Luis Pérez, Carmelo Javier
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Luis Pérez
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Carmelo Javier
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Ingeniería
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INAMAT2 - Institute for Advanced Materials and Mathematics
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Publication Open Access Modeling of surface roughness in honing processes by using fuzzy artificial neural networks(MDPI, 2023) Buj Corral, Irene; Sender, Piotr; Luis Pérez, Carmelo Javier; Ingeniería; IngeniaritzaHoning processes are abrasive machining processes which are commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These processes can be employed to obtain a cross-hatched pattern on the internal surfaces of cylinders. In this present study, fuzzy artificial neural networks are employed for modeling surface roughness parameters obtained in finishing honing operations. As a general trend, main factors influencing roughness parameters are grain size and pressure. Mean spacing between profile peaks at the mean line parameter, on the contrary, depends mainly on tangential and linear velocity. Grain Size of 30 and pressure of 600 N/cm2 lead to the highest values of core roughness (Rk) and reduced valley depth (Rvk), which were 1.741 µm and 0.884 µm, respectively. On the other hand, the maximum peak-to-valley roughness parameter (Rz) so obtained was 4.44 µm, which is close to the maximum value of 4.47 µm. On the other hand, values of the grain size equal to 14 and density equal to 20, along with pressure 600 N/cm2 and both tangential and linear speed of 20 m/min and 40 m/min, respectively, lead to the minimum values of core roughness, reduced peak height (Rpk), reduced valley depth and maximum peak-to-valley height of the profile within a sampling length, which were, respectively, 0.141 µm, 0.065 µm, 0.142 µm, and 0.584 µm.Publication Open Access Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems(Elsevier, 2023) Buj Corral, Irene; Sender, Piotr; Luis Pérez, Carmelo Javier; Ingeniería; IngeniaritzaHoning processes are usually employed to manufacture combustion engine cylinders and hydraulic cylinders. Honing provides a crosshatch pattern that favors the oil flow. In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) models were obtained for tool wear, average roughness Ra, cylindricity and material removal rate in finishing honing processes. In addition, multi-objective optimization with the desirability function method was applied, in order to determine the process parameters that allow minimizing roughness, cylindricity error and tool wear, while maximizing material removal rate. The results showed that grain size and tangential velocity should be at their minimum levels, while density, pressure and linear velocity should be at their maximum levels. If only roughness, cylindricity error and tool wear are considered, then low grain size, low pressure and low linear velocity are recommended, while density and tangential velocity vary, depending on the optimization algorithm employed. This work will help to select appropriate process parameters in finishing honing processes, when roughness, cylindricity error and tool wear are to be minimized.