Person:
Luis Pérez, Carmelo

Last Name

Luis Pérez

First Name

Carmelo

person.page.departamento

Ingeniería

ORCID

0000-0002-1363-1667

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2277

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Now showing 1 - 9 of 9

Open Access
Analysis of AM parameters on surface roughness obtained in PLA parts printed with FFF technology
(MDPI, 2021) Buj Corral, Irene; Sánchez Casas, Xabier; Luis Pérez, Carmelo; Ingeniería; Ingeniaritza
Fused filament fabrication (FFF) 3D printing technology allows very complex parts to be obtained at a relatively low cost and in reduced manufacturing times. In the present work, the effect of main 3D printing parameters on roughness obtained in curved surfaces is addressed. Polylactic acid (PLA) hemispherical cups were printed with a shape similar to that of the acetabular part of the hip prostheses. Different experiments were performed according to a factorial design of experiments, with nozzle diameter, temperature, layer height, print speed and extrusion multiplier as variables. Different roughness parameters were measured—Ra, Rz, Rku, Rsk—both on the outer surface and on the inner surface of the parts. Arithmetical mean roughness value Ra and greatest height of the roughness profile Rz are usually employed to compare the surface finish among different manufacturing processes. However, they do not provide information about the shape of the roughness profile. For this purpose, in the present work kurtosis Rku and skewness Rsk were used. If the height distribution in a roughness profile follows a normal law, the Rku parameter will take a value of 3. If the profile distribution is symmetrical, the Rsk parameter will take a value of 0. Adaptive neural fuzzy inference system (ANFIS) models were obtained for each response. Such models are often employed to model different manufacturing processes, but their use has not yet been extended to 3D printing processes. All roughness parameters studied depended mainly on layer height, followed by nozzle diameter. In the present work, as a general trend, Rsk was close to but lower than 0, while Rku was slightly lower than 3. This corresponds to slightly higher valleys than peaks, with a rounded height distribution to some degree.
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; Ingeniería; Ingeniaritza
Honing 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.
Open Access
Using a fuzzy inference system to obtain technological tables for electrical discharge machining processes
(MDPI, 2020) Luis Pérez, Carmelo; Ingeniería; Ingeniaritza
Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.
Open Access
Optimal machining strategy selection in ball-end milling of hardened steels for injection molds
(MDPI, 2019) Buj Corral, Irene; Ortiz Marzo, Jose Antonio; Costa Herrero, Lluís; Vivancos Calvet, Joan; Luis Pérez, Carmelo; Ingeniería; Ingeniaritza
In the present study, the groups of cutting conditions that minimize surface roughness and its variability are determined, in ball-end milling operations. Design of experiments is used to define experimental tests performed. Semi-cylindrical specimens are employed in order to study surfaces with different slopes. Roughness was measured at different slopes, corresponding to inclination angles of 15 degrees, 45 degrees, 75 degrees, 90 degrees, 105 degrees, 135 degrees and 165 degrees for both climb and conventional milling. By means of regression analysis, second order models are obtained for average roughness Ra and total height of profile Rt for both climb and conventional milling. Considered variables were axial depth of cut a(p), radial depth of cut a(e), feed per tooth f(z,) cutting speed v(c,) and inclination angle Ang. The parameter a(e) was the most significant parameter for both Ra and Rt in regression models. Artificial neural networks (ANN) are used to obtain models for both Ra and Rt as a function of the same variables. ANN models provided high correlation values. Finally, the optimal machining strategy is selected from the experimental results of both average and standard deviation of roughness. As a general trend, climb milling is recommended in descendant trajectories and conventional milling is recommended in ascendant trajectories. This study will allow the selection of appropriate cutting conditions and machining strategies in the ball-end milling process.
Open Access
On the application of a design of experiments along with an anfis and a desirability function to model response variables
(MDPI, 2021) Luis Pérez, Carmelo; Ingeniería; Ingeniaritza
In manufacturing engineering, it is common to use both symmetrical and asymmetrical factorial designs along with regression techniques to model technological response variables, since the in-advance prediction of their behavior is of great importance to determine the levels of variation that lead to optimal response values to be obtained. For this purpose, regression techniques based on the response surface method combined with a desirability function for multi-objective optimization are commonly employed, since it is usual to find manufacturing processes that require simultaneous optimization of several variables, which exhibit in many cases an opposite behavior. However, these regression models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. To deal with this drawback, soft computing techniques are very effective in overcoming the limitations of conventional regression models. This present study is focused on the employment of a symmetrical design of experiments along with a new desirability function, which is proposed in this study, and with soft computing techniques based on fuzzy logic. It will be shown that more accurate results than those obtained from regression techniques are obtained. Moreover, this new desirability function is analyzed in this study.
Open Access
A proposal of an adaptive neuro-fuzzy inference system for modeling experimental data in manufacturing engineering
(MDPI, 2020) Luis Pérez, Carmelo; Ingeniería; Ingeniaritza
In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.
Open Access
Development of a machining strategy to manufacture SiSiC nuts by EDM
(SAGE Publications, 2024) Torres Salcedo, Alexia; Puertas Arbizu, Ignacio; Luis Pérez, Carmelo; Ingeniería; Ingeniaritza
Today, the high-precision manufacturing of small cavities in difficult-to-machine materials is still a challenge, even more so if they need to be threaded. The machining time, the wear suffered by the electrodes and the surface finish are determining factors in the efficiency of the threading process. However, there is scant literature on this subject so there is a need to study the process and the parameters involved. Thus, this study presents a novel machining strategy for the manufacture of nuts using die-sinking electrical discharge machining (EDM). Moreover, the novelty of this strategy is that it is carried out in a single stage and with a conventional EDM generator. To do so, a design of experiments (DOE) methodology has been followed. First, the optimal machining conditions are determined by studying the influence of EDM parameters on operation variables and mathematical models are developed using multiple linear regression. These models allow the behavior of the response variables under study to be predicted. Finally, this machining strategy developed from the previous experimental results is validated in the manufacturing process of a final part, specifically a square nut. It can be concluded that the mathematical model is good enough to predict the experimental results. Thus, the new method presented and described in this present study allowed a nut to be obtained with a real arithmetic mean deviation of the roughness profile (Ra) value of 1.27 μm whereas the predicted value from the model was 1.28 μm. To do so, the machining conditions selected were: 4 A (current intensity), 5 µs (pulse time) and 0.4 (duty cycle), which also gave a material removal rate (MRR) value of 0.5370 mm3/min. The machining strategy proposed here may be used for future research works related to the manufacturing of mechanical joints made of conductive ceramic materials.
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; Ingeniería; Ingeniaritza
Honing 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.
Open Access
Modeling of the influence of input AM parameters on dimensional error and form errors in PLA parts printed with FFF technology
(MDPI, 2021) Luis Pérez, Carmelo; Buj Corral, Irene; Sánchez Casas, Xabier; Ingeniería; Ingeniaritza
As is widely known, additive manufacturing (AM) allows very complex parts to be manufactured with porous structures at a relatively low cost and in relatively low manufacturing times. However, it is necessary to determine in a precise way the input values that allow better results to be obtained in terms of microgeometry, form errors, and dimensional error. In an earlier work, the influence of the process parameters on surface roughness obtained in fused filament fabrication (FFF) processes was analyzed. This present study focuses on form errors as well as on dimensional error of hemispherical cups, with a similar shape to that of the acetabular cup of hip prostheses. The specimens were 3D printed in polylactic acid (PLA). Process variables are nozzle diameter, temperature, layer height, print speed, and extrusion multiplier. Their influence on roundness, concentricity, and dimensional error is considered. To do this, adaptive neuro-fuzzy inference systems (ANFIS) models were used. It was observed that dimensional error, roundness, and concentricity depend mainly on the nozzle diameter and on layer height. Moreover, high nozzle diameter of 0.6 mm and high layer height of 0.3 mm are not recommended. A desirability function was employed along with the ANFIS models in order to determine the optimal manufacturing conditions. The main aim of the multi-objective optimization study was to minimize average surface roughness (Ra) and roundness, while dimensional error was kept within the interval. When the simultaneous optimization of both the internal and the external surface of the parts is performed, it is recommended that a nozzle diameter of 0.4 mm be used, to have a temperature of 197 °C, a layer height of 0.1 mm, a print speed of 42 mm/s, and extrusion multiplier of 94.8%. This study will help to determine the influence of the process parameters on the quality of the manufactured parts.