Statistical analysis of different machining characteristics of EN-24 alloy steel during dry hard turning with multilayer coated cermet inserts

• Published in: Measurement : journal of the International Measurement Confederation 2019-02, Vol.134, p.123-141
• Main Authors: Das, Anshuman, Patel, Saroj Kumar, Hotta, Tapano Kumar, Biswal, Bibhuti Bhushan
• Format: Article
• Language: English
• Subjects: Abrasion; Cermets; Chipping; Cracks; Cutting parameters; Cutting speed; Cutting wear; Design of experiments; Dimensional deviations; Edge dislocations; EN 24 steel; Feed rate; Genetic algorithms; Hard machining; Inserts; Material removal rate (machining); Mathematical models; Mathematical morphology; Morphology; Multilayers; Neuro-genetic algorithm; Nickel chromium molybdenum steels; Regression analysis; Regression models; Shear bands; Statistical analysis; Surface roughness; Tool tip temperature; Tool wear; Turning (machining); Variance analysis; Wear; Wear mechanisms; Wear rate
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2018.10.065

•Neuro genetic algorithm is a better alternative for multiobjective optimization.•Dimensional deviations should be studied in hard turning.•With material removal rate, both crater and flank wear increased. This research article describes the experimental and statistical analysis of flank wear, material removal rate, tool tip temperature, surface roughness parameters (i.e. Ra, Rz and Rt), chip morphology, chip thickness and dimensional deviations (i.e. circularity and cylindricity). Taguchi's L27 orthogonal array has been selected for experimental design and ANOVA (analysis of variance) has been used to study the significance of cutting parameters on the responses. The experimental study exhibits that depth of cut is the predominant machining parameter influencing surface roughness followed by feed rate and cutting speed. Similar result was found for flank wear and dimensional deviations. However, cutting speed was found to be the most crucial input parameter for tool the tip temperature whereas feed rate was the most notable input variable for material removal rate (MRR). Abrasion and chipping are two major wear mechanisms found for flank wear in this study. During chip morphology study using scanning electron microscope material side flow, serrations, shear band, shear cracks, smooth and rough surfaces are observed. Further, the effect of flank wear on surface roughness parameters, dimensional deviations and effect of MRR on different patterns of crater wear were studied. For each response, mathematical model was developed with regression analysis and the models having higher R-Sq values show favorable relationship between predicted and experimented values. Finally, the optimal combination of machining parameters has been obtained using neuro-genetic algorithm.