ANN and RSM approach for modelling and multi objective optimization of abrasive water jet machining process

• 發表在: Decision Science Letters
• Main Authors: Srinath Reddy N., Dinesh Tirumala, Rajyalakshmi Gajjela, Raja Das
• 格式: Article
• 語言: 英语
• 出版: Growing Science 2018-09-01
• 主題: AWJM; Response surface methodology; Artificial neural network; Modeling; Optimization
• 在線閱讀: http://www.growingscience.com/dsl/Vol7/dsl_2017_36.pdf
• ISSN: 1929-5804; 1929-5812

Abrasive Water Jet Machining is one of the novel nontraditional cutting processes found diverse applications in machining different kinds of difficult-to-machine materials. Process parameters play an important role in finding the economics of machining process at good quality. This research focused on the predictive models for explaining the functional relationship between input and output parameters of AWJ machining process. No single set of parametric combination of machining variables can suggest the better responses concurrently, due to its conflicting nature. Hence, an approach of Multi-objective has been attempted for the best combination of process parameters by modelling AWJM process using of ANN. It served a set of optimal process parameters to AWJ machining process, which shows a development with an enhanced productivity. Wide set of trail experiments have been considered with a broader range of machining parameters for modelling and, then, for validating. The model is capable of predicting optimized responses.