loading. 27 28 Keywords: Mechanical behaviors, Thin Monoholar, Solid material, Tension Loading. 29 30 1. Introduction 31 Over the past three decades, there have been developments that lead to extraordinary 32 mechanical behavior of a novel type of materials [1-3]. This novel type of materials could 33 unleash tremendous potential in various areas of applications. Recent advancements of 34 manufacturing technique have sparked great interest in this type of materials. The trick that 35 makes possible a number of extraordinary mechanical behaviors is the so called cellular 36 structure or the repeated geometric pattern. An example of such an idea is the monoholar rubber

146 37 flat slab. The studies of the cellular structure offer new insights into the fabrication of novel 38 materials and devices with tailored properties. The cellular structures of materials with their 39 special mechanical and physical properties play an important role in determining their 40 properties [4-9]. The benefits that arise from cellular structure are various unpredictable 41 properties with multifunctionalities that could not be achieved in conventional materials [8, 10- 42 13]. 43 It is well known that the physical characteristics of the material affect its mechanical 44 properties of the material, even when using the same chemical composition [6, 13-16]. The 45 changes in structural patterns at macroscale can be triggered by elastic instability at the 46 macroscale [6-7]. Thus cellular structure materials’ functionality relies on elastic instabilities, 47 such as the quasi-2D slabs perforated with a square array of holes [6, 8, 17-20]. These materials 48 have since been widely applied in the development of novel products, useful in the automotive, 49 defense, sport, aerospace, energy industries [14, 20-23], shape memory foams, and 50 bioprostheses [20, 24-25]. Certain applications related to compression loading and tension 51 loading are running shoes, helmets, auto parts [20-21], aerospace [22-24] etc. 52 This paper focuses on some notable mechanical behaviors of thin monoholar rubber flat slab 53 under tension loading. This is because the monoholar pattern is simple and exhibiting the 54 highest stiffness [19]. There have been a number of published reports on the behavior of much 55 thicker monoholar rubber flat slab under compression loading, but little or none on tension 56 loading has been reported. In compression loading experiments emphasis is on mechanical 57 behavior during the early stage of loading. [6, 8, 26-29], Increasing the ligament thickness, the 58 loop size is larger and the stress increased. [8] Only hysteretic characteristics and stress-strain 59 curves are of concern herein. Therefore two loading actions are to be performed, the cyclical 60 tension applications and beyond rupture tension loading. Ligament thickness is a parameter of 61 interest in this work. 62 63 2. Experimentation 64 65 This section describes both the specimen preparation and experimental setup. The 66 experiments to be performed are those of tension loading actions on both the solid flat slab 67 (SFS) (the referenced specimen) and the monoholar flat slab (MFS) (the investigated 68 specimen). Both types of specimens are made of compounded rubber. Specimen preparation is 69 to be described first then detail of the experimental setup is given succinctly.

147 70 71 2.1 Specimen preparation 72 73 This research has opted for a compounded rubber made of Standard Thai Rubber (STR20) 74 40 phr, Butadiene Rubber 60 phr, silica 50 phr and other compounds used in the shoe industry. 75 The cellular structure of interest herein is the monoholar array. This is because of its 76 construction simplicity [26]. Figures 1-3 depict geometries of both the solid and monoholar flat 77 slabs employed in the experiment. Both specimens are different in physical structure at the 78 macro level. They are labelled as Model 1(M1) Model 2 (M2) and model 3(M3) as shown in 79 Figs.1-3 respectively. All dimensions are in millimeter. It is evident from all 3 figures that 80 ligament thickness, a parameter of interest in this work, has been varied from 2-4 mm. Both 81 types of flat slabs are 1 mm thick. They are hence thin flat slabs. This is very interesting under 82 tension loading. Specimens preparation has been carried out as illustrated in Fig. 4. 83 84 85 (a) M1- SFS (b) M1-MFS. 86 87 Figure 1 Specimen geometries model 1 (a) M1-SFS (b) M1-MFS.

148 88 89 (a) M2-SFS (b) M2-MFS 90 91 Figure 2 Specimen geometries model 2 (a) M2-SFS (b) M2-MFS 92 93 (a) M3-SFS (b) M3-MFS 94 95 Figure 3 Specimen geometries model 3 (a) M3-SFS (b) M3-MFS 96 97 98 99 100 101 102 103 104 105 106 107