Browsing by Keyword "Milling"
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Item Analysis of Alloy 718 surfaces milled by abrasive waterjet and post-processed by plain waterjet technology(2017) Alberdi, A.; Rivero, A.; Artaza, T.; Lamikiz, A.; FABRIC_INTEL; SGThis work analyzes the surfaces obtained in Alloy 718 when they are milled by Abrasive Waterjet (AWJ) at different conditions. This analysis revealed that all surfaces have a homogeneous roughness in the transversal and the longitudinal directions, present embedded abrasive particles and have hardened about 50% with respect to the untreated bulk Alloy 718. On the other hand, Plain Waterjet (PWJ) technology was used for removing the abrasive particles embedded in surfaces of Alloy 718 milled previously by AWJ technology. The effect of this process on the surface characteristics is also analyzed. For all tested conditions, this technology removed all the particles embedded in the surface. In addition, the PWJ technology process in general smoothened the surfaces produced by AWJ milling and it also released near-surface stresses.Item A machine-learning based solution for chatter prediction in heavy-duty milling machines(2018-11) Oleaga, Ibone; Pardo, Carlos; Zulaika, Juan J.; Bustillo, Andres; MAQUINASThe main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.Item Surface properties and fatigue failure analysis of alloy 718 surfaces milled by abrasive and plain waterjet(2017-09-20) Rivero, A.; Alberdi, A.; Artaza, T.; Mendia, L.; Lamikiz, A.; SG; FABRIC_INTEL; Caracterización y Validación. MaterialesThis work analyzes the surfaces obtained in alloy 718 when they are milled by abrasive waterjet (AWJ) at different conditions, and the effect of main process parameters on the characteristics of these surfaces. This analysis revealed that all surfaces have a homogeneous roughness in the transversal and the longitudinal directions, present embedded abrasive particles and have hardened about 50% with respect to the untreated bulk alloy 718. In addition, plain waterjet (PWJ) technology was used for removing the abrasive particles embedded in surfaces of alloy 718 milled previously by AWJ technology. The effect of this process on the surface characteristics is also analyzed. For all tested conditions, this technology removed all the particles embedded in the surface. In addition, the PWJ technology process in general smoothened the surfaces produced by AWJ milling and it also released near-surface stress. Finally, fatigue tests revealed lower performance of the treated specimens in comparison to untreated specimens, due to crack-like surface irregularities introduced by the treatments.Item Tool-path effect on the geometric deviations in the machining of UNS A92024 aeronautic skins(2017) Del Sol, I.; Rivero, A.; Salguero, J.; Fernández-Vidal, S.R.; Marcos, M.; SGTraditionally, aeronautics skins are being machined by chemical milling, a high-pollutant process. An efficient alternative to this technology is conventional machining. However, to ensure the parts machined with this process keeps the industrial quality controls, the effect of tool-path might be characterized, specially analyzing final thickness and roughness. In this paper, five different tool-paths have been applied under the same machining parameters in the dry milling of Al-Cu UNS A92024 thin plates. Machining time, final thickness and roughness have been evaluated. Most roughness and thickness results are under the industrial quality limits stablished for this type of parts.