Optimization of roller burnishing process using Kriging model to improve surface properties

Abstract

The objective of this work is to investigate the influences of three machining factors (burnishing speed V, feed rate f, and depth of penetration a) on the improved rate of arithmetic average roughness Δ Ra, improved rate of maximum height roughness Δ Ry, and improved rates of surface hardness Δ SH. The internal roller burnishing experiments were conducted with the aid of the computer numerical control machining center and Box–Behnken experimental design. The Kriging models were used to render the highly nonlinear relationships between inputs and outputs. An integrative approach combining a Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by Similarity to Ideal Solution was adopted to generate a set of feasible optimal solutions and determine the best machining conditions. The scanning electron microscopy images were depicted to investigate the surface morphology at the different conditions. The X-ray diffraction was applied to measure the compressive stresses at the external surface. The results showed that the predicted values of the objectives have good agreement with the experimental ones. High surface quality is characterized by an improved average roughness of 95.80%, an enhancement in the maximum roughness of 91.98%, and an improvement in surface hardness of 45.44%, compared to pre-machined surfaces. The selection of optimum process parameters could help the burnishing operators to save the machining costs and time. The combination of Kriging model, Non-dominated Sorting Genetic Algorithm II, and Technique for Order Preference by Similarity to Ideal Solution is considered as an intelligent approach for modeling and optimization of burnishing processes.

Investigation of residual stress distribution induced during deep rolling of Ti-6Al-4V alloy

“…It is also interesting that, in most cases, the position of maximum of residual stresses tends to be subsurface, as seen in many other publications. 3,8 An experiment was also carried out to verify the reliability of the simulation results. The experimental results are shown in Figure 10(b) and it is obvious that the FE simulation is consistent with experimental observation.…”

“…However, the feed rate does not produce any effect at large depth, and only the initial subsurface distribution is slightly influenced. Nguyen and Le 3 found that an increase in burnishing feed results in a decrease in residual stress in both the axial and tangential directions, and an increase in the depth of penetration leads to an increase in residual stresses. El-Axir 4 investigated the effect of the ball burnishing parameters on the residual stress and found that at low speeds, an increase in the burnishing force leads to an increase in the maximum compressive residual stress.…”

Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel

“…In this work, the self-propelled rotary tool has been designed and fabricated. The improved Kriging models are applied to render nonlinear relationships between the process parameters and turning responses [16,17]. The neighborhood cultivation genetic algorithm (NCGA) is employed to select the optimal values of the inputs and objectives [18].…”

Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB2in situ composites using the Entropy–VIKOR method

“…These limitations make it challenging to convert the material into the desired shape. 19 Electrochemical machining (ECM) enables micro-level material removal without needing to consider the hardness of the material to be machined. In ECM, the material machining process is carried out based on Ohm's and Faraday's laws.…”