Regression analysis and neural networks as methods for production time estimation (CROSBI ID 187448)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Ćosić, Predrag ; Lisjak, Dragutin ; Antolić, Dražen
hrvatski
Regression analysis and neural networks as methods for production time estimation
An experienced process planner usually makes decisions based on comprehensive data without breaking it down into individual parameters. So, as the first phase it was necessary to establish a technological knowledge base, define features of the 2D drawing (independent variables), possible dependent variables, size and criteria for sample homogenization (principles of group technology) for carrying out analysis of variance and regression analysis. The second phase in the research was to investigate the possibility for easy automatic, direct finding and applying 3D features of an axial symmetric product to the regression model. The third phase in the research was to investigate the possibility for the application of neural networks in production time estimation and to compare the 224 results between the regression models and neural network models. The most important characteristic of our approach presented in this paper is estimation of production times using group technology, regression analysis and neural networks.
group technology; knowledge base; neural networks; production time; stepwise multiple linear regression; Total Cost Estimation
nije evidentirano
engleski
Regression analysis and neural networks as methods for production time estimation
An experienced process planner usually makes decisions based on comprehensive data without breaking it down into individual parameters. So, as the first phase it was necessary to establish a technological knowledge base, define features of the 2D drawing (independent variables), possible dependent variables, size and criteria for sample homogenization (principles of group technology) for carrying out analysis of variance and regression analysis. The second phase in the research was to investigate the possibility for easy automatic, direct finding and applying 3D features of an axial symmetric product to the regression model. The third phase in the research was to investigate the possibility for the application of neural networks in production time estimation and to compare the 224 results between the regression models and neural network models. The most important characteristic of our approach presented in this paper is estimation of production times using group technology, regression analysis and neural networks.
group technology; knowledge base; neural networks; production time; stepwise multiple linear regression; Total Cost Estimation
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
18 (4)
2011.
479-484
objavljeno
1330-3651