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ASTM E3353-22
Standard Guide for In-Process Monitoring Using Optical and Thermal Methods for Laser Powder Bed Fusion
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STANDARD published on 1.7.2022
The information about the standard:
Designation standards: ASTM E3353-22
Publication date standards: 1.7.2022
SKU: NS-1084187
The number of pages: 29
Approximate weight : 87 g (0.19 lbs)
Country: American technical standard
Category: Technical standards ASTM
Annotation of standard text ASTM E3353-22 :
Keywords:
additive manufacturing, build chamber conditioning monitoring, defects, in-process monitoring, laser powder bed fusion, laser power monitoring, layer imaging, machine conditioning monitoring, machine learning, melt pool monitoring, melt pool signatures, process signatures, process signature taxonomy, statistical process control (SPC),
Additional information
| Significance and Use |
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4.1?Metal additive manufacturing has broadened design space, enabling production of more complex and customized products. Additive technology along with the broadened design space is pushing the limits of inspection capabilities and has led to challenges in process and product qualification, verification, certification, etc. In-process monitoring technologies have been developed to help address these challenges. 4.2?In-process monitoring in AM is emerging from the realm of Research and Development (R&D). As such, there are not yet well-established procedures for incorporating AM process monitoring within a qualification or certification framework outside of a specific company or institutions internal use. Practical application of in-process monitoring data spans multiple disciplines and parts of the production cycle, each with well-established practices, terminology, expectations, etc. This guide draws on these where appropriate. 4.3?Inspection and Statistical Process Control (SPC)A primary motivation for using in-process monitoring technologies is to aid in process and product qualification, verification, certification of AM components that are increasingly difficult to inspect. AM process monitoring functions can be broadly separated into two categories of application: in-process inspection and process control. In-process inspection refers to the identification of in-process signatures that correlate to the formation of physical flaws and defects in additively manufactured component. This is discussed further in 5.2 on Flaw Detection. Statistical Process Control (SPC) encompasses measurement or observation of process signatures or metrics associated with the stability or repeatability of the additive manufacturing process. This is discussed further in 5.3 on Statistical Process Control (SPC). Real-time feed-forward or feed-back control methods and techniques may be considered subcategories under process control, and can make use of the same in-process monitoring measurement tools. Currently, these concepts and techniques are still largely under research and development not generally implemented in commercial LPBF systems. They are not discussed further in this guide. 4.4?Production and Development UsesProduction of finished components using additive manufacturing requires some combination of inspection to ensure the component meets design requirements for the ultimate product functionality and process qualification. Both inspection and process control applications of in-process monitoring may be integrated into an overall product or process qualification, verification, or certification strategy, or a combination thereof, in the production environment. In-process monitoring tools are also valuable in the development both of the additive process and build design, providing support for engineering decisions on parameter selection (for example, laser power, scan speed) for new materials, scan strategy, part geometry, part placement on an AM build platform, etc. A prerequisite to SPC is establishing the normal variation of the process which can be evaluated using in-process monitoring tools during process development. 4.5?Economic JustificationIn-process monitoring can be economically justified through its contribution to cost reduction and yield improvements in addition to its value to the additive manufacturing enterprise as an element of an overall process or product qualification, verification, or certification strategy, or a combination thereof. For high value products, in-process monitoring has been shown to reduce the scrap fraction rate by at least 10 % according to recent literature.7 The realization of the cost/part reduction in the scrap fraction rate over time is dependent on the diagnostic capability of the in-process monitoring strategy as measured in false alarm (false positive) and undetected defect (false negative) performance. Further in-process monitoring can produce per part cumulative yield improvements through enabling process engineering diagnosis capabilities within part manufacturing such that SPC charts can be tuned to optimize the systems diagnostic performance. 4.6?Identifying Part Quality from Process SignaturesUltimately, final part quality metrics and associated mechanical or functional performance of AM parts are of greatest concern. Guide E3166, pertaining to ex-situ NDT, identifies two correlations of interest: process-flaw correlation and flaw-property correlation. In the context of this guide, measurements of material flaws or properties are considered FIG. 1?General Schematic of AM In-process Monitoring High-level Objectives for Inspection to Identify the Correlations, Through Analytical or Numerical Methods, that Relate Process Signatures to Part Quality Metrics and Utilize These as Part of a Broader Inspection or Part Validation Strategy 4.6.1?Process Signature TaxonomyMany different terms have been used in AM to describe process signatures or part quality metrics in the context of in-process monitoring (for example, defect, fault, flaw, anomaly, imperfection, etc.). The following provides a high-level taxonomy used in this guide to further define and categorize deleterious process signatures in AM process monitoring. As noted in 4.3, in-process monitoring is primarily used as part of an overall quality plan, either as a supplement to or replacement of traditional component inspection methods (for example, NDE) or to enable statistical process control. These two functions are mapped to corresponding taxonomies are mapped in Fig. 2. FIG. 2?Description of Higher-level Terms Relating an Observation of Process Signatures From In-process Monitoring for Inspection and Statistical Process Control (SPC) use Cases 4.6.2?For the in-process enabled inspection case, this taxonomy builds upon established standards or work items (see Terminology E1316, Guide E3166, and ISO/ASTM TR 52905). (1)?(2)?(3)?4.6.3?Statistical process control (SPC) uses statistical methods to improve quality by reducing the variability of one or more process outputs. For in-process monitoring enabled statistical process control, one or more process signatures are the outputs of the process to which SPC is applied. Process variation may be classified in one of two categories, common cause variation or special cause variation. (1)?(2)?4.7?Additive Manufacturing Flaws and Flaw Formation MechanismsUnderstanding how in-process flaws and defects form during fabrication is critical to the instrument design, data analysis or interpretation, and general application of AM in-process-monitoring. The following describe flaws that may exhibit in-process, and may be targeted for observation by in-process monitoring instruments. The following is not a comprehensive list or categorization of in-process flaws or defects, but is meant as a guide to better understand how the most commonly observed or understood flaws and defects may relate to in-process monitoring. Additional details regarding in-process defect and flaw formation are provided in regards to each measurement system modality discussed starting in Section 7. 4.7.1?Stochastic versus Systemic Defect FormationSystematic defects are voids resulting from input processing parameters and build plan. In contrast, stochastic flaws result from conditions that are not systematically controlled (that is, are a consequence of random or statistical processes), as shown in Fig. 3. FIG. 3?Example Organization and Categorization of Some Flaws Observable in a Laser Powder Bed Fusion (LPBF) Process, Categorized by 'Systematic' or 'Stochastic' Formation Note 1:?Reprinted from Additive Manufacturing, Vol 36, Snow, Z.,
Nassar, A. R., and Reutzel, E. W., Review of the formation and
impact of flaws in powder bed fusion additive manufacturing, 2020,
101457, https://doi.org/10.1016/j.addma.2020.101457, with
permission from Elsevier.
4.7.2?In-process Defects:? 4.7.2.1?Void FormationThe term voids (voids in Guide E3166, or synonymous with discontinuity in Terminology E1316) includes any material discontinuity within a part that is not a designed feature. This includes pores and cracks. While the methods of formation of voids is not always discernible in post-process inspection, their formation and corresponding signatures may be observable and distinguishable via in-process monitoring. (1)?(a)?(b)?(2)?(3)?(4)?4.7.2.2?Cracking:? (1)?(2)?4.7.3?In-process Flaws:? 4.7.3.1?Overheating, Overmelting, or Thermal HeterogeneityDue to the dynamically moving heat sources used during AM processing, some regions of a fabricated part can experience excessive heat accumulation and elevated temperatures relative to the rest of the part volume. This can generally be attributed to one or two factors: (1) combination of scan-strategy and layer geometry which causes excessive laser exposure over a confined area within the layer (Fig. 4); (FIG. 4?Example From Staring-configuration, Near-infrared (NIR) Spectrum Melt Pool Monitoring Camera. This System Compiles Images from Multiple Camera Exposures and Processes Them Into a Single Image. Left: Image Data Based on Integrated Values, Which Highlight Thermal Heterogeneity Features. Right: Image Data Based on Maximum Value, Which Highlight Spatter or Plume Features Note 1:?Barfoot, M. (2020). Evaluation of In-Situ Monitoring
Techniques (Additive Manufacturing Consortium (AMC) Project
Final Report, EWI Project No. 58279CPQ).
(1)?4.7.3.2?Powder Layer or Recoating FlawsImproper application of metal powder layers during LPBF fabrication can result in part defects. A number of in-process flaws associated with insufficient or improper powder layer formation are known, and are generally easily observed and interpreted. Generally, the source of these flaws can be categorized as stemming from the erroneous recoating process (for example, 4.7.4?Speed, Resolution, and Data ConsiderationsSpeed, resolution, and data considerations specific to each sensor modality will be discussed starting in Section 7. Generally, data rate and storage requirements for process monitoring are relatively high, which largely stems from the multi-scale physics of the AM fabrication process, and the necessity to adequately resolve signatures spatially or temporally. 4.7.4.1?For example, assume a typical 250 mm x 250 mm build area, divided into 0.1 mm x 0.1 mm pixels (25002 pixels/layer). Assume a 200 mm build height divided into 0.02 mm layers (10 000 layers/build). This results in 25002 pixels/layer ? 10 000 layers/build ? 1 byte/pixel = 62.5 GB/build. Similarly, in the temporal domain, consider a sensor acquiring data at 100 kHz, over a 36 h build. This results in a 105 samples/s ? 129 600 s/build ? 1 bytes/sample results in approximately 13 GB/build. These values are only given as typical examples, but indicate the relative volume of data that might be expected to be on the order of 10s of GB per sensor per build. 4.7.5?Data Reduction or CompressionMost often, in-process monitoring data size is reduced either in-line during acquisition, or just prior to storage, so that the raw instrument values are not transferred or stored. This is done by processing the data into a reduced-dimension parameter (for example, obtaining a single-value measurand from a 2D image), reducing the indicated or represented resolution (for example, averaging or binning pixels in an image), removing unnecessary data (for example, dark or saturated pixels in an image), employing data compression algorithms (lossy or loss-less), or employing other data reduction methods. 4.7.6?Data Alignment or RegistrationData alignment, registration, and visualization considerations specific to each sensor modality will be discussed in Sections 7 9. Refer to subcommittee ASTM F42.08 for proposed standards on data alignment and registration. 4.7.6.1?Visualization of in-process monitoring data is typically represented in the spatial domain, such that sensor signals or process signatures derived from those signals are mapped to the spatial position within the 3D part when or where, or both, they were acquired (Fig. 5). Most often, this is represented in three ways: (FIG. 5?Example Registration of 1D Process Monitoring Data (Signal versus Time from Melt Pool Monitoring (MPM) Photodetectors in Co-axial Configuration) into 3D Representation, Which Can Then be Projected onto Different Planar Slices (a) 2D Layer Representation (XY Plane), (b) 2D Slice Representation (YZ Plane), (c) 2D Slice Representation (XZ Plane), (d) 3D Part Representation (Orthographic Projection), Showing Location of the 2D Slice Locations 4.7.6.2?In this manner, the geometric location of those process signatures that may indicate an in-process flaw or defect can potentially be aligned and correlated to the same flaw or defect observed via ex-situ methods (for example, X-ray computed tomography (XCT)). For example, see Fig. 6. FIG. 6?Example Local Anomaly Observed in Co-axial Configuration, Photodetector-based Melt Pool Monitoring (Left), and Corresponding Observation of a Pore Defect (Right) from XCT of the Fabricated Part Note 1:?Barfoot, M. (2020). Evaluation of In-Situ Monitoring
Techniques (Additive Manufacturing Consortium (AMC) Project
Final Report, EWI Project No. 58279CPQ).
4.7.6.3?Alignment of in-process measured process signatures with part geometry requires additional measurements to obtain information that relates the positioning of the sensors field of view or sensing area to a coordinate system shared by the machine or parts. For further description of some of the measurement references, refer to ASTM subcommittee F42.08 for proposed standards on data alignment and registration. Some examples of accessory measurements for data alignment or registration are as follows: (1)?(2)?(3)?4.8?AM Process Monitoring ModalitiesIn the context of this guide, 4.8.1?Physical ConfigurationsProcess monitoring sensors of various types can be fixed to stationary locations onto or within the AM machine. The same type of sensor can be fixed into different configurations, which will change the position, field of view, or coordinate frame in which the sensor data is defined. The two primary configurations used in LPBF in-process monitoring, staring configuration, and co-axial configuration, are shown in Fig. 7. FIG. 7?Example Schematic of Two Common Instrument Physical Configurations in Laser Powder Bed Fusion (LPBF) Process Monitoring: (a) Co-axial Configuration and (b) Staring Configuration 4.8.1.1?Staring Configuration,also known as offline or fixed position configuration. This is a non-contact configuration where the sensor is placed in a fixed position with respect to the build plane or machine coordinate system (see ISO/ASTM 52921). A staring configuration sensor can be fixed either inside or outside the controlled-environment (build) chamber. This configuration is typical with single-point pyrometer, camera or thermal imager, etc. 4.8.1.2?Co-axial Configuration,also known as on-axis or inline. This is a non-contact configuration especially suited for optical or radiometric sensors, where the sensor is mounted in an optical path shared by the laser heat source. The field of view of the sensor is then fixed to the moving reference frame of the laser spot and moves in the same scan trajectories of the laser throughout the fabrication process. This effectively keeps the melt pool stationary within the sensor field of view. Example sensors include filtered radiometers, spectrometers, or high-speed cameras. 4.8.1.3?Other ConfigurationsA variety of other physical instrument configurations can exist that may be unique, specialized, or not easily described by the aforementioned configurations. For example, an acoustic microphone may be suspended within the build chamber, or an oxygen sensor set within the inert gas recirculation system (for example, machine condition monitoring, Section 9). |
| 1. Scope |
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1.1?This guide provides information on emerging in-process monitoring sensors, sensor configurations, sensor data analysis, and sensor data uses for the laser powder bed fusion additive manufacturing process. 1.2?The sensors covered produce data related to and affected by feedstock, processing parameters, build atmosphere, microstructure, part geometry, part complexity, surface finish, and the printing equipment being used. 1.3?The parts monitored by the sensors covered in this guide are used in aerospace applications; therefore, their final inspection requirements for discontinuities are different and more stringent than for materials and components used in non-aerospace applications. 1.4?The metal materials under consideration include, but are not limited to, aluminum alloys, titanium alloys, nickel-based alloys, cobalt-chromium alloys, and stainless steels. 1.5?This guide discusses sensor observation of parts while they are being fabricated. Sensor data analysis may take place concurrently or after the manufacturing process has concluded. 1.6?The sensors discussed in this guide may be used by cognizant engineering organizations to detect both surface and volumetric flaws. 1.7?The sensors discussed in this guide may be used by cognizant engineering organizations to detect process stability or drift, or both. 1.8?The sensors discussed in this guide are primarily configured in staring, co-axial, or mounted configurations. 1.9?This guide does not recommend a specific course of action, sensor type, or configuration for application of in-process monitoring to additively manufactured (AM) parts. It is intended to increase the awareness of emerging in-process sensors, sensor configurations, data analysis, and data usage. 1.10?Recommendations about the control of input materials, process equipment calibration, manufacturing processes, and post-processing are beyond the scope of this guide and are under the jurisdiction of ASTM Committee F42 on Additive Manufacturing Technologies. Standards under the jurisdiction of ASTM F42 or equivalent are followed whenever possible to ensure reproducible parts suitable for NDT are made. 1.11?Recommendations about the inspection requirements and management of fracture critical AM parts are beyond the scope of this guide. Recommendations on fatigue, fracture mechanics, and fracture control are found in appropriate end user requirements documents, and in standards under the jurisdiction of ASTM Committee E08 on Fatigue and Fracture. Note 1:?To determine the deformation and fatigue properties of
metal parts made by additive manufacturing using destructive tests,
consult Guide F3122.
Note 2:?To quantify the risks associated with fracture
critical AM parts, it is incumbent upon the structural assessment
community, such as ASTM Committee E08 on Fatigue and Fracture, to
define critical initial flaw sizes (CIFS) for the part to define
the objectives of the NDT.
1.12?This guide does not specify accept-reject criteria used in procurement or as a means for approval of AM parts for service. Any accept-reject criteria are given solely for purposes of illustration and comparison. 1.13?UnitsThe values stated in SI units are to be regarded as the standard. No other units of measurement are included in this standard. 1.14?This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.15?This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. |
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