Estimation of Friction Coefficients of Soybean Seeds with Soft Computing Approach

Elçin Yeşiloğlu Cevher; Demet Yıldırım; Gürkan Alp Kağan Gürdil

Article Details

Estimation of Friction Coefficients of Soybean Seeds with Soft Computing Approach

Authors:

Elçin Yeşiloğlu Cevher | Demet Yıldırım | Gürkan Alp Kağan Gürdil

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Tarım Makineleri | Hayvansal Üretim (Diğer)

Journal:

Black Sea Journal of Agriculture Vol.8 Issue 4, pp.463-475

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001354609

pISSN:

2618-6578

Keywords:

Abstract:

 Determination of physical and mechanical properties of agricultural products plays an important role in the usage areas of the products and industrial applications. Correct determination and evaluation of physical and mechanical properties of agricultural products is of critical importance in determining the quality, durability and usage potential of the product. In this study, the relationship between moisture content and friction coefficients of Samsoy variety soybean seed, which is a trial material, was determined in order to contribute to making correct decisions in industrial design and material selection. The central aim of this research is to expose with different moisture contents and friction surfaces well-accepted data-driven models to predict friction coefficients for soybean seed using different soft computing techniques. Determination of friction coefficient of agricultural products is important in terms of design and functionality of equipment used in post-harvest technologies and agricultural applications. In the study, 3 different moisture contents and five different friction surfaces steel, stainless steel, galvanized sheet, PVC, court fabric were used. Artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, group method of data handling GMDH are used to predict of friction coefficients. The best accuracy values were recorded as GMDH 7-7-1 for seven input and 7-15-1 model for five input structures for kinetic and static friction that were calculated performance criteria R2  0.99-0.98, RMSE 0.00004-0.00006 , MSE  0.00009 -0.00011, respectively. These selected the best models predicted which can be used in the soft computing techniques determined different conditions to estimating the friction coefficient for soybean seeds.

 Determination of physical and mechanical properties of agricultural products plays an important role in the usage areas of the products and industrial applications. Correct determination and evaluation of physical and mechanical properties of agricultural products is of critical importance in determining the quality, durability and usage potential of the product. In this study, the relationship between moisture content and friction coefficients of Samsoy variety soybean seed, which is a trial material, was determined in order to contribute to making correct decisions in industrial design and material selection. The central aim of this research is to expose with different moisture contents and friction surfaces well-accepted data-driven models to predict friction coefficients for soybean seed using different soft computing techniques. Determination of friction coefficient of agricultural products is important in terms of design and functionality of equipment used in post-harvest technologies and agricultural applications. In the study, 3 different moisture contents and five different friction surfaces steel, stainless steel, galvanized sheet, PVC, court fabric were used. Artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, group method of data handling GMDH are used to predict of friction coefficients. The best accuracy values were recorded as GMDH 7-7-1 for seven input and 7-15-1 model for five input structures for kinetic and static friction that were calculated performance criteria R2  0.99-0.98, RMSE 0.00004-0.00006 , MSE  0.00009 -0.00011, respectively. These selected the best models predicted which can be used in the soft computing techniques determined different conditions to estimating the friction coefficient for soybean seeds.

 Determination of physical and mechanical properties of agricultural products plays an important role in the usage areas of the products and industrial applications. Correct determination and evaluation of physical and mechanical properties of agricultural products is of critical importance in determining the quality, durability and usage potential of the product. In this study, the relationship between moisture content and friction coefficients of Samsoy variety soybean seed, which is a trial material, was determined in order to contribute to making correct decisions in industrial design and material selection. The central aim of this research is to expose with different moisture contents and friction surfaces well-accepted data-driven models to predict friction coefficients for soybean seed using different soft computing techniques. Determination of friction coefficient of agricultural products is important in terms of design and functionality of equipment used in post-harvest technologies and agricultural applications. In the study, 3 different moisture contents and five different friction surfaces steel, stainless steel, galvanized sheet, PVC, court fabric were used. Artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, group method of data handling GMDH are used to predict of friction coefficients. The best accuracy values were recorded as GMDH 7-7-1 for seven input and 7-15-1 model for five input structures for kinetic and static friction that were calculated performance criteria R2  0.99-0.98, RMSE 0.00004-0.00006 , MSE  0.00009 -0.00011, respectively. These selected the best models predicted which can be used in the soft computing techniques determined different conditions to estimating the friction coefficient for soybean seeds.

 Determination of physical and mechanical properties of agricultural products plays an important role in the usage areas of the products and industrial applications. Correct determination and evaluation of physical and mechanical properties of agricultural products is of critical importance in determining the quality, durability and usage potential of the product. In this study, the relationship between moisture content and friction coefficients of Samsoy variety soybean seed, which is a trial material, was determined in order to contribute to making correct decisions in industrial design and material selection. The central aim of this research is to expose with different moisture contents and friction surfaces well-accepted data-driven models to predict friction coefficients for soybean seed using different soft computing techniques. Determination of friction coefficient of agricultural products is important in terms of design and functionality of equipment used in post-harvest technologies and agricultural applications. In the study, 3 different moisture contents and five different friction surfaces steel, stainless steel, galvanized sheet, PVC, court fabric were used. Artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, group method of data handling GMDH are used to predict of friction coefficients. The best accuracy values were recorded as GMDH 7-7-1 for seven input and 7-15-1 model for five input structures for kinetic and static friction that were calculated performance criteria R2  0.99-0.98, RMSE 0.00004-0.00006 , MSE  0.00009 -0.00011, respectively. These selected the best models predicted which can be used in the soft computing techniques determined different conditions to estimating the friction coefficient for soybean seeds.

Article Details

Estimation of Friction Coefficients of Soybean Seeds with Soft Computing Approach

Authors:

Elçin Yeşiloğlu Cevher | Demet Yıldırım | Gürkan Alp Kağan Gürdil

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Tarım Makineleri | Hayvansal Üretim (Diğer)

Journal:

Black Sea Journal of Agriculture Vol.8 Issue 4, pp.463-475

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001354609

pISSN:

2618-6578

Soft computing

Friction coefficients

Soybean seeds

Predict frictionSoft computing

Friction coefficients

Soybean seeds

Predict friction

Issues

Article Details

Authors: Elçin Yeşiloğlu Cevher | Demet Yıldırım | Gürkan Alp Kağan Gürdil

Publisher: Karyay Karadeniz Yayımcılık

Main Topics: Tarım Makineleri | Hayvansal Üretim (Diğer)

Journal: Black Sea Journal of Agriculture Vol.8 Issue 4, pp.463-475

Document Type: Article

Publication Date: 7/2025

Document Language: English

Document ID (Org No.): 2001354609

pISSN: 2618-6578

Issues

Authors:

Elçin Yeşiloğlu Cevher | Demet Yıldırım | Gürkan Alp Kağan Gürdil

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Tarım Makineleri | Hayvansal Üretim (Diğer)

Journal:

Black Sea Journal of Agriculture Vol.8 Issue 4, pp.463-475

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001354609

pISSN:

2618-6578