engleski

Estimation of Production Time by Regression and Neural Networks

The estimation of production times will be the necessary future basis for cost estimation, cost reduction or TCE (Total Cost 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.

stepwise multiple linear regression; group technology; knowledge base; production time; neural networks; TCE

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