Investigation and optimization of the internal cylindrical surface lapping process of 440c steel

Abstract

A new lapping method is proposed for internal cylindrical surfaces finishing. Regression analysis and artificial neural network (ANN) were used for modeling this lapping process and predicting the effects of parameters of rotational speed of the lapping tool (ω), the length of the lapping tool (L) and difference in external diameter of the lapping tool and internal diameter of the workpiece (D) on the material removal rate (MRR), out-of-cylindricity (C) and surface roughness (Ra) of the lapped holes. Comparison of the results of the regression and ANN models with the values obtained from the experiments indicates that the MRR, Ra and C are more accurately predicted using ANN models. MRR, Ra and C of the lapped holes have been optimized using genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The results show that the highest MRR, which is equal to 2029 μg/min, has been achieved at ω of 400 rpm, D of 0.1 mm and L of 45 mm. The lowest Ra of the lapped hole is 64 nm which has been obtained at ω of 100 rpm, D of 0.1 mm and L of 20.82 mm. The minimum C of the lapped hole is 3 μm, which was obtained at ω of 212 rpm, D of 0.1 mm and L of 28.3 mm. The most important problem in the lapping process is the low MRR which causes increased cost and production time. Therefore, in the lapping process, the selection of conditions, that in addition to the production of pieces with geometric accuracy and surface roughness needing a high MRR, is very important. In this study, MRR, Ra and cylindricity of the lapped holes was optimized using multi-objective PSO (MOPSO) algorithm and non-dominated sorting genetic algorithm II (NSGA II), and the Pareto optimal solutions were obtained. Comparison of the results obtained from NSGA II and MOPSO shows that both of these algorithms can achieve optimal Pareto front with the same accuracy, but the time required to reach the MOPSO algorithm to the optimal Pareto front is 25 % less than the NSGA II.