Physics-Based Machine-Learning Approach for Modeling the Temperature-Dependent Yield Strengths of Medium- or High-Entropy Alloys

Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use of reasonable machine-learning models. Here, we present a comprehensive and up-to-date overview of a bilinear log model for predicting temperature-dependent YS of medium-entropy or high-entropy alloys (MEAs or HEAs). In this model, a break temperature, Tbreak, is introduced, which can guide the design of MEAs or HEAs with attractive high-temperature properties. Unlike assuming black-box structures, our model is based on the underlying physics, incorporated in form of a priori information. A technique of global optimization is employed to enable the concurrent optimization of model parameters over low- and high-temperature regimes, showing that the break temperature is consistent across YS and ultimate strength for a variety of HEA compositions. A high-level comparison between YS of MEAs/HEAs and those of nickel-based superalloys reveal superior strength properties of selected refractory HEAs. For reliable operations, the temperature of a structural component, such as a turbine blade, made from refractory alloys may need to stay below Tbreak. Once above Tbreak, phase transformations may start taking place, and the alloy may begin losing structural integrity

Construction of Multi-Dimensional Functions for Optimization of Additive-Manufacturing Process Parameters

The authors present a generic framework for parameter optimization of additive manufacturing (AM) processes, one tailored to a high-throughput experimental methodology (HTEM). Given the large number of parameters, which impact the quality of AM-metallic components, the authors advocate for partitioning the AM parameter set into stages (tiers), based on their relative importance, modeling one tier at a time until successful, and then systematically expanding the framework. The authors demonstrate how the construction of multi-dimensional functions, based on neural networks (NN), can be applied to successfully model relative densities and Rockwell hardness obtained from HTEM testing of the Inconel 718 superalloy fabricated, using a powder-bed approach. The authors analyze the input data set, assess its suitability for predictions, and show how to optimize the framework for the multi-dimensional functional construction, such as to obtain the highest degree of fit with the input data. The novelty of the research work entails the versatile and scalable NN framework presented, suitable for use in conjunction with HTEM, for the AM parameter optimization of superalloys, and beyond.Comment: Submitted to the Journal of Additive Manufacturing on November 10, 202

Ecosystem for Engineering Design Learning: A Comparative Analysis

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 detail 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

Dataset for Fracture and Impact Toughness of High-Entropy Alloys

Measurement(s) Fracture Toughness; Impact Toughness; Impact Energy Technology Type(s) Mechanical Testing Syste