1.1 本指南提供了有关新兴市场的信息 生产中的 监测传感器、传感器配置、传感器数据分析以及用于激光粉末床聚变添加剂制造过程的传感器数据。 1.2 传感器涵盖了与原料、加工参数、制造气氛、微观结构、零件几何形状、零件复杂性、表面光洁度和所用印刷设备相关的和受其影响的生产数据。 1.3 本指南所述传感器监测的部件用于航空航天应用；因此，它们对不连续性的最终检验要求不同于非航空航天应用中使用的材料和部件，并且更加严格。 1.4 正在考虑的金属材料包括但不限于铝合金、钛合金、镍- 基合金、钴铬合金和不锈钢。 1.5 本指南讨论了零件制造过程中的传感器观察。传感器数据分析可以同时进行，也可以在制造过程结束后进行。 1.6 本指南中讨论的传感器可由认可的工程组织用于检测表面和体积缺陷。 1.7 本指南中讨论的传感器可由认可的工程组织用于检测过程稳定性或漂移，或两者兼而有之。 1.8 本指南中讨论的传感器主要配置为启动、同轴或安装配置。 1.9 本指南不建议应用中的特定操作过程、传感器类型或配置- 附加制造（AM）零件的过程监控。它旨在提高对新兴过程中传感器、传感器配置、数据分析和数据使用的认识。 1.10 关于输入材料控制、工艺设备校准、制造工艺和后处理的建议超出了本指南的范围，并受ASTM添加剂制造技术委员会F42的管辖。尽可能遵循ASTM F42或同等标准管辖下的标准，以确保制造出适用于无损检测的可复制零件。 1.11 关于断裂临界AM零件的检查要求和管理的建议超出了本指南的范围。 有关疲劳、断裂力学和断裂控制的建议可在适当的最终用户要求文件中找到，也可在ASTM E08疲劳和断裂委员会管辖的标准中找到。 注1: 为了通过破坏性试验确定添加剂制造金属零件的变形和疲劳性能，请参考指南 3122楼 . 注2: 为了量化与断裂临界AM零件相关的风险，结构评估团体（如ASTM疲劳和断裂委员会E08）有责任定义零件的临界初始缺陷尺寸（CIFS），以定义无损检测的目标。 1.12 本指南未规定采购中使用的验收拒收标准，也未将其作为批准AM部件使用的一种手段。 任何接受-拒绝标准仅用于说明和比较。 1.13 单位- 以国际单位表示的数值应视为标准值。本标准不包括其他计量单位。 1.14 本标准并不旨在解决与其使用相关的所有安全问题（如有）。本标准的使用者有责任在使用前制定适当的安全、健康和环境实践，并确定监管限制的适用性。 1.15 本国际标准是根据世界贸易组织技术性贸易壁垒（TBT）委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认标准化原则制定的。 =====意义和用途====== 4.1 金属添加剂制造拓宽了设计空间，使生产更复杂和定制的产品成为可能。随着设计空间的扩大，加性技术正在推动检验能力的极限，并在工艺和产品鉴定、验证、认证等方面带来了挑战。为了帮助应对这些挑战，已经开发了工艺监测技术。 4.2 AM中的过程监控正在从研发领域中兴起。因此，在特定公司或机构内部使用之外，还没有将AM过程监控纳入资格或认证框架的完善程序。 生产过程监控数据的实际应用涉及生产周期的多个学科和部分，每个学科和部分都有成熟的实践、术语、期望等。本指南在适当的情况下借鉴了这些。 4.3 检验和统计过程控制（SPC）- 使用过程中监测技术的一个主要动机是帮助对越来越难以检查的AM组件进行过程和产品鉴定、验证和认证。AM过程监控功能可大致分为两类应用:过程中检查和过程控制。过程中检查是指识别与附加制造组件中物理缺陷和缺陷的形成相关的过程中签名。 这将在中进一步讨论 5.2 缺陷检测。统计过程控制（SPC）包括测量或观察与添加剂制造过程的稳定性或可重复性相关的过程特征或指标。这将在中进一步讨论 5.3 统计过程控制（SPC）。实时前馈或反馈控制方法和技术可被视为过程控制的子类别，并可使用相同的过程监控测量工具。目前，这些概念和技术在很大程度上仍在研究和开发中，没有在商业LPBF系统中普遍实现。本指南不再进一步讨论这些问题。 4.4 生产和开发用途- 使用添加剂制造的成品部件的生产需要一些检查组合，以确保部件满足最终产品功能和工艺鉴定的设计要求。过程中监控的检查和过程控制应用程序可集成到生产环境中的整体产品或过程鉴定、验证或认证策略中，或两者的组合中。过程中监控工具在附加过程和构建设计的开发中也很有价值，为新材料的参数选择（例如激光功率、扫描速度）、扫描策略、零件几何形状、AM构建平台上的零件放置等方面的工程决策提供支持。 SPC的先决条件是建立过程的正常变化，可以在过程开发期间使用过程监控工具进行评估。 4.5 经济合理性- 过程中监测除了作为整个过程或产品鉴定、验证或认证策略的一个组成部分或其组合对添加剂制造企业的价值外，还可以通过其对成本降低和产量提高的贡献，从经济上证明其合理性。根据最近的文献，对于高价值产品，过程监控已证明可将废品率降低至少10%。 7. 随着时间的推移，废品率中成本/零件减少的实现取决于in的诊断能力- 过程监控策略，以假警报（假阳性）和未检测到的缺陷（假阴性）性能衡量。通过在零件制造中启用工艺工程诊断功能，进一步的过程中监控可以提高每个零件的累积产量，从而可以调整SPC图表以优化系统的诊断性能。 4.6 从工艺特征识别零件质量- 最终，最终零件质量指标和AM零件的相关机械或功能性能是最值得关注的。指导 E3166型 关于现场无损检测，确定了两种相关关系:工艺缺陷相关性和缺陷特性相关性。在本指南中，考虑材料缺陷或性能的测量 零件质量指标 如指南中所述 E3166型 ，零件质量指标可能与工艺或工艺参数相关，例如激光功率、激光扫描速度等，如所示 图1 . 过程中监控 属于观察和测量 流程签名 或AM过程中出现的可观察现象，例如，熔池的电磁发射、声发射等。过程特征与过程参数相关。虽然过程参数通常是命令值或设定值，但过程签名提供了过程的测量声音。过程签名也可能与零件质量度量相关联，如 图1 作为产品检验和验证策略的一部分- 过程监控旨在利用这些过程特征和零件质量度量之间的相关性。因此，过程中的监测可用于结合或代替过程后检查方法（例如，无损检测）。 图1 AM过程中监控高级检验目标的一般示意图，通过分析或数值方法识别将过程特征与零件质量指标关联起来的相关性，并将其作为更广泛的检验或零件验证策略的一部分 4.6.1 流程签名分类- AM中使用了许多不同的术语来描述过程监控背景下的过程特征或零件质量指标（例如，缺陷、故障、缺陷、异常、缺陷等）。 ). 以下提供了本指南中使用的高级分类法，用于进一步定义和分类AM流程监控中的有害流程签名。如中所述 4.3 过程监控主要用作整体质量计划的一部分，作为传统部件检查方法（例如NDE）的补充或替代，或用于实现统计过程控制。这两个函数映射到相应的分类法，映射到 图2 . 图2 检查和统计过程控制（SPC）用例过程中监控过程特征观察的高级术语描述 4.6.2 对于进程内启用的检查用例，此分类基于已建立的标准或工作项（请参阅术语 E1316型 指导 E3166型 和ISO/ASTM TR 52905）。 (1) 指示 （术语 E1316型 ):在过程中启用的检验中，从过程中监测数据中观察到的过程特征，作为潜在材料缺陷的证据，被视为 指示 （术语 E1316型 ). 与传统无损检测一样，该指示应解释为 错误指示 , 无关指示 或 相关指示 （术语 E1316型 ). A. 相关指示 （术语 E1316型 )表明存在材料缺陷，需要进一步评估缺陷是否可接受，或者是否必须根据部件要求拒收零件。 (2) 缺陷 （术语 E1316型 ):缺陷是一种缺陷或不连续性，其形成可以通过- 过程监控，但不一定会被拒绝。 (3) 缺点 （术语 E1316型 ):一个或多个缺陷，其骨料尺寸、形状、方向、位置或性质不符合规定的验收标准，可拒收。 4.6.3 统计过程控制（SPC）使用统计方法通过减少一个或多个过程输出的可变性来提高质量。对于支持进程内监视的统计过程控制，一个或多个过程签名是应用SPC的过程的输出。过程变化可分为两类， 共因变异 或 特殊原因变化 . (1) 共同原因变化 （实践 E2587型 )，也称为 偶然变化 是过程中固有的随机变化，在统计极限内是可预测的。添加剂制造工艺可以说是 统计控制状态 仅观察到共同原因变化时（实践 E2587型 ). (2) 特殊原因变更 （实践 E2587型 )，也称为 可指定原因变更 与过程干扰或不安相关。特殊原因变化可能与过程中信号的峰值、偏移、趋势或变化有关。 4.7 添加剂制造缺陷和缺陷形成机制- 了解制造过程中缺陷和缺陷的形成方式对仪器设计、数据分析或解释以及AM在- 过程监控。以下描述了可能出现过程中的缺陷，并可能被过程中的监测仪器瞄准观察。以下并不是过程中缺陷或缺陷的综合列表或分类，而是为了更好地理解最常见的观察或理解的缺陷和缺陷如何与过程中监控相关。关于第节开始讨论的每个测量系统模式，提供了有关过程中缺陷和缺陷形成的更多详细信息 7. . 4.7.1 随机与系统缺陷形成- 系统性缺陷是输入处理参数和构建计划导致的空洞。相反，随机缺陷是由未系统控制的条件造成的（即随机或统计过程的结果），如 图3 . 图3 激光粉末床熔合（LPBF）过程中观察到的一些缺陷的示例组织和分类，按“系统”或“随机”形成分类 注1: 转载自 添加剂制造 第36卷，Snow，Z.、Nassar，A.R.和Reutzel，E.W.，“粉末床熔合添加剂制造缺陷形成和影响的回顾”，2020，101457，https://doi.org/10.1016/j.addma.2020.101457经爱思唯尔许可。 4.7.2 过程中缺陷: 4.7.2.1 空隙形成- 术语无效( 空洞 在指南中 E3166型 ，或同义词 不连续性 在术语中 E1316型 )包括零件内非设计特征的任何材料不连续性。这包括气孔和裂缝。 虽然在后处理检查中并不总是能够识别孔洞的形成方法，但其形成和相应的签名可以通过过程中的监控进行观察和识别。 (1) 孔隙 （指南 E3166型 )-孔隙是可与裂纹区分的材料不连续性，但也可能类似地作为应力集中或裂纹萌生位置。在2D中观察到的裂纹是一种具有极低纵横比的不连续性。孔隙和裂缝可能是表面连接的。在本指南中，孔隙进一步从指南中的描述中细分 E3166型 根据其形成机制和潜在特征: （a） 钥匙孔孔隙度 （指南 E3166型 和ISO/ASTM TR 52905）-小孔孔隙度与液体熔体池中的不稳定性有关，通常发生在相对较高的激光能量密度下( 7.2.2 ). 观察小孔孔隙度通常需要熔池监测，以捕捉小孔事件或相关熔池特征( 7.2.2 ). 这通常（但不是直接）与观察更深、更宽或更亮的熔池有关。单个小孔孔的大小大约比熔池小一个数量级，或者近似于典型LPBF粉末的规模（例如，10μm）。具体的仪器设计标准，以及过程监测观测值和小孔孔隙形成之间的统计相关性，仍然是一个研究和开发的问题。 （b） 气体孔隙度 （指南 E3166型 )-气体孔隙度被认为是由于粉末制造过程中夹带在粉末颗粒内的气体或由于固化时溶解度降低而释放的间隙气体造成的，通常认为通过电流无法观察到气体孔隙度- 工艺监测技术，因为孔隙被纳入粉末材料中，通常不会到达表面。 (2) 未熔合（LOF） （指南 E3166型 和ISO/ASTM TR 52905）-LOF孔隙形成可分为水平LOF或垂直LOF（ISO/ASTOM TR 5290%）。通常，通过过程中监测，仅可在制造层的顶面上观察到水平LOF孔隙或事件。然而，观察多层同一区域内的多个LOF事件可能表明形成了垂直LOF孔隙。 (3) 图案填充LOF -水平LOF源于相邻扫描轨迹的不完全熔化和润湿。 (4) 图案填充轮廓重叠和短图案填充缺陷 -水平LOF源于轮廓和填充激光扫描轨迹交叉处的不完全熔化和湿润。 4.7.2.2 开裂: (1) 分层开裂 -当AM构建中的层彼此分离，形成空腔或裂纹时，就会发生分层，这通常是由于制造过程中残余应力积累过多，以及零件或支撑材料或两者的设计不佳，或选择适当的AM构建参数造成的。这通常发生在固体零件结构与支撑结构、支撑与基板或固体零件与基板之间的界面。在AM制造过程中，随着零件高于新粉末表面的高度增加，或在开裂事件中出现的声学特征，可能会观察到分层开裂。 (2) 凝固开裂（或热开裂） -凝固- 当熔池熔合边界处的快速冷却导致高热应变和未充分填充熔融材料的材料分离时，就会发生开裂。凝固裂纹可能发生在凝固过程中，或在凝固后很短的时间内，并可能因随后的加热和冷却循环而扩大或加剧。某些材料比其他材料更容易发生热裂纹，可以向合金中引入各种填充材料以降低敏感性。工艺参数的组合及其对熔池形状的影响以及熔池内部和周围产生的热梯度，可能会导致凝固开裂的可能性。可通过声学特征观察到凝固裂纹，但通常太小，无法通过光学手段显示。 4.7.3 过程中缺陷: 4.7.3.1 过热、过熔或热不均匀性- 由于AM加工过程中使用的动态移动热源，相对于零件体积的其余部分，装配零件的某些区域可能会经历过多的热量积累和高温。这通常可归因于一个或两个因素:( 1. )扫描策略和层几何结构的组合，导致激光在层内受限区域过度照射( 图4 ); ( 2. )激光照射在受限区域，周围粉末的相对低导热性抑制了热量从熔池传导。通过几个过程特征可以观察到局部过热: ( 1. )熔池的尺寸、温度或亮度增加（请参见 7.2.5 熔池“强度”）；( 2. )过热区域变色或“烧焦”，以及( 3. )隆起、海拔、异常光滑/流动，或过热区域的表面结构和地形通常不同（见第节 8. 图层成像）。 图4 凝视配置示例，近红外光谱熔池监控摄像头。该系统编译来自多个相机曝光的图像并将其处理为单个图像。左图:基于“综合”值的图像数据，突出了热不均匀性特征。右:基于“最大”值的图像数据，突出显示飞溅或羽流特征 注1: Barfoot，M.（2020年）。 现场监测技术评估 （添加剂制造联盟（AMC）项目最终报告，EWI项目编号58279CPQ）。 (1) 飞溅/喷射过多 -在LPBF熔池规模下，可以观察到许多粒子从熔体附近逸出（或喷射）。这些粒子是由几种现象引起的。当蒸发引起的反冲压力超过熔池内的表面张力压力时，就会发生熔体喷射，导致熔滴逸出。飞溅颗粒还来自蒸发诱导气流中的粉末颗粒夹带。热飞溅颗粒是由于夹带颗粒的激光或蒸汽诱导加热而形成的。过程监控仪器可能会针对相对频繁、强烈或过度的热溅射( 图4 )作为缺陷或缺陷形成或有害制造质量的迹象。 4.7.3.2 粉末层或重涂缺陷- LPBF制造过程中金属粉末层的不当应用可能导致零件缺陷。已知许多与粉末层形成不足或不当相关的过程中缺陷，通常很容易观察和解释。通常，这些缺陷的来源可以归类为错误的重涂过程（例如， 跳跃、刮擦、粉末输送不足、零件打击 )，零件形成错误( 变形、隆起、成球 或 超高 ). 虽然许多缺陷可以通过多种过程监控方式观察到，但它们主要是通过层成像过程观察到的。 请参阅第节 8. 用于详细描述粉末层缺陷的层成像。 4.7.4 速度、分辨率和数据注意事项- 从第节开始，将讨论每个传感器模式的速度、分辨率和数据注意事项 7. 一般来说，过程监控的数据速率和存储要求相对较高，这在很大程度上源于AM制造过程的多尺度物理，以及在空间或时间上充分解析特征的必要性。 4.7.4.1 例如，假设一个典型的250 mm x 250 mm构建区域，划分为0.1 mm x 0.1 mm像素（2500 2. 像素/层）。假设200 mm的构建高度分为0.02 mm层（10 000层/构建）。这导致2500 2. 像素/层×10 000层/构建×1字节/像素=62.5 GB/构建。类似地，在时域中，考虑传感器在36 kHz以上以100 kHz采集数据 h构建。这将导致10分 5. 样本/s×129 600 s/build×1 bytes/sample的结果约为13 GB/build。这些值仅作为典型示例给出，但表示可能预计每个构建的每个传感器10 GB左右的相对数据量。 4.7.5 数据缩减或压缩- 最常见的情况是，在采集期间或存储之前，过程中监测数据的大小会减小，以便不传输或存储原始仪器值。这是通过将数据处理成一个简化的维度参数来实现的（例如，获取一个- 从2D图像测量值）、降低指示或表示的分辨率（例如，平均或“装箱”图像中的像素）、删除不必要的数据（例如，图像中的暗像素或饱和像素）、采用数据压缩算法（有损或无损）或采用其他数据缩减方法。 4.7.6 数据对齐或注册- 各传感器形态的数据对齐、注册和可视化注意事项将在第节中讨论 7 – 9 有关数据对齐和注册的拟议标准，请参阅ASTM F42.08小组委员会。 4.7.6.1 过程中监测数据的可视化通常在空间域中表示，这样，从这些信号中导出的传感器信号或过程特征就被映射到3D零件内的空间位置，无论何时何地，或两者都被采集( 图5 ). 最常见的表现方式有三种:( 1. ) 三维零件表达 特征或特征映射到零件内的3D位置，形成零件的数字表示，但由过程监控数据构建；( 2. ) 二维图层表示 ，其中数据映射到名义上与AM制造层相称的平面（垂直于构建方向）；或( 3. ) 二维切片表示 ，其中三维零件表达中的值或数据投影到平面切片上，该切片的方向与二维图层表达的方向不同。 图5 1D过程监测数据登记示例（Co中熔池监测（MPM）光电探测器的信号与时间- 轴配置）转换为3D表示，然后可以投影到不同的平面切片上（a）2D层表示（XY平面），（b）2D切片表示（YZ平面） 4.7.6.2 通过这种方式，可能指示过程中缺陷或缺陷的工艺特征的几何位置可能与通过现场外方法（例如，X射线计算机断层扫描（XCT））观察到的相同缺陷或缺陷对齐并关联。例如，请参见 图6 . 图6 同轴配置中观察到的局部异常示例、基于光电探测器的熔池监测（左）以及从制造零件的XCT观察到的相应孔隙缺陷（右） 注1: Barfoot，M.（2020年）。 现场监测技术评估 （添加剂制造联盟（AMC）项目最终报告，EWI项目编号58279CPQ）。 4.7.6.3 将过程中测量的过程特征与零件几何结构对齐需要额外的测量，以获取传感器视野或传感区域的定位与机器或零件共享的坐标系相关的信息。有关一些测量参考的进一步说明，请参阅ASTM小组委员会F42.08，以了解数据对齐和注册的拟议标准。以下是用于数据校准或登记的一些附件测量示例: (1) 激光/振镜位置与时间的同步采集 -许多商业过程监控系统能够通过与过程监控仪器并行的检流计（振镜）系统同步采集激光扫描位置。这可以通过读取发送到检流计的数字命令（例如，XY2-100或SL2-100数字命令协议）或读取检流计反馈编码器信号（如果可用）来完成。通过将传感器信号直接映射到同步空间位置（例如，XY位置），实现过程监测仪器信号或图像的对准或注册，同步空间位置是从振镜位置获得的。该方法广泛用于同轴仪器配置（例如，熔池监测，第 7. )或不提供空间信息的单元素探测器（例如，凝视配置光电探测器或安装的声学传感器）。 (2) 参考扫描模式 -特别是对于LPBF系统中的初始配置仪器，可以在裸基板、构建中的初始层或构建中的中间层上扫描具有已知几何形状的参考图案或网格。通过过程监控传感器进行的测量可以与扫描同步进行，也可以在完成后立即进行。参考图形的尺寸可以通过编程到AM机器控制器中的命令参考图形几何形状来确定，也可以通过校准尺寸测量（例如卡尺、光学坐标测量机）进行现场测量来确定。 然后，可将从过程监测仪器获取的信号或图像映射或转换为通过测量的参考扫描模式获取的坐标。 (3) 参考目标 -与扫描的参考图案类似，校准的尺寸目标或伪影可以放置在过程监测仪器的视野或传感区域。例如，成像仪可以观察到尺寸校准伪影，该伪影是用机器或零件坐标系定向的（第 8. ). 可能需要额外的步骤来参考工件相对于机器或零件坐标的位置。 4.8 AM流程监控模式- 在本指南中， 模式 描述了一组类似的过程监控技术，根据测量对象或感兴趣现象或所用测量仪器类型的类似属性进行分组。从第节开始，深入讨论不同模式 7. 不同的模式可以以不同的方式进行细分或分组。流程监控技术的另一个重要描述符是 物理配置 传感器的。 4.8.1 物理配置- 各种类型的过程监控传感器可以固定在AM机器上或内部的固定位置。相同类型的传感器可以固定在不同的配置中，这将改变传感器数据定义的位置、视野或坐标框架。 LPBF进程内监控中使用的两种主要配置， 起动配置 和 同轴配置 ，如所示 图7 . 图7 激光粉末床熔融（LPBF）过程监测中两种常见仪器物理配置的示例示意图:（a）同轴配置和（b）启动配置 4.8.1.1 起动配置， 也称为“脱机”或“固定位置”配置。这是一种非接触配置，传感器相对于构建平面或机器坐标系放置在固定位置（见ISO/ASTM 52921）。凝视配置传感器可以固定在受控环境（建筑）室内或室外。这种配置是典型的单- 点高温计、相机或热成像仪等。 4.8.1.2 同轴配置， 也称为“轴上”或“内联”。这是一种非接触配置，特别适用于光学或辐射传感器，其中传感器安装在激光热源共享的光路中。然后，传感器的视场固定在激光光斑的移动参考框上，并在整个制造过程中沿激光的相同扫描轨迹移动。这有效地使熔池在传感器视野内保持静止。示例传感器包括滤波辐射计、光谱仪或高速摄像机。 4.8.1.3 其他配置- 可以存在多种其他物理仪器配置，这些配置可能是独特的、专门的，或者不容易通过上述配置进行描述。 例如，可以在构建室内悬挂声学麦克风，或在惰性气体再循环系统内设置氧气传感器（例如，机器状态监测，第 9 ).

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 Units— The 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. ====== Significance And Use ====== 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 institution’s 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 Uses— Production 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 Justification— In-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 system’s diagnostic performance. 4.6 Identifying Part Quality from Process Signatures— Ultimately, 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 part quality metrics . As noted in Guide E3166 , part quality metrics may be correlated to the process or process parameters, such as laser power, laser scan speed, etc. as shown in Fig. 1 . In-process monitoring pertains to the observation and measurement of process signatures , or observable phenomena that occur during the AM process, for example, electromagnetic emissions from the melt pool, acoustic emissions, etc. Process signatures are correlated to process parameters. While process parameters are generally commanded or set point values, process signatures provide a measured voice of process. Process signatures may also be correlated to part quality metrics, as shown in Fig. 1 . As part of a product inspection and validation strategy, in-process monitoring aims to utilize the correlation between these process signatures and part quality metrics. In-process monitoring can thus be used to in conjunction with or in-lieu of post-process inspection methods (for example, NDE). 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 Taxonomy— Many 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) Indication (Terminology E1316 ): In an in-process enabled inspection, a process signature observed from the in-process monitoring data that is evidence of a potential material flaw is deemed an indication (Terminology E1316 ). As in traditional NDE, the indication is subject to interpretation as a false indication , nonrelevant indication , or relevant indication (Terminology E1316 ). A relevant indication (Terminology E1316 ) is indicative of a material flaw and requires further evaluation as to whether the flaw is acceptable or the part must be rejected based on the requirements of the component. (2) Flaw (Terminology E1316 ): A flaw is an imperfection or discontinuity, the formation of which may be detectible by in-process monitoring, but is not necessarily rejectable. (3) Defect (Terminology E1316 ): One or more flaws whose aggregate size, shape, orientation, location, or properties do not meet specified acceptance criteria and are rejectable. 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) Common Cause Variation (Practice E2587 ), also referred to as chance variation , is inherent random variation in the process which is predictable within statistical limits. An additive manufacturing process may be said to be in a state of statistical control when only common cause variation is observed (Practice E2587 ). (2) Special Cause Variation (Practice E2587 ), also referred to as assignable cause variation , associated with a process disturbance or upset. Special cause variation may be associated with a spike, shift, trend, or change in variability of the in-process signal. 4.7 Additive Manufacturing Flaws and Flaw Formation Mechanisms— Understanding 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 Formation— Systematic 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 Formation— The 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) Pores (Guide E3166 )—Pores are material discontinuities that are distinguishable from cracks, but may similarly act as stress concentration or crack initiation sites. Cracks, viewed in 2D, are a discontinuity with an extremely low aspect-ratios. Pores and cracks may be surface-connected. In the context of this guide, pores are further sub-categorized from description in Guide E3166 based on their formation mechanisms and potential signatures: (a) Keyhole Porosity (Guide E3166 and ISO/ASTM TR 52905)—Keyhole porosity is related to instability in the liquid melt pool, and typically occurs under relatively high laser energy density ( 7.2.2 ). Observation of keyhole porosity generally requires melt pool monitoring to capture a keyhole event, or related melt pool signature ( 7.2.2 ). This can be generally (but not directly) related to observation of a deeper, wider, or brighter melt pool. Individual keyhole pores are roughly an order of magnitude smaller than the melt pool, or approximately the scale of typical LPBF powder (for example, 10’s of μm). Specific instrument design criteria, and statistical correlation between in-process monitoring observations and keyhole pore formation are still a matter of research and development. (b) Gas Porosity (Guide E3166 )—Gas porosity, thought to result from gas entrapped within a powder particle during manufacturing of the powder or interstitial gases released due to reduced solubility upon solidification, is generally not considered to be observable via current in-process monitoring techniques, since the pores are incorporated into the powder material and do not typically reach the surface. (2) Lack of Fusion (LOF) (Guide E3166 and ISO/ASTM TR 52905)—LOF pore formation can be subcategorized as either horizontal LOF or vertical LOF (ISO/ASTM TR 52905). Generally, only horizontal LOF pores or events are observable on the top surface of the fabricated layer via in-process monitoring. However, observation of multiple LOF events within the same region over multiple layers may be indicative of formation of vertical LOF pores. (3) Hatching LOF —A horizontal LOF stemming from incomplete melting and wetting of adjacent scan tracks. (4) Hatch-contour Overlap and Short-hatch Flaw —A horizontal LOF stemming from incomplete melting and wetting at the intersection of a contour and infill laser scan tracks. 4.7.2.2 Cracking: (1) Delamination Cracking —Delamination occurs when layers within an AM build separate from one another forming a cavity or crack, often due to excessive residual stress buildup during fabrication in conjunction with poor design of the part or support materials, or both, or selection of appropriate AM build parameters. This most often occurs at the interface between a solid part structure and support structure, support and substrate, or the solid part and substrate. During AM fabrication, delamination cracking may be observed as increasing elevation of the part above new powder surface, or acoustic signatures that occur during cracking events. (2) Solidification Cracking (or Hot Cracking) —Solidification-cracking occurs when rapid cooling at the fusion boundary of a melt pool causes high thermal strain and separation of material that is not adequately filled by molten material. Solidification cracks may occur during solidification, or very shortly after, and can be enlarged or exacerbated by subsequent heating and cooling cycles. Certain materials are more susceptible to hot cracking than others, and various filler materials may be introduced to the alloy to reduce susceptibility. Combination of process parameters, and their effect on melt pool shape and resultant thermal gradients in and around the melt pool, can contribute to the likelihood of solidification cracking. Solidification cracks may be observable via acoustic signatures, but are generally too small and occur for indication via optical means. 4.7.3 In-process Flaws: 4.7.3.1 Overheating, Overmelting, or Thermal Heterogeneity— Due 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 ); ( 2 ) laser exposure over a confined region, where the relatively low thermal conductivity of the surrounding powder inhibits conduction of heat away from the melt pool. Local overheating can be observed via several process signatures: ( 1 ) Increased size, temperature, or brightness of a melt pool (see 7.2.5 on Melt pool ‘intensity’); ( 2 ) discoloration or ‘scorching’ of the overheated region, and ( 3 ) humping, elevation, abnormally smooth/fluid, or generally different surface structure and topography in the overheated region (see Section 8 on Layer Imaging). 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) Excessive Spatter/Ejecta —At the LPBF melt pool scale, many particles can be observed escaping (or ejected) from the vicinity of the melt. These particles initiate from several phenomena. Melt ejection occurs when evaporation-induced recoil pressure exceeds the surface tension pressure within the melt pool, causing molten droplets to escape. Spatter particles also result from powder particle entrainment within the evaporation-induced gas flow. Hot spatter particles are formed due to laser- or vapor-induced heating of entrained particles. Relatively frequent, intense, or excessive hot spatter may be targeted by process monitoring instruments ( Fig. 4 ) as an indication of flaw or defect formation, or deleterious fabrication quality. 4.7.3.2 Powder Layer or Recoating Flaws— Improper 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, skipping, scraping, insufficient powder delivery, part strikes ), part formation errors ( distortion, humping, balling , or superelevation ). While many of these flaws may be observable through multiple process monitoring modalities, they are primarily observed through Layer Imaging processes. Refer to Section 8 on Layer Imaging for detailed description of powder layer flaws. 4.7.4 Speed, Resolution, and Data Considerations— Speed, 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 (2500 2 pixels/layer). Assume a 200 mm build height divided into 0.02 mm layers (10 000 layers/build). This results in 2500 2 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 10 5 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 10’s of GB per sensor per build. 4.7.5 Data Reduction or Compression— Most 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 Registration— Data 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: ( 1 ) 3D part representation , where signatures or features are mapped to the 3D location within a part, forming digital representation of the part(s), but constructed from process monitoring data; ( 2 ) 2D layer representation , where the data is mapped to a plane nominally commensurate with an AM fabrication layer (normal to the build direction); or ( 3 ) 2D slice representation , where values or data from a 3D part representation are projected onto a planar slice that is oriented in a direction different than the 2D layer representation. 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 sensor’s 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) Simultaneous Acquisition of Laser/Galvo Position versus Time —Many commercial process monitoring systems enable synchronized acquisition of the laser scan position via the galvanometer (galvo) system in parallel with the process monitoring instruments. This is done either by reading the digital commands (for example, XY2-100 or SL2-100 digital command protocol) sent to the galvanometer, or reading galvo feedback encoder signal, if available. Alignment or registration of process monitoring instrument signals or images is done by directly mapping the sensor signal to the synchronized spatial location (for example, XY position) where it was obtained from the galvo position. This method is widely used for co-axial instrument configurations (for example, melt pool monitoring, Section 7 ), or single-element detectors that do not provide spatial information (for example, staring configuration photodetector or mounted acoustic sensor). (2) Reference Scan Pattern —Particularly for staring configuration instruments in a LPBF system, a reference pattern or grid with known geometry can be scanned on a bare substrate, initial layers within a build, or during intermediate layers within a build. Measurement via the process monitoring sensors may be conducted synchronously with the scan, or immediately after completion. Dimensions of the reference pattern may be known from the commanded reference pattern geometry programmed into the AM machine controller, or via ex-situ measurement by a calibrated dimensional measurement (for example, calipers, optical CMM). Signal or images acquired from the process monitoring instruments may then be mapped or transformed into the coordinates acquired via the measured reference scan pattern. (3) Reference Target —Similar to the scanned reference pattern, a calibrated dimensional target or artifact may be placed in the field of view or sensing area of the process monitoring instrument(s). For example, an imager may observe a dimensional calibration artifact that has been oriented with the machine or part coordinate system (Section 8 ). An additional step may be necessary to reference the position of the artifact with respect to the machine or part coordinates. 4.8 AM Process Monitoring Modalities— In the context of this guide, modality describes a group of similar process monitoring technologies, grouped based on similar attributes regarding the measured object(s) or phenomena of interest, or the types of measurement instruments employed. In-depth discussion of different modalities are discussed beginning in Section 7 . Different modalities may be sub-categorized or grouped in different ways. An additional important descriptor for process monitoring techniques is the physical configuration of the sensor(s). 4.8.1 Physical Configurations— Process 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 Configurations— A 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 ).