Tool Life Distributions—Part 4: Minor Phases in Work Material and Multiple-Injury Tool Failure

1978 ◽  
Vol 100 (2) ◽  
pp. 201-209 ◽  
Author(s):  
S. Ramalingam ◽  
J. D. Watson
Keyword(s):  
Tool Life ◽  
Volume Fraction ◽  
Production Systems ◽  
Carbon Steels ◽  
Multiple Injury ◽  
Analytical Models ◽  
Minor Phase ◽  
Tool Failure ◽  
Steel Making ◽  
Work Material

Distributed tool life under production machining conditions results in the need for unplanned tool changes. In the case of large volume or automated production systems, such production interruptions invariably lead to higher manufacturing costs. When the distribution in tool life is known, logical operating strategies can be devised to minimize the costs associated with unforeseen production interruptions. To facilitate this, analytical models for tool life have been developed and presented in the first two parts of this paper. These stochastic models portray tool failure as resulting from injuries due to damage producing encounters in the course of machining. In Part 3 of this paper, a physically consistent model for damage producing encounters which result in tool fracture has been identified and validated for single-injury tool failure. The case of multiple-injury failure is considered here with emphasis on the tool life scatter due to the variations in minor phase content of the work material (nonsulphide, nonmetallic inclusion content). The role and significance of the oxygen-rich nonmetallics to tool wear and machinability in unalloyed carbon steels is examined. It is shown that given a steel, the chemistry and volume fraction of oxygen-rich nonmetallics in it may well determine the tool life (machinability) and tool life scatter. If this be the case, details of the steel making process can be varied to limit and control the detrimental effects of the oxygen-rich, nonmetallic phases to the tool life. Some such techniques that allow machinability enhancement by steel making process modifications are discussed to illustrate the validity of the concepts postulated here. The analysis suggests that the tool life (or machinability) can be improved by limiting the frequency of damaging encounters. But since the minor phase is dispersed and the encounters are stochastic, the tool life improvement will have to be accompanied by an increase in scatter in agreement with previously reported results.

Tool-Life Distributions—Part 2: Multiple-Injury Tool-Life Model

1977 ◽  
Vol 99 (3) ◽  
pp. 523-528 ◽  
Author(s):  
S. Ramalingam
Keyword(s):  
Tool Life ◽  
Multiple Injury ◽  
Linear Wear ◽  
Life Distribution ◽  
Subsequent Work ◽  
Tool Failure ◽  
Linear Wear Rate ◽  
Life Model ◽  
Life Distributions ◽  
Log Normal

The single-injury tool-life model developed in Part 1 of this paper is extended to the case of tool failure due to a multitude of injuries. The expected tool-life distribution in the case of tool failure from multiple injuries due to constant, time-independent stochastic hazards is shown to be a gamma distribution. The result obtained is based on a linear wear-rate assumption. The model is further extended to ensure applicability in the nonlinear wear region. It is shown that the expectation of a log-normal tool-life distribution when tool failure is due to crater wear is not unrealistic. No specific mechanism of tool wear is used to develop the model. The nature of the hazards and the wear mechanisms that are consistent with the multiple-injury tool-life model will be discussed in a subsequent work.

scholarly journals Modeling And Characterization Of High Carbon Nanobainitic Steels

2021 ◽  
Author(s):  
Gaganpreet Sidhu
Keyword(s):  
Heat Treatment ◽  
Yield Strength ◽  
Volume Fraction ◽  
Isothermal Transformation ◽  
Carbon Steels ◽  
Bainitic Transformation ◽  
Analytical Models ◽  
Isothermal Heat Treatment ◽  
Compression Tests ◽  
High Carbon

Analytical models have been developed for the transformation kinetics, microstructure analysis and the mechanical properties in bainitic steels. Three models are proposed for the bainitic transformation based on the chemical composition and the heat treatment conditions of the steel as inputs: (1) thermodynamic model on kinetics of bainite transformation, (2) improved thermo-statistical model that eliminates the material dependent empirical constants and (3) an artificial neural network model to predict the volume fraction of bainite. Neural networks have also been used to model the hardness of high carbon steels, subjected to isothermal heat treatment. Collectively, for a steel of given composition and subjected to a particular isothermal heat treatment, the models can be used to determine the volume fraction of bainitic phase and the material hardness values. The models have been extensively validated with the experimental data from literature as well as from three new high carbon experimental steels with various alloying elements that were used in the present work. For these experimental steels, data on the volume fraction of phases (via X-ray diffraction), yield strength (via compression tests) and hardness were obtained for various combinations of isothermal heat treatment times and temperatures. The heat treated steels were subjected to compression and hardness tests and the data have been used to develop a new correlation between the yield stress and the hardness. It was observed that while all three experimental steels exhibit a predominantly nanostructured bainite microstructure, the presence of Co and Al in one of the steels accelerated and maximized the nano-bainitic transformation within a reasonably short isothermal transformation time. Excellent yield strength (>1.7 GPa) and good deformability were observed in this steel after isothermal heat treatment at a low temperature of 250C for a relatively short duration of 24 hours.

scholarly journals Modeling And Characterization Of High Carbon Nanobainitic Steels

2021 ◽  
Author(s):  
Gaganpreet Sidhu
Keyword(s):  
Heat Treatment ◽  
Yield Strength ◽  
Volume Fraction ◽  
Isothermal Transformation ◽  
Carbon Steels ◽  
Bainitic Transformation ◽  
Analytical Models ◽  
Isothermal Heat Treatment ◽  
Compression Tests ◽  
High Carbon

Analytical models have been developed for the transformation kinetics, microstructure analysis and the mechanical properties in bainitic steels. Three models are proposed for the bainitic transformation based on the chemical composition and the heat treatment conditions of the steel as inputs: (1) thermodynamic model on kinetics of bainite transformation, (2) improved thermo-statistical model that eliminates the material dependent empirical constants and (3) an artificial neural network model to predict the volume fraction of bainite. Neural networks have also been used to model the hardness of high carbon steels, subjected to isothermal heat treatment. Collectively, for a steel of given composition and subjected to a particular isothermal heat treatment, the models can be used to determine the volume fraction of bainitic phase and the material hardness values. The models have been extensively validated with the experimental data from literature as well as from three new high carbon experimental steels with various alloying elements that were used in the present work. For these experimental steels, data on the volume fraction of phases (via X-ray diffraction), yield strength (via compression tests) and hardness were obtained for various combinations of isothermal heat treatment times and temperatures. The heat treated steels were subjected to compression and hardness tests and the data have been used to develop a new correlation between the yield stress and the hardness. It was observed that while all three experimental steels exhibit a predominantly nanostructured bainite microstructure, the presence of Co and Al in one of the steels accelerated and maximized the nano-bainitic transformation within a reasonably short isothermal transformation time. Excellent yield strength (>1.7 GPa) and good deformability were observed in this steel after isothermal heat treatment at a low temperature of 250C for a relatively short duration of 24 hours.

Characterization of a soft magnetic composite for use in road-embedded wireless-charging systems

2021 ◽  
pp. 1-10
Author(s):  
Kai-Yeung Li ◽  
Bill Trompetter ◽  
Maedeh Amirpour ◽  
Tom Allen ◽  
Simon Bickerton ◽  
...  
Keyword(s):  
Volume Fraction ◽  
Flexural Modulus ◽  
Magnetic Core ◽  
Analytical Models ◽  
Magnetic Composite ◽  
Wireless Charging ◽  
Soft Magnetic ◽  
Soft Magnetic Composite ◽  
Trade Offs ◽  
Wheel Loading

The ferrite magnetic core is an integral component of road-embedded wireless charging systems for electric vehicles. However, the brittleness of ferrite makes it susceptible to premature fracture due to cyclic wheel loading from vehicles. This has motivated the development of a soft magnetic composite (SMC) composed of a flexible polyurethane and crushed ferrite as an alternative. An experimental investigation was conducted into the trade-offs between mechanical, thermal and magnetic properties at ferrite volume fractions between 45.9[Formula: see text]vol% and 80.6[Formula: see text]vol%. A comparison was made between measured properties and predictions from analytical models in order to further investigate the characteristics of the composite. The investigation showed a trade-off between the increase in magnetic permeability and the reduction in strain-to-failure as ferrite volume fraction increased. In addition, a large increase in flexural modulus and thermal conductivity, along with a slight increase in flexural strength was observed. More importantly, the strain-to-failure of the composite was 20 times higher than that of ferrite even at the highest volume fraction, indicating that the SMC was successful in providing a more ductile and flexible alternative.

scholarly journals Comparison of Analytical Approaches Predicting the Compressive Strength of Fibre Reinforced Polymers

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2517 ◽  
Author(s):  
Christian Leopold ◽  
Sergej Harder ◽  
Timo Philipkowski ◽  
Wilfried Liebig ◽  
Bodo Fiedler
Keyword(s):  
Compressive Strength ◽  
Prediction Accuracy ◽  
Volume Fraction ◽  
Fibre Volume Fraction ◽  
Experimental Results ◽  
Analytical Models ◽  
Reinforced Polymers ◽  
Reinforced Polymer ◽  
Fibre Reinforced Polymer ◽  
Analytical Approaches

Common analytical models to predict the unidirectional compressive strength of fibre reinforced polymers are analysed in terms of their accuracy. Several tests were performed to determine parameters for the models and the compressive strength of carbon fibre reinforced polymer (CFRP) and glass fibre reinforced polymer (GFRP). The analytical models are validated for composites with glass and carbon fibres by using the same epoxy matrix system in order to examine whether different fibre types are taken into account. The variation in fibre diameter is smaller for CFRP. The experimental results show that CFRP has about 50% higher compressive strength than GFRP. The models exhibit significantly different results. In general, the analytical models are more precise for CFRP. Only one fibre kinking model’s prediction is in good agreement with the experimental results. This is in contrast to previous findings, where a combined modes model achieves the best prediction accuracy. However, in the original form, the combined modes model is not able to predict the compressive strength for GFRP and was adapted to address this issue. The fibre volume fraction is found to determine the dominating failure mechanisms under compression and thus has a high influence on the prediction accuracy of the various models.

Low C High Mn Cold Rolled TWIP Steel: Kinetics of Isothermal Recrystallization

2012 ◽  
Vol 706-709 ◽  
pp. 2181-2186 ◽  
Author(s):  
Tulio M.F. Melo ◽  
Érica Ribeiro ◽  
Lorena Dutra ◽  
Dagoberto Brandão Santos
Keyword(s):  
Automobile Industry ◽  
Twip Steel ◽  
Volume Fraction ◽  
Microstructural Analysis ◽  
High Strength ◽  
Specific Weight ◽  
Carbon Steels ◽  
Cold Rolled ◽  
Increasing Demand ◽  
Kinetics Of

The increasing demand, mainly from the automobile industry, for materials which combine high strength, high ductility and low specific weight makes steels with the TWIP (TWinning Induced Plasticity) effect a promising material to meet these requirements. This work aimed to study the kinetics of isothermal recrystallization of a TWIP steel (C-0.06%, Mn-25%, Al-3%, Si-2%, and Ni-1%) after cold rolling. The steel was hot and cold-rolled and then annealed at 700°C with soaking times ranging from 10 to 7200 s. Microstructural analysis was performed using light (LM) and scanning electron microscopy (SEM). Furthermore, quantitative metallography was performed in order to evaluate the recrystallized volume fraction and grain size. A JMAK based model was applied to describe the nucleation grain growth process. The restoration of the steel was also evaluated by microhardness tests. A complete recrystallization after 7200 s at 700°C was observed. It was found that with increasing annealing times, the recrystallized volume fraction also increases, while the nucleation and growth rates decrease, in agreement with the results for plain carbon steels.

A Model for Bainite Formation at Isothermal Heat Treatment Conditions

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Gaganpreet Sidhu ◽  
Seshasai Srinivasan ◽  
Sanjiwan Bhole
Keyword(s):  
Heat Treatment ◽  
Volume Fraction ◽  
Isothermal Transformation ◽  
Carbon Steels ◽  
Isothermal Heat Treatment ◽  
Low Carbon ◽  
Treatment Conditions ◽  
Nucleation Sites ◽  
Initial Nucleation ◽  
Improved Model

Abstract An improved model is presented for the formation of bainitic structures during isothermal heat treatment conditions. The model based on displacive mechanism consists of a new expression for the volume fraction of bainite as a function of time, incorporating a temperature and chemical composition-based expression for the number density of initial nucleation sites and limiting the volume fraction of bainite. The model has been validated with respect to experimental data of high- as well as low-carbon steels. It has been found that the isothermal transformation kinetics is well predicted for all steels.

Online Tool Life Prediction in the Machining of Titanium Alloys

2010 ◽  
Vol 458 ◽  
pp. 355-361 ◽  
Author(s):  
Song Lin Ding ◽  
R. Izamshah R.A. ◽  
John Mo ◽  
Quan Sheng Liu
Keyword(s):  
Regression Analysis ◽  
Titanium Alloys ◽  
Tool Life ◽  
Cutting Forces ◽  
Life Prediction ◽  
Machining Process ◽  
Empirical Methods ◽  
Tool Failure ◽  
Online Tool ◽  
Prediction Approach

In order to reduce the risk of expensive tool failure in the machining of Titanium alloys, the paper presents a tool life prediction approach based on the analysis of cutting forces. Regression analysis was applied to develop the prediction model. Detailed steps of implementation are presented. Prediction logics and criteria are introduced. Cutting tests were carried out to validate the reliability of the proposed method. When compared with empirical methods the proposed approach which is based on the analysis of cutting force measured in the machining process appears far more effective in predicting tool life.

Design Optimization of Material Properties in a Particle-Filled Composite Material System

2008 ◽  
Author(s):  
Siva P. Gurrum ◽  
Jie-Hua Zhao ◽  
Darvin R. Edwards
Keyword(s):  
Composite Material ◽  
Young’S Modulus ◽  
Material Properties ◽  
Poisson’S Ratio ◽  
Volume Fraction ◽  
Filler Content ◽  
Young's Modulus ◽  
Random Packing ◽  
Poisson's Ratio ◽  
Analytical Models

This work presents a methodology implementing random packing of spheres combined with commercial finite element method (FEM) software to optimize the material properties, such as Young’s modulus, Poisson’s ratio, coefficient of thermal expansion (CTE) of two-phase materials used in electronic packaging. The methodology includes an implementation of a numerical algorithm of random packing of spheres and a technique for creating conformal FEM mesh of a large aggregate of particles embedded in a medium. We explored the random packing of spheres with different diameters using particle generation algorithms coded in MATLAB. The FEM meshes were generated using MATLAB and TETGEN. After importing the nodes and elements databases into commercial FEM software ANSYS, the composite materials with spherical fillers and the polymer matrix were modeled using ANSYS. The effective Young’s modulus, Poisson’s ratio, and CTE along different axes were calculated using ANSYS by applying proper loading and boundary conditions. It was found that the composite material was virtually isotropic. The Young’s modulus and Poisson’s ratio calculated by FEM models were compared to a number of analytical solutions in the literature. For low volume fraction of filler content, the FEM results and analytical solutions agree well. However, for high volume fraction of filler content, there is some discrepancy between FEM and analytical models and also among the analytical models themselves.

Design of High Thermal Conductivity Particle Filled Polymer Using Effective Thermal Conductivity Models

2012 ◽  
Vol 714 ◽  
pp. 21-24 ◽  
Author(s):  
B. Garnier ◽  
F. Danes
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Thermal Contact Resistance ◽  
Volume Fraction ◽  
Thermal Contact ◽  
Analytical Models ◽  
Finite Element Models ◽  
Inner Diameter ◽  
Conductive Particles ◽  
Conductivity Models

The context of this work is the enhancement of the thermal conductivity of polymer by adding conductive particles. It will be shown how we can use effective thermal conductivity models to investigate effect of various factors such as the volume fraction of filler, matrix thermal conductivity, thermal contact resistance, and inner diameter for hollow particles. Analytical models for lower bounds and finite element models will be discussed. It is shown that one can get some insights from effective thermal conductivity models for the tailoring of conductive composite, therefore reducing the amount of experimental work.

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