scholarly journals Estimating modulus of elasticity (MOE) of particleboards using artificial neural networks to reduce quality measurements and costs

2019 ◽  
Vol 70 (3) ◽  
pp. 257-263
Author(s):  
Rıfat Kurt ◽  
Selman Karayilmazlar
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modulus Of Elasticity ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error ◽  
Artificial Neural

There are a large number of costs that enterprises need to bear in order to produce the same product at the same quality for a more affordable price. For this reason, enterprises have to minimize their expenses through a couple of measures in order to offer the same product for a lower price by minimizing these costs. Today, quality control and measurements constitute one of the major cost items of enterprises. In this study, the modulus of elasticity values of particleboards were estimated by using Artificial Neural Networks (ANN) and other mechanical properties of particleboards in order to reduce the measurement costs in particleboard enterprises. In addition to that, the future values of modulus of elasticity were also estimated using the same variables with the purpose of monitoring the state of the process. For this purpose, data regarding the mechanical properties of the boards were randomly collected from the enterprise for three months. The sample size (n) was: 6 and the number of samples (m): 65 and a total of 65 average measurement values were obtained for each mechanical property. As a result of the implementation, the low Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Error (MSE) performance measures of the model clearly showed that some quality characteristics could easily be estimated by the enterprises without having to make any measurements by ANN.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

scholarly journals ARDL METHOD: Forecasting Data Curah Hujan Harian NTB

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 73-82
Author(s):  
Ulul Azmi ◽  
Zilullah Nazir Hadi ◽  
Siti Soraya
Keyword(s):  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Root Mean Squared Error ◽  
Absolute Percentage Error ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Squared Error ◽  
Parameter Error

Penelitian ini berisi tentang prediksi atau forecasting data iklim di Nusa Tenggara Barat (NTB) tahun 2011, yakni jumlah hari terjadinya hujan dengan menggunakan metode Autoregressive Distributed Lag (ARDL). Data yang digunakan yaitu data iklim di Nusa Tenggara Barat (NTB) dari tahun 2006 -2010, dengan menggunakan beberapa parameter error seperti Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil simulasi data iklim di Nusa Tenggara Barat (NTB) tersebut, diperoleh prediksi jumlah hari terjadinya curah hujan pada tahun 2011 sebesar 226 hari dengan nilai MAD 20,8069, MSE 3,5569, RMSE 1,88597, dan MAPE 11,9297 . Dan prediksi jumlah hari terjadinya hujan pada tahun 2011 sebanyak 225,928 hari atau jika di bulatkan menjadi 226 hari dengan nilai parameter error MAD sebesar 20,8069, sehingga dapat disimpulkan pada tahun 2011 terjadi peningkatan jumlah hari terjadinya hujan di Nusa Tenggara Barat (NTB).

Use of Artificial Neural Networks and Arima to Forecasting Consumption Sawnwood of Pinus sp. in Brazil

2019 ◽  
Vol 21 (1) ◽  
pp. 51-61 ◽  
Author(s):  
D.A. Buratto ◽  
R. Timofeiczyk Junior ◽  
J.C.G.L. Silva ◽  
J.R. Frega ◽  
M.S.S.A. Wiecheteck ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Arima Model ◽  
Secondary Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Percentage Error ◽  
Explanatory Variables ◽  
Artificial Neural

The objective of this study was to analyze the application of an artificial neural networks model and an ARIMA model to predict the consumption of sawnwood of pine. For this, we use real and secondary data collected and obtained from a historical data source, corresponding to the period from 1997 to 2016, which were later tested to generate the forecast models. Based on economic and statistical criteria, six explanatory variables were used to fit the best model. The choice of the model was made based on Mean Squared Error, Mean Absolute Error, Theil U metric, Percentage Error of Forecast and Akaike value information criterion. The results indicated that the models generated through the ARIMA model presented better performance when compared to the artificial neural network. The best adjusted model estimated a reduction of 1.33% in consumption of sawnwood of pine in Brazil for the period between 2017 and 2020.

scholarly journals A Comparison of Performance of Artificial Neural Networks for Prediction of Heavy Metals Concentration in Groundwater Resources of Toyserkan Plain

2017 ◽  
Vol 4 (1) ◽  
pp. 11792-11792 ◽  
Author(s):  
Meysam Alizamir ◽  
Soheil Sobhanardakani
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Groundwater Resources ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Squared Error ◽  
Artificial Neural ◽  
Statistical Indicators

Nowadays, about 50% the world’s population is living in dry and semi dry regions and has utilized groundwater as a source of drinking water. Therefore, forecasting of pollutant content in these regions is vital. This study was conducted to compare the performance of artificial neural networks (ANNs) for prediction of As, Zn, and Pb content in groundwater resources of Toyserkan Plain. In this study, two types of artificial neural networks (ANNs), namely multi-layer perceptron (MLP) and Radial Basis Function (RBF) approaches, were examined using the observations of As, Zn, and Pb concentrations in groundwater resources of Toyserkan plain, Western Iran. Two statistical indicators, the coefficient of determination (R2) and root mean squared error (RMSE) were employed to evaluate the performances of various models. The results indicated that the best performance could be obtained by MLP, in terms of different statistical indicators during training and validation periods.

scholarly journals Previsão dos modelos univariados e rede neural da demanda turística do estado de Mato Grosso do Sul

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Bruno Matos Porto ◽  
Daniela Althoff Philippi ◽  
Vanessa Aline Wagner Leite
Keyword(s):  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Mato Grosso ◽  
Absolute Percentage Error ◽  
Squared Error ◽  
Mato Grosso Do Sul

O objetivo deste artigo foi gerar previsões de curto, médio e longo prazos e comparar a precisão dos modelos em cada horizonte de previsão. Para atender o objetivo foram aplicados os modelos univariados e rede neural (NNAR) nos dados da demanda turística do estado de Mato Grosso do Sul (MS). A amostra foi coletada na ferramenta base de dados extrator do Instituto Brasileiro de Turismo (Embratur) referente as chegadas turísticas por todas as vias registradas no MS entre janeiro de 2007 a dezembro de 2017. As previsões dos modelos de previsão ARIMA, Holt-Winters (HW) versões aditiva e multiplicativa e NNAR foram projetadas, por meio da linguagem de programação R, com uso do software R Studio. O procedimento empírico de execução dos scripts de todos os modelos foi disponibilizado. As predições fora da amostra da procura do turismo abrangeram o intervalo de janeiro até dezembro de 2018, sendo então comparadas aos dados reais do mesmo período. As previsões dos modelos foram comparadas no curto, médio e longo prazo mediante os critérios Mea Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) e Mean Squared Error (MSD). A rede neural (NNAR) superou os modelos testados em diferentes horizontes de previsão e as medidas de erros mostraram que a NNAR é altamente precisa. Em segundo lugar no ranking de acuracidade destacou-se ARIMA. Os resultados mostraram que as previsões da rede neural auxiliam na tomada de decisão dos planejadores turísticos de MS. Para pesquisas futuras recomenda-se realizar previsões fora da amostra num amplo número de séries temporais.

scholarly journals Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City

2020 ◽  
Vol 1 (2) ◽  
pp. 45
Author(s):  
Dewi Darma Pertiwi
Keyword(s):  
Mean Squared Error ◽  
Secondary Data ◽  
Weather Conditions ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Data Smoothing ◽  
Squared Error ◽  
The City

Weather conditions in the city of Mataram tend to be erratic and difficult to predict, such as the condition of rainfall data in 2018 which changes over a certain period of time so that the weather is difficult to predict accurately. In this study, we propose the Exponential Smoothing Holt-Winter method to forecast rainfall in the city of Mataram, so that it can be a decision support for various interested sectors. This method has been tested using secondary data from the Mataram City Central Bureau of Statistics for the period January 2014 to 2018 and evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results of this study indicate that using the Exponential Smoothing Holt-Winter method yields better results, each of which is MAPE 142.3, MAD 95.6 and MSD value 24988.7 and the data smoothing value is obtained for the smallest combination value of α 0.2, β 0.1, and γ 0.1. It can be concluded that the proposed method can provide better information and can be used to predict rainfall in Mataram City for the next 12 periods.

Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

2013 ◽  
Vol 773-774 ◽  
pp. 268-274
Author(s):  
Amir Ghiami ◽  
Ramin Khamedi
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Total Elongation ◽  
Ultimate Tensile Stress ◽  
Dual Phase ◽  
Dual Phase Steels ◽  
Good Ability ◽  
Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

PERENCANAAN PERSEDIAAN BAHAN BAKU AKAR TANJUNG DENGAN METODE MATERIAL REQUIREMENTS PLANNING (MRP) PADA UPT. MAKARTI POMOSDA

2020 ◽  
Vol 16 (3) ◽  
pp. 1-12
Author(s):  
Khoirul Hidayah ◽  
Sukarni Sukarni ◽  
Achmad Syaichu
Keyword(s):  
Standard Error ◽  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Material Requirements Planning ◽  
Safety Stock ◽  
Mean Error ◽  
Squared Error ◽  
Material Requirement

Suatu produksi yang direncanakan dengan baik akan menghasilkan efektivitas dan efisiensi produksi bagi perusahaan. Pentingnya perencanaan material pada perusahaan diharapkan dapat menghasilkan sistem yang baik terhadap proses produksi. Tujuan dari penelitian ini adalah untuk mengetahui penerapan Material Requirement Planning (MRP) sehingga kebutuhan bahan baku selama proses produksi di UPT MAKARTI POMOSDA dapat terpenuhi dengan menggunakan metode peramalan forecasting dalam satu tahun yaitu, moving average dan weighted moving average.  Metode ini terpilih untuk mengetahui safety stock nya produk setiap bulan dan setiap tahun. Berdasarkan detail dan analisa kesalahan metode moving average dengan menggunakan program POM QM forWindows Versi 3 Basic (Mean Error) 42,455, MAD (Mean Absolute Deviation) 259,545, MSE (Mean Squared Error) 118490,6, Standard Error (denom=n-2=9) 380,555, MAPE (Mean Absolute Percent Error) 643, dan next period 480. Sedangkan detail dan analisa kesalahan metode ini dengan menggunakan program POM QM For Windows Versi 3 Basic (Mean Error) 38,827, MAD (Mean Absolute Deviation) 212,257, MSE (Mean Squared Error) 83586,58, Standard Error (denom=n-2=9) 323,239, MAPE (Mean Absolute Percent ) 495, dan next period 464,893. Berdasarkan hasil proses diatas juga diketahui (safety stock) pada UPT MAKARTI POMOSDA pada tahun 2017 yaitu sejumlah 5209 unit, setelah dilakukan penelitian mengalami kenaikan sebesar 6758 dengan prosentase sebesar 129,7%, sehingga tidak ada penumpukan barang digudang. Hal ini juga didukung dengan penurunan biaya simpan bahan baku dari Rp 120.850/Periode (bulan) menjadi Rp 109.350/Periode (bulan).

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