An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

The honing operation is maned as an effective and efficient finishing solution to achieve better surface properties and machining efficiency. However, quality and productivity indicators are primary considerations of published investigations. In this study, the rough honing operation of the hardened 5150 alloy has been addressed and optimized to decrease the energy efficiency (EF), surface roughness (SR), and material removal rate (MRR). The optimizing factors include tangential speed (T), linear speed (L), honing pressure (P), and honing depth (D). The artificial neural network (ANN) method is utilized to present the non-linear relations between the process parameters and performance measures. The Taguchi method is applied to select the best architecture of ANN models. The Entropy and the vibration and communication particle swarm optimization (VCPSO) algorithm is applied to compute the weight factors of response and find optimal data. The total honing cost (THC) model is proposed to compute the honing expenses. The results indicated that the optimal values of the T, L, P, and D are 32.0 m/min, 8.0 m/min, 3.3 MPa, and 0.01 mm, respectively, while the improvements in the EF, SR, MRR, and THC were 15.17%, 44.7%, 134.19%, and 25.0%, as compared to the initial values. The optimizing technique comprising the ANN, Entropy, and VCPSO could be named as an effective and efficient method to solve complicated optimization issues. The optimal data could help the machine operator to improve the honing performances with regard to saving trial costs and essential efforts.