Search Results for ICME

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.

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.

News about ASM members, chapters, events, awards, conferences, affiliates, and other society activities. Topics include: appointment of 2012 ASM Nominating Committee; Integrated Computational Materials Engineering (ICME); Author Spotlight on Taylan Altan.

This article discusses the thinking behind the U.S. Materials Genome Initiative (MGI), the central role of integrated computational materials engineering (ICME), the expected gains, and the prospects for success.

Integrated computational materials engineering has proven effective in shrinking materials development timelines and accelerating the implementation of new materials in manufactured components. This article presents examples of aerospace alloys developed using ICME techniques and provides a glimpse of future applications.

To conclude the celebration of ASM International's 100-year anniversary, AM&P asked several thought leaders to share their perspectives on what the future may hold in various areas of materials science and engineering, including R&D, ICME, additive manufacturing, and nanotechnology. This article provides a summary of their responses.

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.

Single crystal and directionally solidified casting processes are among the most complex techniques available, requiring stringent process control and a thorough understanding of engineered materials and specialty alloys. This article examines how an aerospace material company utilizes advanced investment casting techniques to create components that meet the increasingly demanding requirements of modern aerospace and gas turbine engines. The article highlights how solidification modeling and integrated computational materials engineering (ICME) have accelerated development processes by predicting material behaviors and optimizing casting parameters without extensive experimental testing.

High strength, corrosion-resistant Ferrium C64 is the culmination of a rigorous design process that bypassed extensive physical experimentation—delivering a carburizable steel with unmatched capability.

Today’s materials engineering leadership role is expected to expand and have a greater impact on evolving the organizational, technological, and strategic initiatives of original equipment manufacturers.

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.

The Center for Heat Treating Excellence (CHTE) was founded with the goal of bringing the industrial sector and university researchers together to find solutions to real-world problems. The Center, located at Worcester Polytechnic Institute (WPI) in Massachusetts, has 19 members from all areas of industry working together with WPI researchers to improve both heat treat and thermal processing methods worldwide. This article describes some of the current CHTE research projects.

This article describes highlights from a panel held at IMAT 2024. Representatives from industry, government, and academia discussed the potential that artificial intelligence and machine learning offer the materials science and manufacturing communities. David Furrer, Pratt & Whitney, moderated the panel. The panelists were Seth Kimble, ASM International; Joshua Stuckner, NASA Glenn Research Center; James E. Saal, Citrine Informatics; and S. Mohadeseh Taheri-Mousavi, Carnegie Mellon University. Each panelist provided their perspective on how AI and ML are impacting current and future materials design and manufacturing processes.

Profile of David Furrer, the 2018-2019 President of ASM International

The 15th World Conference on Titanium included papers on titanium alloy development, efforts to reduce Ti powder costs, additive manufacturing, and computational materials modeling tools. This article reviews some of the key advances reported at the conference.

This article reports on outcomes from an additive manufacturing workshop that developed guidelines for AM data management to realize the promise of Materials 4.0 and achieve process qualification for AM parts.

The Heat Treating Society R&D Committee, under the direction of Michael Pershing, Caterpillar, has compiled a list of organizations conducting research related to heat treating. Each listing includes contact information and selected projects.

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.

Advanced thermoelectric materials open new horizons for use in solid state power generation and refrigeration applications. This article describes a project that used state-of-the-art computational modeling and high-throughput experimentation to strategically screen the extensive material space and accelerate learning for materials development.

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.