Optimization of multi-pass turning of slender bar using artificial neural networks and genetic algorithm

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 obtained model, cutting parameter was optimized to satisfy the specified drum-shaped error and economics criterion in multi-pass turning of slender bar. Due to the high complexity of the machining optimization problem, genetic algorithm was employed to resolve this problem. Experimental results show that the proposed optimization method is both effective and efficient for slender bar turning operations.