The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.
Fused deposition modelling is an extrusion-based automated fabrication process for making 3D physical objects from part digital information. The process offers distinct advantages, but the quality of part lacks in surface finish when compared with other liquid or powder based additive manufacturing processes. Considering the important factors affecting the part quality, the chapter attempted to optimize the raster angle, air gap, and raster width to minimize overall part roughness. Experiments are designed using face-centered central composite design and analysis of variance provides the effects of processing parameters on roughness of part. Suitability of developed model is tested using Anderson-darling normality test. Desirability method propose that roughness of different part faces are affected differently with chosen parameters, and thus, hybrid approach of WPCA based TOPSIS is used to break the correlation between part faces and reduce the overall part roughness. Optimizing shows that lower raster angle, lower air gap, and larger raster width minimizes overall part roughness.
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