Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy

Fuat Kartal; Arslan Kaptan

Makale detayları

Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy

Yazarlar:

Fuat Kartal | Arslan Kaptan

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Biochemistry and Cell Biology (Other)

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 2, pp.516-528

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001316869

pISSN:

1304-7191

Anahtar kelimeler:

Özet:

 This study aims to predict machinability and high performance optimum surface roughness Ra by developing multiple regression models and artificial neural network ANN model for abrasive water jet cutting AWJC of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure 3500-4000 Bar, nozzle distance 2-5 mm, abrasive flow rate 200-350 gmin, abrasive grain size 100-110 mesh, and nozzle traverse speed 240- 300 mmmin were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Ad- ditionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance ANOVA. The Artificial Neural Network ANN model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error MSE and mean absolute error MAE. The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65 from the multiple regression models created to estimate the surface rough-ness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5, 25.4 and 21.9, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contrib-ute to improving process planning and control.

 This study aims to predict machinability and high performance optimum surface roughness Ra by developing multiple regression models and artificial neural network ANN model for abrasive water jet cutting AWJC of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure 3500-4000 Bar, nozzle distance 2-5 mm, abrasive flow rate 200-350 gmin, abrasive grain size 100-110 mesh, and nozzle traverse speed 240- 300 mmmin were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Ad- ditionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance ANOVA. The Artificial Neural Network ANN model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error MSE and mean absolute error MAE. The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65 from the multiple regression models created to estimate the surface rough-ness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5, 25.4 and 21.9, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contrib-ute to improving process planning and control.

 This study aims to predict machinability and high performance optimum surface roughness Ra by developing multiple regression models and artificial neural network ANN model for abrasive water jet cutting AWJC of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure 3500-4000 Bar, nozzle distance 2-5 mm, abrasive flow rate 200-350 gmin, abrasive grain size 100-110 mesh, and nozzle traverse speed 240- 300 mmmin were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Ad- ditionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance ANOVA. The Artificial Neural Network ANN model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error MSE and mean absolute error MAE. The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65 from the multiple regression models created to estimate the surface rough-ness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5, 25.4 and 21.9, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contrib-ute to improving process planning and control.

 This study aims to predict machinability and high performance optimum surface roughness Ra by developing multiple regression models and artificial neural network ANN model for abrasive water jet cutting AWJC of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure 3500-4000 Bar, nozzle distance 2-5 mm, abrasive flow rate 200-350 gmin, abrasive grain size 100-110 mesh, and nozzle traverse speed 240- 300 mmmin were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Ad- ditionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance ANOVA. The Artificial Neural Network ANN model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error MSE and mean absolute error MAE. The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65 from the multiple regression models created to estimate the surface rough-ness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5, 25.4 and 21.9, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contrib-ute to improving process planning and control.

Makale detayları

Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy

Yazarlar:

Fuat Kartal | Arslan Kaptan

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Biochemistry and Cell Biology (Other)

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 2, pp.516-528

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001316869

pISSN:

1304-7191

Abrasive Water Jet Cutting

Artificial Neural Network

Aerospace Alloy

Al 7068

Surface Roughness

ANOVA

Sayı

Makale detayları

Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy

Yazarlar: Fuat Kartal | Arslan Kaptan

Yayıncı: Yildiz Technical University

Ana Başlıklar: Biochemistry and Cell Biology (Other)

Dergi: Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 2, pp.516-528

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001316869

pISSN: 1304-7191

Abrasive Water Jet Cutting

Artificial Neural Network

Aerospace Alloy

Al 7068

Surface Roughness

ANOVA

Sayı

Yazarlar:

Fuat Kartal | Arslan Kaptan

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Biochemistry and Cell Biology (Other)

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 2, pp.516-528

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001316869

pISSN:

1304-7191