Comparison of different mathematical models for prediction of self-excited vibrations occurance in milling process

In modern production, despite the existence of other production methods, metal cutting still plays an important role. The performance of machine tools has a decisive role in terms of productivity and quality of production increase. Undoubtedly, productivity and quality of production are two mail requirements which are key elements to stay on top in a competitive market. One of the most influencing factor that affect the machine tools are vibrations. The most unwanted vibrations that can appear during metal cutting process are self-excited vibrations, which are one of the three kinds of mechanical vibration, free vibration, forced vibration, and self-excited vibration. When it comes to improving the performance of machine tools, the analysis of the appearance of self-excited vibrations and their isolation occupy a significant place. The aim of this paper derives from trends and limitations exists in metal production. The way to isolate the self-excited vibrations is to predict their occurrence by defining the stability lobe diagram. The paper presents two popular analytical methods for identifying stability lobe diagrams in milling, which shows the boundary between stable and unstable zone of machining operations, depending on the number of revolutions of the spindle and cutting depth. First considered method is Fourier series approach and second one id average tooth angle approach. Lather, both stability lobe diagrams were compared with results obtained experimentally

MACHINE TOOLS HARMONIZATION WITH EU TECHNICAL LEGALIZATIONS REQUIREMENTS

This paper presents a general approach to the product conformity assessment which is required by EU New and Global Approach. As an example, some of the results of the project TD-7082B are presented relating to the harmonization of machine tools with the requirements of the relevant EU directives and harmonized standards

Automated Acquisition of Proximal Femur Morphological Characteristics

The success of the hip arthroplasty surgery largely depends on the endoprosthesis adjustment to the patient's femur. This implies that the position of the femoral bone in relation to the pelvis is preserved and that the endoprosthesis position ensures its longevity. Dimensions and body shape of the hip joint endoprosthesis and its position after the surgery depend on a number of geometrical parameters of the patient's femur. One of the most suitable methods for determination of these parameters involves 3D reconstruction of femur, based on diagnostic images, and subsequent determination of the required geometric parameters. In this paper, software for automated determination of geometric parameters of the femur is presented. Detailed software development procedure for the purpose of faster and more efficient design of the hip endoprosthesis that ensures patients’ specific requirements is also offere

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training

A study of thermo-elastic characteristics of the machine tool spindle

In order to avoid the failure of machine tools spindles in the real machining process due to an increase in temperature, it is essential to predict its thermal behavior in the designing phase. The characteristics of machine tools significantly depend on the thermal-elastic behavior of the spindle. These parameters directly affect the productivity and quality of machining operations. This paper presents a thermal - elastic model of the machine tool spindle which was based on the quasi-static model of bearings and the finite element (FE) model of the spindle shaft. Based on quasi-static model of bearings with angular contact, heat generated and thermal contact resistances (TCR) are determined for each position of the balls. The aforementioned constraints have been applied to the 3D FE model of the spindle which allowed for establishing non-stationary change of temperature and thermal deformation. In order to prove the efficacy of the proposed model, experimental measurements of spindle and bearing temperatures were done using thermocouples and thermal imager