CN109477737A - 增材制造工艺中原位与实时质量控制 - Google Patents
增材制造工艺中原位与实时质量控制 Download PDFInfo
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Abstract
所公开的发明公开了具有至少一个光纤传感器(50)的传感器读出系统的使用,所述至少一个光纤传感器(50)通过至少一个信号线(51)连接到处理单元(52),传感器读出系统(5)作为增材制造装置的一部分,用于正进行的离子和电子束、微波或激光增材制造工艺的原位和实时质量控制,其中,通过具有布拉格光栅、光纤干涉仪或法布里‑珀罗结构(500)的光纤形式的至少一个光纤传感器(50)测量声发射,接着是信号传输(51)以及处理单元(52)中的测量的信号的分析、由于烧结或熔化质量与测量的声发射信号之间的相关性进行的烧结或熔化工艺质量的评估,随后是通过反馈回路(53)实时进行的增材制造装置的离子和电子束、微波或激光电子装置(1)的离子和电子束、微波或激光烧结或熔化参数的适应,作为在处理单元(52)中用算法框架解译之后的测量的声发射信号的结果。
Description
技术领域
本发明描述了:一种具有至少一个光纤传感器(50)的传感器读出系统(5)的使用,所述至少一个光纤传感器(50)通过至少一个信号线(51)连接到处理单元(52),传感器读出系统(5)作为增材制造装置的一部分,用于正进行的离子和电子束、微波或激光增材制造工艺的原位和实时质量控制;一种离子和电子束、微波或激光烧结或熔化装置中的增材制造工艺的原位和实时质量控制方法,在正进行的增材制造工艺期间用可控的离子和电子束、微波或激光参数,使用由离子和电子束、微波或激光电子装置(1)控制的离子和电子束、微波或激光源(2)以及离子和电子束、微波或激光聚焦装置(3)将离子和电子束、微波或激光束以离子和电子束、微波或激光照射聚焦光斑(30)聚焦在烧结或熔化体(4)的工艺表面上;以及一种增材制造装置,包括离子和电子束、微波或激光电子装置(1)、离子和电子束、微波或激光源(2)以及离子和电子束、微波或激光聚焦装置(3),用于利用可控的离子和电子束、微波或激光参数将离子和电子束、微波或激光束以离子和电子束、微波或激光照射聚焦光斑(30)聚焦在烧结或熔化体(4)的工艺表面上。
背景技术
特别是使用激光诱导粉末烧结或熔化的增材制造是最近有前途的技术,其已经涉及广泛的应用。与传统制造方法相比,它具有明显的经济和技术优势,但其进一步渗透到工业应用市场受到两个因素的强烈制约:与通过传统方法(例如材料固体的机械加工的等效物相比,1)生产线中组件的机械性能的重复性低;以及2)组件的机械强度低(美国国家标准与技术研究院(NIST):金属类增材制造的测量科学发展规划(Measurement ScienceRoadmap for Metal-Based Additive Manufacturing),2013))。
其中一个原因是潜在物理现象的复杂性,尤其是微/纳米颗粒介质内的激光和热传输。这意味着在工艺的精确物理模型的创建和已经积累的统计数据的分类/分析方面存在多重困难。这就需要开发有效的烧结或熔化质量控制的新型的稳健方法。
增材制造质量控制领域的主流是烧结或熔化区域的温度测量。
到目前为止,该领域的研究一直主要聚焦在工艺区域的直接温度测量。在US5,427,733或US5,508,489中已经公开:为了在增材制造工艺期间测量温度,采用了诸如高温计、光电二极管和矩阵CCD探测器的不同传感器。
提出了增材制造质量和温度动态之间的多重关联模型,但到目前为止,这种方法不能单独使用。原因在于温度测量出现了许多技术问题。需要额外的微观系统测量窄光斑(narrow spot)的温度,这增加了机器的成本和复杂性,其中窄光斑对应于离子束、微波或激光聚焦区域。通常,进行离子、微波或激光束同轴温度测量是不可能的,这会影响制造质量。与热影响材料粉末中的温度瞬变相比,许多出版物中报道的使用矩阵光电探测器(CCD)提供了相对低的时间分辨率。这导致获取的信号的时间分辨率低。所有这些因素加上数据解释的不准确性(由于非线性动力学)和建模复杂性使得不能提供有效的解决方案。
关于这类方法的更详细的概述可以在(Zeng,K.,Pal,D.和Stucker,B.,A Reviewof Thermal Analysis Methods in Laser Sintering and Selective Laser Melting(激光烧结与选择性激光熔化的热分析方法综述),Proceedings of the Solid FreeformFabrication Symposium(实体自由成形制造会议记录论文集),奥斯汀,德克萨斯州,pp.796-814,2012)中找到。即使具有关于温度动态的丰富信息,这种方法也不允许创建完整的成熟质量控制系统。主要原因在于离子束、微波或激光-粉末相互作用工艺的复杂性,因此传热模型不准确。
为了避免仅温度方案的缺点,在(Kleszczynski,S.,zur Jacobsmühlen,J.,Sehrt,J.,和Witt,G.,2012,Error Detection in Laser Beam Melting Systems by HighResolution Imaging(通过高分辨率成像在激光束熔化系统中进行误差检测),Proceedings of the Solid Freeform Fabrication Symposium(实体自由成形制造会议记录论文集),pp.975-987页)中提出了替代解决方案。这些文献描述了图像处理系统,其中,简单的可见光CCD摄像机捕获烧结或熔化部分的图像并使用特殊图像处理算法检测缺陷。缺点在于在可见光中摄像机的相对较低的帧采集速率(其由每秒数百帧计数)。由此,缺陷检测大规模地进行(当打印部件的层时,并且不再能改善质量)。此外,可检测的缺陷尺寸由摄像机物镜的空间分辨率限定,并且在生产大部件时趋于增加。最大的缺点是在制造大部件时,例如事后逐层进行质量控制,这不是很划算。已知没有任何方法可以实时改进增材制造工艺。
视频处理和高温测量的组合方法可以在(Doubenskaia,M.,Pavlov,M.,和Chivel,Y.;Optical System for On-line Monitoring and Temperature Control in SlectiveLaser Melting Technology(选择性激光熔化技术中用于在线监测和温度控制的光学系统),Key Eng.Mater.,437,pp.458-461,2010)中找到。该方法利用两种方法的优点并显示出良好的结果,但是它仍然在宏观尺度上分析已经制造的层并且不能消除缺陷。
根据WO2016081651,其尝试通过测量声发射,然后传输信号并用合适硬件进行信号处理来实时和原位改善增材制造工艺,由于熔化质量和测量的声发射信号之间的相关性,因此可以用于评估增材制造工艺质量。尽管使用的发射传感器检测到工件内的微裂纹,但质量控制的准确性和灵敏度可能不够好。只有通过使用更多传感器来提高质量控制装置的复杂性,才能实现改进。根据CN102680263,光学传感器的使用可以因光学参数的实时检测而改善增材制造方法的质量,但是仍存在许多待解决的未解问题,现有技术文献的简单组合并不能达到解决这些问题。
发明内容
本发明的目的是能够以颗粒/粉末样品的激光烧结或熔化工艺的形式在增材制造中实现原位和实时质量控制,而没有上述缺点。
通过用于离子束、微波或激光烧结或熔化的质量控制装置的使用和新方法以及新型装置解决了该问题。特别是通过使用光纤传感器,在离子束、微波或激光烧结或熔化工艺中使用声发射(AE)信号及时监测烧结或熔化质量。基于测量的信号,可以通过独立的反馈控制回路来优化烧结或熔化离子束、微波或激光参数。因此,能够实现质量控制和下一个烧结或熔化参数的适应。
已经发现,可以检测材料粉末的局部加热期间发出的声信号的级别,并且信号的内容包括关于烧结或熔化质量的信息。该内容是独特的并且取决于烧结或熔化区域内的热分布和颗粒相互作用,因此可以评估质量。尽管在烧结或熔化期间AE信号很微弱,但光纤传感器读出系统仍能检测到该信号。
所公开的质量控制方法使用与现有方法完全不同的方法,并且可以单独使用或与现有技术方法一起使用。
附图说明
下面结合附图描述本发明主题的优选示例性实施例以及烧结或熔化工艺的质量控制的记录和计算结果。
图1示出了具有质量控制装置的用于离子束、微波或激光烧结或熔化的整个系统的方案。
图2a示出了在用不同能量的单个激光脉冲加热时来自烧结或熔化的颗粒的声发射信号,而
图2b示出了不同激光脉冲能量的相关频谱图。工艺参数如下:
脉冲宽度:500μs,
聚焦光斑直径:30μm,
铁颗粒的平均尺寸:50μm,
脉冲数:1。
图3a示出了来自通过向已经烧结或熔化的大量颗粒施加第二脉冲而重复烧结或熔化的颗粒的声发射信号,而
图3b示出了相关的频谱图。工艺参数类似于图2b的描述中列出的参数。
图4示出了通过施加一个激光脉冲(图4a)和两个激光脉冲(图4b)烧结或熔化之后的铁微粒的图像。
相应的AE信号如图2a和3a所示。
图5a示出了存储在小波包树节点中的能量,以及
图5b是发现最大方差的每对事件的节点,最大方差用主成分分析(PCA)限定。
图6示出了用于图5的使用线性离散分析(LDA)算法的分类结果,其中,图6a中显示分类结果的系数,图6b是分配给不同类别信号的分数。使用小波包树的每个节点的能量作为特征,并且通过选择具有最大方差的节点来减少这些特征的数量(那些所选节点在前图中示出)。
图7a示出了从不同的激光照射功率获得的AE信号的小波包节点中的能量分布,以及图7b示出了使用例如主成分分析(PCA)算法在特征减少之后选择的节点(这些节点具有最大方差)。
图8示出了在时频域中从之前图所选的节点的动态范围:整个范围(A)和放大(B)。
图9示出了具有附加可选光传感器的另一系统的方案。
具体实施方式
本发明的目的是介绍一种增材制造(AM)工艺的质量控制的新型方法,特别是基于离子束、微波或激光粉末烧结或熔化(用于粉末床和离子和电子束、微波或激光粉末沉积)。该方法旨在成为一个独立的反馈控制回路,提供增材制造工艺的实时在线监测。而且,公开了一种使用所述方法的装置。
增材制造装置包括离子和电子束、微波或激光照射系统,离子和电子束、微波或激光照射系统包括离子和电子束、微波或激光电子装置1控制的离子和电子束、微波或激光源2、离子和电子束、微波或激光聚焦装置3,离子和电子束、微波或激光聚焦装置3在粉末材料上将离子和电子束、微波或激光源2的离子和电子束、微波或激光束照射聚焦成离子和电子束、微波或激光照射聚焦光斑30,形成烧结或熔化体4。从离子和电子束、微波或激光烧结或熔化工艺可知,离子和电子束、微波或激光烧结或熔化粉末材料。
对于增材制造工艺的质量控制,传感器读出系统布置在离子和电子束、微波或激光烧结或熔化体4附近。传感器读出系统5包括至少一个光纤传感器50、信号线51、处理单元52和反馈回路53。处理单元52分析来自声发射光纤传感器50的数据。
在进行离子和电子束、微波或激光烧结或熔化时,至少一个光纤传感器50读出声发射信号,该声发射信号由信号线51转发到处理单元52。在处理单元52中,如下所述,进行声发射信号的分析。在分析测量的AE信号之后,可以根据测量和分析的信号将离子和电子束、微波或激光电子装置1以及离子和电子束、微波或激光聚焦装置3适应为新的改进参数,接下来进行下一离子和电子束、微波或激光烧结或熔化(可以是增材制造)步骤。信号处理单元52经由反馈回路53连接到离子和电子束、微波或激光电子装置1,用于离子和电子束、微波或激光增材制造工艺参数的适应。
增材制造工艺通过使用多个光纤传感器50进行声学检测,所述光纤传感器50围绕工艺轴L对称地布置。至少一个光纤传感器50必须在离子和电子束、微波或激光源2与离子和电子束、微波或激光照射聚焦光斑30之间布置在工艺轴L的旁边。这里,光纤传感器50处于烧结或熔化环境中,与粉末材料4接触或不接触。光纤传感器50可以远离烧结或熔化体4放置或与烧结或熔化体4接触或者甚至放置在粉末材料4内。通过将光纤传感器50放置在相对于工艺轴L的特定位置、距离和定向,可以调整动态范围以进行质量控制测量。
通过使用多个光纤传感器50实现了优化的结果。所有光纤传感器50的光纤轴f相对于工艺轴L分别以相等的角度θ倾斜。发现光纤传感器50朝向工艺轴L的定向影响传感器50的灵敏度。通过改变角度θ,可以改变光纤传感器50的灵敏度。θ的范围在90度和0度之间变化,必须优化θ以在烧结或熔化工艺中达到所需的灵敏度。10°到70°之间的角度θ可以实现最佳结果。
具体使用的光纤传感器50示意性地示出为布拉格光栅,其在图1中标记为500。光纤传感器不限于这种特定类型。除了具有布拉格光栅的光纤传感器50之外,还使用法布里-珀罗光纤、光纤干涉仪或这些的任意组合。通过以下方法可以在光纤中形成法布里-珀罗腔:放置镜面端面;或将两个FBG紧密内接;或在FBG长度内引入相移;或使用单个FBG和切割的光纤边缘。有许多方法,而我们使用具有法布里-珀罗腔的光纤。这些光纤传感器50对AE非常敏感,这反过来又引起光纤中的振动并因此改变其光学性质。
在现实生活条件下,声波在空气/周围气体中传播时失真很微弱,因此,这些失真并不重要,可以忽略不计。在距离工艺区一定距离处可检测到声波。通过在粉末内放置放大元件也可以放大信号。由于颗粒的尺寸和颗粒之间的多个界面,所以粉末是声学绝缘体。
信号极其微弱并且仅在光纤传感器读出系统5的诸如光纤应变、布拉格光栅的光谱反射率(反射率光谱区的锐度)、工作波长和光纤芯材料的特定参数下可检测到。
这里,用于控制增材制造离子和电子束、微波或激光烧结或熔化强度的主要反馈参数是声发射(AE),其由照射区的温度的快速改变和粉末材料4内的温度梯度引起。当离子和电子束、微波或激光烧结或熔化时,来自所有光纤传感器50的所有数据经由信号线51传输到信号处理单元52并在信号处理单元52中被数字化。
在信号处理单元52中,分析测量和数字化的声信号。该分析可以以不同方式完成。
根据分析,可以通过与已知(预期值)比较或通过计算来评估当前的烧结或熔化质量。
信号处理单元52可以检测烧结或熔化的组件是否包含在烧结或熔化质量方面不可接受的缺陷。如果烧结或熔化质量不满足要求,则在离子和电子束、微波或激光电子装置1中自动地经由反馈回路53通过信号处理单元52适应性地调整离子和电子束、微波或激光参数,以用于即将进行的烧结或熔化。除了离子和电子束、微波或激光电子装置1之外,可选地,离子和电子束、微波或激光聚焦装置3可以通过处理单元52经由反馈回路53进行调节。通过适应的照射参数,可以在即将进行的烧结或熔化中实现优化的烧结或熔化质量。此外,使用声发射信号进行质量控制,可以在相同的制造工艺中,在单次烧结或熔化事件的级别上,直接优化烧结或熔化质量。
使用AE进行烧结或熔化控制正在研究中。可以观察到,AE与烧结或熔化事件的特性相关,即与激光照射的功率密度、粉末颗粒的尺寸和材料、粉末表面上的熔体或烧结颗粒的构造相关(图4)。
信号本身非常微弱,这对探测器灵敏度(提供可接受的噪声/信号比)和处理程序提出了额外要求。提供烧结或熔化工艺的声发射检测的FBG传感器的光纤应该提供244-0.1nm/MPa范围内的对压力波的伸长率。
为了检查检测信号差异的可行性,我们使用机器统计和分类方法,应用于记录的AE信号(图2-3)。这些方法代表了一种自动算法框架,允许确定每个烧结或熔化事件的独特AE特征,并使用这些特征进行质量控制的自动分类。
设计基本算法框架以检查下面提出和描述的方法的可行性(图5-8)。通常,算法框架包括两个阶段:
1)提取AE特征(这些特征的组合描述了AE的独特性),以及
2)分类本身。据此,结果呈现在图中。
下图示出了使用具有布拉格光栅的光纤传感器50的实验结果。图2和图3示出了由激光诱导的热量影响的颗粒粉末发射的AE信号。使用这些信号获得的所有进一步的计算结果呈现在以下附图中。
图5和图6示出了用于自动识别AE的算法框架步骤的序列,同时应用1个激光脉冲和2个激光脉冲。以这种方式,使用小波包分解图2和图3中使用FBG记录的信号。使用具有10个消失矩的Daubechies进行分解。计算所有信号的小波包树节点的能量并将其作为进一步分析的特征。一个和两个发射的声信号的相应特征在图5a中示出。然后,将主成分分析(PCA)应用于提取的特征,并计算1个和2个激光照射的声信号的特征之间的方差。存储在特征内的方差在图5b中示出。为了进一步分析,仅选择提供最大方差的特征。以这种方式选择的特征进一步馈送到评估烧结或熔化质量的分类器。
图6、7和8示出了使用1个激光脉冲但具有不同照射功率的烧结或熔化的AE信号的差异。相应的评述如下。
图2a示出了在铁微粒的烧结或熔化期间光纤传感器50(FBG)对AE的响应。通过一个激光脉冲加热颗粒,并且使用具有布拉格光栅500的光纤形式的光纤传感器50(FBG传感器)记录AE信号,布拉格光栅500远离烧结或熔化区域放置。脉冲宽度为500微秒;激光功率在1100-1500W的范围内变化,并且以30微米的直径聚焦成激光照射聚焦光斑30。
图2b还包括整个工艺持续时间的AE信号的频谱图。每个AE信号的记录时间是50毫秒。从频谱图中可以看出,主要信号能量存储在0-100kHz的范围内。信号的离散特性归因于传感器传输函数。
图3示出了光纤传感器(FBG)对通过两个激光脉冲序列加热大量颗粒而重复烧结或熔化的AE的响应。激光脉冲的参数与图2中描述的相同。相应的光谱图(图3b)示出了时域中信号的光谱含量。AE信号带来的大部分能量也存储在0-100kHz的范围内(如同在一个脉冲的情况下)。
图4示出了使用一个(图4a)和两个(图4b)激光脉冲序列的烧结或熔化体的图像。从图像中可以看出,烧结或熔化具有不同的体积配置,这取决于所施加的光功率。在一个脉冲的情况下,主要的烧结或熔化处于照射区域中,在照射区域周边上几乎没有烧结或熔化的颗粒。可以观察到,烧结或熔化处于光焦点的中心并且具有球形。观察到,烧结区或熔化区的尺寸取决于激光照射功率。
施加两个脉冲后(图4b),烧结或熔化的区域膨胀很大,而主要烧结或熔化区域不均匀并且包封一些未熔化的颗粒粉末的裂缝。这会影响烧结或熔化的区域的机械性能。
图5示出了图2信号的小波分解的结果。特征是信号的模式(pattern),该模式对于外部条件是不变的。在统计方法中,从初步测量的集合中提取这些模式,该集合称为训练数据集。
特征提取的更多细节和不同技术可以在(Long F.,Xue H.,Reserch on SignalAnalysis Method of Acoustic Emission of Material 2.25Cr-1Mo Based on WaveletFilter and Clustering(基于小波滤波器和聚类的材料2.25Cr-1Mo声发射的信号分析方法研究);Recent Advances in Computer Science and Information Engineering(计算机科学与信息工程的最新进展),Lecture Notes in Electrical Engineering(电气工程讲义),第126卷,2012年,第821-827页,Lyons R.G.Understanding Digital SignalProcessing(了解数字信号处理)(第三版),11,2010)中找到。
提取的特征的集合创建了信号的独特特征,因此即使在与训练数据集的环境不同的环境中也可用于识别信号(Zheng J.,Shen S.,Fan H.,Zhao J.An online incrementallearning support vector machine for large-scale data(一种用于大规模数据的在线增量学习支持向量机);Neural Computing and Application(神经计算与应用),第22卷,第5期,pp.1023-1035,2013)。特征集包含有关真实物理现象的信息,因此可以根据工艺质量进行解释。
作为特征提取技术,可以使用AE的小波包分解(WPD)。在该方法中,通过存储在单独的小波包中的能量来描述特征。WPD是标准小波理论的延伸(Mallat S.Wavelet Tour ofSignal Processing(信号处理的小波之旅),第三版:The Sparse Way,2008,DaubechiesI.,Ten lectures on wavelets(小波十讲),1992,ISBN:978-0-89871-274-2,eISBN:978-1-61197-010-4),在各种实际应用中证明了WPD是一种强大的方法。小波变换背后的思想是使用由特定函数(称为小波)形成的标准正交基的信号分解。与标准频率方法(A.Papandreou-Suppappola,Applications in Time-Frequency Signal Processing(时频信号处理的应用),CRC出版社,伯克莱屯,佛罗里达州,2002)相比,小波分解和WPD的优势是在频域和时域中信号局部模式的定位。尽管该方法具有明显的优点,但其结果在很大程度上取决于基小波的选择。
对于我们的分析,我们使用了Daubechies小波(db10),它被证明适用于各种非确定性信号。
WPD过程如下操作。首先,如上所述,使用标准小波分解将原始信号分成两部分。这些部分对应于原始信号的低频和高频分量,两侧都有指定的频率范围。重复该过程并将该过程依次施加到已经分开的部分,导致分解元素的增加。操作迭代的次数由分解级别限定,整个过程可以表示为树。
树节点被称为小包,并且每个分解级别(例如,树中的节点级别的数量)限定了时频域中的分辨率。在我们的研究中,所选择的分解级别为11(适合获取的信号的最大值),并且将存储在小包中的能量作为特征(对应于特定频段的能量)。
由于在选定的分解级别出现大量小包(参见图5b),应该减少小包。该操作旨在减少那些不会对所有信号中呈现的识别效率产生任何影响且因此不能用于信号区分的小包。
为了选择主要(main)(或所谓的基本(principal))特征,可以使用主成分分析(PCA)(Jolliffe I.T.,Principal Component Analysis(主成分分析),Series:SpringerSeries in Statistics,第2版,Springer,NY,2002,XXIX,487p.28illus.ISBN 978-0-387-95442-4)。该方法的思想是仅选择WPD树的那些具有最高方差且因此提供几个烧结或熔化事件最佳分离的小包。
从信号中提取重要特征还有其他几种可能方法。例如,通过使用标准傅立叶变换(Smith S.W.,The scientist and engineers guide to digital signal processing(科学家和工程师数字信号处理指导);California technical publishing(加利福尼亚技术出版),2011),或通过使用神经网络进行特征提取和分类(Keynote talk:“Achievementsand Challenges of Deep Learning–From Speech Analysis and Recognition ToLanguage and Multimodal Processing”(主题演讲:“深度学习的实现和挑战-从语音分析和识别到语言和多模态处理”),Interspeech,2014年9月)。
图6示出了前一阶段提取的特征的自动分类结果。包含在主要分量中的减少数量的小波包树节点创建可以被分类的特征集。为此,使用线性判别分析(LDA)(Duda,R.0.;Hart,PE;Stork,D.H.Pattern Classification(模式分类);(第2版).WileyInterscience.ISBN 0-471-05669-3.MR 1802993)。LDA的主要思想是设计一系列线性预测器(线性函数),转换特征集,为特征集分配一些特定的数字(分数)。每类事件都有自己的分数,因此是可识别的。转换函数的系数在图6a中示出,同时几个信号分类后的分数在图6b中示出。可以观察到,最简单的线性分类方案识别用1个和2个脉冲烧结或熔化粉末的情况下获得的AE信号之间的差异。可以观察到,分类器为不同类别的信号分配不同的分数,从而来区分不同类别的信号。这里,可以应用若干其他分类方案来提高算法框架的效率。例如,可以应用LDA的非线性扩展(Gu S,Tan Y.,He X.Discriminant analysis via support(通过支持向量进行判别分析);Neurocomputing(神经计算),73,1669-1675,2010),或可以应用支持向量机(Vapnik和Chervonenkis,Theory of Pattern Regoginition(模式识别理论),1974(Wapnik和Tschervonenkis,Theorie der Mustererkennung,1979),或可以应用神经网络(D.Ciresan,A.Giusti,L.Gambardella,J.Schmidhube.Deep Neural NetworksSegment Neuronal Membrances in Electron Microscopy Images(电子显微镜图像中的深度神经网络片段神经元膜),In Advances in Neural Information ProcessingSystems(神经信息处理系统进展),2012)。
图7示出了区分AE信号的差异的结果,所述AE信号通过一个激光脉冲但改变激光功率进行烧结或熔化获得。这里,我们类似地使用小波包分解方法来分解图2a和3a的信号。使用Daubechies小波(db10)分解AE信号。附图中示出了不同信号的节点能量分布。将PCA应用于节点以找出限定所有呈现信号之间的主要差异的主要节点。图(7b)中同样示出了PCA的结果。可以观察到,对于所有信号,几个节点是不同的,因此为机器可识别的每个信号创建独特的描述。
在图8a中,存储在主要节点中的频段(在图7中示出)在时频域中显示。从图中可以观察到,不同信号之间的主要差异存储在0-60kHz的频率范围内,因此可以用于使用图6的注释中描述的技术进行自动识别。
图8b描绘了所选择的主要节点,所述主要节点在图7的注释中描述并且被传输到时频域。它显示了光谱范围,其中,信号的差异可以检测到并用于进一步的自动分类。这里的分类方案可以完全类似于图4-6的描述中所示的分类方案。
在图9中,描绘了用于烧结和熔化工艺的另一质量控制装置5。如图所示,光纤传感器54可以放置在烧结或熔化体4内,或者光纤传感器55可以在与入射辐射侧相对的一侧放置在烧结或熔化体4外。
在辐射侧,可以放置附加的可选光传感器56,连接到处理单元52。
可选地,可以使用与图5中描述的相同的方法来执行光发射的处理(背反射的光/工艺区发射的光)。然后可以将提取的信息与从AE中提取的信息统一,并且如图6、7、8所示,可以进一步计算统一数据的分类。相应的附加的可选光传感器56可以放置在聚焦系统外部,并且该位置如图9所示。或者,附加的可选光传感器56或多个可选光传感器56可以设置在聚焦装置3中。利用至少一个可选光传感器56,能够检测入射照射的背反射(离子和电子束、微波或激光)和/或工艺区的光发射。
附图标记列表
1:离子和电子束、微波或激光电子装置
2:离子和电子束、微波或激光源
3:离子和电子束、微波或激光聚焦装置
30:离子和电子束、微波或激光照射聚焦光斑
4:烧结或熔化体/粉末材料
5:质量控制装置/传感器读出系统
50、54、55:光纤传感器
500:光纤布拉格光栅
51:信号线
52:处理单元
53:反馈回路
56:附加可选光电传感器
L:工艺轴
f:光纤轴
Claims (13)
1.具有至少一个光纤传感器(50)的传感器读出系统(5)的使用,所述至少一个光纤传感器(50)通过至少一个信号线(51)连接到处理单元(52),传感器读出系统(5)作为增材制造装置的一部分,用于正进行的离子和电子束、微波或激光增材制造工艺的原位和实时质量控制,其中,通过具有布拉格光栅、光纤干涉仪或法布里-珀罗结构(500)的光纤形式的至少一个光纤传感器(50)测量声发射,接着是信号传输(51)以及处理单元(52)中的测量的信号的分析、由于烧结或熔化质量与测量的声发射信号之间的相关性进行的增材制造工艺质量的评估,随后是通过反馈回路(53)实时进行的增材制造装置的离子和电子束、微波或激光电子装置(1)的增材制造参数的适应,作为在处理单元(52)中用算法框架解译之后的测量的声发射信号的结果,其中,所述至少一个光纤传感器(50)在离子和电子束、微波或激光源(2)与离子和电子束、微波或激光照射聚焦光斑(30)之间、在工艺轴(L)旁边与样本和源(2)分开布置,并且所述至少一个光纤传感器(50)的光纤轴(f)相对于工艺轴(L)分别以0°至90°范围内的角度(θ)倾斜。
2.离子和电子束、微波或激光烧结或熔化装置中的增材制造工艺的原位和实时质量控制方法,在正进行的增材制造工艺期间用可控的离子和电子束、微波或激光参数,使用由离子和电子束、微波或激光电子装置(1)控制的离子和电子束、微波或激光源(2)以及离子和电子束、微波或激光聚焦装置(3)将离子和电子束、微波或激光束以离子和电子束、微波或激光照射聚焦光斑(30)聚焦在烧结或熔化体(4)的工艺表面上,
其特征在于步骤:
-通过使用具有布拉格光栅、光纤干涉仪或法布里-珀罗结构(500)的光纤形式的至少一个光纤传感器(50)实时检测通过工艺区发射并由烧结或熔化脉冲引起的声波,其中,所述至少一个光纤传感器(50)在离子和电子束、微波或激光源(2)与离子和电子束、微波或激光照射聚焦光斑(30)之间、在工艺轴(L)旁边与样本和源(2)分开布置,并且所述至少一个光纤传感器(50)的光纤轴(f)相对于工艺轴(L)分别以0°至90°范围内的角度(θ)倾斜,
-通过至少一条信号线(51)将测量的传感器信号传输到处理单元(52),
-通过算法框架在处理单元(52)中数字化测量的传感器信号,然后分析数字化的传感器信号,所述算法框架提供测量的声发射特征的提取和分类,用于烧结或熔化工艺的质量控制,
-确定改进的未来增材制造参数,然后
-通过反馈回路(53)将改进的增材制造参数从处理单元(52)传输到离子和电子束、微波或激光电子装置(1),以便在即将进行的离子和电子束、微波或激光烧结或熔化步骤中应用改进的增材制造参数。
3.根据权利要求2所述的原位和实时烧结或熔化质量控制方法,其中,作为算法框架的一部分,通过作为提取技术的小波包分解WPD来完成声发射与增材制造质量的关联。
4.根据权利要求3所述的原位和实时烧结或熔化质量控制方法,其中,作为算法框架的一部分,对于选择主要节点,在处理单元(52)中使用主成分分析PCA。
5.根据权利要求2至4之一所述的原位和实时烧结或熔化质量控制方法,其中,作为处理单元(52)中算法框架的一部分,使用标准傅里叶变换从测量的传感器信号提取重要的特征。
6.根据权利要求2至5之一所述的原位和实时烧结或熔化质量控制方法,其中,作为处理单元(52)中算法框架的一部分,使用神经网络从测量的传感器信号提取重要的特征,用于特征提取和分类。
7.根据权利要求2至6之一所述的原位和实时烧结或熔化质量控制方法,其中,使用对称地围绕工艺轴(L)的多个光纤传感器(50)测量声发射,同时所有光纤传感器(50)的光纤轴(f)相对于工艺轴(L)分别以相等的角度(θ)倾斜。
8.增材制造装置,包括离子和电子束、微波或激光电子装置(1)、离子和电子束、微波或激光源(2)以及离子和电子束、微波或激光聚焦装置(3),用于利用可控的离子和电子束、微波或激光参数将离子和电子束、微波或激光束以离子和电子束、微波或激光照射聚焦光斑(30)聚焦在烧结或熔化体(4)的工艺表面上,
其特征在于包括:
集成的传感器读出系统(5),包括至少一个光纤传感器(50),所述至少一个光纤传感器(50)在离子和电子束、微波或激光源(2)与离子和电子束、微波或激光照射聚焦光斑(30)之间布置在工艺轴(L)旁边,集成的传感器读出系统(5)具有在光纤传感器(50)和处理单元(52)之间的至少一条信号线(51)以及从处理单元(52)到离子和电子束、微波或激光电子装置(1)的反馈回路(53),用于在即将进行的激光烧结或熔化中应用改进的烧结或熔化参数。
9.根据权利要求8所述的增材制造装置,其中,选择具有布拉格光栅、光纤干涉仪或法布里-珀罗结构(500)的光纤形式的所述至少一个光纤传感器(50)。
10.根据权利要求8或9所述的增材制造装置,其中,对称地围绕工艺轴(L)的多个光纤传感器(50)用于测量声发射。
11.根据权利要求9至10之一所述的增材制造装置,其中,所述光纤传感器(50)的光纤轴(f)分别以范围为0°到90°的相等的即为所有光纤传感器(50)与工艺轴(L)之间的角度的角度倾斜。
12.根据权利要求11所述的增材制造装置,其中,所述光纤传感器(50)的光纤轴(f)特别地以10°到70°之间的在所有光纤传感器(50)和工艺轴(L)之间的角度(θ)倾斜。
13.根据权利要求12至11之一所述的增材制造装置,其中,加热照射的背反射以及熔化/烧结区域的温度和其他光学发射可以通过至少一个光电探测器(56)收集,所述至少一个光电探测器(56)安装在工艺区附近并且连接到处理单元(52),其中,附加信息另外用于控制烧结/熔化质量。
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US11534979B2 (en) | 2022-12-27 |
EP3472569A1 (en) | 2019-04-24 |
US20190329498A1 (en) | 2019-10-31 |
EP3472569B1 (en) | 2020-06-03 |
WO2017216059A1 (en) | 2017-12-21 |
US10821674B2 (en) | 2020-11-03 |
EP3258219A1 (en) | 2017-12-20 |
CN109477737B (zh) | 2021-05-18 |
US20210039324A1 (en) | 2021-02-11 |
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