Structural design of an agricultural backhoe using TA, FEA, RSM and ANN

Bustos-Gaytán, A.; Peña, José Ángel; Saldaña‐Robles, Adriana; Alcántar-Camarena, V.; Balvantín-García, Antonio de Jesús; Saldaña-Robles, Noé

doi:10.1016/j.compag.2020.105278

Cited by 10 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tahap post processing merupakan tahapan terakhir yang berhubungan dengan intrepretasi data hasil simulasi FEA yang telah dilakukan. Pada tahap post processing, diperoleh data berupa nilai tegangan von Misses, safety factor, dan besar displacement yang terjadi pada rangka [8], [9], [10]. Pemodelan FEA menggunakan software autodesk inventor 2017 merupakan pendekatan sifat fisik komponen struktur dengan asumsi material dalam model bersifat homogen dengan respon linier terhadap tegangan [11], [12].…”

Section: Metode Simulasi Fea (Finite Element Analysis)unclassified

Pengembangan Konsep Desain dan Fabrikasi Mesin Penyortir Buah Duku (Lansium Parasiticum)

Hariyanto

Himarosa

Aditya

et al. 2020

JMPM

View full text Add to dashboard Cite

Pengembangan konsep desain mesin penyortir buah duku (Lansium Parasiticum) dilakukan menggunakan metode Five Step Method. Diperoleh tiga konsep mesin yang kemudian dipilih satu dari tiga konsep terbaik menggunakan metode product champion dilanjutkan dengan pembuatan desain 3D dan Finite Element Analysis (FEA) menggunakan software autodesk inventor 2017. Analisis FEA menunjukkan nilai von Misses stress sebesar 18 MPa bernilai lebih kecil dibanding yield strength material penyusun rangka sebesar 207 MPa, displacement yang terjadi sebesar 0,99 mm dengan nilai safety factor 15. Fabrikasi dan pengujian mesin menunjukkan nilai persentase keberhasilan proses penyortiran pada mesin lebih dari 85% dengan kapasitas sortir 400 kg/jam.

show abstract

Section: Metode Simulasi Fea (Finite Element Analysis)unclassified

Pengembangan Konsep Desain dan Fabrikasi Mesin Penyortir Buah Duku (Lansium Parasiticum)

Hariyanto

Himarosa

Aditya

et al. 2020

JMPM

View full text Add to dashboard Cite

show abstract

“…Using artificial intelligence methods is quick, robust, and accurate when compared with the FEA. Also, Saldaña-Robles et al (2020) used both FEA and ANN models to design an agricultural backhoe. They claimed that ANN decreases the number of numerical case studies and the solution time with satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the onset of shear localization based on machine learning

Akar

Ayli

Ulucak

et al. 2023

AIEDAM

View full text Add to dashboard Cite

Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors’ knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.

show abstract

“…Conversely, the artificial neural network (ANN) is a highly robust and excellent modeling tool generally used in nonlinear and complex processes, which can effectively overcome the limitations of RSM [25]. At present, ANN and RSM have been widely used in structural design optimization of agricultural machinery [26], design optimization of food processing machinery [27], food process optimization [28], and industrial process optimization [29,30]. In fact, GFM harvesting is a complex process, affected by many factors, and conducting trials requires a great deal of time and high costs.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Xue,

Fu,

et al. 2023

Agriculture

View full text Add to dashboard Cite

Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R2 values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R2 of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.

show abstract

Structural design of an agricultural backhoe using TA, FEA, RSM and ANN

Cited by 10 publications

References 8 publications

Pengembangan Konsep Desain dan Fabrikasi Mesin Penyortir Buah Duku (Lansium Parasiticum)

Pengembangan Konsep Desain dan Fabrikasi Mesin Penyortir Buah Duku (Lansium Parasiticum)

Prediction of the onset of shear localization based on machine learning

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Contact Info

Product

Resources

About