Search for: All records

Abstract Fracture dictates the service limits of metallic structures. Damage tolerance of materials may be characterized by fracture toughness rigorously developed from fracture mechanics, or less rigorous yet more easily obtained impact toughness (or impact energy as a variant). Given the promise of high-entropy alloys (HEAs) in structural and damage-tolerance applications, we compiled a dataset of fracture toughness and impact toughness/energy from the literature till the end of the 2022 calendar year. The dataset is subdivided into three categories, i.e., fracture toughness, impact toughness, and impact energy, which contain 153, 14, and 78 distinct data records, respectively. On top of the alloy chemistry and measured fracture quantities, each data record also documents the factors influential to fracture. Examples are material-processing history, phase structures, grain sizes, uniaxial tensile properties, such as yield strength and elongation, and testing conditions. Data records with comparable conditions are graphically visualized by plots. The dataset is hosted in Materials Cloud, an open data repository. 

In the pursuit of developing high‐temperature alloys with improved properties for meeting the performance requirements of next‐generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature‐dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature,T_break, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black‐box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low‐ and high‐temperature regimes, is presented. The results presented extend previous work on high‐entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature‐dependent strength behavior of superalloys as well as HEAs. 

Design is a human activity that encompasses a broad array of tasks. In engineering design, individual efforts can be aggregated into teams to maximize collective progress. Effective teamwork, however, requires extensive management, organization and communication. Furthermore, modern challenges encompass complicated multi-disciplinary problems with faster schedules, fewer resources, and greater demands. Design, as a process, can be dissected into characteristic phases. Within each phase, design solutions are gradually developed. Technological tools have prioritized the structured analyses of the detailed and final design phases and have proven to be powerful multipliers for effective design efforts. It has long been the case, however, that major commitments of intangible resources are made as a result of efforts in the less emphasized earlier phases. These commitments and lack of modern toolsets for requirement development and conceptual design activities materialize as major sources of design pitfalls, both in industry and on student design projects. This paper presents a digital Ecosystem for Engineering Design Learning as a comprehensive, yet flexible, framework for capstone design teams. The digital Ecosystem has been developed as a feasible technology to bolster student information management, teamwork, communication, and proficiency in fundamental design principles, and as a technology capable of alleviating rework and process-related productivity interruptions. Its primary innovation, for capstone applications, is the ability to assess design work automatically against the design process, as well as against ABET compliant learning objectives, and provide prompt advisories in case of design oversights. The digital Ecosystem is compared to tools for project management, team communication, and requirement management.