Highlights from a member survey and panel discussion outlining the challenges and benefits of advanced manufacturing for the materials community. The article covers research being conducted in academia and industry to develop and integrate advanced manufacturing techniques; methods for in-process monitoring and machine learning; and challenges in adopting new digital manufacturing technologies.

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.

Vision-based machine learning systems for microstructural characterization and analysis are being successfully used for image classification, semantic and instance segmentation, and object detection, leading to accurate, autonomous, objective, and repeatable results.

Modeling and simulation are used to solve the problem of folded grooves, a common occurrence during aluminum wheel forging.

NitrideTool software, developed by the the Center for Heat Treating Excellence (CHTE), simulates the gas nitriding of steels to understand nitriding potential, temperature, time, and surface condition.

This article is an informal discussion among three industry leaders about how additive manufacturing is impacted by new data-driven technologies. The panelists are Elizabeth A. Holm, James C. Williams, and Edward D. Herderick; the discussion is moderated by Hanchen Huang. The discussion is a preview for an IMAT 2020 panel session on the same topic organized by the ASM Emerging Technologies Awareness Committee.

A novel framework including experimental and model-based techniques saves time and enables the introduction of new alloys for additive manufacturing. This article describes the first phase of the probabilistic machine learning framework that was successfully demonstrated to rapidly define optimum parameter sets for commercial high-temperature nickel superalloys, as well as to guide alloy design and selection for compatibility with laser powder bed fusion additive manufacturing.

Application of heat treat simulation using the finite element method is ideal to troubleshoot, improve, and design heat treating processes. This article presents examples of heat treating simulation used in the design of a tooling component and in refining a low-pressure carburizing 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.

In this article, the authors demonstrate the use of simulation software to optimize low-pressure carburizing (LPC) processes for high-alloy steels with strong carbide-forming elements.

Using virtual tools to study aluminum cylinder head quenching processes delivers valuable information for process design and optimization. In this study, cooling curves and temperature gradients generated by air and water quench modeling methods were used to evaluate quenching performance for various quenching configurations.

An in-depth understanding of technology trends not only keep materials engineers relevant, but also puts them in the driver’s seat when it comes to formulating strategies and executing projects. Some of today’s technology trends that are relevant to materials engineers include data analytics, artificial intelligence (machine learning and deep learning), blockchain, digital thread and digital twins, Internet of Things (IoT) or Industry 4.0, additive manufacturing, electric vehicles, and autonomous vehicles.

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.

Computer modeling is used in the design and development stages of induction hardening to optimize the process and to improve component quality including hardness, beneficial stress distributions, and reduced distortion.

Simulation of components and joints made of hybrid materials enables innovative designs for corrosion protection in automotive applications. This article gives examples of simulations used to gain insight into the corrosion behavior of riveted joints.

Computational thermodynamics and CALPHAD modeling prove useful for selecting and developing new magnesium alloys. This article summarizes an ongoing effort to establish a scientific foundation of computational thermodynamics and kinetics of magnesium alloys to achieve accelerated design and optimization of these alloys for weight reduction in the transportation industries.

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.

Computer simulation is an important tool for medical device companies and designers to reduce expensive prototyping and testing. The U.S. Food and Drug Administration’s Center for Device and Radiological Health (CRY) is seeing a growing number of submissions for medical devices that include a simulation-data component.

This work describes the development of a model to simulate the compound layer growth kinetics for nitriding AISI 4140 steel based on a customized Lehrer diagram. The model can be used to calculate concentration profiles of nitrogen as a function of time and position during the nitriding process and to predict the thickness of the compound layer. Winner of the 2012 HTS-Bodycote Best Paper in Heat Treating Award endowed by Bodycote Thermal Process-North America.

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.