Modeling shows that achieving required gear performance in a reduced gear size is possible by changing the steel grade and heat treatment parameters during the design stage. This article describes work in which virtual computer models using DANTE software are applied to help achieve gear size reduction by including steel grade hardenability and heat treatment parameters in the design process.

4D printing enables fabrication of complex objects that transform over time (the fourth dimension) when subjected to external stimuli. 4D printing of metallic functional materials is of special interest due to their capacity for self-assembly and multifunctionality, with the added benefit of higher actuation capability, in comparison with polymeric materials.

Synergistic performance can be achieved by integrating judgment-focused humans and prediction-focused AI agents. This article discusses general developments in this area and potential implications for materials innovation in semiconductor devices.

This article describes research at Los Alamos National Laboratory that is aimed at building a coupled experimental and computational methodology that supports the development of predictive damage capabilities by: (1) capturing real distributions of microstructural features from real material and implementing them as digitally generated microstructures in damage model development, and (2) distilling structure-property information to link microstructural details to damage evolution under a multitude of loading states.

Engineers at the Ford Motor Co. are using integrated computational materials engineering (ICME) tools, allowing them to accurately simulate the heat treatment of gears and predict phase transformation kinetics and distortion.

Corrosion is commonly defined as the deterioration of a material or its properties because of a reaction with its environment. It is a natural process due to a high energy state induced in metals and alloys during refining, processing, and manufacturing. Modern methods for preventing and controlling corrosion can reduce or eliminate its impact on public safety, the economy, and the environment. New technologies push materials to withstand increasingly demanding conditions as a result of higher temperatures and pressures, and environments with greater corrosivity. Therefore, the risks of new types of corrosion failures need to be considered along with methods to avoid them, as described in this article.

This article starts with a synopsis of machine learning (ML) and explores the characteristics of ML algorithms. It then reports on the results of two recently completed research projects investigating the potential use of ML to establish additive manufacturing materials property allowables. Although continued research and development work is required, the results are very promising.

The effects of Nitinol micropurity on the durability of superficial femoral artery stents offers a potential way to enhance durability and reduce fatigue fractures. This article describes recent work on superior grades of “microclean” Nitinol and the effects on fatigue and durability of the devices.

Recent advances in neutron sources and detector technologies enable new contrast mechanisms to determine crystalline information for metal components. This new capability can help validate emerging additive manufacturing processes used to produce parts with complex geometries.

Integrated computational materials engineering (ICME) is a relatively new discipline that shows great promise for reducing the cost and time required to design and deploy new materials, manufacturing technologies, and products. This article introduces an ICME implementation framework for product development using modeling of a friction stir welding process as an example.

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 describes new software that helps predict properties of high-entropy alloy compositions under high-temperature conditions. The tool was developed by extensive testing on the quinary Al-Co-Cr-Fe-Ni alloy system with both first-principles density functional theory calculations and machine learning to establish a substantial database for modeling.

This article demonstrates how FEA-based tools are used to model residual stress and distortion in a full-float truck axle induction hardened and cooled at different rates. The effect of cooling rate on axial displacement is discussed.

In order to take full advantage of the promise of recent computational efforts, new microstructural models that consider realistic shapes, connectivities, and distributions are required. Characterizing materials in 3D is necessary to reveal the actual distributions of relevant microstructural characteristics and thereby enable development of realistic models to predict the behavior of new materials. This article describes methods to model grain size distributions and presents case studies in cementite precipitation morphology and coarse martensite analysis.

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.

Typical vehicles contain more than 3000 spot welds, and failure parameters for these welds must be accurately predicted in crash simulations. A testing protocol developed at EWI for the purpose of creating spot weld failure parameters has been used since 2010 to establish these parameters for automotive manufacturers for a range of steel and aluminum stack-ups. Crash simulations in full-vehicle models show that the failure parameters created by this method are accurate for predicting the initiation of spot weld failure by comparing results with experimental data.

Despite challenges, research and development scientists are increasingly turning to artificial intelligence when creating new materials. This article outlines what AI can do and why businesses are using it, illustrated by examples.

This article reports on a multifaceted testing and modeling approach to investigate the fatigue life of medical grade Nitinol to one billion cycles. The goal of a the research is to understand the mechanism of Nitinol ultra-high cycle fatigue and to eventually be able to predict when fracture will occur.

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.

Automotive manufacturers are making significant investments in the design and development of bioplastics and biocomposites-based components.