The finishing honing process is an effective machining to enhance surface properties. The objective of this work is to optimize the machining parameters, including the tangential speed (H), linear speed (L), and grit size (G) for minimizing the average roughness (R a ), maximum height roughness (R y ), and machining time (T M ). The honing experiments were performed with the aids of an industrial machine and the Box-Behnken experimental matrix. The nonlinear relationships between machining parameters and honing responses were developed using response surface method models. Subsequently, two optimization techniques, including the desirability approach and non-dominated sorting genetic algorithm II (NSGA II), were used to solve the trade-off analysis among three technological responses and find the optimal factors. Finally, the machining time reductions were assessed in consideration of constrained roughness properties. The obtained results showed that surface roughness and machining time were strongly influenced by abrasive grit size, followed by the tangential speed and linear speed. The optimal values of the H, L, and G were 36.0 m/min, 9.5 m/min, and 220 FEPA, respectively. The reductions in the average roughness, maximum height roughness, and machining time are 53.13%, 8.93%, and 13.95%, respectively, as compared to common values used. Moreover, the genetic algorithm-based approach could be employed to produce reliable values in comparison with the desirability approach. The outcome is expected as a technical solution to enhance the surface properties and productivity of the finishing honing process.

The rotary turning is an effective manufacturing method to machine hardened metals due to longer tool life, higher production rate, and acceptable quality. However, sustainability-based optimization of the rotary turning has not been thoroughly considered because of the huge efforts. This study presents an optimization to enhance the energy efficiency (EFR), turning cost (CT), average roughness (Ra), and the operational safety (POS) for the rotary turning of the hardened steel. Four key process parameters considered are the inclined angle (α), depth of cut (ap), feed rate (f), and cutting speed (vc). The improved Kriging (IK) models were used to construct the relations between the parameters and performances. The optimum varied factors were obtained utilizing the neighborhood cultivation genetic algorithm (NCGA). The findings revealed that the performance models are primarily affected by the feed rate, depth of cut, speed, and inclined angle, respectively. The optimal values of the α, ap, f, and vc are 26°, 0.44 mm, 0.37 mm/rev, and 200 mm/min, respectively. The improvements in energy efficiency, average roughness, and cost are 8.91%, 20.00%, and 14.75%, as compared to the initial values. Moreover, the NCGA may perform an efficient operation to obtain the optimal outcomes, as compared to conventional algorithms.

Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for turning operations. In this work, a machining condition-based optimization has been performed to decrease the total specific energy (SEC), carbon emission (CE), and average roughness (AR) of the actively driven rotary turning (ADRT) process. The processing factors are the tool rotational speed (Tv), depth of cut (a), feed rate (fr), and workpiece speed (Wv). The turning experiments of the mold material labeled SKD11 have been conducted on a CNC lathe. The regression method is employed to develop comprehensive models of the total specific energy, carbon emissions, and average roughness. The entropy approach is then applied to drive out the weight value of each ADRT response. Finally, the non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. The findings indicated that the optimal values of the Tv, a, fr, and Wv are 77 m/min, 0.32 mm, 0.25 mm/rev., and 128 m/min, respectively. The SEC, AR, and CE are decreased by 18.07%, 10.46%, and 5.02%, respectively, as compared to the initial approach. Moreover, the developed active rotary turning operation can be considered as an effective technical solution to boost the machining efficiency of hardened steels.

Improving the technical performance of the wire electro-discharge machining (WEDM) process is an effective solution to decrease manufacturing costs. This paper addresses a multi-response optimization to simultaneously improve the cutting area rate CAR and decrease the kerf width AKW, while the average surface roughness ASR is predefined as a constraint. The processing conditions considered include the pulse-on time T on , the current I, the voltage V and the wire speed S. A WEDM machine was adopted in conjunction with the Box-Behnken matrix to conduct experimental trials for machining of SKD61 steel. Highly nonlinear relationships between machining parameters and technological outputs were developed using the Kriging models. Finally, an archive-based micro-genetic algorithm (AMGA) was used to resolve the trade-off analysis among three responses and determine the optimal values of the processing factors. The results showed that a set of feasible solutions can be determined for the low kerf width as well as the surface roughness and the high cutting area rate. The selection of optimum parameters could help the WEDM operators to save the machining costs and time. The combination of the Kriging model and AMGA could be considered as an intelligent approach for modelling WEDM processes and predicting optimal results.