High-alloy steels, like Ferrium C64, are used in powertrain components due to their corrosion resistance and high temperature resistance properties. These steels undergo a tempering temperature that is well above traditional steel, and during this process alloy carbides or compounds form, increasing the materials hardness, mechanical strength, and high temperature resistance properties. In the early stages of tempering, softening occurs due to the formation and coarsening of iron carbide, followed by a hardening as the alloy elements combine to form nano-scale dispersoids. These alloy carbides block the path of dislocations in the grain, strengthening the material. At longer tempering times or high temperatures, the coarsening of these alloy carbides and compounds can cause softening. A predictive material model for the high-tempering response of steels is needed to ensure peak hardening properties are met. For a robust heat treatment model, the material response for every step of the process needs to be modeled. These material properties include austenitization rates and thermal expansion during heating, carbon diffusivity and saturation limits for carburization, phase transformation rates and thermal contraction rates per phase during cooling and quenching, deep-freeze kinetics for further martensitic transformation, tempering kinetics for formation of the tempered martensite phase, and carbide kinetics for formation, coarsening, and size. Additionally, mechanical properties of each phase as a function of carbon need to be defined to ensure the proper mechanical response during and after heat treatment. After the material model is developed it can be used to design and optimize the high-temperature tempering process for any part using the same material.

During forging operations, strain can occur through three primary mechanisms: strain due to load applied through dies, strain due to thermal contraction, and strain due to creep. In materials behavior models, strain due to applied load and thermal contraction are directly considered and predictions are based on thermophysical properties and flow stress behaviors as inputs to the models. Strain due to creep after forging (during cooling) is often more difficult to predict and capture due to lack of materials data. In particular, data that capture the changing flow stress behavior during cooling (rather than from isothermal testing) are not commonly available. In this project, creep strain behavior during cooling was investigated by physical simulations using a Gleeble 3500. Standard cylinder-shaped Ti-6Al-4V samples with 10 mm diameter were heated to below β-transus temperature (1775°F) or above β-transus (1925°F), followed by constant cooling rates of 250°F/min and 1000°F/min with and without applied load during cooling to 1000°F. Total strain for the tests ranged from 2 – 6%. Characterization of prior microstructure and texture was carried out using XRD, optical microscopy, and SEM. The results provide insights on the relationship of flow stress behavior and microstructure as a function of temperature and cooling rate and are applicable to forging practices. These materials data can be used as input for future process modeling, potentially giving better prediction accuracy in industry applications.

Many alternative ecofriendly quenchants have been developed to replace mineral oil such as vegetable oils, polymer quenchants, and nanofluids. Although vegetable oils show superior cooling performance to mineral oil, their use is limited due to high production costs and low thermal stability. In this study, used coconut oil was chemically treated and its cooling and heat transfer characteristics were compared with that of refined coconut oil and mineral oil. The thermophysical properties of chemically treated waste coconut oil were found to be higher than that of the other oils tested, and its wettability proved to be better as well. Quenching experiments using an Inconel 600 probe (as per ISO 9950 and ASTM D 6200 standards) showed that the vapor blanket stage was shorter for the chemically treated oil than either of the others. The treated waste oil was also found to have the highest average peak heat flux based on the solution to the inverse heat conduction problem.

This paper investigates the effect of various types of errors on the accuracy of finite-element models used to simulate electromagnetic induction heat treating processes. By comparing simulation outputs, it shows how FEA calculations are affected by incorrect material specifications, incorrectly entered data, imprecise data, misassigned elements, unsuitable mesh sizing, inadequate current or power, and failure to properly account for skin effect depth. The paper includes relevant data and equations in addition to computer generated plots.

This paper describes a new grade of 3D carbon composite materials that are more durable than alloys at high temperatures as well as lighter and stronger. Advantages over other materials in the construction of heat treat furnace fixtures are illustrated in several comparative case studies.

A variety of test systems have been developed to determine the cooling characteristics of quenchants. Although current test standards specify cylindrical probes for measuring quenchant temperatures and cooling rates, this review concerns the development, implementation, and potential of test systems that use ball probes instead. It assesses the strengths and limitations of different types of ball probes and describes prototype test systems that leverage ball probe capabilities while compensating for inherent weaknesses.

While the induction heating of non-ferrous alloys is fundamentally no different than that of other metals, the unique physical properties of different non-ferrous alloys have a number of critical induction heating implications. This paper addresses a number of physical characteristics and practical subtleties associated with the induction heating of non-ferrous alloys, focusing particularly on the influence of electromagnetic and thermal material properties. A mathematical optimization routine for continuous induction heating processes is also presented. Utilizing coupled electromagnetic-thermal FEA computer simulation results and taking into account real-world process requirements, this routine is used to maximize induction heating quality and equipment performance.

Accurate simulation of phase transformation during quenching of steels requires comprehensive knowledge of thermal and physical properties of the material. In cases when reliable material data are not available they can be obtained by a two-stage inverse method proposed in the paper. It includes a Jominy test of a specimen with thermocouples. At the first stage, we obtain TTT diagrams by means of analyzing cooling curves for several regions of the specimen obtained from experimental results. The second stage includes correction of material thermo-physical properties, i.e. the thermal conductivity and specific heat for each phase as well as estimation of the latent heat for each phase transformation. Parameters fitting is carried out iteratively by comparing FEM simulation and experimental results. Varying of parameters is performed with evolutionary methods of multi-parameter optimization. The developed method is implemented in QForm commercial software.

The temperature profile in the Heat Affected Zone (HAZ) during induction welding is one of the most important factors determining the weld quality of High-Frequency Induction (HFI) welded steel tubes. In this work, numerical computation of the 3D temperature profile in the steel tube has been done by coupling the electromagnetic model with the thermal model. The high-frequency current and the magnetic fields in the tube, coil and impeder have been evaluated. The resulting power from the induced current is used to evaluate the temperature in the joining edges of the tube. The continuous tube movement has been implemented by considering an additional transport term in the heat equation. The simulations consider non-linear electromagnetic and thermal properties of the steel when it undergoes temperature rise to the welding temperature. The temperature profile from the resulting simulation gives information to control the subsequent process of joining the edges of the steel tube.