Comparison of ANN and DoE for the prediction of laser machined micro-channel dimensions

This paper presents four models developed for the prediction of the dimensions of laser formed micro-channels. Artificial Neural Networks (ANNs) are often used for the development of predictive models. Three feed-forward, back-propagation ANN models varied in terms of the number and the selection of training data, were developed. These ANN models were constructed in LabVIEW coding. The performance of these ANN models was compared with a 33 statistical design of experiments (DoE) model built with the same input data. When compared with the actual results two of the ANN models showed greater prediction error than the DoE model. The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model

Surface Roughness, Machining Force and Flank Wear in Turning of Hardened AISI 4340 Steel with Coated Carbide Insert: Cutting Parameters Effects

Abstract The current experimental study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (surface roughness, machining force and flank wear) in hard turning of AISI 4340 steel with multilayer CVD (TiN/TiCN/Al2O3) coated carbide insert. Combined effects of cutting parameter (v, f, d) on performance outputs (Ra, Fm and VB) are explored employing the analysis of variance (ANOVA). An L9 Taguchi standard design of experiments procedure was used to develop the regression models for machining responses, within the range of parameters selected. Results show that, feed rate has statistical significance on surface roughness and the machining force is influenced principally by the feed rate and depth of cut whereas , cutting speed is the most significant factor for flank wear followed by cutting speed. The desirability function approach has been used for multi-response optimization. Based on the surface roughness, machining force and flank wear, optimized machining conditions were observed in the region 147 m/min cutting speed and 0.10 mm/rev feed rate and 0.6 mm depth of cut