Overview of Materials Qualification Needs for Metal Additive Manufacturing
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This overview highlights some of the key aspects regarding materials qualification needs across the additive manufacturing (AM) spectrum. AM technology has experienced considerable publicity and growth in the past few years with many successful insertions for non-mission-critical applications. However, to meet the full potential that AM has to offer, especially for flight-critical components (e.g., rotating parts, fracture-critical parts, etc.), qualification and certification efforts are necessary. While development of qualification standards will address some of these needs, this overview outlines some of the other key areas that will need to be considered in the qualification path, including various process-, microstructure-, and fracture-modeling activities in addition to integrating these with lifing activities targeting specific components. Ongoing work in the Advanced Manufacturing and Mechanical Reliability Center at Case Western Reserve University is focusing on fracture and fatigue testing to rapidly assess critical mechanical properties of some titanium alloys before and after post-processing, in addition to conducting nondestructive testing/evaluation using micro-computerized tomography at General Electric. Process mapping studies are being conducted at Carnegie Mellon University while large area microstructure characterization and informatics (EBSD and BSE) analyses are being conducted at Materials Resources LLC to enable future integration of these efforts via an Integrated Computational Materials Engineering approach to AM. Possible future pathways for materials qualification are provided.
While AM is also increasingly being explored for the development of new products, variation in the part quality and mechanical properties due to inadequate dimensional tolerance, presence of defects, surface roughness, and residual stress can limit its use in high-value or mission-critical applications. Various roadmap efforts have been conducted for AM4,5 as well as updates regarding the status of research needs for qualification and certification.6,7 The recent review of the metal AM process by Frazier8 highlights the drastic variability in the various stages in process that produces multiple challenges for the development of qualification standards. Other recent reviews9,10 dealing with qualification/certification highlight additional needs.
The current overview will focus on the direct metal laser melting (DMLM; e.g., EOS) and electron beam melting (EBM; e.g., Arcam) powder bed processes since they are viewed as the most successful direct metal AM processes in the automated building of high-quality shapes. Our objective is to summarize ongoing work and issues as well as provide an update to the previous work11 that has demonstrated location- and orientation-dependent properties on Ti-6Al-4V that are affected by microstructure variations and process-induced defects in as-deposited material. Although the as-deposited materials reported in that published work11 exhibited some properties (i.e. fatigue crack growth, fracture toughness) approaching those of some cast/wrought materials, industrial input from both the aerospace and biomedical communities indicate the desire to use as-deposited materials in various applications where high cycle fatigue (HCF) properties are also critical. To produce functional orientation-dependent properties (e.g., HCF, toughness) required by both industries, the source(s) of process-induced defects and microstructure spatial heterogeneities must first be understood and then manipulated by control of the AM process(es). This will facilitate minimization and/or elimination of various costly post-processing techniques (e.g., heat treatment, hot isostatic pressing, etc.). The alternative is using energy-intensive and costly processes to achieve the qualification of each part, which is practiced widely today in the industry for AM-processed metallic components.
Global Activities, Special Journal Issues on AM
List of journals special issues on additive manufacturing
Date of publication
Journal of Materials Research (JMR)
Journal of Manufacturing Science and Engineering (JMSE)
NIST Journal of Research
Materials Science and Technology (MST)
End of 2015
Surface Topography: Metrology and Properties
Annual Review of Materials Research
International Journal of Fatigue
Industrial Examples of Evolving and Potential AM Applications
Need for Multi-Scale Integrated Computational Materials Engineering (ICME) Approach
While AM processing of a part can be conducted all within one company (from CAD to the final product), qualification of such a part for use in civilian or military applications can require the collection of a large amount of labor-intensive processing-, microstructure-, and property-based information. In many cases, these must be obtained from many different organizations that must then make the information available to pave the path for eventual qualification and certification. However, there are many potential intellectual property hurdles in both the generation and analysis of such data. One approach that can provide the necessary infrastructure to accelerate qualification and certification involves ICME,25,26 as outlined below and captured in Fig. 2. In the following sections, the individual components highlighted in Fig. 2 will be covered, followed by a discussion indicating the need to integrate such efforts via an ICME approach.
AM Alloys, Processes, and Equipment
The range of metals available for use in AM continues to grow as new technologies and applications emerge. Currently, the most common metallic materials are steels (tool steel and stainless), pure titanium and titanium alloys, aluminum casting alloys, nickel-based superalloys, cobalt-chromium alloys, gold, and silver.8 To utilize the full potential of AM, alloy development specifically for AM processing will require further attention. Although preliminary work on new AM materials is underway,27 a number of challenges remain including contamination issues, chemistry control during the melting process and solidification cracking, amongst others. Resulting AM parts must meet desired specifications for chemistry, surface roughness, damage tolerance, fatigue, strength, and other properties that may be sensitively affected by subtle changes to the chemistry and/or resulting microstructure and defect population. Currently, such understanding is not widely available due, in part, to a lack of detailed understanding of the processing–structure–property relationships, insufficient testing, lack of shared knowledge and materials test results across the AM community, and lack of standardized test methods for AM materials.12
The selection of an AM material is highly dependent on the AM process that will utilize the material. Some of the most common AM processes employed for metallic materials are powder bed fusion (PBF), directed energy deposition (DED) and wire-fed AM.8 DED potentially has the capability to build parts with gradient materials (combination of two or more powders). Other metal-based processes in use, but not as common, include material jetting and ultrasonic bonding. Most AM systems melt the metal materials and produce parts with close to 100% density, with some baseline properties (e.g., tensile strength) that can match or exceed those of a cast part.8
While much progress has been made in the development of AM processes and equipment, some significant challenges remain for more widespread implementation. Improving product quality with respect to surface roughness and/or residual stresses, increasing the efficiency of production, gaining the ability to rapidly produce larger and more diverse parts, and lowering production costs are a few of the important challenges that are directly impacted by processing techniques. In addition, one of the biggest challenges relates to barriers associated with accessing and/or sharing information on the details of various AM machine setup parameters that prevents users from overriding machine-preset processing conditions, thereby preventing optimization of these conditions. Unfortunately, in many cases, overcoming these barriers requires approaches that are unique to the type of processes, equipment, and materials employed.
Process Control and Process Mapping
Feed Stock, Variables (Power, Velocity, Hatching, etc.), Scan Strategies, Build Orientation
Determining the properties of the powder used for metal-based AM, as well as the properties of the solidified metal part, is a necessary condition for the industry to be able to confidently select the powders and produce consistent parts with known and predictable properties. A number of projects sponsored by America Makes28,29 are addressing the use/reuse of powder as well as powder flow characteristics. The project involving Carnegie Mellon University (CMU), North Carolina State University (NCSU), and a large number of industry and government laboratory collaborators29 is seeking to broaden the range of powders useable in laser and electron beam powder bed processes to allow machine users to balance constraints on cost and part precision through their choice of powder systems. That project has recently demonstrated the successful use of two nonstandard Ti-6Al-4V powder systems in the Arcam machine through process variable changes.
In Situ Process Monitoring and Feedback Control
In situ process monitoring and control are one of the key evolving areas that could impact qualification and certification of parts where quality demands are extremely high, such as aerospace and medical devices. The ability to produce multiple parts consistently across machines, operators, and manufacturing facilities could require the integration of various process monitoring and measurement tools. While careful and consistent process control can limit variability, the lack of adequate process measurement methods hinders more widespread use of the technology. Currently, process control based on heuristics and experimental data has only yielded limited improvements in part quality. As documented in a recent NIST report, traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of AM processes.41
Variability in AM machines and/or beam source/material interactions can create inconsistency in the microstructure, presence of defects, and variability in the mechanical performance. While in situ monitoring has been used in the past for DED, its use in the PBF processes is challenging due to limited access to the melt pool to enable imaging which is further complicated by the higher process velocity (Fig. 6). Recent attempts on process monitoring of PBF focused on defect detection and melt pool characterization that can be interrogated in situ. Melt pool monitoring is being used to capture the distribution of temperature within deposited layers in order to generate a heat map that can be compared to melt pool size.42,43 However, high bandwidth data acquisition (e.g. 1000 s of Hz) and processing can generate enormous datasets that are difficult to manage while also requiring enormous amounts of data storage (e.g., 1000 s of GB). Nonetheless, various machine OEMs have begun to deploy process monitoring options for their equipment. One example, Concept LaserTM, provides a quality assurance monitoring system (e.g., QMmeltpool 3D) that intends to detect processing defects at early stages, thereby providing a means for process optimization.42 In the next generation of machines under development, one of the desires is the retention of x–y location information for all data throughout the build, thereby permitting the generation of a high-resolution 3D rendering of sensor signals. Ultimately, this may provide a quality assurance diagnostic similar to non-destructive testing (NDT) akin to computed tomography. Comparable systems are also being provided by other vendors such as ArcamTM (LayerQam), SLM SolutionsTM, and EOSTM. As indicated in recent reviews,41 while process monitoring is necessary and has received significant interest, this technology for PBF is still in its infancy.
In the research community, feedback control approaches are being pursued at CMU in collaboration with significant efforts on process sensing. For the LENS process, indirect control of microstructure through thermal imaging and control of the melt pool is being pursued with Penn State and Stratonics through the linking of observable melt pool dimensions to the formation of microstructural features. Thermal control of the Arcam process is being implemented through thermal imaging of the entire build after layers are fused via an infrared system installed at the University of Texas at El Paso. CMU-developed process maps are being used to prescribe in-process changes in beam power and/or beam velocity to compensate for changes in average temperature in the build, yielding consistent microstructure sizes with part height. LLNL44 is also leading various modeling and simulation activities including melt pool monitoring.
Microstructure and Defect Characterization/Quantification
Various works have shown that electron beam AM of Ti-6Al-4V parts with a preheated powder bed produce solidification rates and beta grain sizes consistent with that shown in Figs. 5 and 7, depending on the P–V combinations employed. However, the finer scale microstructural features post-solidification also depend on cooling rate as well as any thermal transients that occur during multi-layer deposition that may produce a basketweave microstructure with alpha-lath thicknesses less than 1 µm. In contrast, laser-based techniques without preheated powder beds produce more rapid solidification and cooling rates in the solid state, along with the observation of martensitic microstructures37 in the as-deposited condition. In the case of electron beam AM processing where the solidification/cooling rates are somewhat slower, the alpha laths (hcp) are produced via phase transformation of prior beta (bcc) upon cooling below the beta transus (~1000°C), while the morphology and crystallography of the prior beta grains are dependent on the cooling rate and build direction.38,45 As such, electron backscatter diffraction (EBSD) techniques are often used first to capture the crystallography and morphology of the room temperature alpha phase, followed by reconstruction of the prior beta phase using the Burger orientation relationship between alpha and beta,46 as explained below.
Large Area BSE/EBSD, Macro/Micro Texture Evolution
Practical microstructure characterization requires designing the inspection volume to be large enough to capture relevant statistics about the feature of interest (FOI)45 at a high enough resolution for the lowest possible cost (i.e. least recording time). In AM parts made of Ti-6Al-4V by electron beam techniques, one of the major FOI is the high-temperature beta grain size. However, the beta phase has a small volume fraction at room temperature,45 which makes it difficult/expensive to record using EBSD. Alternatively, the alpha phase is recorded at room temperature, and the beta phase is then reconstructed from the alpha phase using the Burger orientation relationship [TiBor @ www.MiCloud.AM], although the inspection volume must be selected carefully as demonstrated below.
In order to address this and illustrate the importance of scan area, our EBSD data generation has been designed to satisfy two conditions: (1) high enough resolution to record alpha orientations from thin laths and (2) covering an area that is large enough to record data from many prior beta grains for reliable statistics, recognizing that each beta grain can be multiple millimeters in size depending on the AM process and P–V conditions employed. While many modern scanning electron microscopes can record EBSD scans from large areas (e.g., 5 mm × 5 mm) at high resolution (e.g., 1 μm step size), analyzing large files (e.g. 25,000,000 alpha orientations) is a challenge for many commercial software running on standalone PCs. To address this challenge, many tools were developed within MiCloud.AM to conduct data analytics on Big EBSD datasets (as large as 100,000,000 orientations covering 10 mm × 10 mm areas at 1 μm resolution) using various data mining techniques and cloud computing.
Figure 9a and b clearly shows that both the morphology of the prior beta and texture of the alpha phase are unreliably presented in commonly used 500 µm × 500 µm EBSD scans for AM Ti-6Al-4V parts, in contrast to the large EBSD scans shown in Fig. 9b. In particular, analyses of the pole figures of the alpha phase obtained from a 500 µm × 500 µm region showed much sharper texture intensity (~13–21 times random) than has been recorded by 150,000 µm × 10,000 µm scans (~2 times random) (Fig. 9b). In addition, the locations of various texture components were drastically different.
Mechanical Property Measurements (Fracture/Fatigue)
One of the key aspects for qualification of AM parts/components is the mechanical performance that requires a wide range of mechanical testing/characterization. Measurement science for the AM industry to determine material properties in a standardized way has gathered a significant amount of interest over the last 3 years.41,47,48 Currently, there are no consensus-based public standards in this area, except for a few examples related to terminology and data file formats.49,50 The ASTM International Committee F42 on Additive Manufacturing Technologies and the International Organization for Standardization (ISO) TC261 Committee on Additive Manufacturing have started separately, and in some cases jointly, standard development to address this deficiency.
Most recently, America Makes co-sponsored an event51 to help coordinate U.S. standards development activities for AM. Key standards developing organizations (SDOs), including ASTM, SAE, ASME, SME, AWS, etc., along with a number of OEMs, gathered to discuss and facilitate collaborative efforts with the goal of initiating a dialogue on joint standards development for AM. These activities are being viewed as one mechanism that can facilitate product qualification and certification. For example, aero engine parts could be certified by FAA while biomedical parts could be certified by FDA. The overarching goal of these coordination efforts is to produce a roadmap that will minimize the amount of overlap activities across the various standardization organizations.
Non-destructive Inspection/Evaluation (μCT) of Defect Distribution
Microstructural/Mechanical/Defect Interaction or Competition
Probabilistic Modeling of Fracture and Fatigue
While the goal of producing defect-free parts in as-deposited materials remains an area of extreme interest, inspection processes (e.g., x-ray tomography, UT, acoustics, etc.) and various post-processing techniques (e.g., HIP, heat treatment, etc.) may continue to need to be implemented for use in fracture-critical applications. However, non-critical locations (i.e. low stress, strain) in fracture critical parts may not require the same damage tolerance/properties required in highly stressed areas (Fig. 2). There has been increased use of probabilistic methods for design assessment of reliability with inspection (DARWIN)62 for a variety of processing techniques (e.g., casting, forging, heat treatment, etc.) to deal with local variations in microstructure/defects/properties in lifing estimates. Programs such as DARWIN and others have been shown to reduce significantly computation time compared to other methods (e.g., Monte Carlo). In addition, proper sampling strategies can be employed to achieve a desired sampling accuracy result for a given confidence interval, thereby focusing on variables that should have the most effect on risk reduction.62 While probabilistic approaches have been used successfully in previous work for predicting thresholds for cracking in commercial aluminum alloys,63,64 such approaches would also appear to be useful for modeling/predicting the effects of changes in AM process variables on subsequent performance as well as modeling location-specific properties.
Microstructure Informatics, Modeling and Simulation—An ICME Approach
Due to the complicated nature of the AM process, a coherent integration between various stages and scales of modeling the materials behavior and the corresponding measurements is critically needed within the framework of the Materials Genome Initiative (MGI)65, 66, 67 and ICME.25,26,68, 69, 70, 71, 72, 73, 74 In particular, AM will benefit from the ICME goal of enabling optimization of materials, manufacturing processes, and component design long before components are fabricated by integrating computational processes involved into a holistic system. Developing and implementing such a system will enable a more efficient qualification process using big data science approaches.71,72
As a demonstration of the practical implementation of an ICME approach, the team involved in this effort integrated efforts from academia, OEMs, and a small business all working on various projects with different funding sources. However, all members recognized the value of collaboration and integration of results obtained using various tools. To enhance such a collaboration and integration of efforts, three major elements were identified: (1) a part that could be produced, (2) datasets to be collected, and (3) a platform to consolidate all the data for analysis using state of the art data-mining algorithms. In addition, an efficient workflow45 is needed to promote transparency and efficient collaborations between materials experts and manufacturing/design specialists by providing an understanding of the various mesoscale heterogeneities that develop naturally in the workpiece as a direct consequence of the inherent heterogeneity imposed by the AM process.
While each team member was responsible for one or more of the items above, the communication was led by CWRU under various non-disclosure agreements that allowed the exchange of information while respecting the Intellectual property (IP) of each team member. To avoid any issues with proprietary designs, the team also selected to demonstrate such collaboration on rectangular coupons. Each team member maintained the details of characterization procedures while sharing the inputs and outputs for each process with the team. The shared data were provided in standard formats that are common in the public domain, such as ASCII files and images. A practical implementation of such a workflow has been deployed at www.MiCloud.AM as the first microstructure-based AM software-as-a-service (SaaS). It is designed on three main pillars: (1) data science protocols for efficient analysis of large datasets, (2) protocols for extracting reduced descriptions of salient microstructure features for insertion into simulations (e.g., regions of homogeneity), and (3) protocols for direct and efficient linking of materials models/databases into process/performance simulation codes45 (see Fig. 2).
In the initial phases of this collaboration, data generated/provided to CWRU were then imported to MiCloud.AM for data mining. The results of microstructure informatics and data mining (i.e. data products) were then shared with the rest of the team towards establishing correlations for qualification. Such a data flow is made possible via using MiCloud.AM with multiple functionalities including Big Data storage, analytics, and visualization capabilities.
Development of accurate AM modeling and simulation tools is an important fundamental building block for an ICME approach for AM. The availability of good validated physics-based modeling and simulation tools decreases the need for experimental testing of technologies and processes and gives product designers a predictive capability to optimize part designs. Accurate models are also vital for developing the required control technologies and software for AM, developing standards, and establishing qualification/certification procedures.
The accuracy of predictions using various simulation tools is heavily dependent on the availability of comprehensive data on materials and deposition processing for calibration, validation, and verification processes. While there have been many recent attempts to develop such models,75 the complicated nature of the AM process affect the current accuracy of predictions for developing comprehensive simulation-based qualification tools. Furthermore, to increase the accuracy of predictions, these tools require a better understanding of the fundamental processes and physical phenomena that underlie AM feedstock inputs, approaches, and technologies which are not possible without generating and analyzing a large amount of data and sharing them among multiple collaborating companies as described herein.
Statistically-based legacy qualification processes for metallic materials require extensive testing that may cost millions of dollars and take up to 15 years to complete.76 This approach is not practical for qualifying AM parts that are known for drastic variability in processes and processing parameters within each process. On the other hand, model-based qualification requires a smaller number of tests to validate the model. However, the rapid and complicated AM process adds many challenges to developing physics-based models with repeatability and reproducibility of predictions across varying processes and process parameters. The proprietary nature of process controls that are imposed by commercial machine manufacturers drastically reduces the availability of data needed to calibrate and validate models that are necessary for the model-based qualification.
Recently, Dave Abbott of GE77 demonstrated the success of GE in certifying the GE9X T25 Sensor and the LEAP Fuel Nozzle without the need for new qualification standards. While this approach is very promising and an excellent example for parts that are produced in mass production, it will be crucial to establishing its implementation on small(er) batches of other AM parts.
In the realm of components or repairs produced by direct deposition of Ti-6Al-4V, AMS standard 4999A78 prescribes certain conditions for feedstock composition, atmosphere, and post-processing (i.e. HIP and/or heat-treatment) to be used in production, as well as minimum standards for tensile and fracture toughness properties in finished products and standardized acceptance testing procedures for each production run (consisting of composition, mechanical properties along and perpendicular to the build direction, microstructure and surface contamination, and NDT by ultrasound and radiography) along with re-test and rejection criteria. The standard also provides for a qualification pathway consisting of several stages: (1) source qualification, including >50 tensile test results from each principal build direction (i.e. X, Y, Z) covering 3 different configurations and 3 different feedstock heats and meeting specified property and variation limits; (2) approval of deposition or deposition/geometry parameters, with sufficient tensile and strain-controlled fatigue test coupons produced to cover the space of processing and geometrical parameters in the component, including deposition power and feed rate, feedstock, deposit height, width, and length, inclination angle, scan strategy, intersection angle and orientation, etc.; and (3) production qualification by destructive testing of one or more production-level parts by >12 tensile tests in each direction. Upon qualification, each of the production parameters is fixed, with any deviations requiring additional testing.
The qualification procedure prescribed in AMS4999A is a classic example of statistically-based qualification, wherein the uncertainty in the production of a particular component is understood and mitigated by massive upfront testing, followed by ongoing quality control testing during production. It is very similar to the procedure that has long been used for aerospace castings,79 where any other than very minor deviation from the qualified procedure triggers a re-qualification process. While such a procedure is suitable for serial production of numerous identical parts (such as the fuel nozzle mentioned above), it represents a high barrier for production of customized, repair, and low-volume components where AM techniques are often most desirable, and demonstrates a clear need for holistic, ICME-based qualification schemes that encompass pre-process, in-process, and post-process data to facilitate demonstration of part suitability according to a “qualify as you go” paradigm.80
Summary and Future Trends
This article is provided as an attempt to capture an overview of the various challenges to be considered in the qualification of metal AM. These include the need for various modeling and experimental activities, along with the integration of such efforts at the size and length scales relevant for intended applications. In addition, a proposed example of multi-organization collaboration towards addressing some of the qualification challenges was demonstrated via an implementation of an ICME approach via BigData analytics and cloud computing.
This work was partly supported by America Makes, the National Additive Manufacturing Innovation Institute, under Project No. 4009: ‘‘Rapid Qualification Methods for Powder Bed Direct Metal Additive Manufacturing Processes’’ through Contract No. FA8650-12-2-7230 and it is highly appreciated. Additional support was provided by two ASTM International Scholarship Awards (M. Seifi) and the Armington Professorship (J. J. Lewandowski). Various discussions with academic team members as well as industrial partners and government laboratories during monthly webinars are appreciated. These include four other university partners (NCSU, CMU, U of L, and WSU), five industrial partners (Lockheed Martin, Pratt & Whitney, GE, Kennametal, and Bayer) and two government laboratories (ORNL and NIST). M. Seifi appreciates various discussions with ASTM F42/E08/E07 committee members. A. Salem and EBSD work are supported under a NAVAIR SBIR “Adaptive Microstructure-Based Approach for Rapid Qualification by Similarity of Ti-6Al-4V Parts Manufactured by Additive Manufacturing (AM) Techniques” under Contract No. N6833515C0209. The help of Dr. Daniel Satko of MRL in generating large-scale EBSD data is appreciated. The tomography analysis shown in Fig. 13 was conducted by Ms. Whitney Yetter at GE Inspection Technologies while the tomography analysis shown in Fig. 14 was conducted by Mr. Daniel Rankin at YXLON, a division of COMET Technologies, Inc.
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