Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

Abstract:

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

• Periodical: Advanced Materials Research (Volumes 314-316)
• Pages: 341-345
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.314-316.341
• Online since: August 2011
• Authors: Bo Di Cui
• Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), High Speed Turning, Principal Component Analysis (PCA), Surface Roughness (SR)

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