Optimal machining parameters for achieving the desired surface roughness in fine turning of cold pre-formed steel workpieces

By J. Kopac, M. Bahor, M. Sokovic. 2002

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Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels (2009, methodology): “…or surface roughness prediction models have been reported by the Taguchi design or other methods (Cheng and Hong, 2003; Davim, 2001; Davim and Figueira, 2006; Kơpac et al., 2002; Sahin, 2006; Sahin et al., 2004; Sahin and Motorcu, 2008; Yang et al.1998; Ozel and Nadgir, 2002; Ozel and Karpat.2005).”
Optimization of turning parameters for surface roughness (2015, background): “It also depends on the several other exogenous factors such as: work piece and tool material combination and their mechanical properties, quality and type of the machine tool used, auxiliary tooling, lubricant used and vibrations between the work piece, machine tool and cutting tool [2-8].”
Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks (2005, methodology): “Kopac et al. [ 18 ] utilized a Taguchi experimental design to determine the optimal machining parameters for a desired surface roughness for traditional turning.”
Predicting surface roughness in machining: a review (2003, methodology): “Further work, regarding the influence of workpiece material properties, cutting parameters and TiN (PVD) hard coating on the surface roughness of fine-turned workpieces by using Taguchi’s signal-to-noise response tables and interaction graphs was also conducted by the same researchers [29].”