Browsing by Keyword "Chatter"
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Item Influence of Axial Depth of Cut and Tool Position on Surface Quality and Chatter Appearance in Locally Supported Thin Floor Milling(2022-01-19) Casuso, Mikel; Rubio-Mateos, Antonio; Veiga, Fernando; Lamikiz, Aitzol; Tecnalia Research & Innovation; FABRIC_INTELThin floor machining is a challenging and demanding issue, due to vibrations that create poor surface quality. Several technologies have been developed to overcome this problem. Ad hoc fixtures for a given part geometry lead to meeting quality tolerances, but since they lack flexibility, they are expensive and not suitable for low manufacturing batches. On the contrary, flexible fixtures consisting of vacuum cups adaptable to a diversity of part geometries may not totally avoid vibrations, which greatly limits its use. The present study analyses the feasibility of thin floor milling in terms of vibration and roughness, in the cases where milling is conducted without back support, a usual situation when flexible fixtures are employed, so as to define the conditions for a stable milling in them and thus avoid the use of ad hoc fixtures. For that purpose, the change of modal parameters due to material removal and its influence on chatter appearance have been studied, by means of stability lobe diagrams and Fourier Transform analysis. Additionally, the relationship between surface roughness and chatter frequency, tooth passing frequency, and spindle frequency have been studied. Ploughing effect has also been observed during milling, and the factors that lead to the appearance of this undesirable effect have been analyzed, in order to avoid it. It has been proven that finish milling of thin floors without support in the axial direction of the mill can meet aeronautic tolerances and requirements, providing that proper cutting conditions and machining zones are selected.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 Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches: A review of models and industrial approaches(2019-06-01) Del Sol, Irene; Rivero, Asuncion; López de Lacalle, Luis Norberto; Gamez, Antonio Juan; SGThin-wall parts are common in the aeronautical sector. However, their machining presents serious challenges such as vibrations and part deflections. To deal with these challenges, different approaches have been followed in recent years. This work presents the state of the art of thin-wall light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the causes of both instability and deformation through analytical models, summarizing the computational techniques used, and presenting the solutions proposed by different authors from an industrial point of view. Finally, some further research lines are proposed.Item Vibrations characterization in milling of low stiffness parts with a rubber-based vacuum fixture(2021-06) RUBIO-MATEOS, Antonio; CASUSO, Mikel; RIVERO, Asuncion; UKAR, Eneko; LAMIKIZ, Aitzol; Tecnalia Research & Innovation; FABRIC_INTEL; SGFixtures are a critical element in machining operations as they are the interface between the part and the machine. These components are responsible for the precise part location on the machine table and for the proper dynamic stability maintenance during the manufacturing operations. Although these two features are deeply related, they are usually studied separately. On the one hand, diverse adaptable solutions have been developed for the clamping of different variable geometries. Parallelly, the stability of the part has been long studied to reduce the forced vibration and the chatter effects, especially on thin parts machining operations typically performed in the aeronautic field, such as the skin panels milling. The present work proposes a commitment between both features by the presentation of an innovative vacuum fixture based on the use of a vulcanized rubber layer. This solution presents high flexibility as it can be adapted to different geometries while providing a proper damping capacity due to the viscoelastic and elastoplastic behaviour of these compounds. Moreover, the sealing properties of these elastomers provide the perfect combination to transform a rubber layer into a flexible vacuum table. Therefore, in order to validate the suitability of this fixture, a test bench is manufactured and tested under uniaxial compression loads and under real finish milling conditions over AA2024 part samples. Finally, a roughness model is proposed and analysed in order to characterize the part vibration sources.