A predictive surface profile model for turning based on spectral analysis

This article presents a predictive approach of surface topography based on the FFT analysis of surface profiles. From a set of experimental machining tests, the parameters investigated are: feed per revolution, insert nose radius, depth of cut and cutting speed. The first step of the analysis consists of normalizing the measured profiles with the feed per revolution. This results in normalized profiles with a feed per revolution and a signal period equal to 1. The effect of each cutting parameter on the surface profile is expressed as a spectrum with respect to the period length. These effects are quantified and can be sorted in descending order of importance as feed per revolution, insert nose radius, depth of cut and cutting speed. The second part of the paper presents a modeling of the surface profile using the parameters effects and one interaction. The proposed model gives the spectrum of the profile to be predicted. The inverse Fourier transform applied to the spectrum yields the expected surface profile. Measured and simulated profiles are compared for two cutting conditions and results correlate well

Surface roughness prediction in milling based on tool displacements

In this paper, an experimental device using non-contact displacement sensors for the investigation of milling tool behaviour is presented. It enables the recording of high frequency tool vibrations during milling operations. The aim of this study is related to the surface topography prediction using tool displacements and based on tool center point methodology. From the recorded signals and the machining parameters, the tool deformation is modeled. Then, from the calculated deflection, the surface topography in 3D can be predicted. In recent studies, displacements in XY plane have been measured to predict the surface topography in flank milling. In this article, the angular deflection of the tool is also considered. This leads to the prediction of surfaces obtained in flank milling as well as in end milling operations. Validation tests were carried out: the predicted profiles were compared to the measured profile. The results show that the prediction corresponds well in shape and amplitude with the measurement

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Surface roughness prediction in milling based on tool displacements

In this paper, an experimental device using non-contact displacement sensors for the investigation of milling tool behaviour is presented. It enables the recording of high frequency tool vibrations during milling operations. The aim of this study is related to the surface topography prediction using tool displacements and based on tool center point methodology. From the recorded signals and the machining parameters, the tool deformation is modeled. Then, from the calculated deflection, the surface topography in 3D can be predicted. In recent studies, displacements in XY plane have been measured to predict the surface topography in flank milling. In this article, the angular deflection of the tool is also considered. This leads to the prediction of surfaces obtained in flank milling as well as in end milling operations. Validation tests were carried out: the predicted profiles were compared to the measured profile. The results show that the prediction corresponds well in shape and amplitude with the measurement

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

An Innovative Experimental Study of Corner Radius Effect on Cutting Forces

The cutting forces are often modelled using edge discretisation methodology. In finish turning, due to the smaller corner radii, the use of a local cutting force model identified from orthogonal cutting tests poses a significant challenge. In this paper, the local effect of the corner radius on the forces is investigated using a new experimental configuration: corner cutting tests involving the tool nose. The results are compared with inverse identifications based on cylindrical turning tests and elementary cutting tests on tubes. The results obtained from these methods consistently show the significant influence of the corner radius on the cutting forces

Analyse des phénomènes vibratoires des fraises disques

Lors d’une opération d’usinage, les efforts de coupe entrainent un déplacement relatif entre la pièce et l’outil qui fait varier les efforts de coupe. Ce phénomène, appelé vibration régénérative, nuit grandement à la durée de vie des outils et à l’état de surface de la pièce. Être capable de prédire ces phénomènes permet donc de mieux choisir les conditions de coupe afin de gagner en productivité. Ces vingt dernières années, beaucoup de modèles théoriques ont été développé pour diverses applications, mais il y a eu très peu d’études concernant le cas particulier des fraises disques. Dans cet article, nous allons donc étudier la stabilité des fraises disques via une méthode numérique de simulation temporelle

Etude du comportement dynamique de broche d'un centre d'usinage dans son espace de travail : application en fraisage

Une méthodologie est proposée dans cet article dans le but de faciliter l’évaluation de la stabilité d’une opération de fraisage, pour une broche, un outil et une position connue de la broche dans son espace de travail. L’approche se base sur une procédure d’indentification du comportement dynamique de la broche qui se suit par un couplage des FRF du système (broche et attachement) avec le tronçon avant de l’outil d’usinage pour prédire la FRF en sa pointe. Celle-ci permet à l’aide d’une résolution analytique de l’équation caractéristique de la dynamique de fraisage dans le domaine fréquentiel de prédire la limite de stabilité critique. Dans le but d’étudier la variabilité du comportement dynamique du système usinant dans son espace de travail, la même démarche est appliquée dans différentes positions. Les profondeurs de passe critiques obtenu pas simulations sont comparées à celles qui sont obtenues par fraisage.The aim of this work is to provide a methodology helping on the evaluation of the milling process stability for a given spindle, tool and work space position of the spindle. The proposed approach is based on the spindle dynamic behavior identification. Then, a FRF coupling is made between the identified system and the tool model in order to obtain the FRF at the tool tip. Therefore, the critical depth of cut can analytically be calculated from the characteristic equation of the dynamic milling process in the frequency domain. In order to study the variability of the dynamic behavior of the spindle in the work space, the same approach is then, applied at different positions and compared against experimental milling.Financement de thèse CIFR

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc) for high accuracy. However it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper an approach for surface profile pre-evaluation in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and free rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy

Tool-life and wear mechanisms of CBN tools in machining of Inconel 718

The demand for increasing productivity when machining heat resistant alloys has resulted in the use of new tool materials such as cubic boron nitride (CBN) or ceramics. However, CBN tools are mostly used by the automotive industry in hard turning, and the wear of those tools is not sufficiently known in aerospace materials. In addition, the grade of these tools is not optimized for superalloys due to these being a small part of the market, although expanding (at 20% a year). So this investigation has been conducted to show which grade is optimal and what the wear mechanisms are during finishing operations of Inconel 718. It is shown that a low CBN content with a ceramic binder and small grains gives the best results. The wear mechanisms on the rake and flank faces were investigated. Through SEM observations and chemical analysis of the tested inserts, it is shown that the dominant wear mechanisms are adhesion and diffusion due to chemical affinity between elements from workpiece and insert