Masters' Theses A Design on Cost-Estimation Support System for Injection Mold Using AI Method
2015.07.29 13:51
| year | 2004 |
|---|---|
| author | Gunhee Kim |
| Keyword | Injection Mold, Feature Extraction, CBR, Cost Estimation, Evaluation & Feedback, Bagging MLP |
| Abstract | This study was initially designed for time efficiency and organizing the process of the cost of injection molds. Estimating the prompt costs of the mold precisely and setting the reasonable prices are absolutely important in the mold industries. The reason for this is mold industries have ETO, which stands for Engineering To Order, formats. Currently, the way they, who set the prices for the mold, assign the cost of mold is not based on the reasonable and scientific processes decisions are made upon one´s own experiences. Subsequently, there are problems of time consuming and organizing the process. For the ideal solutions, it requires analyzing and re-designing the current process also, the processing time and assigning proper values are required. This proposed Solution is based on the four modules which are on the process from the ordering products to assigning the costs. Those four modules are Feature Extraction module, CBR (Case-Based Reasoning) module, Cost Estimation module and Evaluation & Feedback module. In the case of the CBR module, especially, it was proved that the CBR model with Bagging MLP (Multiple Layer Perceptron) performed better than others such as Multiple Linear Regression, Single MLP, Bagging MLP and Simple CBR. |
| c | MS |
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