Existing strain measurement techniques do not offer enough information about how failures occur, so alternative methods are needed. One technique involves using digital image correlation (DIC) to measure full field strain over the entire material surface. DIC works by capturing a series of images throughout a test and analyzing them afterwards.

Recent advances in hardware and software provide materials testers with improved productivity and enhanced capabilities. This article describes some innovations in testing composites and aluminum alloy formability and new capabilities in digital image correlation and testing machine software.

Researchers at the University of Michigan are investigating the complex heterogeneous phase transformations associated with deformation in shape memory alloys by means of in-situ tensile loading in combination with SEM, EBSD, and digital image correlation. This article describes the general approach and presents and interprets test results.

Digital image correlation data and Bayesian inference used together facilitate rigorous quantification of the uncertainty in material input parameters for finite element simulations of superelastic deformation.

Non-contacting extensometry offers a wide range of benefits in materials tests. Depending on the specific application, non-contacting extensometry may be the only option for strain measurement.

A look at how companies in regulated industries can test materials to produce higher quality metal components with additive manufacturing.

This article provides an overview of nondestructive test methods and their application in the study of material properties and structures. It covers the most widely used NDT technologies, including optical examination, radiography, acoustic emission, and ultrasonic testing, and discusses the impact of recent advancements.

While today's composites encompass a broad and well-established family of materials, the commercial market for high-performance, structural composites has actually existed for well over 30 years. Adoption in the aerospace industry has resulted in a wide range of static test methods offering reliable results. This article reviews some recent innovations in composites testing, including developments related to fatigue testing, high strain rate testing, thermography, and high capacity test systems.

New FDA guidance for Nitinol used in medical devices includes clarification on the determination of safety factors, the requirements for corrosion testing, and proper labeling of potentially harmful elements.

Developments in titanium technology and opportunities for increased use in several markets were highlighted at the 13th World Conference on Titanium. This article reviews some of the key technical topics covered at the conference, including new alloy development and innovative characterization methods.

This article, the second in a two-part series on titanium alloys, examines the effects of microtexture on deformation behavior, slip dislocations, and fatigue properties. Part I, which appeared in the April 2021 issue, described microstructural evolution processes that result in microtexture formation.

A microscale fatigue testing technique for characterizing mechanical response and relating it to microstructure promises to improve fatigue life prediction for turbine engine components.

Systems integration expertise is valuable for innovative high-temperature testing because it reduces data variability and allows for fewer tests in order to achieve accurate results. Grips, extensometers, furnaces, and environmental chambers are potential sources of experimental error. Variability that arises from these components tends to be systemic, so solving an issue with one tends to raise issues with another.

Scientists and engineers working on behalf of the U.S. automotive industry are nearing completion of a multiyear effort to accelerate the incorporation of advanced high-strength steels in American-made cars and trucks. This article reports on work to develop and validate an integrated computational materials engineering (ICME) model optimized for third-generation advanced high-strength steels.

This article provides a brief overview of the many ways that artificial intelligence and machine learning are being used for materials and manufacturing research. Several case studies show how the discovery, development, and deployment of novel materials are being dramatically accelerated through automation and data-driven models.

Advances in software algorithms and design enable automation of microstructure image analysis, leading to cost savings, reduction in measurement variability, and access to important metrics.

New electrical, mechanical, and chemical testing capabilities give scanning electron microscopes the feel of a self-contained “nano-laboratory.” Several developments in SEM technology allow new and more flexible approaches to the analysis of novel systems, such as Li battery-related materials, nanostructured and nanoporous materials, and novel approaches to 3D analysis.

Stony Brook celebrates a milestone by reflecting on its Center for Thermal Spray Research from the early years of growth and expansion to the development of an industrial consortium.

To learn about the latest trends in advanced materials, we turned to four of ASM’s thought leaders: John Ågren, KTH Royal Institute of Technology; George T. (Rusty) Gray III, Los Alamos National Laboratory; Jennie S. Hwang, H-Technologies Group; and David K. Matlock, Colorado School of Mines. This article presents their perspectives on the future of engineered materials, key areas of opportunity, and what ASM can do to support the next generation of materials scientists and engineers.

Scanning electron microscopy and energy dispersive x-ray spectroscopy open new windows into glassmaking processes.