This workshop was in some respects a follow-up to a 2007 National Research Council Workshop on Materials State Awareness (MSA). Whereas the first workshop dealt with themes such as how to define MSA and what its future prospects might be (see the introductory presentation, by James Malas, of highlights from the 2007 workshop), this second workshop focused on current and emerging MSA applications across a number of aspects of system life cycle management. Condition-based maintenance (CBM) recurred as a major topic throughout the workshop, and in many ways CBM provided a defining context for the perspectives on MSA offered by both presenters and audience participants. But other aspects of system life cycle management were also addressed, including system life prediction (SLP), system life extension, structural health monitoring (SHM), qualifying a new material for an application or qualifying a known material for a new application (qualification), and life cycle cost management. For purposes of this summary, “MSA applications” includes all of these aspects of system life cycle management.
The workshop’s 14 presentations and 2 crosscutting discussion sessions covered a wealth of technical detail. To help readers trace connections among all of these details, the rapporteur has identified four themes that ran through multiple presentations and came up repeatedly during discussions. These themes are offered solely to aid comprehension and do not represent findings or conclusions of the workshop participants as a group.
THEME 1—WHAT IS MATERIALS STATE AWARENESS? WHAT SHOULD IT BE?
In his presentation of highlights from the 2007 MSA workshop, Dr. Malas said the participants in that workshop debated how to define and delineate MSA but did not arrive at a comprehensive definition. He expressed approval of the characterization included in the invitation-agenda document for this workshop:
Materials state awareness seeks to quantify the current state of a material and/or damage [to a material or structure] with statistical metrics of accuracy located in individual systems, structures, or components and is the heart of condition-based management strategies. In principle, such quantitative evaluation should be based on knowledge of the initial state, damage or failure process, operational environment, and nondestructive evaluation (NDE) assessment of state. However, most frequently the initial state is not known and the assessment must be done from an unknown reference state.
Whereas the 2007 workshop focused on MSA for bulk materials such as metal alloys, this workshop expanded the scope of MSA to include composites, interfaces and complex assemblies, and hierarchically structured materials. Robert E. Schafrik encouraged the participants to think beyond MSA of monolithic structures to consider how it could be applied to degradation mechanisms at interfaces such as those between coatings and substrates or at material joins.
Another participant said that the degradation information from MSA needs to be related to functional characteristics of the system and its subsystems to provide a practical CBM solution. Mr. Eric Lindgren expressed a similar view, saying that ensuring the integrity of a system is the rationale behind trying to understand material state or the state of the system.
Jan D. Achenbach distinguished between the MSA methodologies for quantitative NDE (QNDE) and SHM. The former, he explained, consists of a toolbox of sensor applications and techniques used for periodic inspections of a structure, particularly safety-critical structures such as aircraft, bridges, or nuclear reactor facilities. In SHM, by contrast, the sensors are permanently installed in the structure, and near-real-time prediction of material properties of interest is possible if there are sufficient sensors and if the data from them can be transferred readily to a data processing facility. Other characteristics differentiating QNDE and SHM are listed in Table 3.1 in the summary of Dr. Achenbach’s presentation.
Dale L. Ball focused his presentation on applications of integrated computational materials engineering (ICME) and integrated computational structural engineering at the airframe level, particularly in the design phase of an aircraft structures development program. He stressed that what the materials science community does with MSA has direct and important impacts on directions in the
structures community. He sees physics-based modeling as a key technology in the set of evolving MSA technologies and capabilities that not only will be applied throughout the operational lives of engineered systems but also will enable higher-fidelity definition of the initial state (post-manufacture) of a materials system.
With respect to what MSA should be, the workshop discussion at the end of Day 1 led Michael F. McGrath to frame the question, “If we had perfect MSA, what would we do differently [in applications such as materials specification for design or CBM for legacy systems]?” Following up on this question, the facilitators of the closing discussion on Day 2 asked the participants, “What are the implications for perfected MSA?” The suggestions they received are summarized in Box 3.9. They also asked for participants’ views on how CBM and SHM might change as MSA improves over time, and the responses are summarized in Box 3.10.
THEME 2—MSA REQUIRES THE INTERPLAY OF MODELING AND CHARACTERIZATION-DETECTION CAPABILITIES
The presentations by Dr. Philip Withers, Dr. Jan D. Achenbach, Dr. Joannie W. Chin, Dr. Kevin J. Hemker, Dr. D.J. Luscher, and Dr. Susan B. Sinnott each noted the necessity of studying material structure and damage state on multiple spatial scales, particularly for composite materials and components. Each presentation shows how this multiscale problem requires the interplay between modeling methods and techniques to detect and characterize microstructure properties in the material of interest. For instance, Dr. Withers emphasized, using several detailed examples, that the various mechanisms and effects of degradation or damage in a heterogeneous composite structure have to be identified and followed across a range of spatial and temporal scales, using multiple tools, including multiple sensor modalities and their associated imaging-modeling systems.
Dr. Achenbach stressed the need for probabilistic approaches to modeling SLPs from NDE and SHM sensor data. Measurement models are needed, he said, that incorporate probabilistic considerations in arriving at an overall interpretation of sensor readings. His presentation elaborated on how this general point can be applied to probabilistic predictions of fatigue crack growth.
Dr. Chin explained how her team at the National Institute of Standards and Technology incorporated a Total Effective Dosage Model into a reliability-based cumulative damage model for SLP. Exposure data from both outdoor testing and laboratory-based exposure chamber experiments are used as inputs to this model.
Speaking as an experimentalist, Dr. Hemker discussed ways that multiscale modeling for MSA needs experimental input to improve the models themselves. He gave examples related to operative failure mechanisms, three-dimensional structures with salient resolution, and benchmarking of model results at relevant length scales. He sees the kinds of detailed quantitative data coming from an increasing
number of laboratories, such as Dr. Withers’s, as providing “a tremendous opportunity” to couple microstructure with physics-based models.
Dr. Luscher described how models for properties and behaviors on at least three different scales—the microscale (e.g., single crystals and grain boundaries), mesoscale (e.g., polycrystalline microstructures), and macroscale (the length scale of engineered components and systems)—have to be coupled to successfully simulate how actual materials and components will behave in extreme environments. Abdel E. Bayoumi described how the Smart Predictive System his team has been developing incorporates data fusion of inputs from a range of condition indicators into measurement-based models. The final phase of development will involve iterated correlation and comparison between results from the measurement-based models and predictions from physics-based models that incorporate algorithms based on theories of materials behavior.
Dr. Sinnott’s presentation focused on the smallest spatial scales in this hierarchy, where computational methods are used to model the electronic structure and atomic-scale properties and behavior of materials. Among her examples was a collaboration with two experimentalists to simulate the behavior of the intermetallic phases at the interface of platinum contacts with thin-film piezoelectric components of microelectromechanical systems. A second example was modeling the defect formation energies in a nickel-based superalloy, where confirmation of the computational results with experimental data was critical. The question period after her presentation included several enlightening discussions with workshop participants on the interaction of atomic-scale models with the models used to capture properties and behaviors at larger scales and on the interplay between these multilevel models and experimental systems.
THEME 3—FUTURE VISIONS FOR MSA, CBM, SLP, AND OTHER ASPECTS OF SYSTEM LIFE CYCLE MANAGEMENT
The plural “visions” in this theme refers to the plurality of long-term views expressed by various workshop participants. These views overlap but also diverge in some respects.
- Dr. Malas viewed MSA as becoming a critical input to a number of Air Force and Department of Defense programs for system life management, but he also said that much work remains to be done and that MSA implementation will have to be tailored to the application. He asked the participants, “Do we need a national initiative in Integrated NDE for Life Management of Advanced Materials?”
- Dr. Achenbach foresaw computational mechanics and multiscale modeling, supplemented with experimental information (see Theme 2), being
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used in the future to provide a computational link from microstructure to material properties at the macrostructural level. These models, he suggested, together with signals from diagnostic embedded sensors, will provide ways to monitor the evolution of damage and enable what he called intelligent system health monitoring. Near the close of his presentation, he discussed the elements of his vision for a “structural health monitoring grand plan.”
- Dr. Bayoumi contrasted CBM with corrective maintenance, which is reactive and event driven, and with preventive maintenance approaches that are time-based (hours of operation) or usage-based (duration and duty-cycle conditions of use). CBM is an approach to proactive maintenance based on one or more condition indicators, and he sees it as part of a paradigm shift from reactive maintenance to a holistic, systems-engineering approach that combines historical and logistics (current use) data on components and subsystems with onboard smart sensing and integration of data from electronics and avionics systems to optimize system operations. Furthermore, this systems engineering approach can be carried forward from maintenance of existing operational systems to optimizing the design and manufacture of new components and systems.
- Dashiell Kolbe described the holistic view of CBM (or Integrated Vehicle Health Management) that his company, an original equipment manufacturer of aircraft, uses. The aim is to provide aircraft customers with a “total solution” incorporating the entire chain, from health monitoring data inputs to decision support and user action.
THEME 4—CHALLENGES AND OPPORTUNITIES FOR MSA AND ITS APPLICATIONS IN SYSTEM LIFE CYCLE MANAGEMENT
Mr. Lindgren contrasted the relative maturity of modeling for bulk metals in propulsion-system materials with the status of modeling for composite materials, where he does not yet see a unifying theory of failure progression from an initiating event emerging, despite a great deal of past and ongoing work. He illustrated the difficulties for MSA of complex fabrications with the example of corrosion, noting that there is still no way to predict the time course of corrosion in an assembled complex aircraft system. Similarly, Haydn N.G. Wadley cautioned that, as high-strength material systems become more heterogeneous, the challenge to metrology for adequate MSA increases.
- Dr. Withers ended his presentation on correlative tomography by listing both the promising possibilities for this MSA methodology and several technical challenges that still need to be addressed.
- Dr. Achenbach expects that SHM will have to justify its existence by showing a favorable cost-benefit profile built on factors such as reduced maintenance with increased safety, advantages in affordability and maintainability, a near-zero rate of false alarms, and reduced design margins that do not compromise performance and safety goals.
- Service life prediction (SLP) is difficult, according to Dr. Chin, because remaining service life is not a fundamental material property; it is measured with respect to a minimum acceptable value for one or more critical performance properties. The SLP challenge is to relate the performance properties of interest to the fundamental material properties that govern them.
- Dr. Sinnott described how multicomponent microscale systems can be modeled with “next generation” energy potentials, but she also noted technical/tactical limitations to more widespread use of these atomic-scale computational approaches. She concluded her presentation with a list of four “big-picture” challenges for computational methods at this small end of the scale and suggested directions for addressing the challenges.
- Dr. Kolbe described the real-world challenges for an aircraft manufacturer in applying system health management technology to practical CBM and system life extension decisions. But he also stressed that many components and subsystems on modern commercial and military aircraft would be high-value areas for applying CBM and system health management approaches.
- Dr. Ball described the vision for ICME as developing both computational tools and experimental tools, then integrating these tools with information technologies, manufacturing-process simulations, and computer-based component design systems to develop and deliver optimized materials and manufacturing processes and to provide improved product performance at reduced time and cost.
- Prasun K. Majumdar addressed the challenges of SLP for composites again, going into even greater detail. He said that research on predicting the life of composites has been a moving target because the problems are more complex than for homogenous materials. His presentation brought out the complex interrelationships between the scale hierarchy for composite materials and the evolving material state as it affects service life.
- James A. Warren highlighted opportunities for MSA technologies stemming from the Materials Genome Initiative (MGI) and its goal of facilitating access to materials data. But he also raised “foundational issues” that he believes must be addressed if the full promise of the MGI is to be realized.
- Stephen Freiman picked up on the theme of uncertainty in MSA modeling, which Dr. Achenbach, Dr. Luscher, and Dr. Hemker addressed earlier in
