Abstract
This study focused on the Grey-Based Fuzzy Logic Algorithm for the prediction and optimization of multiple performance characteristics of oblique turning process. Experiments have been constructed according to Taguchi’s L16 orthogonal array design matrix. Cutting speed, rate of feed and depth of cut were selected as input parameters, whereas material removal rate, cutting force and surface roughness were selected as output responses. Using grey relation analysis (GRA), grey relational coefficient (GRC) and grey relation grade (GRG) were obtained. Then, Grey based fuzzy algorithm was applied to obtain grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the application feasibility of the grey based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.
Highlights
Turning is a very important machining process in which a single-point cutting tool removes material from the surface of a rotating cylindrical workpiece [1]
Overall Grey relational grade is determined by averaging the Grey relational coefficient corresponding to selected responses [13,14,15,16]
The pre-processed data of the normalized experimental results, grey relational coefficients and the overall grey relational grade for each combination of parameters is tabulated in Table 4, Table 5 and Table 6
Summary
Turning is a very important machining process in which a single-point cutting tool removes material from the surface of a rotating cylindrical workpiece [1]. The desired cutting parameters are determined based on experience or by use of a handbook [1]. This does not ensure that the selected cutting parameters have optimal or near optimal cutting performance for a particular machine and environment. The grey based fuzzy logic is a powerful tool for the design of multivariable complex systems It provides a robust systematic and efficient way in order to model the multivariable complex systems. Three controlling factors including cutting speed (V), depth of cut (d) and feed rate (f ) were selected as input parameters whereas material removal rate, cutting force
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