Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

Chatter avoidance in the milling of thin floors with bull-nose end mills: Model and stability diagrams

The milling of thin parts is a high added value operation where the machinist has to face the chatter problem. The study of the stability of these operations is a complex task due to the changing modal parameters as the part loses mass during the machining and the complex shape of the tools that are used. The present work proposes a methodology for chatter avoidance in the milling of flexible thin floors with a bull-nose end mill. First, a stability model for the milling of compliant systems in the tool axis direction with bull-nose end mills is presented. The contribution is the averaging method used to be able to use a linear model to predict the stability of the operation. Then, the procedure for the calculation of stability diagrams for the milling of thin floors is presented. The method is based on the estimation of the modal parameters of the part and the corresponding stability lobes during the machining. As in thin floor milling the depth of cut is already defined by the floor thickness previous to milling, the use of stability diagrams that relate the tool position along the tool-path with the spindle speed is proposed. Hence, the sequence of spindle speeds that the tool must have during the milling can be selected. Finally, this methodology has been validated by means of experimental tests.

Comparación de la rugosidad media y máxima obtenida mediante diferentes instrumentos: análisis del filtro y Lc empleados

[ES]Este trabajo pretende analizar la influencia que tiene la longitud de corte elegida y el filtro empleado en la medida de la rugosidad. Se realizan medidas de rugosidad en tres probetas que han sido sometidas a diferentes procesos de fabricación (fresado, rectificado y electroerosión). Además, se utilizan tres instrumentos diferentes para la medición. Tras realizar las medidas, se comparan los resultados y se extraen las conclusiones

Estudio del rectificado de Inconel 718

[ES]Cada vez más industrias como las aeronáuticas o espaciales buscan nuevos materiales con excelentes propiedades hoy en día. Sin embargo, estos materiales suelen presentar hándicaps como por ejemplo su maquinabilidad. Mediante este TFG, se estudiará el rectificado de uno de estos materiales, el Inconel 718. Para conseguir los objetivos, este TFG se elaborará en varias etapas. Primero es necesario conocer las propiedades del Inconel 718 y analizarlas. Tras esto, se realizará el mismo proceso con el proceso de rectificado, haciendo hincapié en el desgaste de muelas; para finalmente, encontrar soluciones y alternativas que optimicen el proceso de rectificado. La ejecución de este TFG permitirá comprender y minimizar el desgaste de las muelas durante el proceso de rectificado.