An optimized artificial neural network for estimating design effort of jigs and fixtures used in aviation industry

Umut Nazmi Aktan; Mehmet Dikmen

Article Details

An optimized artificial neural network for estimating design effort of jigs and fixtures used in aviation industry

Authors:

Umut Nazmi Aktan | Mehmet Dikmen

Publisher:

Ankara University

Main Topics:

Engineering

Journal:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.130-141

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001289792

pISSN:

1303-6009

Keywords:

Abstract:

 This paper investigates the usefulness of the machine learning methods to predict the design effort of jigs and fixtures used in the aviation industry. Reaching the best possible result by determining the ideal machine learning model to obtain the best estimate and the most appropriate set of inputs and parameters forms the basis of this study. To that end, most popular machine learning models that can be used for regression are combined with various data encoding methods. The best combination is optimized as well. The results showed that an optimized Artificial Neural Network architecture with binary encoding applied to the input data can be applied satisfactorily in the aviation industry for the solution of the given problem.

 This paper investigates the usefulness of the machine learning methods to predict the design effort of jigs and fixtures used in the aviation industry. Reaching the best possible result by determining the ideal machine learning model to obtain the best estimate and the most appropriate set of inputs and parameters forms the basis of this study. To that end, most popular machine learning models that can be used for regression are combined with various data encoding methods. The best combination is optimized as well. The results showed that an optimized Artificial Neural Network architecture with binary encoding applied to the input data can be applied satisfactorily in the aviation industry for the solution of the given problem.

 This paper investigates the usefulness of the machine learning methods to predict the design effort of jigs and fixtures used in the aviation industry. Reaching the best possible result by determining the ideal machine learning model to obtain the best estimate and the most appropriate set of inputs and parameters forms the basis of this study. To that end, most popular machine learning models that can be used for regression are combined with various data encoding methods. The best combination is optimized as well. The results showed that an optimized Artificial Neural Network architecture with binary encoding applied to the input data can be applied satisfactorily in the aviation industry for the solution of the given problem.

 This paper investigates the usefulness of the machine learning methods to predict the design effort of jigs and fixtures used in the aviation industry. Reaching the best possible result by determining the ideal machine learning model to obtain the best estimate and the most appropriate set of inputs and parameters forms the basis of this study. To that end, most popular machine learning models that can be used for regression are combined with various data encoding methods. The best combination is optimized as well. The results showed that an optimized Artificial Neural Network architecture with binary encoding applied to the input data can be applied satisfactorily in the aviation industry for the solution of the given problem.

Article Details

An optimized artificial neural network for estimating design effort of jigs and fixtures used in aviation industry

Authors:

Umut Nazmi Aktan | Mehmet Dikmen

Publisher:

Ankara University

Main Topics:

Engineering

Journal:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.130-141

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001289792

pISSN:

1303-6009

Tool design effort

aviation industry

artificial neural network

machine learning

Issues

Article Details

An optimized artificial neural network for estimating design effort of jigs and fixtures used in aviation industry

Authors: Umut Nazmi Aktan | Mehmet Dikmen

Publisher: Ankara University

Main Topics: Engineering

Journal: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.130-141

Document Type: Article

Publication Date: 12/2023

Document Language: English

Document ID (Org No.): 2001289792

pISSN: 1303-6009

Tool design effort

aviation industry

artificial neural network

machine learning

Issues

Authors:

Umut Nazmi Aktan | Mehmet Dikmen

Publisher:

Ankara University

Main Topics:

Engineering

Journal:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.65 Issue 2, pp.130-141

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001289792

pISSN:

1303-6009