3D opportunity for assurance and parts qualification Additive manufacturing clears the bar

A Deloitte series on additive manufacturing 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Deloitte Consulting LLP’s Supply Chain and Manufacturing Operations practice helps companies understand and address opportunities to apply advanced manufacturing technologies to impact their ’ performance, innovation, and growth. Our insights into additive manufacturing allow us to help organizations reassess their people, process, technology, and innovation strategies in light of this emerging set of technologies. Contact the authors for more information, or read more about our alliance with 3D Systems and our 3D Discovery Center on www.deloitte.com. A Deloitte series on additive manufacturing About the authors

Ian Wing

Ian Wing is a senior consultant in Deloitte Consulting’s Strategy & Operations practice. He brings nearly a decade of experience solving complex challenges for federal and military custom- ers to bear on his client engagements. Wing’s prior experience was in research and development at a major defense and intelligence contractor, and he now focuses on helping clients realize supply chain efficiencies and develop new capabilities with additive manufacturing and other advanced technologies.

Rob Gorham

Rob Gorham is the director of operations for America Makes, the National Additive Manufacturing Innovation Institute at the National Center for Defense Manufacturing and Machining. Prior to joining America Makes, he was senior manager of the manufacturing exploration and development group at Lockheed Martin Aeronautics - Advanced Development Programs, also known as The Skunk Works. In this position, Gorham was responsible for leading the transition-focused develop- ment and application of affordable manufacturing technologies for Lockheed Martin.

Brenna Sniderman

Brenna Sniderman is a senior manager with Deloitte Services LP, affiliated with Deloitte’s Center for Integrated Research. Her research focuses on issues related to the application of advanced tech- nologies in manufacturing and their impact on growth.

iii 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Contents

Introduction | 1

Toward a solution: The AM Quality Pyramid | 4

Addressing the business challenges of quality assurance | 13

Raising the bar | 17

Endnotes | 18

Acknowledgements | 22

Contact | 22

iv A Deloitte series on additive manufacturing

Introduction

One of the most serious hurdles to the broad adoption “of additive manufacturing of metals is the qualification of additively manufactured parts.1

DDITIVE manufacturing (AM) produces Figure 1. Facets of AM quality Aobjects by layering materials such as ” metals, composites, or polymers to produce a three-dimensional part rather than, for example, machining parts from blocks of raw material, as with conventional manufactur- Guarantee quality 2 without direct input ing. However, while companies have widely Guarantee quality in any location, under any set of from the part’s explored AM’s potential to shrink the scale environment conditions designer and scope necessary for manufacturing, bring to life previously impossible designs, and alter the makeup of organizational supply Ensure repeatable 2 3 chains, several significant hurdles prevent its quality with the same wider adoption. printer, materials, and operators One of the most important barriers is the Recognize 3 qualification of AM-produced parts. So cru- situations in cial is this issue, in fact, that many characterize which quality is quality assurance (QA) as the single biggest 4 not or cannot be 1 guaranteed hurdle to widespread adoption of AM technol- ogy, particularly for metals.4 Put simply, many manufacturers and end users have difficulty stating with certainty that parts or products Graphic: Deloitte University Press | DUPress.com produced via —whether all on the same printer or across geographies—will be addressed for parts qualification—and AM’s of consistent quality, strength, and reliability. potential—to be more fully realized. Figure 1 Without this guarantee, many manufacturers summarizes the major facets. will remain leery of AM technology, judging In order to address the challenge of certify- the risks of uncertain quality to be too costly a ing quality for AM-produced parts along these trade-off for any gains they might realize.5 four facets, manufacturers can develop capa- QA presents a multifaceted challenge, bilities that will enable them to: encompassing both the scale and scope of production. Indeed, quality doesn’t just exist • Identify the level of QA their products on one dimension, and each area should be need, and what level of risk they are willing to assume

1 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

The roots of 3D printing go back nearly three decades. Its importance is derived from its ability to break existing performance trade-offs in two fundamental ways: First, AM reduces the capital required to achieve economies of scale; second, it increases flexibility and reduces the capital required to achieve scope.6

Capital versus scale: Considerations of minimum efficient scale can shape supply chains. AM has the potential to reduce the capital required to reach minimum efficient scale for production, thus lowering the manufacturing barriers to entry for a given location.

Capital versus scope: Economies of scope influence how and what products can be made. The flexibility of 3D printing facilitates an increase in the variety of products that a unit of capital can produce, reducing the costs associated with production changeovers and customization and, thus, the overall amount of required capital.

Changing the capital-versus-scale relationship has the potential to change how supply chains are configured, and changing the capital-versus-scope relationship has the potential to change product designs. These impacts present companies with choices on how to deploy AM across their businesses.

Companies pursuing AM capabilities choose between divergent paths (figure 2):

Path I: Companies do not seek radical alterations in either supply chains or products, but they may explore AM technologies to improve value delivery for current products within existing supply chains.

Path II: Companies take advantage of scale economics offered by AM as a potential enabler of supply chain transformation for the products they offer.

Path III: Companies take advantage of the scope economics offered by AM technologies to achieve new levels of performance or innovation in the products they offer.

Path IV: Companies alter both supply chains and products in pursuit of new business models.

Figure 2. Framework for understanding AM paths and value

High product change

Path III: Product evolution Path IV: Business model evolution • Strategic imperative: Balance of growth, innovation, and • Strategic imperative: Growth and performance innovation • Value driver: Balance of profit, risk, • Value driver: Profit with revenue and time focus, and risk • Key enabling AM capabilities: • Key enabling AM capabilities: – Customization to customer – Mass customization requirements – Manufacturing at point of use – Increased product functionality – Supply chain disintermediation High supply chain change – Market responsiveness – Customer empowerment – Zero cost of increased complexity

Path I: Stasis Path II: Supply chain evolution • Strategic imperative: Performance

No supply chain change • Value driver: Profit with a cost • Strategic imperative: Performance focus • Value driver: Profit with a cost • Key enabling AM capabilities: focus, and time – Design and • Key enabling AM capabilities: – Production and custom tooling – Manufacturing closer to point of use – Supplementary or “insurance” capability – Responsiveness and flexibility – Low rate production/no – of demand changeover uncertainty – Reduction in required inventory

No product change Graphic: Deloitte University Press | DUPress.com

2 A Deloitte series on additive manufacturing

• Accurately predict whether parts will meet the same level of QA. Thus, the same robust specifications when built under “idealized” approach may not be suitable for all organi- conditions zations. To help manufacturers consider the appropriate QA level, we illustrate a continuum • Ensure repeatability, consistency, and of options, with simple inspection on one reliability across different AM machines extreme and the complete pyramid on the and geographies other. By identifying their position along this spectrum, manufacturers can begin to under- • Incorporate the appropriate technolo- stand the level of QA their products require gies and capabilities necessary to qualify and, thus, recognize which strategies to use to AM-produced parts, based on the target qualify AM-produced parts. QA level Historically, most firms exploring AM technology fall into path I, using it largely Based on a review of the technical lit- for rapid prototyping and iterative design for erature, we have developed the AM Quality parts meant to be manufactured via AM.7 As Pyramid to provide an organizational schema additive technologies advance, opportunities through which to approach QA for additively for their use will continue to grow and may manufactured parts and to describe approaches eventually prompt a strategic shift to paths II, for accurately predicting quality pre-build, III, or IV. A needs-based promoting repeatability, and incorporating the approach is essential to any such shift, as the technologies necessary for QA. ability to qualify and certify parts remains In doing so, we argue that not all essential to moving to wider use of AM. AM-produced end-use parts will require

3 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Toward a solution The AM Quality Pyramid

ODAY, firms seeking to qualify the qualification process to which engineers TAM-produced parts generally apply the are accustomed: the development of a means same processes used for parts produced by to certify AM parts based on design, as well as traditional methods: namely, extensive non- observations and corrections made during the destructive and destructive testing of hundreds build process, rather than verifying perfor- of copies of the final part.8 This is expensive for mance after fabrication. any type of production; it also negates many To address the differences between AM of AM’s identified economic and operational and conventional processes, the science and engineering community is gravitat- ing toward an AM solution centered on three pillars: QA derived from Unlocking the full potential of build planning and build monitoring/ inspection, linked together with feed- AM may necessitate a reversal back control, described in table 1.11 Later in this paper, we discuss in detail of the qualification process to some of the “intelligent” machine con- trol methods in development meant to which engineers are accustomed. preemptively adjust build parameters to avoid situations that increase the risk of defects. advantages, which include low-volume or one- In addition, several supporting factors off printing.9 Thus, the prospect of printing underpin build planning, build monitoring, hundreds of parts, one by one, solely for testing and feedback control—and, by extension, can be daunting. effective QA schemas. These include enabling Still, some firms find AM’s demonstrated factors such as standards to guide the process, benefits so compelling that they pur- sue this process anyway, a testament Table 1. Key elements of quality assurance in AM to the value of 3D printing in many instances.10 To make this leap on a AM pillar Description wider scale, however, most organi- zations require a more sustainable, The use of advanced modeling and Build simulation to develop a plan for a machine feasible approach to qualifying and planning certifying parts. to produce a specific part Thus, a different methodol- Build Monitoring with sensors the build process as ogy—one taking AM processes into monitoring the part is being constructed account—may offer greater benefits. Using data from the build monitoring sensors Indeed, unlocking the full potential Feedback to iteratively update the build planning of AM may necessitate a reversal of control process in real time

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calibration, raw materials, and a build data accurate data about the design or process, the “body of knowledge” that enables manufac- pyramid cannot remain structurally sound. turers to catalog and leverage past experi- In this section, we explore current research ences. Deloitte has developed the AM Quality at each level of the pyramid, moving from Pyramid to capture these key elements of AM QA and map the ways in As manufacturers seek to qualify parts, which they inter- relate and build they must first understand and articulate upon each other (figure 3). what they are striving for. The ultimate goal—quality parts—rests at the apex. Directly supporting this goal are the top-down to illuminate various consider- key components necessary for successful QA: ations and approaches relevant to each, along build planning and build monitoring, linked by with illustrative examples of ongoing real- feedback control. Supporting these processes is applications. the third tier, consisting of the enabling factors. It is important to note that this paper does Finally, at the base rest information manage- not attempt an exhaustive review of research ment and information assurance, which under- literature related to the topic of parts quali- pin the entire QA process; without reliable, fication or QA for additive manufacturing.

Figure 3. The AM Quality Pyramid

Quality parts

Build planning Build monitoring Modeling & simulation In-situ sensing

Feedback control Standards Raw materials Build data body Calibration of knowledge

Information management

Information assurance

Graphic: Deloitte University Press | DUPress.com

5 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Rather, the intent is to provide a few illustrative Each of these factors depends directly examples of research in each area; several more on build process parameters, including raw detailed reviews are available on this topic.12 materials.14 Thus, controlling and assuring the build process—and, by extension, ensur- Starting at the top: ing uniformity among these three crite- Defining “quality” ria—figure strongly in the overall quality of a finished part. The most important questions of all must be addressed at the outset of any QA develop- Build planning: Increasing ment: What does “quality” look like, and how complexity, growing is it defined in relation to this particular AM process? Without an understanding of what data requirements constitutes quality in each particular case, it will be difficult, if not impossible, to develop a In most AM applications, a three-dimen- consistent approach to achieving it. sional shape is digitally sliced into thin layers, At the same time, however, quality will and a tool path15 is defined for each slice to cre- not—and should not—look the same for every ate the part layer by layer.16 Traditionally, part type of part and product. Quality exists on geometry has dominated tool path planning, a spectrum and is often contingent on the but achieving in AM involves intended use of the part; a 3D printed action greater command over parameters beyond figure is held to a different type of quality con- geometry. These parameters can include laser trol than a component on a fighter jet. In the power, laser scan speed, and build chamber former case, a difference of a few microns in temperature, to name a few. Each of these the geometry of the final part makes little dif- factors contributes to the outcome of a build, ference; in the latter, it could be a matter of life and aberrations in any could impact final and death. As manufacturers seek to qualify part quality. parts, they must first understand and articulate Advanced computational models, which what they are striving for. can simulate the physical phenomena associ- Furthermore, part quality is more than just ated with AM processes, are useful build-plan- the shape of the finished part. Fundamentally, ning tools. Computational models can predict quality is about a part’s ability to perform the how a part will behave in response to envi- task for which it was designed, while maintain- ronmental stresses. Consider the example of a ing structural integrity. Contributing factors passenger jet with engines mounted beneath are usually included in a part’s specifications the wings: Engineers can use simulation to and typically include geometry, surface finish, estimate the mechanical stress that the pull of and material properties (table 2).13

Table 2. Dimensions of quality

Quality dimension Definition

Geometry The shape of the finished part and how it will fit with other parts

Surface finish The desired smoothness, roughness, or other functional surface treatment of the finished part

Material A variety of attributes, including mechanical strength, stiffness, and fatigue life properties

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the engines will cause on the wings, without for developing build plans that adjust input conducting a physical experiment (figure 4). parameters dynamically to avoid defects and, For AM build planning, engineers seek to ultimately, guarantee quality. extend this computational approach to solv- Due to these simulations’ complexity and ing the multiphysics process of AM, which scale, most computational models are run in includes the mechanics of the part being built, high-performance computing facilities, as the the surface tension of the liquefied metal in wide range of data requirements increases the build area, and the way that heat from, for computational load to the point where it example, a laser is applied and dissipated.18 approaches current limits of high-performance And these data points represent merely the computing technology.21 Currently, some of the tip of the iceberg. Researchers cite more than most sophisticated models of AM processes are 130 variables for which designers may need to found at the US National Laboratories, which account in a fully representative simulation, possess massive computing capabilities.22 including a wide range of time and length Thus, availability of specialized computing scales.19 These phenomena are complex enough resources becomes a potential limiting factor to simulate individually; creating a concurrent for QA, as most companies lack ready access model that builds in multiple factors can make to this level of computing power. Fortunately, the process even more challenging.20 As with approaches to clearing this barrier may be the 3D printing process itself, every additional materializing: Computing power continues to factor adds another layer of modeling com- grow, and some commercially available simula- plexity. Yet this process represents a founda- tion and software solutions specifically geared tional area: Accurate simulations are essential toward AM in a production environment,

Figure 4. Results of a numerical simulation showing peak stresses on a passenger aircraft in flight, with stress concentrations near the engine mounts, a result of the engines pushing the plane forward.17

Image reproduced with permission from Dr. Yijun Liu, University of Cincinnati. Graphic: Deloitte University Press | DUPress.com

7 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

such as 3DSIM’s EXASIM, are emerging.23 The tools developed in-house—dependent on indi- TRUCHAS code, originally developed by Los vidual organizational needs.27 Alamos National Laboratory to model the cast- Proliferation of computing power has ing processes for nuclear fuel rods, provides a helped lower barriers to entry for complex trenchant example. TRUCHAS simulates many modeling and may even be setting the stage of the important parameters for modeling the for in-the-loop computing on the production AM process: solid mechanics, multiple types of floor. Given the enormous computing power heat transfer, and the changeover between solid requirements, it remains a challenge to use and liquid phases for a metal.24 It is available existing models in a production environment, via open source to anyone with the computing although these requirements may become power to use it. more realistic as computing power increases Aside from computing and data manage- and more solutions enter the marketplace. ment capabilities, engineers must also consider A recent National Academies of Sciences Predictive Theoretical & Computational Approaches for Additive Manufacturing Workshop prioritized the development of Researchers cite more than 130 “reduced order models,”28 which focus on dramatically reducing the computational variables for which designers intensity of simulations by making assump- tions to constrain them, and by leverag- may need to account in a fully ing already-existing libraries of similar, 29 representative simulation, catalogued features. Build monitoring: Measuring including a wide range of time the build process in real time and length scales. During the parts qualification process, AM build monitoring systems must docu- ment the build process to ensure specifica- tions are met.36 Several key sensing modalities modeling software and the code that enables can be used to measure relevant parameters, all calculations. Approaches include open-source of which are within reach with today’s technol- code,25 commercial software,26 and proprietary ogy (table 3).

Table 3. Examples of measurement modalities

Sensing modality Function

Measure vibration of the print head during fused deposition modeling and detect potential Accelerometers anomalies37

Ensure the final part is free of internal voids, an important capability since voids create stress Ultrasound sensors concentrations that can lead to premature part failure38

High-resolution Allows for near real-time inspection of parts in the build chamber39 photography

Thermal imaging Monitors size, shape, and relative temperature distribution of the melt pool40

Pyrometry (photodiode) Measures light intensity at a single point and correlates to temperature41

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ADDRESSING COMPUTING AND DATA CHALLENGES The physical challenges of guaranteeing quality for AM are substantial and widely accepted. However, equally important—and less often discussed—is the issue of data management. Both build planning and build monitoring add enough data to challenge today’s most advanced high-performance computers. The data requirements are, quite simply, staggering.

For example, the Accelerated Certification of Additively Manufactured Metals Initiative at Lawrence Livermore National Laboratory runs some of the most sophisticated models of the powder-bed fusion AM processes available today.30 In simulating the builds of relatively small and simple parts at only moderate resolutions, their supercomputer runs routinely produce outputs of hundreds of gigabytes of data, spread over hundreds of thousands of files.31 While the current volume of data is not that challenging, as part volume and simulation resolution both grow, data requirements will increase by orders of magnitude in the near future.

Video data from process monitoring drives even larger requirements. Berumen et al. describe a technique for monitoring the build process with full-frame video.32 To simultaneously capture the full chamber and provide sufficient detail to resolve the melt pool at a frame rate fast enough to keep up with the motion of the laser results in 1.5 petabytes (1500 TB) of data for the same six-hour build. This volume of data is roughly the equivalent of more than 57 years of high-definition streaming video.33

Scientists and engineers are innovating in both these areas to reduce data requirements. Leaders in the field of computational modeling for AM describe “reduced order models” which bound the problem and allow various shortcuts.34 Berumen et al. describe a creative solution to the data challenge around video monitoring: use of mirrors to image down the axis of the laser beam, providing a “tracking view” of the melt pool and dramatically reducing the data requirements.35 Although the resulting video data is considerably smaller, this approach still results in a 12.7 TB file for a six-hour build. In some cases, engineers will need to perform this process—with its attendant data requirements—for every part. Further adding to data load, organizations may need to maintain that data for a prescribed amount of time post-build, and be able to access and analyze it on demand.

In the future, a combination of data reduction techniques such as these, paired with steadily increasing computing power, will help open the door for real-time processing and feedback control.

Together or separately, these technolo- temperature and size over time, and proposes a gies can measure multiple aspects of a build. capability to use sensor information to docu- Of principal interest for powder bed-based ment the build process for applications with AM technologies, for example, is the size and stringent quality requirements.43 temperature of the melt pool, which has been It is important to note the criticality of demonstrated to drive microstructure, mate- sensor calibration for this type of measure- rial properties, surface finish, and overall part ment. Many factors govern the resolidification performance.42 Measuring the melt pool in process; put very simply, metals subjected to near real time can be accomplished with the different melt temperatures during the build combination of a calibrated digital infrared process will have different strength levels, camera and a photodiode sensor to measure which could ultimately impact function and the intensity of light. quality.44 To this end, another study from Such a technique is described in a 2010 the University of Texas’s Keck Center for 3D joint publication from CONCEPT Laser Innovation described development of a tech- and Katholieke Universiteit (KU) Leuven nique that complements the CONCEPT Laser/ in Belgium. This study imaged the melt- KU Leuven work: a physics-based method for ing/resolidification process with high-speed calibrating infrared cameras to ensure accu- video and a photodiode to estimate melt pool rate temperature measurements. The research

9 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

highlights the importance of proper camera the consistent geometries, surface finishes, and calibration in providing accurate monitoring material properties that underpin quality. during the build process, and argues that pre- Emerging examples of feedback control can cise temperature readings are essential.45 illustrate its impact: In one case, researchers Melt pool monitoring data alone is valu- at the University of Texas designed a system able. Measuring these parameters can be used to modulate laser power and scan rate46 based alongside other models to verify microstruc- on the temperature and size of the melt pool, ture and ultimately guarantee part specifica- adjusting power accordingly when these attributes changed.47 In a related effort, a team at Pennsylvania State University developed a system to measure tem- Maintaining control over build perature at the start of each layer. In the event that the temperature exceeded a processes enables manufacturers predetermined threshold at the planned start location, an algorithm adjusted to achieve the consistent the process to start building at a lower temperature location.48 And at least geometries, surface finishes, one commercially available AM system already offers feedback control, using a and material properties that thermal camera to measure laser power and scan rate in response to size, shape, underpin quality. and temperature of the melt pool.49 Additionally, we believe several major aerospace and defense firms are finding success in this area as well. tions or, conversely, to identify defects as An illustrative example of feedback control they occur and stop the build process early. also comes from the KU Leuven group, which However, a more effective application of such demonstrated the effect of simple feedback data would be to adjust input parameters in control based on the temperature of the melt real time when sensors detect non-ideal condi- pool, applied to a challenging build: closed tions—a process known as feedback control. overhangs.50 The results show a dramatic increase in the surface integrity of the over- Feedback control: Linking build hangs.51 (See figure 5.) planning and build monitoring As AM technology continues to develop, feedback controls should be tightly inte- It’s often not enough to detect anomalies. grated with multiphysics simulations used in Ideally, systems should be able to take action build planning. Instead of simply controlling to correct them. Feedback control refers to conditions to keep the melt pool at a constant the ability to detect build-plan deviations and size and temperature, sensing systems will automatically adjust systems to correct for report actual readings back to the simulation, them. Applying this capability to AM build which can then recalculate and prescribe an planning and build monitoring is crucial to updated build pattern in order to meet the achieving QA. Maintaining control over build desired specifications. processes enables manufacturers to achieve

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Figure 5. Experimental results demonstrating the effect of feedback control on a closed overhang with a length or diameter of 5MM—a particularly challenging AM application.52

Without feedback control With feedback control

Images used with permission from J. P. Kruth, P. Mercelis, et al., "Feedback control of selective laser melting," Katholieke Universiteit Leuven. Graphic: Deloitte University Press | DUPress.com

Supporting factors: published standards for the production of AM Underpinning the AM parts. The area is, however, evolving rap- Quality Pyramid idly. The American Society for Testing and Materials has designated a committee to define Supporting build planning and build moni- and issue standards for test methods, design, toring are multiple factors, including stan- materials, processes, environment, health and dards, engineering and management controls, safety, terminology, and potentially file for- and a build data body of knowledge. At the mats.55 Ideally, these standards will be applica- base of the AM Quality Pyramid, informa- ble in the near future across multiple machines tion management and information assurance and processes, to help maintain consistency in underpin and reinforce the structure. a variety of situations.56 Working with those who understand this evolving space can help Standards manufacturers get a handle on newly accepted Maintaining controls over the size, shape, standards and assess how to incorporate them and chemical composition of powders used in into existing AM approaches—or develop AM processes helps ensure consistent results, wholly new approaches instead. and standards are emerging to control these factors.53 Standards are also available for Calibration, maintenance, and raw destructive and non-destructive evaluation of material quality and handling finished AM parts.54 Adopters must develop detailed mainte- Despite these developments, as of October nance and calibration plans for equipment, 2015 there are no broadly recognized, as well as define guidelines for raw material

11 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

quality and handling. This represents an Institute, and the EU-based Standardization important competitive advantage; companies in AM are using collective knowledge to help performing well in this area may hold their drive standardization.58 practices close to the vest. Production man- agers should carefully consider these factors Information management as they develop and apply engineering and and information assurance management controls At the base of across the AM pro- the AM Quality duction environment, Pyramid, informa- perhaps incorporating Working with those tion management skills and processes and information they may already who understand this assurance enable have in place around the management process design and evolving space can of design/build documentation and information and error minimization, help manufacturers its protection from such as . unauthorized access get a handle on newly or tampering.59 The Build data body advancement and of knowledge accepted standards. proliferation of The “build data AM technology is body of knowledge” expected to drive refers to shar- considerable data ing detailed information about a variety of requirements, increasing data generation by build situations. In this way, all can learn orders of magnitude, as this process will need from collective experiences, advancing QA to be repeated for every part and the data must capabilities as a whole.57 Should one team be accessible for analysis on demand. Likewise, observe a process defect, sharing technical the data will need to be transmitted and information about that feature, process, and secured both in advance, to help prevent hack- result via a searchable database can help oth- ing designs and, afterward, to help prevent cor- ers avoid the same mistakes. Organizations ruption and/or loss. A manufacturer’s ability to such as America Makes, the US-based store, manage, and protect this information is National Additive Manufacturing Innovation likely to become an important differentiator.

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Addressing the business challenges of quality assurance

HE AM Quality Pyramid offers a com- than 10 microns.61 Failure here could mean Tplete vision for QA in AM: a point where, more than just a disappointing breakfast—it through constant and robust monitoring, might well be catastrophic. Thus, A&D will objects can be printed and certified, at a level likely need to move toward rigorous, feedback of dependability and quality comparable to control-based system over the next several that of conventional manufacturing. Over time, years; indeed, the sector is already relatively as AM technology continues to develop and more advanced than others in this area. proliferate, many manufacturers will find that The majority of parts and products will a robust QA schema similar to that of the pyra- likely fall between these two extremes, and mid is likely the approach best suited to their AM production needs. However, not all manufactur- ers have need for the same level of They should first ascertain where consistency, and not all parts war- rant the same vigorous level of QA. on the spectrum they fall by In these cases, a more modest QA scheme may be appropriate—as well developing a deep understanding as more cost-effective. As such, orga- nizations should choose what level of of the requirements underpinning QA is necessary for each part to help determine the most fitting approach. the need for QA, then develop

A spectrum of capabilities a means to achieve it in a

On one end of the spectrum, cost-effective way. some applications may require little to no QA. Consider the forthcom- ing PancakeBot, a relatively simple 3D printer that extrudes pancake batter onto manufacturers here have a more complex a hot skillet to create edible shapes.60 Even for choice to make. They should first ascertain the most exacting engineer, “close enough” for where on the spectrum they fall by develop- a dinosaur-shaped pancake may be enough to ing a deep understanding of the requirements pass muster. underpinning the need for QA, then develop Aerospace and defense firms, on the other a means to achieve it in a cost-effective way. hand, land on the much more stringent end Figure 6 depicts Deloitte’s view of the various of the QA continuum. The tolerance limit QA approaches based on the level of quality for aerospace manufacturers is typically less required for the end product.

13 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Figure 6. AM quality assurance continuum Example: Aerospace & technology parts

Example: PancakeBot™ HIGH

MEDIUM LOW

Principal QA tool and description Manual inspection: Visual or manual Auditable process control: QAAM pyramid: Advanced measurement of finished parts and Rigorous testing of a part printed modeling, sensing, and feedback comparison against specifications under known conditions, control work together to guarantee Mechanical testing: Testing of parts quantification/codification of those the quality of any part, on any under laboratory loading conditions to conditions, and traceable, auditable machine with the capability to print it design load (non-destructive) or to reproduction of those conditions or Result: Guaranteed quality for almost failure (destructive) other printers any part, or rejection of build plan up Result: Individual parts pass/fail Result: All parts pass as long as front if it cannot be built desired conditions are maintained Business enablers

Investment in existing test and Creation of auditable manufacturing Significant investment in R&D to inspection technology process, enabled by manufacturing IT develop modeling, sensing, and Training of workforce in traditional Robust protocols to manage calibration feedback control capabilities T&E methods Integration of sensing technologies to Marriage of high-performing Low QA requirements or non-critical verify compliance computing with manufacturing application Information assurance becomes Supported by enablers (see pyramid) important Information management (10s–100s of TB) and information assurance are critical Business outcomes

Path 1: Can continue to produce Path II: Can apply audit in remote Paths II, II, IV: Ability to produce tooling, fixtures, and limited locations to guarantee quality and almost any part and guarantee quality production parts allow supply chain evolution allows fundamental changes to the Path III: Can qualify novel business model, both in terms of products, but cannot assure quality product and supply chain at scale or scope

Graphic: Deloitte University Press | DUPress.com

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Low QA requirements: Taking (building stopgap measures for keeping dam- a conventional manufacturing aged equipment running while awaiting more approach to QA permanent repairs). However, it is important to consider that manufacturers at this level In an environment with minimal or low may find it difficult to achieve scale by -per QA requirements—such as, again, custom- forming exhaustive inspection and testing of shaped pancakes—a simple “eyeball” check every part. Furthermore, firms operating at might be enough to declare that a product this level of QA may not realize more extensive has met standards. In situations with slightly supply chain benefits of AM, as they will be more demanding requirements, engineers may unable to certify any parts manufactured under require measurements or mechanical testing— different conditions.

In fact, detailed process control of production operations is already fairly common for quality programs; adding AM represents a new layer on what is, for many, a well-established practice.

a simulation of loading conditions in a labora- Medium QA requirements: tory—to verify performance. An audit-based approach Inspection technology like this is widely available today and is already regularly used Organizations with the need for stricter in a variety of industries. Implementation QA considerations that still fall short of full may thus prove a lower barrier, provided that implementation of the highly technical ele- manufacturers invest in both training and ments of the AM Quality Pyramid can con- equipping their workforce, and that customers sider taking some conventional approaches to will tolerate the result. Additionally, due to its developing and testing a part. They can then relative ease of implementation, this can prove codify the results of those tests into a “recipe”: a useful interim approach for manufactur- a detailed build procedure and prescription ers as they develop more a robust, long-term of build conditions that, through experience, QA approach. have been shown to produce a part that passes Organizations focusing on the least strin- inspection. During the actual build process, gent quality levels may find themselves limited audit techniques can be applied to document in their applications of AM. This sort of that “recipe” conditions are met, regardless approach may be best suited for manufacturers of where the part is built. This level of QA within path I, using 3D printing for tooling, implementation relies on the creation of an rapid prototyping, and other indirect applica- auditable manufacturing process, and could tions. Some firms can also consider adopting require detailed calibration management pro- low QA requirements within path III (develop- cedures and integration of sensing technolo- ing and certifying new products with a high gies. Adopting an audit-based QA solution, tolerance for variability) or within path II and applying that audit capability in remote

15 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

locations to guarantee quality, can enable and capabilities in place that will allow for data firms to more fully leverage path II, supply collection, storage, protection, and analysis. chain evolution. Many manufacturers may be well posi- High QA requirements: Scaling tioned to adopt this level of QA relatively the AM Quality Pyramid rapidly. Leading aerospace and defense firms already apply tightly controlled and docu- Manufacturers with the strictest QA mented processes to ensure quality on flight- requirements, such as those in the aerospace critical parts.62 In fact, detailed process control and sectors, may require a of production operations is already fairly full application of the AM Quality Pyramid: advanced build planning and monitoring capa- bilities, linked together with feedback control. This can help certify the quality of any part, on Guaranteeing quality on any machine with the capability to print it, in any location. any part anywhere also Internal investment in R&D will likely be essential to developing high-level QA capa- unlocks both dimensions bilities; due to the competitive advantage it typically creates, early leaders may be tight- of demonstrated lipped about their techniques. To address the supporting factors described in the AM AM value: product Quality Pyramid, manufacturers may need to integrate high-performance computing and evolution and supply manufacturing. Information management also becomes critical, as single builds can result in chain evolution. 10s to 100s of terabytes of data. While initial investments may be high, however, they can pay strong dividends. The ability to fully certify AM-manufactured parts, and thus reliably common for quality programs; adding AM print them on demand, anywhere, will likely be represents a new layer on what is, for many, a a significant competitive advantage. well-established practice.63 Guaranteeing quality on any part anywhere At this level, manufacturers may begin to also unlocks both dimensions of demonstrated see an exponential increase in data require- AM value: product evolution and supply ments due to the level of monitoring and the chain evolution. In this way, manufacturers potential inclusion of supply chain partners can explore path IV: evolving supply chain in the QA process. (See sidebar “Addressing with distributed manufacturing, developing computing and data challenges.”) Thus, advanced new products previously impossible information management and assurance—the to create through conventional methods, and bottom level of the Quality Pyramid—grow in potentially creating new operating models. importance. Indeed, firms should make sure they have information management strategies

16 A Deloitte series on additive manufacturing

Raising the bar

S additive manufacturing continues to status not only during the build but well Aadvance and moves beyond rapid proto- beforehand, via simulations and modeling. typing into development of truly innovative This can enable manufacturers to avoid the parts and more efficient supply chains, it is often costly and inefficient process of having important for manufacturers looking to take to test copies of a part post-production, an fuller advantage of AM’s potential to find a approach ill suited to AM. Given the current way to ensure the parts they produce can be state and availability of high-performance of consistent and reliable quality. To do so computing required for this sort of approach, effectively, manufacturers should consider the it will be crucial to consider what plans—or following actions: partnerships—should be in place in order to Evaluate the level of QA needed for each make progress. part. Not all parts or products will need the Understand the data management chal- same level of scrutiny. Even within a single lenges that may lie ahead. QA can require end product, manufacturers may find that strong data management and access to high- one part—such as a hinge or bracket—can performance computing. It will be important be held to a lower QA standard, while others, to determine what can be accomplished inter- such as engine parts for the same aircraft, will nally and whom to partner with to develop need to pass the most stringent specifications. these skills. Assessing the level of QA actually needed can When developing a plan for developing help ensure that manufacturers do not under- QA capabilities, assess not only where you prepare—and also that they do not over-invest are but where you wish to be. Manufacturers in QA technologies that they may not need. residing in path I whose long-term strategy Consider adhering to lower standards on involves supply chain evolution may wish to the AM QA continuum in the interim while concentrate on QA approaches that enable developing capabilities to enable a more the ability to print across wide geographical stringent QA process in the long term. Rome distances. Conversely, those whose long-term wasn’t built in a day. Planning, building, and strategy focuses more on leveraging AM implementing a full QA process such as the to manufacture entirely new products may AM Quality Pyramid takes time, training, and instead focus initial QA efforts on other tech- investment in new technologies, processes, nologies. Taking a strategic approach to growth and talent. Manufacturers can consider using helps develop a QA process that enables and approaches that fall lower on the QA con- supports business goals. tinuum as a stopgap measure while develop- As manufacturers look to adopt AM, ing more in-depth strategies. This may also creating a clear map to assuring consistent ease the implementation process internally, quality will remain a significant challenge. A as it gives an organization time to gradually systematic approach to quality assurance can incorporate and acclimate to new processes help additive manufacturing continue to reach over time. its potential. Take a full view of the production process. With AM, it will be important to understand

17 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Endnotes

1. Rose Hansen, “Building the future: Modeling 11. Billy Short, “Quality metal additive and uncertainty quantification for accelerated manufacturing (quality made) enabling certification,” Science and Technology Review, capability,” Office of Naval Research, July January/February 2015, https://str.llnl.gov/jan- 28, 2015, slideplayer.com/slide/6642475/, uary-2015/king, accessed November 5, 2015. accessed November 5, 2015. 2. For a full discussion of these dynamics, 12. Gustavo Tapia and Alaa Elwany, “A review see Mark Cotteleer and Jim Joyce, 3D op- of process monitoring and control in metal- portunity: Additive manufacturing paths to based additive manufacturing,” Journal of performance, innovation, and growth, Deloitte Manufacturing Science and Engineering, 136, Review 14, January 2014, http://dupress.com/ no. 6, October 24, 2014; William E. Frazier, articles/dr14-3d-opportunity/, or visit http:// “Metal additive manufacturing: A review,” dupress.com/collection/3d-opportunity/ to Journal of Materials Engineering and Perfor- view the full 3D Opportunity collection. mance 23, pp. 1917–28, April 8, 2014, http:// 3. T. AlGeddawy and H. ElMaraghy, “Product lib2013.mirdc.org.tw/temp/cisc_news/144/ variety management in design and manufac- Metal%20Additive%20Manufacturing-%20 turing: Challenges and strategies,” Enabling A%20Review.pdf, accessed November 5, 2015. Manufacturing Competitiveness and Economic 13. Frazier, “Metal additive manufacturing.” Sustainability, September 2011, pp. 518–23. 14. Ibid. 4. US Government Accountability Office 15. “Tool path” refers to the path a cutting or report to the chairman, Committee on shaping tool makes through space or material Science, Space, and Technology, House of as it produces a desired part, product, or shape. Representatives, 3D printing: Opportunities, challenges, and policy implications of additive 16. American Society for Testing and manufacturing, June 2015, www.gao.gov/ Materials, Standard terminology for ad- assets/680/670960.pdf; National Institute of ditive manufacturing technologies, www. Standards and Technology, “Measurement astm.org/Standards/F2792.htm. science for additive manufacturing program,” 17. Yijun Liu, “Fast multipole boundary October 1, 2013, www.nist.gov/el/isd/sbm/ element method (FastBEM) software for msam.cfm; both accessed November 5, 2015. education, research and further develop- 5. US Government Accountability Office ment,” http://urbana.mie.uc.edu/yliu/ report to the chairman, Committee on Software/, accessed November 6, 2015. Science, Space, and Technology, House 18. Wayne King et al., “Overview of modelling of Representatives, 3D printing. and simulation of metal powder bed fusion 6. See Cotteleer and Joyce, 3D opportunity. process at Lawrence Livermore National Laboratory,” Materials Science and Technol- 7. For further information about uses of AM ogy 31, issue 8, June 2015, pp. 957–68, www. in manufacturing approaches, see Mark maneyonline.com/doi/full/10.1179/17432847 Cotteleer, Jonathan Holdowsky, and Monika 14Y.0000000728, accessed November 6, 2015. Mahto, The 3D opportunity primer, Deloitte University Press, March 6, 2014, http://dupress. 19. Deepankar Pal et al., “An integrated approach com/articles/the-3d-opportunity-primer- to cyber-enabled additive manufacturing the-basics-of-additive-manufacturing/ using physics based, coupled multi-scale process modeling,” proceedings of the 8. Hansen, “Building the future.” Solid Freeform Fabrication Symposium, 9. Cotteleer and Joyce, 3D opportunity. August 12–14, 2013, http://sffsymposium. 10. Hansen, “Building the future.” engr.utexas.edu/Manuscripts/2013/2013- 01-Pal.pdf, accessed November 6, 2015; Hansen, “Building the future.”

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20. D. Hu, M. Labudovic, and R. Kovacevic, 30. Hansen, “Building the future” “Modelling and measuring the thermal be- 31. Interview with Neil Hodge, solid mechan- haviour of the molten pool in closed-loop ics/code developer, Methods Development controlled laser-based additive manufacturing,” Group, Lawrence Livermore National proceedings of the Institution of Mechanical Laboratory, November 3, 2015. Engineers, Part B: Journal of Engineering Manufacture 217, no. 4, pp. 441–52, April 32. Sebastian Berumen et al., “Quality control of 1, 2003, www.researchgate.net/publica- laser- and powder bed-based additive manu- tion/245386050_On-line_sensing_and_estima- facturing (AM) technologies,” Physics Procedia tion_of_laser_surface_modification_by_com- 5, pp. 617–22, 2010, www.sciencedirect. puter_vision, accessed November 6, 2015. com/science/article/pii/S1875389210005158, accessed November 6, 2015. 21. Wayne King et al., “Overview of modelling and simulation of metal powder bed fusion process 33. Assumes 3GB per hour for full high-definition at Lawrence Livermore National Laboratory.” video, per https://help.netflix.com/en/node/87. 22. Top 500 Supercomputing Sites, www.top500. 34. USNC/TAM, “A Workshop on Predictive org/lists/2015/06/, accessed November 6, 2015. Theoretical and Computational Ap- proaches for Additive Manufacturing.” 23. Mark William Schraad and Marianne M. Francois, “ASC additive manufacturing,” 35. Sebastian Berumen et al., “Quality control Los Alamos National Laboratory, June 8, of laser- and powder bed-based additive 2015, http://permalink.lanl.gov/object/ manufacturing (AM) technologies.” tr?what=info:lanl-repo/lareport/LA- 36. H. Krauss, C. Eschey, and M. F. Zaeh, UR-15-24605, accessed November 6, 2015. “Thermography for monitoring the selective 24. Ibid. laser melting process,” proceedings of the Solid Freeform Fabrication Symposium, 25. Carolin Körner, Elham Attar, and Peter Heinl, August 12–14, 2013, http://sffsymposium. “Mesoscopic simulation of selective beam melt- engr.utexas.edu/Manuscripts/2012/2012-76- ing processes,” Journal of Materials Processing Krauss.pdf, accessed November 6, 2015. Technology 211, no. 4, pp. 978–87, June 2011, www.scribd.com/doc/48415551/Korner-Meso- 37. Prahalad K. Rao et al., “Online real-time scopic-simulation-o-selective-beam-melting- quality monitoring in additive manufactur- processes#scribd, accessed November 6, 2015. ing processes using heterogeneous sensors,” Journal of Manufacturing Science and 26. I.A. Roberts et al., “A three-dimensional Engineering 137, no. 6, September 9, 2015. finite elements analysis of the temperature field during laser melting of metal pow- 38. Hans Rieder et al., “Online monitoring of ders in additive layer manufacturing,” additive manufacturing processes using ultra- International Journal of Machine Tools and sound,” 11th European Conference on Non- Manufacture 49, issues 12–13, July 2009. Destructive Testing, October 6–10, 2014, www. ndt.net/events/ECNDT2014/app/content/Pa- 27. 3DSIM, “Technology,” http://3dsim.com/ per/259_Spies.pdf, accessed November 6, 2015. technology, accessed November 6, 2015. 39. Stefan Kleszczynski et al., “Error detection 28. USNC/TAM, “A Workshop on Predictive in laser beam melting systems by high Theoretical and Computational Ap- resolution imaging,” proceedings of the proaches for Additive Manufacturing,” Twenty Third Annual International Solid October 7–9, 2015, http://sites.nation- Freeform Fabrication Symposium, August alacademies.org/PGA/biso/IUTAM/ 2012, http://sffsymposium.engr.utexas.edu/ PGA_168737, accessed November 6, 2015. Manuscripts/2012/2012-74-Kleszczynski. 29. For further information regarding how use pdf, accessed November 6, 2015. of collaboratively developed databases can 40. Sebastian Berumen et al., “Quality control build bodies of knowledge and ease analysis of laser- and powder bed-based additive practices without increasing data load in manufacturing (AM) technologies.” other types of AM assessments, see Beth McGrath, Jaymes Hanna, Runze Huang, 41. Ibid. and Amar Shivdasani, 3D opportunity for life cycle assessments: Additive manufactur- ing branches out, Deloitte University Press, October 16, 2015, http://dupress.com/articles/ additive-manufacturing-in-lca-analysis/.

19 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

42. Corey Dunsky, “Process monitoring in laser 51. J. P. Kruth et al., “Feedback control of additive manufacturing,” Industrial Laser selective laser melting,” Katholieke Uni- Solutions for Manufacturing, September 12, versiteit Leuven, https://lirias.kuleuven. 2014, www.industrial-lasers.com/articles/ be/bitstream/123456789/185342/1, print/volume-29/issue-5/features/process- accessed November 6, 2015. monitoring-in-laser-additive-manufacturing. 52. Ibid. html, accessed November 6, 2015. 53. American Society for Testing and Materials, 43. Sebastian Berumen et al., “Quality control Standard Standard guide for character- of laser- and powder bed-based additive izing properties of metal powders used for manufacturing (AM) technologies.” additive manufacturing processes, www. 44. Makhabo Ramosoeu et al., “Additive astm.org/Standards/F3049.htm. manufacturing: Characterization of TI- 54. American Society for Testing and Materials, 6AI-4V alloy intended for biomedical “New guide for nondestructive testing of addi- applications,” South African Institute of tive manufactured metal parts used in aero- Mining and Metallurgy, October 2010. space applications,” www.astm.org/WorkItems/ 45. Emmanuel Rodriguez et al., “Approximation WK47031.htm, accessed November 6, 2015. of absolute surface temperature measure- 55. American Society for Testing and Materials, ments of powder bed fusion additive Committee F42 on Additive Manufacturing manufacturing technology using in situ Technologies, www.astm.org/COMMITTEE/ infrared thermography,” Additive Manu- F42.htm, accessed November 6, 2015. facturing 5, pp. 31–39, January 2015. 56. Ibid. 46. “Scan rate” refers to the speed, typically measured in times per second, 57. National Institute of Standards and Technology, at which an imaging device is able to Measurement science for additive manufactur- sample and capture its line of vision. ing Program, October 1, 2013, www.nist.gov/el/ isd/sbm/msam.cfm, accessed October 1, 2015. 47. Jorge Mireles et al., “Automatic feedback control in electron beam melting using 58. Ibid. infrared thermography,” proceedings of the 59. While much of the conversation has focused on Twenty Fourth Annual International Solid inadvertent errors as the root cause of quality Freeform Fabrication Symposium, August lapses, it is important to note that business 2013, http://sffsymposium.engr.utexas. leaders must also consider deliberate defects edu/Manuscripts/2013/2013-57-Mireles. introduced for purposes of sabotage. This issue pdf, accessed November 6, 2015. is of particular concern in the highly connected 48. Abdalla R. Nassar et al., “Intra-layer closed- manufacturing infrastructure required for loop control of build plan during directed AM at scale. To begin addressing this chal- energy additive manufacturing of Ti–6Al–4V,” lenge, the National Institute for Standards Additive Manufacturing 6, March 2015. and Technology hosted a symposium in February 2015, finding that existing guidance 49. EOS International, “Quality assurance in failed to adequately address cybersecurity additive manufacturing,” www.eos.info/ in AM and calling for proposed guidance. quality-assurance, accessed October 11, 2015. 60. Miguel Valenzuela, “PancakeBot—the 50. “Closed overhang” refers to a feature supported world’s first pancake printer!.” Kickstarter, on two edges, with an unsupported gap located March 1, 2015, www.kickstarter.com/proj- in the center. This is a particularly challeng- ects/1853707494/pancakebot-the-- ing feature for powder-bed AM systems, first-pancake-printer, accessed October 16, as the un-solidified powder beneath the 2015; also see Kim Porter et al., 3D op- overhang dramatically reduces the ability of portunity serves it up, Deloitte University the in-process part to dissipate heat from the Press, June 18, 2015, http://dupress.com/ laser. Thus, as the overhang is being built, articles/3d-printing-in-the-food-industry/. it is prone to “over baking” by the laser.

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61. John Coykendall et al., 3D opportunity for aerospace and defense: Additive manufacturing takes flight, Deloitte University Press, June 2, 2014, http://dupress.com/articles/additive- manufacturing-3d-opportunity-in-aerospace/. 62. National Aeronautics and Space Ad- ministration, “Standard materials and processes requirements for spacecraft,” July 11, 2008; download at https://standards. nasa.gov/documents/detail/3315591. 63. Bell Helicopter, “Flight safety, primary, and critical parts,” www.bellhelicopter.com/ en_US/Suppliers/ProductionDevelopment/ FlightSafetyPrimaryandCriticalParts/ Flight_Safety_Primary_and_Critical_Parts. html, accessed November 6, 2015.

21 3D opportunity for quality assurance and parts qualification: Additive manufacturing clears the bar

Acknowledgements

The authors would like to acknowledge several other contributors, who provided assisted in various ways throughout the process of developing this paper.

Dr. Mark Cotteleer of Deloitte Services LP for his leadership in the development of the 3D Opportunity series of articles, including this piece.

Dr. Wayne King and Dr. Neil Hodge, both of the Accelerated Certification of Additively Manufactured Metals Initiative at Lawrence Livermore National Laboratory, for lending their expertise around numerical simulation of additive manufacturing processes.

Kagan Yaran, Joseph Vidalis, Ed Dobner, and Kevin Hom, of Deloitte Consulting LLP for conducting an initial literature review.

Contact

Mark J. Cotteleer Center for Integrated Research Research director Deloitte Services, LP +1 414 977 2359 [email protected]

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