Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes

This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min-1. The other variables, density and pressure, would not change significantly from the first situation.Peer ReviewedPostprint (published version

Neural network modelling of Abbott-Firestone roughness parameters in honing processes

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.Peer ReviewedPostprint (author's final draft

Improved neural models for roughness in honing processes

In the present work improved neural network models for average roughness Ra in rough honing processes are studied. Four different adaptive models were tested, which integrate previously obtained direct and indirect models. Such models allow defining values for process variables from required average roughness Ra values. A control parameter d is employed for determining the error of the model, and a sensitivity parameter m measures the convergence speed of the models. Models were tested for m=1, m=10, m=100 and m=1000. Best model was selected having lowest relative error between experimental and simulated values.Peer ReviewedPostprint (published version

Neural network model for surface roughness in semifinish honing

In the present work, neural networks were used for modelling average roughness Ra as a function of process parameters: grain size, density of abrasive, pressure of honing stones on the workpiece’s surface, linear speed and tangential speed. For doing this, first experimental semifinish honing tests were performed. Then results were used for selecting best configuration of the neural network, taking into account either one or two hidden layers. In addition, neural models were compared to regression models.Peer ReviewedPostprint (published version

CINEMATICA I DINAMICA DE MAQUINES (Examen 1r quadrimestre)

Examen parcial sense solucion

CINEMATICA I DINAMICA DE MAQUINES (Examen 2n quadrimestre)

Examen fina

Examen parcial [Curs 2012-2013]

Resolve

SISTEMES MECÀNICS (Examen 1r quadrimestre)

Examen final sense solucion

CINEMATICA I DINAMICA DE MAQUINES (Examen 1r quadrimestre)

Examen final sense soluci

CINEMÀTICA I DINÀMICA DE MÀQUINES (Examen 2n quadrimestre)

Examen final amb solucióResolve