Annealing and normalizing both involve heating metal to a temperature and cooling back to room temperature and are differentiated by the metals involved and rate of cooling. This article is an overview of annealing and normalizing processes.

This article describes sample preparation to create a visually impressive micrograph image of single-phase cartridge brass, C26000, an alloy composition of 70% copper and 30% zinc. C26000 was chosen due to the striking twins and large grain size that are easily generated by annealing a cold-worked sample, the large range of contrast and colors that can be created by etching, and the wide availability of the material. This entry won the Jacquet-Lucas Award for Best in Show at the 2021 International Metallographic Contest.

Three types of thermal treating processes are commonly applied to magnesium alloys, including annealing, solution heat treatment, and precipitation, or aging.

This study looks at whether adding an annealing step to the heat treating process has an effect on residual stress and distortion on hypoid gear sets. Test results indicate that the annealing heat treatment introduced between gear set soft cutting and case carburizing helped to minimize distortion.

Despite decades of research, fundamental questions still exist regarding the role of microtexture on the deformation, fatigue, and fracture behavior of titanium alloys. This article describes the microstructural evolution processes that result in microtexture formation, while Part II of this series will further elaborate on the effects of microtexture on deformation behavior.

Proper specification, measurement, and control of annealing furnace atmospheres are critical to reduce sooting, oxidation, sticking, and distortion. This article describes how to effectively design and utilize a nitrogen-hydrogen annealing atmosphere.

This article describes a procedure for optimizing the annealed microstructure of a high Al KAB steel. The main focus is directed toward characterization of microstructural development during annealing of different phases in a nanostructured bainitic steel using high resolution transmission electron microscopy (HRTEM). This entry won the prestigious 2019 Jacquet-Lucas Award for Excellence in Metallography at the International Metallographic Contest held in Portland, Oregon, October 2019.

Variable valve timing (VVT) plates are part of a system designed to optimize engine performance by changing the lift, duration, and timing of valve lift events. This article reviews a case study involving cracking of AISI 1045 steel VVT plates after heat treating.

An in-situ annealing technique combined with electron backscatter diffraction was used to characterize the microstructural evolution of ZE20 magnesium alloy as a function of temperature. This work sets the stage for future experiments to understand and control the recrystallization behavior of commercial alloys.

This article presents a broad overview of key annealing processes such as recovery, recrystallization, and grain growth in magnesium and its alloys. It explores the role of recovery in recrystallization behavior, evolution of recrystallization microstructure and texture, current understanding of grain coarsening in magnesium alloys, and concludes with a perspective on future research.

Manufacturing data together with heat treating knowledge can be synthesized with physics and data-based modeling approaches in a closed loop to provide insight for improving process efficiency and product quality for overall reduction in operating and energy costs. This article provides case studies of data analytics for coil batch annealing and batch carburizing.

Laser surface modification (LSM) techniques provide new pathways to improving the performance of engineered structures and components, either by altering surface metallurgy or chemistry or by adding a functional surface layer.

Research conducted at a brass-strip plant in Zutphen, Netherlands shows how nonlinear modeling can be used to predict the grain size and hardness of annealed strip based on composition and process variables. Production data gathered over the course of a year were used to train a feed-forward neural network that achieved a correlation coefficient of more than 86% when presented with new input data. The standard deviation between predicted and measured grain size was about 4.2 µm.