Most people probably remember the TV show CSI: Crime Scene Investigation. One of the more popular “police procedural” television franchises in the recent past, CSI: (and its similarly named spin-off shows) differentiated itself by giving the crime scene’s evidence the starring role. Much of the drama in the show centered on how it painstakingly built the investigation process – understanding the “how” and the “why” – instead of focusing on the “what”. Rather than relying on intuition, investigators pieced together the physical evidence throughout the program, leading to witnesses, suspects, motives, locations, and timelines. Often, high-tech instruments would be used in the crime lab to analyze things like blood traces or bullet casings. Reports would be written which would then be used in the arrest and conviction of the perps, or conversely, to exonerate the wrongly-accused.

While real life crime scene forensic analysis may not always be as glamorous/gory as it’s depicted in Hollywood, distinct parallels can be drawn between the procedures involved in dissecting a crime scene and those used in component failure analysis. In fact, the term Forensic Engineering is used to describe the investigation of physical failures. And Forensic Materials Engineering categorizes the methods used and processes followed to study why structures and materials fail and who (or what) could have been at fault. As they say, it’s usually not good enough to determine when something broke (i.e. “number of fatigue cycles to failure”); the most interesting information comes as answers to the questions of how it broke (failure mechanisms) and why it broke (external conditions). A thorough failure analysis can answer all of these questions and more.

When manufacturing companies receive broken components from their end users (known as “field returns” or “field failures”), those components will usually go through a level of laboratory process very similar to a crime scene investigation. All available evidence will be collected, documented and analyzed, using both high-tech and low-tech instruments (tabletop light microscopes for metallography, scanning electron microscopes [SEM] for fractography, chemical analysis, etc.). Traditionally, these tests can be time-consuming, and as with any CSI time is of the essence (as well as efficiency and accuracy). Finally, reports will be produced to justify the conclusions made from the analyses. If a company is well-integrated, these failure reports will act as feedback to the design and materials teams on the front end of product lifecycle management (PLM). This would hopefully point out flaws that may have been missed during the original development activities, and bring about continuous improvement in the product. The insights can better inform the qualification testing testing procedures, so that reliability can be more realistically assessed. However, this feedback process can also take a lot of time because the field failures first have to occur, be reported, be adequately analyzed, and then results have to make their way to the proper teams in the organization. Wouldn’t it be more effective for there to be a way to perform this analysis beforehand, and help make the product better before it even has a chance to fail?

VEXTEC’s VPS-MICRO® software functions as a computational forensic engineering tool. By inputting component design and material information, you can virtually perform field tests of your parts under many different circumstances and conditions, with varying material capabilities. When the simulations complete, the output answer is not simply the predicted number of cycles to failure (the typical result of the prevalent fatigue estimation software packages around), but it also provides the entire digital trail of evidence that led to that failure. Each of the images below represents fatigue crack damage emanating from an initial defect; physical evidence on the left, and a computational simulation output of the damage from VPS-MICRO on the right. The physical evidence can show you where something failed, and after enough in-depth analysis, can give an idea of fatigue crack growth rate (da/dN). The simulation results on the right are even more informative, providing details such as crack growth (a vs. N), grain individual orientation, localized stresses, and microcracking. These simulation outputs capture the variability inherent in fatigue testing, and give quantitative evaluation of the likelihood of such events.

You can investigate the initiation mechanisms of the damage (whether they be from non-metallic defects, rogue large grains in the microstructure, surface roughness, etc.), and follow the damage right through to failure. The locations, motives, timelines, and suspects of component failure are all studied in the virtual realm, at a fraction of the time spent in physical test labs, reducing risk to end use customers as well as risk to important company metrics like PLM and warranty budgets. Just as “persons of interest” can be vindicated and set-free by the physical evidence that is found and properly processed, the predictive models used in VPS-MICRO can validated by comparison to results from a limited amount of physical testing. Once this verification and validation (V&V) activity is complete, the software can be used for a multitude of product design trade studies (geometrical effects, material second sourcing, manufacturing process changes). The evidence is still the star of the show here, and VEXTEC has added another high-tech resource to the CSI engineering toolbox!

The largest time chunks in any product’s life cycle are in the design and engineering phases. This is because there are questions that need to be answered, both in how the product will perform and how the product itself will be made. Each of these parallel design inquiries are rooted in materials science, which at its basic level employs the evaluation and application of a material’s physical properties to make engineering decisions. Over the last 3 decades, shifts to digitization by the design and manufacturing worlds have contributed to the year-over-year advancements in the design loop. The use of finite element analysis (FEA) has revolutionized the way companies attack their structural designs; workstation processor speeds and high-end graphics cards have kept pace to give engineers in-depth knowledge of how their components react to service loads. In manufacturing, robots can repeatedly perform intricate machining, welding, or even build full components in the case of Additive Manufacturing (3-D Printing). However, the lynchpin between these design and manufacturing sectors, materials science, has remained a mostly analog endeavor. Relying on testing, measurement, and analysis, materials science has been necessarily slower in comparison.

The pivot to predictive, analytics-driven strategies is well underway in many industries and is certainly bearing fruit. In healthcare, forests full of patient information on paper have been digitized into electronic health records (EHRs), and health trends are now being predicted with astonishing accuracy. Even in the (slightly) less academic world of internet searches, aggregation of data (search terms, geographies, times of year, among others) reveals a very accurate picture of seasonal illness trends around the globe. The retail sector’s “loyalty card” programs may offer discounts for shoppers, but the habitual data received in return is much more valuable. Manufacturing’s use of the Big Data concept of IoT- Internet of Things (adapted to the Industrial Internet of Things – IIoT, a.k.a. Industry 4.0) is giving plant managers actionable data for improving production rates. Suddenly, the “necessarily slow” process of materials science has become a “cripplingly slow” bottleneck. The drumbeat for progress is persistent from all critical industries (aerospace, automotive, energy, medical devices, etc.). It is inevitable that the products and production methods of the future will demand a quantum leap in materials science. This leap will be facilitated by three main aspects: fundamental changes to materials science education, increasing reliance on desktop prototyping, and the maturation of Additive Manufacturing.

Materials Science Education

Using systematic numerical modeling to analyze and solve complex mechanics problems has been the basis of FEA techniques since their initial development in the late-1960s. The underlying math (algebraic matrices and differential equations) had been around for much longer than this, but the computational capability for solving anything more than the most basic geometries was realized only in the latter-half of the 20th century. It really is amazing how fast FEA has become entrenched in the design process for most industries, but not completely surprising given the amount of attention being paid to it at the university level. As FEA is a natural extension of math and computer science, you will often find entire courses in these disciplines being devoted to the finite element method. These courses place particular emphasis on using software as an assisting tool to visualize the problem (structural analysis, fluid dynamics, etc.). Mechanical engineering students are now required to have at least introductory-level knowledge of this method and one of its fundamental tenets: not every location on a component sees the same stress. On the other side of campus sits the materials science department. Aspiring engineers taking introductory materials courses are told to neglect the reality that materials are not homogeneous and isotropic (the same everywhere), and are also exposed to the physical tests used to assess material properties. Unlike their experience with finite element methods, students’ takeaway from materials science is that it is some kind of “black box” with parameters that are difficult to quantify; only upper-level students would understand. This type of thinking must change, as materials science undergoes the digital transformation necessary to keep up with industry.

Desktop Prototyping

Desktop prototyping goes hand-in-hand with FEA-assisted design. Engineers can rapidly assess the effects of loading components in different ways, or the effects of putting the same loads on components with different geometries. A vanguard of new technologies to digitize materials science is approaching critical mass; these form the basis of Integrated Computational Materials Engineering (ICME). VEXTEC’s VPS-MICRO® software is an ICME tool that efficiently marries quantifiable microstructural characteristics with FEA-supplied stresses, to visualize and predict the durability of a component, or even a system of components. Indeed, these material properties are not the “black box” many engineers imagine, nor are they the single deterministic values that are presented to them on material specifications and lot certification reports. These properties are now being leveraged computationally, to achieve efficient production rates and improved end-user performance. The efficient linkage of all of these digital methods to virtually prototype from “cradle to grave”, will give engineers and other decision-makers enormous capability in many aspects of their business (design, sustainment, warranty outlay, and supply chain, just to name a few).

Additive Manufacturing

Arguably, the two most prominent manufacturing buzzwords of the last 5 years have been “Additive Manufacturing” (AM). Companies have been investing heavily in these types of 3-D printing technologies that build components layer by layer, so much so that it has quickly become the third major manufacturing method for metallic components next to conventional forging and casting processes. Earlier this year, VEXTEC’s blog highlighted this shift in manufacturing, and how our VPS-MICRO® technology plays a key role in durability certification for AM. The benefits of additive are obvious: production of near-net shapes with intricate geometries, in controllable volumes with very little waste. However, unlike forging and casting, the materials science related to AM is not yet well-established. Industries are racing to find effective means of qualifying AM components, because the last thing anyone needs is a critical part made by AM to fail when it was not expected to. But the lure of the “on-demand production” that AM offers, much like “on-demand” taxi services like Uber and Lyft in the transportation service industry, will necessarily disrupt and pull materials science into the digital age.

Companies who embrace the analytical digitization of materials science will see outstanding returns both in the near-term and long-term, with technologies that can take full advantage of insatiable consumer demands, and with engineers who are better-equipped to adapt to those demands.

The inaugural Additive Manufacturing Benchmarks (AM-Bench) event was held last month (June 18-21) at the headquarters of the National Institute of Standards and Technology (NIST) near Washington, D.C. The benchmark tests are a set of ongoing experiments, designed to enable modelers to evaluate and compare their simulations with rigorously-accumulated physical data. These activities will greatly help the AM industry as a whole, by helping to define a path for widespread adoption of these manufacturing methods. I was privileged to represent VEXTEC at the event, where we were both a main sponsor and a technical contributor.

Our presentation on June 19 was entitled “Integrated Computational Materials Engineering to Quantify the Effect of Uncertainty in Microstructure on the Fatigue Performance of Additively Manufactured Parts”. The researchers (both in metals and polymers) presented on Benchmark Class 1 testing, which focuses on modeling the AM process parameters and the resultant microstructural and mechanical properties. VEXTEC’s simulation software VPS-MICRO® extends the simulation to the in-service durability of metallic components. The upcoming Benchmark Class 3 testing’s objective aligns with our software’s capabilities, so we are excited to see the near-future needs of AM designers and modelers being addressed by our technology!

It was great to meet with so many people collaborating to make AM more viable in the future. As VEXTEC is based in Nashville, Tennessee, I also distributed nearly 250 treats from a local Nashville candy company: the infamous GooGoo cluster! If you haven’t had the pleasure of trying one of these, make your way to Nashville and I’ll personally buy one for you…they’re THAT good.

 

My thanks to NIST as well as The Minerals Metals & Materials Society (TMS) for their hosting and organization of the week-long event.

 

A turbocharger client of ours wanted to improve durability and reduce warranty costs on cast wheels made from a nickel superalloy with a radially-solidified (RS) microstructure. A significant portion of their previous field failures had been attributed to high cycle fatigue (HCF). Our client already had ideas about how to reduce these HCF failures by changing the wheel’s microstructure to an equiaxed (EQ) morphology. General representations of RS and EQ microstructures are shown here.

Since design changes like these would require a hefty amount of physical validation testing, they needed a way to predictively quantify the costs/benefits to the product line, should some of these proposed changes be implemented. So they turned to VPS-MICRO®. The VPS-MICRO simulation platform combines structural finite element analysis of the component (FEA, seen below) with a 3-D spatial model of the material’s microstructure to predict component durability risk. It is a probabilistic framework, accounting for variability in microstructure and strength properties, applicable damage mechanisms, and usage over time.

The primary inputs to VPS-MICRO are the design input file (stresses from the FEA and the corresponding stressed area in terms of elemental surface area) and the material input file. VEXTEC has developed plug-ins to extract the design input information from several commercial FEA software programs. The material input file contains all the relevant material properties of the component, from macro-scale to the microstructural level. These properties include those you would normally find in an FEA analysis (material modulus and Poisson’s ratio), but also microstructural properties such as grain size, population density of variously-sized inclusions/defects, and grain-level strength and energy parameters. It is the inherent variability of these microstructural properties that is a key factor of component-level fatigue life variability. The good news is that these properties can be statistically evaluated using industry standard (ASTM) tests.

Now, back to our client’s specific issue. Their RS material microstructure was originally developed to resist the onset of damage at high temperatures. The likelihood of initiating damage is low due to fewer grain boundaries. However once damage initiates, the failure probability goes up because there aren’t as many grain boundaries to arrest crack growth. Their proposed design change, using an equiaxed (EQ) microstructure instead for the turbocharger wheels, was thought to be more damage tolerant. The likelihood of initiating damage would be higher, but so would the opportunity for fatigue crack arrest (more grain boundaries). Using VPS-MICRO, our client was able to pursue a quantitative assessment of the risk of HCF failure versus grain type (radially-solidified vs. equiaxed), before any re-designed wheels were even produced or tested.

Shown below is the VPS-MICRO simulated fatigue life comparison of the current-state RS wheel, and the proposed EQ wheel (baseline average grain size = 2.7 mils). The comparison results are presented using a simulated S-N (Stress-Life) plot. The figure shows considerable variability at each stress level for both materials. Run-outs (the points on the right marked with arrows) are predicted at every stress level. A “run-out” means the simulated specimen did not fail within the number of cycles analyzed. These results indicate the RS wheel would have a lower endurance (fatigue limit) compared to the baseline EQ wheel. Generally speaking, this would seem to indicate that the EQ material is better than the RS material. These results appeared to correlate with published industry reports.

Because the wheel was a casting, there is an expected grain size variation throughout the part. Our client’s quality group thought they could maintain the EQ grain size between 1.7 and 3.8 mils, but acknowledged that sizes as high as 15 mils could occur. So they used VPS-MICRO in a different way: to evaluate the sensitivity of grain size to the risk of wheel failure. Their virtual analysis revealed that EQ wheels are not always better than RS wheels.  The figure below shows that failure probability is low for small EQ grains, but is very sensitive to grain size.  At a grain size of 15 mils, the EQ wheel is actually more likely to fail than the RS wheel (which has an average grain size of 87 mils). Probability of failure is not as sensitive to grain size for the RS wheel. Did the reversing trend make sense?

VEXTEC and our turbocharger client investigated this relationship between the grain size and HCF failure risk. After analyzing the output of the VPS-MICRO simulations, we determined that competing failure mechanisms were present:

• The area effect: it takes more small-sized grains to fill a given surface area compared to fewer, larger grains. A smaller average grain size means a statistically-higher probability of having a weaker grain in a given area. This is analogous to the “weakest link theory“, where increasing the number of links in a chain increases its probability of failure. This explains why larger grains are producing fewer failures compared to small grains (the downward trend of the figure above).
• The grain-level strength effect: as the grain size increases, an initiating fatigue crack has a larger size as well. These larger-sized starter cracks are more likely to grow (with minimal arresting) to final failure. Therefore, the local strength properties of the grains become key gatekeepers to either prevent or allow these cracks to propagate from their initial sizes.

The final conclusions reached by our client, with the assistance of VPS-MICRO, were

• Using EQ material (2.7 mils) would reduce turbocharger wheel HCF failures by at least 60%
• Not all EQ materials are equal; small changes in grain size yield large changes in durability
• The probability of wheel failure was not as sensitive to grain size for the RS material
• Replacing RS material with EQ material requires significantly-tighter production control

Our client could now make a more-informed decision about the proposed design change (producing and testing the EQ wheel). They knew they would have to cast the wheel in a production environment to capture realistic variations, and to assess their capability to hold tighter tolerance on grain size than what was previously required on the RS wheel.

We’ve said it before, and we’ll say it again:

The topic of corrosion makes recurring appearances in the media; it seems that when you hear about one corrosion-related problem, invariably there will be others reported-on at around the same time. There has recently been a spate of articles confirming that corrosion is currently a headache to the oil and gas sector (undersea bolt failures), as well as to the aviation sector (corrosion-induced fatigue of turbine engine blades in the new Dreamliner aircraft). Oftentimes these stories are first published by financial-leaning news outlets (Wall Street Journal, CNN Money, Bloomberg), a result of the high visibility and cost that these incidents bring in terms of replacement and downtime to their respective industries. Enough of these stories circulating over the span of a few news cycles will make any investor wary, and will prompt questions on what is being done from a regulatory standpoint to restore confidence in companies’ operations. This is particularly true when these reports of corrosion failures have impacts (real, or perceived) on public and environmental safety.

Of course, corrosion is not a new phenomenon. We have been observing the process of corrosion for centuries in our manmade structures, and have developed ways to physically mitigate its effects (painting, inspection methods, et cetera). However, it has only been in recent history that we a) have deeper understanding of the electrochemical processes that describe corrosion, and b) have the industrial engineering prowess to design and build ever greater machines and superstructures that help make modern life possible (economically-available energy sources and air travel, being prime examples). The confluence of these two factors drive the need for more development of mechanistic approaches to corrosion mitigation, through the use of computer-assisted modeling and simulation.

To that end, more and more resources are being appropriated for the research of these corrosion mechanisms in many of the materials that are used today. For example, members of the LIFT Consortium (Lightweight Innovations for Tomorrow) have begun work on the development of new models and a material properties database that will allow for more accurate simulations of corrosion in aluminum alloys used in aerospace and other transportation sectors (focusing on aluminum alloys containing copper, lithium, magnesium, manganese, and zinc). The materials database will be characterized to such a degree so that precise information is obtained about the interaction between microstructure and corrosion. The team will begin with the characterization of the industry’s workhorse alloys, and then extend work to evaluate newer alloys crated using various manufacturing techniques. The goal is to mitigate corrosion in a broad spectrum of aluminum alloys through improved simulator capabilities.

However, only half of the equation is being studied by LIFT: the corrosion impact on metals…with no discussion of how that corrosion introduces damage states, from which stress corrosion cracking and other types of corrosion-fatigue can arise. VEXTEC has pioneered development of a software for the U.S. Navy that predicts the statistical distribution of stress corrosion cracking in an alloyed aluminum microstructure that has been exposed to a corrosive environment. This software serves as a basis for all types of materials that are impacted by corrosion: the material modelers can provide the inputs of the corroded damage states into the VEXTEC software, which will in turn simulate the result of in-service loading on the durability of the critical structures of interest.

Until such time as corrosion has been completely removed as a mechanism in a critically-stressed component (and that time is not approaching anytime soon), it isn’t enough to just model the corrosion characteristics…we must also be able to effectively model the subsequent damage growth throughout the component’s service life.