Modeling and predicting the tensile strength of poly (lactic acid)/graphene nanocomposites by using support vector regression

2016 ◽  
Vol 30 (10) ◽  
pp. 1650052
Author(s):  
W. D. Cheng ◽  
C. Z. Cai ◽  
Y. Luo ◽  
Y. H. Li ◽  
C. J. Zhao
Keyword(s):  
Tensile Strength ◽  
Lactic Acid ◽  
Support Vector Regression ◽  
Process Parameters ◽  
Poly Lactic Acid ◽  
Percentage Error ◽  
Support Vector ◽  
Graphene Nanocomposites ◽  
Absolute Percentage Error ◽  
Rsm Model

According to an experimental dataset under different process parameters, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization was employed to establish a mathematical model for prediction of the tensile strength of poly (lactic acid) (PLA)/graphene nanocomposites. Four variables, while graphene loading, temperature, time and speed, were employed as input variables, while tensile strength acted as output variable. Using leave-one-out cross validation test of 30 samples, the maximum absolute percentage error does not exceed 1.5%, the mean absolute percentage error (MAPE) is only 0.295% and the correlation coefficient [Formula: see text] is as high as 0.99. Compared with the results of response surface methodology (RSM) model, it is shown that the estimated errors by SVR are smaller than those achieved by RSM. It revealed that the generalization ability of SVR is superior to that of RSM model. Meanwhile, multifactor analysis is adopted for investigation on significances of each experimental factor and their influences on the tensile strength of PLA/graphene nanocomposites. This study suggests that the SVR model can provide important theoretical and practical guide to design the experiment, and control the intensity of the tensile strength of PLA/graphene nanocomposites via rational process parameters.

Investigation on the Processing-Properties of Hot Deformed TA15 Titanium Alloy via Support Vector Regression

2011 ◽  
Vol 689 ◽  
pp. 134-143
Author(s):  
J.F. Pei ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang
Keyword(s):  
Tensile Strength ◽  
Titanium Alloy ◽  
Strain Rate ◽  
Support Vector Regression ◽  
Process Parameters ◽  
Percentage Error ◽  
Support Vector ◽  
Test Set ◽  
Novel Approach ◽  
Ta15 Titanium Alloy

According to an experimental dataset on the tensile strength and elongation of TA15 titanium alloy under different hot deformation process parameters including temperature, strain, strain rate and cooling condition, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, is proposed to establish a model for prediction of the tensile strength and elongation of hot deformed TA15 titanium alloy. For tensile strength, the mean absolute percentage error (MAPE) achieved by SVR is 0.65% and 0.68% for the training and test set, respectively. At the same time, the MAPE for elongation achieved by SVR is 1.51% and 3.36% for the training and test set, respectively. The MAPEs for both tensile strength and elongation achieved by SVR are much smaller than those of BPNN by using identical training and test samples. Accordingly, the established SVR model was adopted to illustrate the relationships among tensile strength, elongation, and the process parameters. From the 3D surface of tensile strength vs. temperature and strain rate, it is found that to reach a higher tensile strength, a strain rate lower than 0.01s-1 is required, and a lower strain will be helpful for achieving the maximum elongation. These suggest that SVR as a novel approach has a theoretical significance and potential practical value in fabrication of TA15 titanium alloy with desired properties.

SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL

2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG
Keyword(s):  
Fuel Cell ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.

scholarly journals Prediksi Jumlah Pengiriman Barang Menggunakan Kombinasi Metode Support Vector Regression, Algoritma Genetika dan Multivariate Adaptive Regression Splines

2020 ◽  
Vol 7 (6) ◽  
pp. 1169
Author(s):  
Nendi Nendi ◽  
Arief Wibowo
Keyword(s):  
Support Vector Regression ◽  
Mean Absolute Percentage Error ◽  
Multivariate Adaptive Regression Splines ◽  
Percentage Error ◽  
Support Vector ◽  
Regression Splines ◽  
Absolute Percentage Error ◽  
Logistics Company ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

<p>Sektor usaha logistik telah berkembang sangat pesat di Indonesia saat ini. PT. XYZ  adalah sebuah perusahaan logistik yang menyediakan jasa pengiriman barang dari satu tempat menuju ke tempat yang lain. Sebagai perusahaan logistik dengan jumlah kendaraan 2.100 unit armada truk dan akan terus bertambah seiring dengan target yang dicanangkan perusahaan, dimana pada 2020 jumlah armada truk harus mencapai 6.000 unit truk. Saat ini strategi operasional logistik dihasilkan berdasarkan pengalaman dari steakholder. Hal ini tentu tidak bisa dipertanggung jawabkan secara ilmiah. Prediksi jumlah pengiriman barang harian dapat menjadi solusi dalam membantu perusahaan dalam merencanakan, memonitoring dan mengevaluasi strategi operasional logistik. Hasil pengujian menunjukkan penggabungan metode <em>Support Vector Regression</em> (SVR), algoritma genetika dan <em>Multivariate Adaptive Regression Splines </em>(MARS) dapat menghasilkan prediksi jumlah pengiriman barang harian dengan nilai <em>Mean Absolute Percentage Error</em> (MAPE) yaitu 0.0969% dengan parameter <em>epsilon</em>(𝜀) 1.92172577675873E-20, <em>complexitas</em>(𝑐) 62 dan <em>gamma</em>(γ) 1.0.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Abstract"><em>The logistics business sector has developed very rapidly in Indonesia today. PT XYZ is a national logistics company that provides freight forwarding services from one place to another. As a national-scale logistics company, the company is supported by a fleet of 2,100 trucks. The number of fleets will continue to grow in line with the target set by the company, namely in 2020 the number of truck fleets must reach 6,000 trucks. Currently the logistics operational strategy is produced based on stakeholder experience, this certainly causes problems in the company's overall operations. Prediction of the number of daily goods shipments can be a solution in helping companies in planning, monitoring and evaluating logistical operational strategies, based on the company's ability in the availability of a fleet of vehicles for shipping. This study proposes a combination of Support Vector Regression (SVR) methods, genetic algorithms and Multivariate Adaptive Regression Splines (MARS) for problem solving in the prediction process, including in the selection of appropriate training data. The test results show that the combination of the three methods can produce predictions of the number of daily shipments with values of Mean Absolute Percentage Error (MAPE) 0.0969%, epsilon (𝜀) 1.92172577675873E- 20, complexity (𝑐) 62, and gamma (γ) 1.0.</em></p><p class="Judul2"><strong><em><br /></em></strong></p>

scholarly journals Multiple-Forecasting Approach: A Prediction of CO2 Emission from the Paddy Crop in India

2021 ◽  
Author(s):  
Pawan Kumar Singh ◽  
Alok Kumar Pandey ◽  
Sahil Ahuja ◽  
Ravi Kiran
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Co2 Emission ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Paddy Crop

Abstract This paper compares four prediction methods namely Random Forest Regressor (RFR), SARIMAX, Holt-Winters (H-W), and the Support Vector Regression (SVR) to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models to suggest an effective model to predict the total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crop in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE), highlights the differences in accuracy among the four models. The mean absolute percentage error (MAPE) and the mean square error (MSE) for the four methods are: RFR (MAPE: 5.67; MSE: 549900.02), SARIMAX (MAPE:1.67; MSE:70422.35), H-W (MAPE:0.75; MSE:16648.58), and SVR (MAPE: 0.91; MSE: 17832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the Support Vector Regression (SVR) is relatively low as compared to SARIMAX and RFR. On the basis of these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crop. Holt-Winters the model predicted 14364.97 for the year 2025 and SVR predicted 13696.67 for the year 2025. These predictions can be used by the decision-maker to build a suitable policy for future studies. For further research, this approach can be contrasted with other approaches, such as the Neural Network or other forecasting methods, using more important datasets to train the model to achieve better forecast accuracy.

Effect of Polyethylene Glycol in Nanocellulose/PLA Composites

2019 ◽  
Vol 821 ◽  
pp. 89-95
Author(s):  
Wanasorn Somphol ◽  
Thipjak Na Lampang ◽  
Paweena Prapainainar ◽  
Pongdhorn Sae-Oui ◽  
Surapich Loykulnant ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Lactic Acid ◽  
Polyethylene Glycol ◽  
Aspect Ratio ◽  
Mechanical Performance ◽  
Tensile Modulus ◽  
Poly Lactic Acid ◽  
Solvent Casting ◽  
Casting Method ◽  
Mediated Oxidation

Poly (lactic acid) or PLA was reinforced by nanocellulose and polyethylene glycol (PEG), which were introduced into PLA matrix from 0 to 3 wt.% to enhance compatibility and strength of the PLA. The nanocellulose was prepared by TEMPO-mediated oxidation from microcrystalline cellulose (MCC) powder and characterized by TEM, AFM, and XRD to reveal rod-like shaped nanocellulose with nanosized dimensions, high aspect ratio and high crystallinity. Films of nanocellulose/PEG/PLA nanocomposites were prepared by solvent casting method to evaluate the mechanical performance. It was found that the addition of PEG in nanocellulose-containing PLA films resulted in an increase in tensile modulus with only 1 wt% of PEG, where higher PEG concentrations negatively impacted the tensile strength. Furthermore, the tensile strength and modulus of nanocellulose/PEG/PLA nanocomposites were higher than the PLA/PEG composites due to the existence of nanocellulose chains. Visual traces of crazing were detailed to describe the deformation mechanism.

scholarly journals Biocomposites of Epoxidized Natural Rubber/Poly(lactic acid) Modified with Natural Fillers (Part I)

2021 ◽  
Vol 22 (6) ◽  
pp. 3150
Author(s):  
Anna Masek ◽  
Stefan Cichosz ◽  
Małgorzata Piotrowska
Keyword(s):  
Tensile Strength ◽  
Lactic Acid ◽  
Natural Rubber ◽  
Epoxidized Natural Rubber ◽  
Poly Lactic Acid ◽  
Uv Aging ◽  
High Elasticity ◽  
Natural Fillers ◽  
Natural Additives

The study aimed to prepare sustainable and degradable elastic blends of epoxidized natural rubber (ENR) with poly(lactic acid) (PLA) that were reinforced with flax fiber (FF) and montmorillonite (MMT), simultaneously filling the gap in the literature regarding the PLA-containing polymer blends filled with natural additives. The performed study reveals that FF incorporation into ENR/PLA blend may cause a significant improvement in tensile strength from (10 ± 1) MPa for the reference material to (19 ± 2) MPa for the fibers-filled blend. Additionally, it was found that MMT employment in the role of the filler might contribute to ENR/PLA plasticization and considerably promote the blend elongation up to 600%. This proves the successful creation of the unique and eco-friendly PLA-containing polymer blend exhibiting high elasticity. Moreover, thanks to the performed accelerated thermo-oxidative and ultraviolet (UV) aging, it was established that MMT incorporation may delay the degradation of ENR/PLA blends under the abovementioned conditions. Additionally, mold tests revealed that plant-derived fiber addition might highly enhance the ENR/PLA blend’s biodeterioration potential enabling faster and more efficient growth of microorganisms. Therefore, materials presented in this research may become competitive and eco-friendly alternatives to commonly utilized petro-based polymeric products.

scholarly journals Bonding Wood Veneer with Biobased Poly(Lactic Acid) Thermoplastic Polyesters: Potential Applications for Consolidated Wood Veneer and Overlay Products

Fibers ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 50
Author(s):  
Warren J. Grigsby ◽  
Arpit Puri ◽  
Marc Gaugler ◽  
Jan Lüedtke ◽  
Andreas Krause
Keyword(s):  
Tensile Strength ◽  
Lactic Acid ◽  
Cold Water ◽  
Performance Criteria ◽  
Poly Lactic Acid ◽  
Wood Veneer ◽  
Pressing Temperature ◽  
Strength Performance ◽  
Potential Applications ◽  
Laminated Panels

This study reports on the use of poly(lactic acid) (PLA) as a renewable thermoplastic adhesive for laminated panels using birch, spruce, and pine veneers. Consolidated panels were prepared from veneer and PLA foils by hot-pressing from 140 to 180 °C to achieve minimum bondline temperatures. Evaluation of panel properties revealed that the PLA-bonded panels met minimum tensile strength and internal bond strength performance criteria. However, the adhesion interface which developed within individual bondlines varied with distinctions between hardwood and softwood species and PLA grades. Birch samples developed greater bondline strength with a higher pressing temperature using semi-crystalline PLA, whereas higher temperatures produced a poorer performance with the use of amorphous PLA. Panels formed with spruce or pine veneers had lower bondline performance and were also similarly distinguished by their pressing temperature and PLA grade. Furthermore, the potential for PLA-bonded laminated panels was demonstrated by cold water soak testing. Samples exhibiting relatively greater bondline adhesion had wet tensile strength values comparable to those tested in dry state. Our study outcomes suggest the potential for PLA bonding of veneers and panel overlays with the added benefits of being renewable and a no added formaldehyde system.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

Tensile and water absorption properties of solvent cast biofilms of sugarcane leaves fibre-filled poly(lactic) acid

2018 ◽  
Vol 33 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Kuhananthan Nanthakumar ◽  
Chan Ming Yeng ◽  
Koay Seong Chun
Keyword(s):  
Tensile Strength ◽  
Lactic Acid ◽  
Water Absorption ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Poly Lactic Acid ◽  
Infrared Analysis ◽  
Absorption Properties ◽  
Elongation At Break ◽  
Bleaching Treatment

This research covers the preparation of poly(lactic acid) (PLA)/sugarcane leaves fibre (SLF) biofilms via a solvent-casting method. The results showed that the tensile strength and Young’s modulus of PLA/SLF biofilms increased with the increasing of SLF content. Nevertheless, the elongation at break showed an opposite trend as compared to tensile strength and Young’s modulus of biofilms. Moreover, water absorption properties of PLA/SLF biofilms increased with the increasing of SLF content. In contrast, the tensile strength and Young’s modulus of biofilms were enhanced after bleaching treatment with hydrogen peroxide on SLF, but the elongation at break and water absorption properties of bleached biofilms were reduced due to the improvement of filler–matrix adhesion in biofilms. The tensile and water properties were further discussed using B-factor and Fick’s law, respectively. Furthermore, the functional groups of unbleached and bleached SLF were characterized by Fourier transform infrared analysis.

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