Open Access
Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization
(Elsevier, 2024-01-22) Luis Pérez, Carmelo Javier; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
This manuscript presents an efficient multi-objective optimization method based on using particle swarm optimization together with a desirability function that can be applied where the response variables may have an opposite behavior and where the range of variation of the independent variables as well as those of the responses are subjected to constraints, which has a great deal of industrial interest. For example, maintaining roughness and dimensional tolerances within a tolerance range is determined by the design requirements of the manufactured parts (shape errors, microgeometry errors, etc.) and these requirements must be met in the manufacture of parts. It is demonstrated that it is possible to obtain optimal results in the ranges of variation considered for the independent variables, with regard to those obtained by experimentation. Similarly, models based on Adaptive Network-based Fuzzy Inference Systems are used to solve the problem that may arise from the inadequate fitting of the regression models. Thus, thanks to this present study a fast and efficient method is available for the multiple-optimization of response variables, subject to constraints on both response and independent variables, which are obtained from experiments and modelled by means of soft computing techniques. Furthermore, it is also demonstrated that it is possible to obtain technology tables for various manufacturing processes, which is of great interest from a technological point of view so as to obtain the most suitable processing conditions.
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; 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
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 Javier; 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
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; 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
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 Javier; 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
A proposal of an adaptive neuro-fuzzy inference system for modeling experimental data in manufacturing engineering
(MDPI, 2020) Luis Pérez, Carmelo Javier; 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
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 Javier; 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.