Charles H. Ward

An Integrated Collaborative Environment for Materials Research

Creating an environment to enable the seamless integration of experiment, computation, and data within a laboratory environment is essential to enabling the practice of Integrated Computational Materials Engineering. Such an environment depends on the connection of experimental equipment and high performance computing resources to a collaborative software environment that supports research teams through simulation tool sharing and archival data management in a secure manner. Key functions of such a system include project management, workflow management, tool staging, data provenance tracking, and user authentication. An overview will be provided on efforts to establish such an integrated collaborative environment in a research laboratory involved in material and process discovery and development in both structural and functional materials.

Integrating Materials and Manufacturing Innovation: a new forum for the exchange of information to integrate materials, manufacturing, and design engineering innovations

A number of contemporary studies have concluded that the research and engineering activities across materials, manufacturing, and product design need to be more closely coupled to enable responsive product innovation and delivery [1-3]. The recurrent themes in these studies point out that the fields of materials and manufacturing must strive to become more quantitative and predictive and have to reshape and integrate their engineering practices and outputs to better synchronize with product design engineering frameworks. This philosophy is strongly embraced by a relatively new discipline known as Integrated Computational Materials Engineering (ICME) which seeks to accelerate the development and deployment of advanced materials [3]. The complexities and opportunities of a fully engaged global research and manufacturing enterprise and the rapid speed of present day product design cycles demand a new ICME-based paradigm for how we work, collaborate, and share knowledge. Hence the time is right to introduce Integrating Materials and Manufacturing Innovation (IMMI), a new journal that focuses on the issues and opportunities facing the materials and manufacturing community in building this new paradigm. The community is becoming ever more reliant on global collaboration, integrated design teams, digital data, computational modeling, and more complex and high throughput experimentation. In addition to integrating these themes in one publication venue, it is clear we need to evolve not only the way in which we disseminate ideas and information, but the format and richness in which it is shared. This journal will promote a discourse that fosters ICME, the accelerated implementation of advanced materials in the product design cycle, and the minimization of the disciplinary

Making the Case for a Model-Based Definition of Engineering Materials

For over 100 years, designers of aerospace components have used simple requirement-based material and process specifications. The associated standards, product control documents, and testing data provided a certifiable material definition, so as to minimize risk and simplify procurement of materials during the design, manufacture, and operation of engineered systems, such as aerospace platforms. These material definition tools have been assembled to ensure components meet design definitions and design intent. They must ensure the material used meets “equivalency” to that used in the design process. Although remarkably effective, such traditional materials definitions are increasingly becoming the limiting challenge for materials, design, and manufacturing engineers supporting modern, model-based engineering. Demands for cost-effective, higher performance aerospace systems are driving new approaches for multi-disciplinary design optimization methods that are not easily supportable via traditional representations of materials information. Furthermore, property design values having the definitions based on statistical distributions from testing results can leave substantial margin or material capability underutilized, depending on component complexity and the application. Those historical statistical approaches based on macroscopic testing inhibit innovative approaches for enhancing materials definitions for greater performance in design. This can include location-specific properties, hybrid materials, and additively manufactured components. Development and adoption of digital and model-based means of representing engineering materials, within a design environment, is essential to span the widening gap between materials engineering and design. We believe that the traditional approach to defining materials by chemistry ranges, manufacturing process ranges, and static mechanical property minima will migrate to model-based material definitions (MBMDs), due to the many benefits that result from this new capability. This paper reviews aspects of the challenges and opportunities of model-based engineering and model-based definitions.

Introducing the Data descriptor article

C a u w Earlier this year, we announced the launch of a new type of scholarly publication for Integrating Materials and Manufacturing Innovation (IMMI): the Data descriptor article. By introducing the Data descriptor article, we are responding to the growing dependence of research on having access to high-quality materials data. The Data descriptor article aims to provide researchers with the necessary supporting information, or metadata, needed to fully understand and use the data described with a high degree of confidence. A number of compelling needs underpin this new, peer-reviewed forum for the presentation of high-quality, high-value materials data. Most would agree that materials data can be extraordinarily rich in information, but we typically only use a subset of the data collected to explore a fairly focused hypothesis. Thus, it makes good sense, as research budgets are always constrained, to provide a forum to facilitate the reuse of materials data for a more efficient research process. Others can use available data to augment or corroborate their own research, perhaps testing a secondary hypothesis not even considered by the data originator. And as we become more dependent on models of materials behavior to guide advances in science or support engineering design, we have a growing need to develop, refine, and validate these models. Ready access to high-quality data will aid theoreticians and modelers in these pursuits. With the emergence of data analytic techniques, we also have the opportunity to gain new insights into material behavior from complex datasets where we do not yet have robust physics-based models of behavior. And finally, it is just sound scientific practice, and the core of the peer-review process, to allow others to examine the data upon which new scientific claims are based. There are several key elements to the Data descriptor article that are aimed at ensuring the data described is at least as valuable to others as it was to the originator. First and foremost is a requirement for a significantly more detailed description of the materials and experimental and/or simulation techniques and conditions used to generate the data than found in a typical research article. Also, we are asking authors to specifically address attributes of data quality, to appropriate levels, including steps taken in verification, validation, uncertainty quantification, and sensitivity to assumptions and variables. We are requiring the data be made available for peer review prior to publication to ensure the data is interpretable by someone else other than the originator. Finally, we are requiring that the data described be made accessible to all without restriction and that the data be assigned a persistent identifier to ensure it can be more easily discovered and properly cited.