Tuğrul Özel

Determination of workpiece flow stress and friction at the chip-tool contact for high-speed cutting

Abstract This paper presents a methodology to determine simultaneously (a) the flow stress at high deformation rates and temperatures that are encountered in the cutting zone, and (b) the friction at the chip–tool interface. This information is necessary to simulate high-speed machining using FEM based programs. A flow stress model based on process dependent parameters such as strain, strain-rate and temperature was used together with a friction model based on shear flow stress of the workpiece at the chip–tool interface. High-speed cutting experiments and process simulations were utilized to determine the unknown parameters in flow stress and friction models. This technique was applied to obtain flow stress for P20 mold steel at hardness of 30 HRC and friction data when using uncoated carbide tooling at high-speed cutting conditions. The average strain, strain-rates and temperatures were computed both in primary (shear plane) and secondary (chip–tool contact) deformation zones. The friction conditions in sticking and sliding regions at the chip–tool interface are estimated using Zorevs stress distribution model. The shear flow stress ( k chip ) was also determined using computed average strain, strain-rate, and temperatures in secondary deformation zone, while the friction coefficient ( μ ) was estimated by minimizing the difference between predicted and measured thrust forces. By matching the measured values of the cutting forces with the predicted results from FEM simulations, an expression for workpiece flow stress and the unknown friction parameters at the chip–tool contact were determined.

Process simulation using finite element method — prediction of cutting forces, tool stresses and temperatures in high-speed flat end milling

Abstract End milling of die/mold steels is a highly demanding operation because of the temperatures and stresses generated on the cutting tool due to high workpiece hardness. Modeling and simulation of cutting processes have the potential for improving cutting tool designs and selecting optimum conditions, especially in advanced applications such as high-speed milling. The main objective of this study was to develop a methodology for simulating the cutting process in flat end milling operation and predicting chip flow, cutting forces, tool stresses and temperatures using finite element analysis (FEA). As an application, machining of P-20 mold steel at 30 HRC hardness using uncoated carbide tooling was investigated. Using the commercially available software DEFORM-2D™, previously developed flow stress data of the workpiece material and friction at the chip–tool contact at high deformation rates and temperatures were used. A modular representation of undeformed chip geometry was used by utilizing plane strain and axisymmetric workpiece deformation models in order to predict chip formation at the primary and secondary cutting edges of the flat end milling insert. Dry machining experiments for slot milling were conducted using single insert flat end mills with a straight cutting edge (i.e. null helix angle). Comparisons of predicted cutting forces with the measured forces showed reasonable agreement and indicate that the tool stresses and temperatures are also predicted with acceptable accuracy. The highest tool temperatures were predicted at the primary cutting edge of the flat end mill insert regardless of cutting conditions. These temperatures increase wear development at the primary cutting edge. However, the highest tool stresses were predicted at the secondary (around corner radius) cutting edge.

High-speed machining of cast iron and alloy steels for die and mold manufacturing

Abstract This paper gives a brief overview of HSC technology and presents current progress in high performance machining of cast iron and alloy steels used in die and mold manufacturing. This work covers: (a) theoretical and experimental studies of tool failure and tool life in high-speed milling of hard materials, (b) optimization of CNC programs by adjusting spindle RPM and feed rate (program OPTIMILL) to maintain nearly constant chip load in machining sculptured surfaces, and (c) prediction of chip flow, stresses and temperatures in the cutting tool as well as residual stresses in the machine surface layer. Experimental studies are conducted using a 4-axis high-speed milling machine. Tool materials evaluated include carbides, coated carbides, and PCBN. Workpiece materials investigated include H-13 at 46 HRC, P-20 at 20–40 HRC and cast iron.

Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel

This article focuses on modeling and optimizing process parameters in pulsed laser micromachining. Use of continuous wave or pulsed lasers to perform micromachining of 3-D geometrical features on difficult-to-cut metals is a feasible option due the advantages offered such as tool-free and high precision material removal over conventional machining processes. Despite these advantages, pulsed laser micromachining is complex, highly dependent upon material absorption reflectivity, and ablation characteristics. Selection of process operational parameters is highly critical for successful laser micromachining. A set of designed experiments is carried out in a pulsed Nd:YAG laser system using AISI H13 hardened tool steel as work material. Several T-shaped deep features with straight and tapered walls have been machining as representative mold cavities on the hardened tool steel. The relation between process parameters and quality characteristics has been modeled with artificial neural networks (ANN). Predictions with ANNs have been compared with experimental work. Multiobjective particle swarm optimization (PSO) of process parameters for minimum surface roughness and minimum volume error is carried out. This result shows that proposed models and swarm optimization approach are suitable to identify optimum process settings.

Identification of Constitutive Material Model Parameters for High-Strain Rate Metal Cutting Conditions Using Evolutionary Computational Algorithms

Advances in plasticity-based analytical modeling and finite element methods (FEM) based numerical modeling of metal cutting have resulted in capabilities of predicting the physical phenomena in metal cutting such as forces, temperatures, and stresses generated. However, accuracy and reliability of these predictions rely on a work material constitutive model describing the flow stress, at which work material starts to plastically deform. This paper presents a methodology to determine deformation behavior of work materials in high-strain rate metal cutting conditions and utilizes evolutionary computational methods in identifying constitutive model parameters. The Johnson–Cook (JC) constitutive model and cooperative particle swarm optimization (CPSO) method are combined to investigate the effects of high-strain rate dependency, thermal softening and strain rate-temperature coupling on the material flow stress. The methodology is applied in predicting JC constitutive model parameters, and the results are compared with the other solutions. Evolutionary computational algorithms have outperformed the classical data fitting solutions. This methodology can also be extended to other constitutive material models.

A Methodology to Determine Work Material Flow Stress and Tool-Chip Interfacial Friction Properties by Using Analysis of Machining

In this paper, we develop a methodology to determine flow stress at the machining regimes and friction characteristics at the tool-chip interface from the results of orthogonal cutting tests. We utilize metal cutting analysis originally developed by late Oxley and present some improvements. We also evaluate several temperature models in calculating the average temperatures at primary and secondary deformation zones and present comparisons with the experimental data obtained for AISI 1045 steel through assessment of machining models (AMM) activity. The proposed methodology utilizes measured forces and chip thickness obtained through a basic orthogonal cutting test. We conveniently determine work material flow stress at the primary deformation zone and the interfacial friction characteristics along the tool rake face. Calculated friction characteristics include parameters of the normal and frictional stress distributions on the rake face that are maximum normal stress Nmax, power exponent for the normal stress distribution, a, length of the plastic contact, lp, length of the tool-chip contact, lc, the average shear flow stress at tool-chip interface, kchip, and an average coefficient of friction, e, in the sliding region of the tool-chip interface. Determined flow stress data from orthogonal cutting tests is combined with the flow stress measured through split-hopkinson pressure bar (SHPB) tests and the Johnson-Cook work material model is obtained. Therefore, with this methodology, we extend the applicability of a Johnson-Cook work material model to machining regimes. DOI: 10.1115/1.2118767

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.  2001 Elsevier Science Ltd. All rights reserved.

Predictive Analytical and Thermal Modeling of Orthogonal Cutting Process—Part I: Predictions of Tool Forces, Stresses, and Temperature Distributions

In this paper, a predictive thermal and analytical modeling approach for orthogonal cutting process is introduced to conveniently calculate forces, stress, and temperature distributions. The modeling approach is based on the work material constitutive model, which depends on strain, strain rate, and temperature. In thermal modeling, oblique moving band heat source theory is utilized and analytically combined with modified Oxleys parallel shear zone theory. Normal stress distribution on the tool rake face is modeled as nonuniform with a power-law relationship. Hence, nonuniform heat intensity at the tool-chip interface is obtained from the predicted stress distributions utilizing slip line field analysis of the modified secondary shear zone. Heat sources from shearing in the primary zone and friction at the tool-chip interface are combined, heat partition ratios are determined for temperature equilibrium to obtain temperature distributions depending on cutting conditions. Model validation is performed by comparing some experimental results with the predictions for machining of AISI 1045 steel, AL 6082-T6, and AL 6061-T6 aluminum. Close agreements with the experiments are observed. A set of detailed, analytically computed stress and temperature distributions is presented.

Modeling of hard part machining: effect of insert edge preparation in CBN cutting tools

Abstract High speed machining of hardened steels for manufacturing dies and molds offers various advantages, but the productivity often limited by mainly tool life. This study investigates the influence of edge preparation in cubic boron nitrite (CBN) cutting tools on process parameters and tool performance by utilizing practical finite element (FE) simulations and high speed orthogonal cutting tests. The predicted process parameters through FE simulations in high speed orthogonal cutting are expected to help optimize tool life and surface finish in hard machining of AISI H-13 hot work tool steel. A set of orthogonal cutting experiments using honed and chamfered CBN tools was performed and primary cutting force and thrust force were measured by using a force dynamometer along with a PC-based data acquisition system. The same set of cutting conditions was used in numerical FE simulations to predict forces, stresses and temperatures developed at the honed and chamfered CBN tools. Simulation results provided a distribution of stresses and temperatures at the cutting zone, chip–tool and workpiece–tool interfaces. Numerical simulations include testing different edge preparation geometry for CBN tools at different cutting speeds and feeds. The results show that a zone of workpiece material is formed under the chamfer acting as an effective rake angle during cutting. The presence of a chamfer affects the cutting forces and temperatures while no significant change in chip formation observed.

3D FINITE ELEMENT MODELLING OF CHIP FORMATION PROCESS FOR MACHINING INCONEL 718: COMPARISON OF FE SOFTWARE PREDICTIONS

Many efforts have been focused on the development of Finite Element (FE) machining models due to growing interest in solving practical machining problems in a computational environment in industry. Most of the current models are developed under 2D orthogonal plane strain assumptions, or make use of either arbitrary damage criterion or remeshing techniques for obtaining the chip. A complete understanding of the material removal process together with its effects on the machined parts and wear behaviour of the cutting tools requires accurate 3D computational models to analyze the entire physical phenomenon in materials undergoing large elastic-plastic deformations and large temperature changes as well as high strain rates. This work presents a comparison of 3D machining models developed using commercially available FE softwares ABAQUS/Explicit© and DEFORM™3D Machining. The work material is chosen as Inconel 718, a difficult-to-cut nickel-based alloy material. Computational results of temperature, strain and stress distributions obtained from the FE models for the effect of cutting speed are presented in comparison with results obtained from experimental tests. In addition, modified material model for Inconel 718 with flow softening is compared with the Johnson-Cook model. The predictions of forces and chip formation are improved with the modified material model.