Predictive analytical and numerical modeling for orthogonal cutting

In this thesis, a predictive analytical and numerical modeling approach for the orthogonal cutting process is proposed to calculate temperature distributions and subsequently, forces and stress distributions. The models proposed include a constitutive model for the material being cut based on the work of Weber, a model for the shear plane based on Merchants model, a model describing the contribution of friction based on Zorev’s approach, a model for the effect of wear on the tool based on the work of Waldorf, and a thermal model based on the works of Komanduri and Hou, with a fraction heat partition for a non-uniform distribution of the heat in the interfaces, but extended to encompass a set of contributions to the global temperature rise of chip, tool and work piece. The models proposed in this work, try to avoid from experimental based values or expressions, and simplifying assumptions or suppositions, as much as possible. On a thermo-physical point of view, the results were affected not only by the mechanical or cutting parameters chosen, but also by their coupling effects, instead of the simplifying way of modeling which is to contemplate only the direct effect of the variation of a parameter. The implementation of these models was performed using the MATLAB environment. Since it was possible to find in the literature all the parameters for AISI 1045 and AISI O2, these materials were used to run the simulations in order to avoid arbitrary assumption.

Development of an experimental setup for metal cutting research

Analytical, numerical and experimental models have been developed over time to try to characterize and understand the metal cutting process by chip removal. A true knowledge of the cutting process by chip removal is required by the increasing production, by the quality requirements of the product and by the reduced production time, in the industries in which it is employed. In this thesis an experimental setup is developed to evaluate the forces and the temperature distribution in the tool according to the orthogonal cutting model conditions, in order to evaluate its performance and its possible adoption in future works. The experimental setup is developed in a CNC lathe and uses an orthogonal cutting configuration, in which thin discs fixed onto a mandrel are cut by the cutting insert. In this experimental setup, the forces are measured by a piezoelectric dynamometer while temperatures are measured by thermocouples placed juxtaposed to the side face of the cutting insert. Three different solutions are implemented and evaluated for the thermocouples attachment in the cutting insert: thermocouples embedded in thermal paste, thermocouples embedded in copper plate and thermocouples brazed in the cutting insert. From the tests performed in the experimental setup it is concluded that the adopted forces measurement technique shows a good performance. Regarding to the adopted temperatures measurement techniques, only the thermocouples brazed in the cutting insert solution shows a good performance for temperature measurement. The remaining solutions show contact problems between the thermocouple and the side face of the cutting insert, especially when the vibration phenomenon intensifies during the cut. It is concluded that the experimental setup does not present a sufficiently robust and reliable performance, and that it can only be used in future work after making improvements in the assembly of the thermocouples.

Model Order Reduction for Determining Bubble Parameters to Attain a Desired Fluid Surface Shape

In this paper, a new methodology for predicting fluid free surface shape using Model Order Reduction (MOR) is presented. Proper Orthogonal Decomposition combined with a linear interpolation procedure for its coefficient is applied to a problem involving bubble dynamics near to a free surface. A model is developed to accurately and efficiently capture the variation of the free surface shape with different bubble parameters. In addition, a systematic approach is developed within the MOR framework to find the best initial locations and pressures for a set of bubbles beneath the quiescent free surface such that the resultant free surface attained is close to a desired shape. Predictions of the free surface in two-dimensions and three-dimensions are presented.

Tight-binding model for carbon nanotubes from ab initio calculations

Here we present a parametrized tight-binding (TB) model to calculate the band structure of single-wall carbon nanotubes (SWNTs). On the basis of ab initio calculations we fit the band structure of nanotubes of different radii with results obtained with an orthogonal TB model to third neighbors, which includes the effects of orbital hybridization by means of a reduced set of parameters. The functional form for the dependence of these parameters on the radius of the tubes can be used to interpolate appropriate TB parameters for different SWNTs and to study the effects of curvature on their electronic properties. Additionally, we have shown that the model gives an appropriate description of the optical spectra of SWNTs, which can be useful for a proper assignation of SWNTs` specific chirality from optical absorption experiments.

The cutting performance of sialon ceramic tools

The thesis deals with a research programme in which the cutting performance of a new generation of ceramic cutting tool material is evaluated using the turning process. In part one, the performance of commercial Kyon 2000 sialon ceramic inserts is studied when machining a hardened alloy steel under a wide range of cutting conditions. The aim is to formulate a pattern of machining behaviour in which tool wear is related to a theoretical interpretation of the temperatures and stresses generated by the chip-tool interaction. The work involves a correlation of wear measurement and metallographic examination of the wear area with the measurable cutting data. Four main tool failure modes are recognised: (a) flank and crater wear (b) grooving wear (c) deformation wear and (d) brittle failure Results indicate catastrophic edge breakdown under certain conditions. Accordingly in part two, the edge geometry is modified to give a double rake tool; a negative/positive combination. The results are reported for a range of workpiece materials under orthogonal cutting conditions. Significant improvements in the cutting performance are achieved. The improvements are explained by a study of process parameters; cutting forces, chip thickness ratio, chip contact length, temperature distribution, stress distribution and chip formation. In part three, improvements in tool performance are shown to arise when the edge chamfer on a single rake tool is modified. Under optimum edge chamfer conditions a substantial increase in tool life is obtained compared with the commercial cutting geometry.

Jatkuvavaletun teräksen leikkaussuunnitelman optimointi

Teollisuuden tuotannon eri prosessien optimointi on hyvin ajankohtainen aihe. Monet ohjausjärjestelmät ovat ajalta, jolloin tietokoneiden laskentateho oli hyvin vaatimaton nykyisiin verrattuna. Työssä esitetään tuotantoprosessi, joka sisältää teräksen leikkaussuunnitelman muodostamisongelman. Valuprosessi on yksi teräksen valmistuksen välivaiheita. Siinä sopivaan laatuun saatettu sula teräs valetaan linjastoon, jossa se jähmettyy ja leikataan aihioiksi. Myöhemmissä vaiheissa teräsaihioista muokataan pienempiä kokonaisuuksia, tehtaan lopputuotteita. Jatkuvavaletut aihiot voidaan leikata tilauskannasta riippuen monella eri tavalla. Tätä varten tarvitaan leikkaussuunnitelma, jonka muodostamiseksi on ratkaistava sekalukuoptimointiongelma. Sekalukuoptimointiongelmat ovat optimoinnin haastavin muoto. Niitä on tutkittu yksinkertaisempiin optimointiongelmiin nähden vähän. Nykyisten tietokoneiden laskentateho on kuitenkin mahdollistanut raskaampien ja monimutkaisempien optimointialgoritmien käytön ja kehittämisen. Työssä on käytetty ja esitetty eräs stokastisen optimoinnin menetelmä, differentiaalievoluutioalgoritmi. Tässä työssä esitetään teräksen leikkausoptimointialgoritmi. Kehitetty optimointimenetelmä toimii dynaamisesti tehdasympäristössä käyttäjien määrittelemien parametrien mukaisesti. Työ on osa Syncron Tech Oy:n Ovako Bar Oy Ab:lle toimittamaa ohjausjärjestelmää.

Orientação exterior de imagens orbitais usando modelos rigorosos com feições retas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Some aspects of drill performance

High speed twist drills are probably the most common of all metal cutting tools and also the least efficient. In this study, detailed research was undertaken into aspects of drill performance and ways in which drilling could be improved in short hole depths of up to two diameters. The work included an evaluation of twist drill geometry and grinding parameters. It was established that errors in point grinding lead to increased hole oversize and reduced drill life. A fundamental analysis was made to establish predictive equations for the drill torque and thrust using modified orthogonal cutting equations and empirical data. A good correlation was obtained between actual and predicted results. Two new techniques for extending twist drill life by the use of coolant feeding holes and also the application of titanium nitride coatings were evaluated. Both methods were found to have potential for improving drill performance. A completely new design of carbide tipped drill was designed and developed. The new design was tested and it compared favourably with two commercially available carbide tipped drills. In further work an entirely different type of drill point geometry was developed for the drill screw. A new design was produced which enabled the drilling time to be minimised for the low thrust forces that were likely to be used with hand held power tools.

Some new three-level orthogonal main effects plans robust to model uncertainty

In this paper, we list some new orthogonal main effects plans for three-level designs for 4, 5 and 6 factors in IS runs and compare them with designs obtained from the existing L-18 orthogonal array. We show that these new designs have better projection properties and can provide better parameter estimates for a range of possible models. Additionally, we study designs in other smaller run-sizes when there are insufficient resources to perform an 18-run experiment. Plans for three-level designs for 4, 5 and 6 factors in 13 to 17 runs axe given. We show that the best designs here are efficient and deserve strong consideration in many practical situations.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

M-estimator, and D-optimality model construction using orthogonal forward regression

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

Orthogonal packing of identical rectangles within isotropic convex regions

A mixed integer continuous nonlinear model and a solution method for the problem of orthogonally packing identical rectangles within an arbitrary convex region are introduced in the present work. The convex region is assumed to be made of an isotropic material in such a way that arbitrary rotations of the items, preserving the orthogonality constraint, are allowed. The solution method is based on a combination of branch and bound and active-set strategies for bound-constrained minimization of smooth functions. Numerical results show the reliability of the presented approach. (C) 2010 Elsevier Ltd. All rights reserved.