Francisco Mata Cabrera

Fuzzy Logic-Based Modeling of Surface Roughness Parameters for CNC Turning of PEEK CF30 by TiN-Coated Cutting Tools

An important range of existing engineered industrial parts consists of plastic materials that are reinforced with carbon fibers. Due to their excellent mechanical and thermal properties, machined mechanical parts made from reinforced polyetheretherketone (PEEK) composite materials have become standard in many high-technology engineering fields such as aerospace, automotive, and electronics. There is however a crucial need to predict the machining criteria for reinforced PEEK composite materials in order to optimize their fabrication process. In this article, the process parameters including cutting speed, feed rate, and depth of cut are investigated. A fuzzy rule-based model was derived to predict the surface roughness parameters Ra and Rt, in dry turning of reinforced PEEK with 30% of carbon fibers using TiN-coated cutting tools. The model was identified using results of experiments carried out according to Taguchi method. Predictions of the fuzzy-based model were found to fit, very well, experimental data with a correlation coefficient as high as 99%.

Prediction of surface roughness in turning of PEEK cf30 by using an artificial neural network

Surface roughness parameters Ra and Rt are mostly used as an index to determine the surface finish quality in the process of machining. Because of the strong nonlinear character of relationships between the process inputs and outputs, it is difficult to accurately estimate roughness characteristics by using traditional modeling techniques. In this work, accurate prediction of the Ra and Rt values during machining of reinforced poly ether ether ketone (PEEK) CF30 with TiN coated tools is achieved. The modeling is performed by using artificial neural network approach to represent the complex relationships between cutting conditions and surface roughness parameters. The input cutting parameters include cutting speed, depth of cut and feed rate. The network was trained with pairs of inputs and outputs datasets generated by machining experimental results that were obtained according to a full factorial design of experiment table. Predictions of the ANN based model were found to fit experimental data very well with a correlation coefficient as high as 99%. Complementary results that were not used during derivation of the ANN model have enabled one to assess the validity of the obtained predictions.

UTILIZACIÓN DE MODELOS DE REDES NEURONALES ARTIFICIALES PARA PREDECIR LA INFLUENCIA DEL TIPO DE FRESADO EN LA CALIDAD DEL PRODUCTO

RESUMEN: Durante el proceso de fresado de piezas de superficies complejas, la eleccion de las distintas estrategias de mecanizado sugeridas por el software CAM conduce a desviaciones de la pieza mecanizada con respecto a la superficie ideal disenada. El conocimiento de las desviaciones generadas respecto de la geometria final de la pieza permite desarrollar modulos de correccion en el propio software, basados en las diferentes estrategias de mecanizado, posibilitando asi que el ejecutor genere las oportunas correcciones antes de la fabricacion, de manera que los productos acabados se encuentren dentro de las especificaciones de diseno. Al mismo tiempo, se ha estudiado el trabajo SIM en los procesos de fabricacion mediante la aplicacion de redes neuronales artificiales (ANN) como solucion a los problemas no lineales y parametros conflictivos. Por lo tanto, este documento evalua la influencia de la geometria y el acabado superficial de tres estrategias diferentes de fresado, sugeridas por un software de CAM en la fabricacion de un producto, por medio de RNA.

Grey-fuzzy optimisation model for multi performance in CNC turning processes

The reinforced polyetheretherketone with 30% of carbon fibres (PEEK-CF30) reveals a particularly good combination of strength, rigidity, and hardness, which prove ideal for industrial applications. Considering these properties and potential areas of application, it is necessary to investigate and optimise the machining of PEEK-CF30. The Grey-fuzzy logic based on orthogonal array for optimising the machining process with multi-response has been reported. An orthogonal array and Grey-fuzzy reasoning grade are applied to study the multi performance characteristics of the machining process. The machining parameters (cutting speed, feed rate and depth of cut) with considerations of multiple responses (roughness surface and cutting force) are effective. The experimental results using the optimal setting easily clarified that the abovementioned optimum procedure greatly improved the manufacturing process in this study.

Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks

In the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks’ inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models’ predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity.

REQUISITOS DE ACTUACIÓN PARA ENTIDADES DE INSPECCIÓN Y CONTROL EN EL ÁMBITO REGLAMENTARIO

Action requirements for Inspection Entities in the Industrial safety area have followed an evolution that could be considered as natural and consistent with the environment, social, economic, industrial and administrative changes carried out until the year 2010. At this time, several regulatoy facts occurred causing an alteration in the legislative framework, whose consequences will be of great importance for industrial safety. As a way for example the 2006/123/CE directive on services in the internal market, the regulation 765/2008/CE regarding accreditation and market surveillance requirements and the Decision 768/2008/CE on a common framework for the marketing of products. The Services Directive transposition into Spanish low has motivated the need of change of more than 50 laws and regulations such as the Law of Industry and the Regulation of the Infrastructure for the Quality and Industrial Safety, changing substantially several precepts related to the control of industrial safety, which directly affect the mode of regulation. Moreover, the administrative authorization required until that time and certain regulatory requirements for different agents are eliminated by judicial judgments of the Supreme Court, This authorization enables unfair competition and a possible rigor and quality of inspections deterioration. Through this article, the impact of these changes that can result in the quality of control of industrial security services and, therefore, the existence of increased risk for citizens, it discusses.

One-Dimensional Consolidation Modelling for Unlayered Soil

In this work, one-dimensional problem has a well-known linear solution and, thus, provides a simple verification of the consolidation capability using numerical solution. The coupling is approximated by the effective stress principle, which treats the saturated soil as a continuum, assuming that the total stress at each point is the sum of an effective stress carried by the soil skeleton and a pore pressure in the fluid permeating the soil. This fluid pore pressure can change with, and the gradient of the pressure through the soil that is not balanced by the weight of fluid between the points in question will cause the fluid to flow: the flow velocity is proportional to the pressure gradient in the fluid according to Darcys law. A typical case is a consolidation problem. Here the addition of a load to a body of soil causes pore pressure to raise initially; then, as the soil skeleton takes up the extra stress, the pore pressures decay as the soil consolidates. The Terzaghi problem is the simplest example of such a process. For illustration purposes, the problem is treated with and without finite-strain effects. The numerical solution agrees reasonably well with the analytical solution, with some loss of accuracy at later times.

Evolutionary Computation in Combinatorial of Machining Parameters of Reinforced PEEK Composites Using NSGA-II

The machining parameters for turning of PEEK CF30 using TiN coated tools under dry conditions have been optimized by using Non dominated Sorting Genetic Algorithm (NSGA-II), a non dominated solution set is obtained. The objectives considered are the minimisation of machining force thereby minimising specific cutting pressure as function of the main operating parameters. The results indicated that the minimal cutting parameters are preferred for reducing the machining force, and the minimal cutting speed, medium depth of cut and high feed rate are recommended for minimal specific cutting machining. As per the requirement, the manufacturing engineer should select the proper cutting parameters.

Application of Desirability Function Based on Neural Network for Optimizing the Process Parameters in Turning of PEEK CF30 Composites

This work deals with optimization of multiple characteristics in CNC turning of reinforced Poly Ether Ether Ketone (PEEK CF30) with TiN coated tools. The considered criteria included surface roughness, machining force and cutting power. Three controllable factors of the turning process consisting of cutting speed, depth of cut and feed rate were incorporated. Taguchi design of experiments method was used to arrange the experimentation task. Artificial Neural Network approach was used for modeling the obtained responses. Then, desirability function approach was used to conduct single and multiple response optimizations.