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This study proposed to employ Cross-Validation (CV) to evaluate reliability of the strength models generated by nonlinear regression analysis (NLRA), artificial neural network (ANN), and genetic operation tree (GOT), to make more sound comparisons between them. It was found that (1) the ANN was the most accurate modeling tool for the Low, Medium, and High water-binder ratio (w/b) data sets; (2) using...
In this paper, an ordinal optimization based approach is proposed to solve for a good enough schedule that minimizes expected sum of storage expenses and tardiness penalties of stochastic classical job shop scheduling problem using limited computation time. The proposed approach consists of exploration and exploitation stage. The exploration stage uses a genetic algorithm to select a good candidate...
Optimization of cutting parameters is very important issues in manufacturing engineering. For slender bar turning operations, drum-shaped error is one of the most important product quality characteristics. In this work, an artificial neural network model was developed firstly to describe the relationship between cutting parameters and drum-shaped error in slender bar turning process. Based on the...
This paper presents a systematic and cost-effective approach for process optimization with minimal experimental runs. Based on the experimental design scheme of orthogonal arrays, artificial neural network is used to establish the process model. Moreover, Taguchi-genetic algorithm (TGA) is used to search for the global optimum of the fabrication conditions. The procedure starts planning and conducting...
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