TWI761892B - Defect detection mechanism and defect identification method for metal lamination manufacturing - Google Patents
Defect detection mechanism and defect identification method for metal lamination manufacturing Download PDFInfo
- Publication number
- TWI761892B TWI761892B TW109124749A TW109124749A TWI761892B TW I761892 B TWI761892 B TW I761892B TW 109124749 A TW109124749 A TW 109124749A TW 109124749 A TW109124749 A TW 109124749A TW I761892 B TWI761892 B TW I761892B
- Authority
- TW
- Taiwan
- Prior art keywords
- signals
- workpiece
- metal powder
- base material
- frequency domain
- Prior art date
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
Landscapes
- Powder Metallurgy (AREA)
Abstract
本發明提供了一種金屬積層製造的缺陷檢測機構,其包含有一平台、一夾具、一雷射光束與金屬粉末射出總成、一麥克風(或一聲射感測器)與一分析電腦。其中,夾具是架設於平台上並用以固定基礎材料。雷射光束與金屬粉末射出總成是架設於平台上方並同時投射雷射光束與金屬粉末至基礎材料上以進行直接能量沉積。麥克風與聲射感測器是用以感測金屬粉末於熔化與堆疊過程中所產生的一組聲音與聲射訊號。分析電腦接收該組聲音與聲射訊號並進行分析,並利用一辨識器來辨識工件的缺陷。通過上述缺陷檢測機構,能有效地檢測工件的缺陷。The invention provides a defect detection mechanism manufactured by metal lamination, which includes a platform, a fixture, a laser beam and metal powder injection assembly, a microphone (or a sound sensor) and an analysis computer. Wherein, the clamp is erected on the platform and used to fix the base material. The laser beam and metal powder injection assembly is erected above the platform and simultaneously projects the laser beam and the metal powder onto the base material for direct energy deposition. The microphone and the acoustic sensor are used to sense a set of sounds and acoustic signals generated during the melting and stacking process of the metal powder. The analysis computer receives and analyzes the set of sound and sound emission signals, and uses an identifier to identify the defects of the workpiece. Through the above-mentioned defect detection mechanism, the defects of the workpiece can be effectively detected.
Description
本發明係有關於金屬積層製造的缺陷檢測,特別是指一種使用直接能量沉積技術的金屬積層製造中的缺陷檢測機構與其辨識方法。The present invention relates to defect detection in metal lamination manufacturing, in particular to a defect detection mechanism and identification method in metal lamination manufacturing using direct energy deposition technology.
積層製造技術(Additive Manufacturing;AM)又稱3D列印技術雖已發展多年,但針對金屬材料的積層製造(以下稱金屬積層製造),由於其尚有許多技術問題須克服,例如材料性質的提升與製程中缺陷產生之改善,但因為金屬積層製造在商業上的巨大潛力,目前有許多先進國家投入大量資源在積極研究相關的製造設備以及製程的設計。Additive Manufacturing (AM), also known as 3D printing technology, has been developed for many years, but for the additive manufacturing of metal materials (hereinafter referred to as metal additive manufacturing), there are still many technical problems to be overcome, such as the improvement of material properties. Due to the improvement of defects in the process, because of the huge commercial potential of metal laminate manufacturing, many advanced countries currently invest a lot of resources in actively researching related manufacturing equipment and process design.
目前金屬積層製造的主流技術大致可分為粉床熔融成型技術(Power Bed Fusion;PBF)與直接能量沉積技術(Directed Energy Deposition;DED),其中直接能量沉積技術可應用於異質材料的成形以及大型機具的零件,因而具有相當大潛力。直接能量沉積技術的原理是透過高能量的電磁波(一般是使用高功率雷射)與金屬粉末同時輸出到一基礎材料(例如一基板)上,並在基礎材料上形成熔池(molten pool),使金屬粉末在熔池上熔化並且相互結合與堆疊而形成焊珠,最後成形成工件。然而,如果製程條件設定不佳,例如雷射功率輸出不足,容易在工件的結構上產生各種缺陷,例如球狀不連續缺陷(Balling)、孔隙(porosity)等缺陷。然而,就申請人所知,目前現有的檢測缺陷的方式多採用光學檢測(例如採用CCD拍攝工件表面的影像以進行分析)或者是採用製程上的監控(監控材料的溫度)來判斷工件在結構上是否具有缺陷,但上述檢測方式是難以判斷工件的內部結構是否實際存有缺陷,因而尚有改善的空間。At present, the mainstream technologies of metal lamination manufacturing can be roughly divided into powder bed fusion technology (Power Bed Fusion; PBF) and direct energy deposition technology (Directed Energy Deposition; DED). Parts of implements and thus have considerable potential. The principle of direct energy deposition technology is to transmit high-energy electromagnetic waves (usually using high-power lasers) and metal powder to a base material (such as a substrate) at the same time, and form a molten pool on the base material (molten pool), The metal powder is melted on the molten pool and combined and stacked to form a weld bead, which is finally formed into a workpiece. However, if the process conditions are not well set, such as insufficient laser power output, various defects such as spherical discontinuity defects (Balling), porosity (porosity) and other defects are easily generated on the structure of the workpiece. However, to the best of the applicant's knowledge, the existing methods for detecting defects mostly use optical inspection (for example, using CCD to capture images of the workpiece surface for analysis) or use process monitoring (monitoring the temperature of the material) to determine the structure of the workpiece. However, the above detection method is difficult to judge whether the internal structure of the workpiece is actually defective, so there is still room for improvement.
本發明的其中一個目的乃在於針對現有金屬積層的檢測技術的缺失進行改良,進而提出一種嶄新的缺陷檢測機構與缺陷辨識方法,能夠檢測並辨識工件的缺陷。One of the objectives of the present invention is to improve the lack of the existing metal lamination detection technology, and further propose a brand-new defect detection mechanism and defect identification method, which can detect and identify the defects of the workpiece.
緣是,依據本發明所提供的一種金屬積層製造的缺陷檢測機構,係用於判斷一工件是否具有結構上的缺陷,工件係成形於一基礎材料上,上述缺陷檢測機構包含有一平台、一夾具、一雷射光束與金屬粉末射出總成、一麥克風與一分析電腦。其中,夾具是架設於平台上並固定上述基礎材料。雷射光束與金屬粉末射出總成是架設於平台上方並指向基礎材料,雷射光束與金屬粉末射出總成同時投射雷射光束與金屬粉末至基礎材料上,以使金屬粉末經由直接能量沉積的方式而熔化並堆疊於基礎材料上。麥克風是架設於平台上並指向基礎材料,麥克風感測金屬粉末於熔化與堆疊過程中所產生的一組時域上的聲音訊號。分析電腦電連接麥克風並接收該組時域上的聲音訊號,分析電腦轉換該組時域上的聲音訊號成一組第一頻域訊號,分析電腦還從該組第一頻域訊號而獲得複數個第一特徵向量,分析電腦執行一辨識器並輸入該等第一特徵向量至辨識器以判斷該工件結構上的缺陷。其中,平台可被驅動而相對於雷射光束與金屬粉末射出總成而水平地移動,或者雷射光束與金屬粉末射出總成可被驅動而相對於平台而水平地移動。The reason is that, according to a defect detection mechanism for metal lamination manufacturing provided by the present invention, it is used to judge whether a workpiece has structural defects. The workpiece is formed on a base material, and the defect detection mechanism includes a platform and a fixture. , a laser beam and metal powder injection assembly, a microphone and an analysis computer. Wherein, the fixture is erected on the platform to fix the above-mentioned basic material. The laser beam and metal powder injection assembly is erected above the platform and directed to the base material. The laser beam and metal powder injection assembly simultaneously project the laser beam and the metal powder onto the base material, so that the metal powder is deposited by direct energy. way to melt and stack on the base material. The microphone is erected on the platform and pointed at the base material. The microphone senses a set of sound signals in the time domain generated during the melting and stacking process of the metal powder. The analysis computer is electrically connected to the microphone and receives the set of sound signals in the time domain. The analysis computer converts the set of sound signals in the time domain into a set of first frequency domain signals. The analysis computer also obtains a plurality of first frequency domain signals from the set of first frequency domain signals. For the first eigenvectors, the analyzing computer executes an identifier and inputs the first eigenvectors to the identifier to judge defects on the workpiece structure. Wherein, the platform may be driven to move horizontally relative to the laser beam and metal powder ejection assembly, or the laser beam and metal powder ejection assembly may be driven to move horizontally relative to the platform.
藉此,使用者便能在通過雷射光束與金屬粉末射出總成在基礎材料上進行直接能量沉積作業,使金屬粉末能熔化並堆疊於基礎材料上,通過麥克風感測金屬粉末熔化並堆疊於基礎材料過程中的聲音,並通過分析電腦加以分析並通過執行一辨識器來辨識成形後的工件是否存在結構上的缺陷。In this way, the user can perform direct energy deposition operations on the base material through the laser beam and the metal powder injection assembly, so that the metal powder can be melted and stacked on the base material, and the metal powder can be sensed by the microphone to melt and stack on the base material. The sound of the basic material process is analyzed by analyzing the computer and by executing an identifier to identify whether there is a structural defect in the formed workpiece.
本發明另提供一種金屬積層製造的缺陷辨識方法,係用於辨識一工件於結構上的缺陷,該工件係由金屬粉末所製成並且是通過直接能量沉積的方式而成形於一基礎材料上,該缺陷檢測方法的步驟包含有:步驟S1:獲得該工件於成形於該基礎材料的過程中所產生的一組時域上的聲音訊號;步驟S2:轉換該組時域上的聲音訊號成頻域上的一組第一頻域訊號,並從該組第一頻域訊號而獲得複數個第一特徵向量;步驟S3:輸入該等第一特徵向量至一辨識器以辨識該工件的缺陷。The present invention further provides a defect identification method for metal lamination manufacturing, which is used to identify structural defects of a workpiece, the workpiece is made of metal powder and formed on a base material by direct energy deposition, The steps of the defect detection method include: step S1: obtaining a set of sound signals in the time domain generated during the process of forming the workpiece on the base material; step S2: converting the set of sound signals in the time domain into frequencies A set of first frequency domain signals on the domain, and a plurality of first feature vectors are obtained from the set of first frequency domain signals; Step S3: Input the first feature vectors to an identifier to identify defects of the workpiece.
以下實施例的內容是依據楊惟鈞的研究論文「金屬積層製造球化與接合缺陷偵測之聲音及聲射訊號分析」所撰寫的(指導教授為盧銘詮老師),因此以下實施例所揭露的範圍應及於上揭論文的全部內容,合先敘明。The contents of the following embodiments are written according to Yang Weijun's research paper "Analysis of Sound and Acoustic Radio Signals for Spheroidization and Joint Defect Detection in Metal Lamination Manufacturing" (the instructor is Mr. Lu Mingquan), so the following embodiments disclose The scope should cover the entire contents of the above-mentioned dissertation, which will be explained together first.
為了詳細說明本發明的技術特點所在,茲舉以下實施例並配合圖式說明如後,其中:In order to illustrate the technical features of the present invention in detail, the following examples are given and described in conjunction with the drawings as follows, wherein:
如圖1所示,本發明實施例所提供的一種金屬積層製造的缺陷檢測機構1,其係用於判斷一工件W是否具有結構上的缺陷,工件W係成形於一基礎材料21(本實施例是一基板)上,缺陷檢測機構1包含有一平台10、一夾具20、一雷射光束與金屬粉末射出總成30、一麥克風40、一聲射感測器50與一分析電腦60。As shown in FIG. 1, a
平台10於本實施例中為一雙軸運動平台,其可被驅動而水平地移動。The
夾具20是架設於平台10上並固定基礎材料21(base material),於本實施例中,基礎材料21為一基板,是用來讓金屬粉末P能夠熔化並堆疊於基板上。The
雷射光束與金屬粉末射出總成30是固定地架設於平台10上方並指向基礎材料21,雷射光束與金屬粉末射出總成30包含有一噴嘴31,噴嘴31的結構可參考圖2所繪示的示意圖,噴嘴31的結構包含有一中央開口32、一中央通道33與二外側通道34,內側通道與外側通道34都連通中央開口32,高功率雷射的雷射光束L經由中央通道33與中央開口32而聚焦於基礎材料21的表面以在基礎材料21的表面形成熔池,高功率雷射的輸出功率是可調整的,例如20瓦特、40瓦特或是200瓦特。金屬粉末P與惰性氣流在外側通道34內傳輸,並經由中央開口32而噴射至熔池,金屬粉末P吸收雷射光束L的能量而熔化並堆疊排列於基礎材料21,通過同時與定點投射雷射光束L與金屬粉末P至基礎材料21的熔池上,使得金屬粉末P能經由直接能量沉積的方式而熔化並堆疊於基礎材料21上以形成工件W。The laser beam and metal
需說明的是,雖然在本實施例中,雷射光束與金屬粉末射出總成30是以同一個噴嘴31而射出雷射光束L與金屬粉末P,在某些情況下,也可能是以分開的方式而設置有二個噴嘴31,其中一個射出雷射光束L,另一個射出金屬粉末P,因此不應以本實施例為限。另外,雖然在本實施例中,是以平台10可被驅動而相對於雷射光束與金屬粉末射出總成30而水平地移動,在某些情況下,也可以讓雷射光束與金屬粉末射出總成30可被驅動而相對於平台10作水平地移動,或者是二者皆可受驅動而移動。It should be noted that, although in this embodiment, the laser beam and the metal
請回到圖1。麥克風40是架設於平台10的上方並指向基礎材料21,於本實施例中,麥克風40是採用Knowles公司生產,型號為SPM0408LE5H-TB的微機電麥克風,其靈敏度為-18dB±3dB,有效頻寬是100Hz~10kHz並具有指向性。麥克風40主要是用來感測金屬粉末P於熔化與堆疊過程中因金屬粉末P熱變形與振盪所產生的一組時域上的聲音訊號。Please go back to Figure 1. The
聲射感測器50是通過扭力扳手而固定於夾具20上並距離熔池的位置約略5公分。於本實施例中,聲射感測器50是採用Kistler公司生產,型號為8152C003032的聲射感測器50,其靈敏度為57dB±10dB,有效頻寬是50Hz~400kHz。聲射感測器50主要是用來感測金屬粉末P於熔化與堆疊過程中因金屬粉末P熱變形與振盪所產生的一組時域上的聲射訊號,其為一固體波的訊號。須說明的是,在某些狀況下,也可以將聲射感測器50架設於基礎材料21(基板)上,而不以本實施例為限。The
分析電腦60是通過一訊號擷取卡61而電連接麥克風40與聲射感測器50,並擷取麥克風40與聲射感測器50所感測的時域上的聲音訊號與聲射訊號。訊號擷取卡61是採用National Instruments所生產,型號為PXle-6132的訊號擷取卡61。分析電腦60內部建置有程式軟體LabVIEW所撰寫的程式以控制訊號擷取卡61擷取上述聲音訊號與聲射訊號,並做訊號的類比數位轉換。另外,分析電腦60還建置有由Python編寫的分析程式,藉以對所擷取到的聲音訊號與聲射訊號進行訊號分析。The
在訊號分析方面,分析電腦60通過Python的分析程式執行快速傅立葉轉換或者是小波包轉換以分別將該組時域上的聲音訊號轉換成一組第一頻域訊號並將該組時域上的聲射訊號轉換成一組第二頻域訊號,之後分析電腦60從上述該組第一頻域訊號中與上述該組第二頻域訊號中獲得複數個第一特徵向量與第二特徵向量,分析電腦60再執行一辨識器並將該等第一頻域訊號與該等第二頻域訊號輸入至上述辨識器,藉以辨識工件W在結構上是否具有缺陷,並辨識缺陷的種類。In terms of signal analysis, the
辨識器的建立方式說明如下:使用上述雷射光束與金屬粉末射出總成30以直接能量沉積的方式製作二個不同工件W,其中一個工件W不具有缺陷而另一個工件W具有缺陷(例如接合缺陷(bonding defect),並通過麥克風40與/或聲射感測器50感測金屬粉末P於熔化與堆疊過程中所產生的一組時域上的聲音訊號與/或時域上的聲射訊號。之後,利用分析電腦60將上述聲音訊號與/或聲射訊號轉換成頻域上的訊號,並且從中獲得對應於無缺陷工件W的多個特徵向量與對應於具有接合缺陷的工件W的多個特徵向量,並利用該等多個特徵向量來建立一個隱藏式馬可夫模型辨識器。辨識器的建立方式的詳細內容可以參考自以下論文:「回饋型隱藏式馬可夫模型於鎳基材料切削刀具磨耗多重感測器偵測系統開發」,論文作者為王崇穎,指導老師為盧銘詮老師。須說明的是,也可以採用其他種類的模型,例如類神經網路模型來建立辨識器。The construction of the identifier is described as follows: two different workpieces W are fabricated by direct energy deposition using the above-mentioned laser beam and metal
在實際所擷取到的聲音訊號與聲射訊號中,其可能來自多種訊號來源。就以聲音訊號來說,其來源可能來自機台背景聲音訊號、單純只有金屬粉末P撞擊基礎材料21的聲音訊號、單純雷射光束L於基礎材料21上形成熔池所產生的聲音訊號、金屬粉末P未到達基礎材料21前已熔化所產生的聲音訊號、金屬粉末P到達基礎材料21並且熔化且堆疊於基礎材料21上所產生的聲音訊號,其中,申請人進行了一連串的實驗,並從實驗結果來看,聲音訊號的來源主要是自於金屬粉末P熔化且堆疊於基礎材料21時所產生的聲音。聲射訊號的來源則不同。金屬粉末P單純撞擊基礎材料21時即會有明顯的聲射訊號,並且聲射訊號不受環境噪音的影響。In the actual captured sound signal and acoustic signal, it may come from a variety of signal sources. As far as the sound signal is concerned, the source may come from the background sound signal of the machine, the sound signal that only the metal powder P hits the
申請人進行了單軌直接能量沉積的實驗(亦即在基礎材料21上形成一條金屬線),將正常單軌沉積與單軌沉積接合缺陷的聲射訊號(如圖3A與圖4A)進行小波包轉換時,申請人檢測到正常單軌沉積所產生聲射訊號於312.5kHz~375kHz的頻段以及375kHz~437.5kHz頻段的聲射訊號的波動程度(如圖3B與圖3C)是大於單軌沉積接合缺陷時的聲射訊號波動程度(如圖4B與圖4C)。至於聲音訊號(如圖5A與圖6A),申請人檢測到正常單軌沉積所產生聲音訊號於0~6.25kHz的頻段的聲音訊號(如圖5B)的波動程度是大於單軌沉積接合缺陷時的聲音訊號(如圖6B)波動程度。The applicant has carried out the experiment of single-track direct energy deposition (that is, forming a metal line on the base material 21), and when the acoustic signals of normal single-track deposition and single-track deposition bonding defects (as shown in FIG. 3A and FIG. 4A ) are subjected to wavelet packet transformation The applicant has detected that the fluctuation of the acoustic signal generated by the normal monorail deposition in the frequency band of 312.5kHz~375kHz and the acoustic signal in the frequency range of 375kHz~437.5kHz (as shown in Figure 3B and Figure 3C) is greater than that of the monorail deposition joint defect. The degree of fluctuation of the radio signal (as shown in Figure 4B and Figure 4C). As for the sound signal (as shown in Fig. 5A and Fig. 6A ), the applicant has detected that the sound signal generated by normal single-track deposition in the frequency range of 0-6.25 kHz (as shown in Fig. 5B ) fluctuates more than the sound when the single-track deposition is defective The degree of fluctuation of the signal (as shown in Figure 6B).
另外,針對球化缺陷,單純靠時序上的聲音訊號(如圖7A與圖8A)即可辨認出球化缺陷的位置,而且正常單軌沉積所產生聲音訊號於高能量區頻域以及低能量區頻域的頻譜分布(如圖7B與圖7C)是不同於單軌球化缺陷的聲音訊號於高能量區頻域以及低能量區頻域的頻譜分布(如圖8B與圖8C)。聲射訊號則看不出特徵。如此,即可針對這些聲射訊號與聲音訊號所轉換的第一與第二頻域訊號的差異作為基礎的分析資料,配合建立的辨識器,辨識出不同型態的缺陷。In addition, for the spheroidization defect, the location of the spheroidization defect can be identified only by the sound signal in the time series (as shown in Figure 7A and Figure 8A ), and the sound signal generated by the normal single-track deposition is in the high-energy frequency region and the low-energy region. The spectral distribution in the frequency domain (as shown in FIGS. 7B and 7C ) is different from the spectral distribution of the sound signal of the single-track spheroidization defect in the high-energy region frequency region and the low-energy region frequency region (as shown in FIGS. 8B and 8C ). Acoustic signals do not see features. In this way, based on the analysis data based on the difference between the acoustic emission signal and the first and second frequency domain signals converted from the acoustic signal, the established identifier can be used to identify different types of defects.
須說明的是,直接能量沉積在結構上的缺陷至少包含有接合缺陷(bonding defect)、球化缺陷(Balling)、孔隙(porosity)等多種型態,雖然本實施例只以接合缺陷與球化缺陷進行舉例說明,但本實施例的檢測機構1與檢測方法也能應用於其他型態的缺陷檢測。另外,本實施例是同時採用了聲音訊號與聲射訊號來進行檢測,但應該可以了解的是,單純使用聲音訊號或者是單純使用聲射訊號來進行缺陷也應該是做得到的,只是辨識程度可能稍差而已。It should be noted that the defects of direct energy deposition on the structure at least include bonding defects, balling defects, porosity and other types, although this embodiment only uses bonding defects and spheroidization. Defects are exemplified, but the
本發明另提供一實施例,說明金屬積層製造的缺陷辨識方法,其係用於辨識一工件W於結構上的缺陷,工件W係由金屬粉末P所製成並且是通過直接能量沉積的方式而成形於基礎材料21上,缺陷檢測方法的步驟包含有:Another embodiment of the present invention is to describe a defect identification method for metal lamination manufacturing, which is used to identify structural defects of a workpiece W, which is made of metal powder P and is formed by direct energy deposition. Formed on the
步驟S1:通過麥克風40而獲得工件W於成形於基礎材料21的過程中所產生的一組時域上的聲音訊號。通過聲射感測器50而獲得工件W於成形於基礎材料21的過程中所產生的一組時域上的聲射訊號。Step S1 : obtaining a set of sound signals in the time domain generated during the process of forming the workpiece W on the
步驟S2:通過快速傅立葉轉換或者是小波包轉換,轉換該組時域上的聲音訊號成頻域上的一組第一頻域訊號,轉換該組時域上的聲射訊號成頻域上的一組第二頻域訊號,並從該組第一頻域訊號與該組第二頻域訊號而獲得複數個第一特徵向量與複數個第二特徵向量。Step S2: Convert the group of sound signals in the time domain into a group of first frequency-domain signals in the frequency domain through fast Fourier transform or wavelet packet transformation, and convert the group of sound-radiation signals in the time domain into the frequency domain. A set of second frequency domain signals, and a plurality of first eigenvectors and a plurality of second eigenvectors are obtained from the set of first frequency domain signals and the set of second frequency domain signals.
步驟S3:輸入該等第一特徵向量與該等第二特徵向量至辨識器以辨識工件W於結構上的缺陷。Step S3: Input the first feature vectors and the second feature vectors to the identifier to identify the structural defects of the workpiece W.
最後,必須再次說明的是,本發明於前述實施例中所揭露方法及構成元件僅為舉例說明,並非用來限制本發明的專利範圍,舉凡未超脫本發明精神所作的簡易結構潤飾或變化,或與其他等效元件的更替,仍應屬於本發明申請專利範圍涵蓋的範疇。Finally, it must be reiterated that the methods and constituent elements disclosed in the foregoing embodiments of the present invention are merely illustrative, and are not intended to limit the scope of the invention. Or replacement with other equivalent elements should still fall within the scope covered by the scope of the patent application of the present invention.
1:檢測機構 10:平台 20:夾具 21:基礎材料 30:雷射光束與金屬粉末射出總成 31:噴嘴 32:中央開口 33:中央通道 34:外側通道 40:麥克風 50:聲射感測器 60:分析電腦 61:訊號擷取卡 L:雷射光束 P:金屬粉末 W:工件 S1-S3:步驟 1: Testing agency 10: Platform 20: Fixtures 21: Basic Materials 30: Laser beam and metal powder injection assembly 31: Nozzle 32: Central opening 33: Central channel 34: Outside channel 40: Microphone 50: Acoustic sensor 60: Analytical Computers 61:Signal capture card L: laser beam P: metal powder W: workpiece S1-S3: Steps
有關金屬積層製造的缺陷檢測機構與缺陷辨識方法的詳細結構、特點、與辨識方式將於以下的實施例予以說明,然而,應能理解的是,以下將說明的實施例以及圖式僅只作為示例性地說明,其不應用來限制本發明的申請專利範圍,其中:The detailed structure, features, and identification methods of the defect detection mechanism and defect identification method for metal lamination manufacturing will be described in the following embodiments. However, it should be understood that the embodiments and drawings described below are only examples. It should not be used to limit the scope of the patent application of the present invention, wherein:
圖1係實施例的金屬積層製造的缺陷檢測機構的結構示意圖; 圖2係實施例的雷射光束與金屬粉末射出總成的示意圖; 圖3A係正常單軌沉積的聲射訊號與時間的關係圖; 圖3B與圖3C係圖3A經由小波包轉換下於不同頻率段聲射訊號與時間的關係圖; 圖4A係單軌沉積接合缺陷的聲射訊號與時間的關係圖; 圖4B與圖4C係圖4A經由小波包轉換下於不同頻率段聲射訊號與時間的關係圖; 圖5A係正常單軌沉積的聲音訊號與時間的關係圖; 圖5B係圖5A經由小波包轉換下於0~62.5kHz頻率段聲音訊號與時間的關係圖; 圖6A係單軌沉積接合缺陷的聲音訊號與時間的關係圖; 圖6B係圖6A經由小波包轉換下於0~62.5kHz頻率段聲音訊號與時間的關係圖; 圖7A係正常單軌沉積的聲音訊號與時間的關係圖; 圖7B與圖7C係圖7A經由小波包轉換下於不同頻率段聲音訊號與時間的關係圖; 圖8A係單軌球化缺陷的聲音訊號與時間的關係圖; 圖8B與圖8C係圖8A經由小波包轉換下於不同頻率段聲音訊號與時間的關係圖;以及 圖9係實施例的辨識方法的步驟流程圖。 FIG. 1 is a schematic structural diagram of a defect detection mechanism manufactured by a metal laminate according to an embodiment; FIG. 2 is a schematic diagram of a laser beam and a metal powder injection assembly according to an embodiment; FIG. 3A is a graph showing the relationship between acoustic emission signal and time of normal monorail deposition; FIG. 3B and FIG. 3C are diagrams of the relationship between acoustic emission signals and time in different frequency bands through wavelet packet transformation in FIG. 3A; FIG. 4A is a graph showing the relationship between acoustic radiation signal and time of a single track deposition bonding defect; FIG. 4B and FIG. 4C are diagrams of the relationship between acoustic emission signals and time in different frequency bands through wavelet packet transformation in FIG. 4A; FIG. 5A is a graph showing the relationship between the acoustic signal and time of normal single-track deposition; FIG. 5B is a graph of the relationship between the sound signal and time in the frequency range of 0 to 62.5 kHz through wavelet packet transformation in FIG. 5A; FIG. 6A is a graph showing the relationship between the acoustic signal and time of a single track deposition bonding defect; FIG. 6B is a graph of the relationship between the sound signal and time in the frequency range of 0 to 62.5 kHz through wavelet packet transformation in FIG. 6A; FIG. 7A is a graph showing the relationship between the acoustic signal and time of normal single-track deposition; FIG. 7B and FIG. 7C are diagrams of the relationship between the sound signal and time in different frequency bands through wavelet packet transformation in FIG. 7A; 8A is a graph showing the relationship between the acoustic signal and time of the monorail spheroidization defect; FIG. 8B and FIG. 8C are graphs of the relationship between the sound signal in different frequency bands and time under the wavelet packet transformation of FIG. 8A; and FIG. 9 is a flow chart of the steps of the identification method of the embodiment.
1:檢測機構 10:平台 20:夾具 21:基礎材料 30:雷射光束與金屬粉末射出總成 40:麥克風 50:聲射感測器 60:分析電腦 61:訊號擷取卡 W:工件 1: Testing agency 10: Platform 20: Fixtures 21: Basic Materials 30: Laser beam and metal powder injection assembly 40: Microphone 50: Acoustic sensor 60: Analytical Computers 61:Signal capture card W: workpiece
Claims (6)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW109124749A TWI761892B (en) | 2020-07-22 | 2020-07-22 | Defect detection mechanism and defect identification method for metal lamination manufacturing |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW109124749A TWI761892B (en) | 2020-07-22 | 2020-07-22 | Defect detection mechanism and defect identification method for metal lamination manufacturing |
Publications (2)
Publication Number | Publication Date |
---|---|
TW202204890A TW202204890A (en) | 2022-02-01 |
TWI761892B true TWI761892B (en) | 2022-04-21 |
Family
ID=81323442
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW109124749A TWI761892B (en) | 2020-07-22 | 2020-07-22 | Defect detection mechanism and defect identification method for metal lamination manufacturing |
Country Status (1)
Country | Link |
---|---|
TW (1) | TWI761892B (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115166049B (en) * | 2022-09-07 | 2022-12-02 | 广东工业大学 | Laser ultrasonic real-time detection system and method based on additive manufacturing |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105619818A (en) * | 2015-12-31 | 2016-06-01 | 浙江大学 | Fused deposition modeling 3D printing monitoring system based on acoustic emission |
CN107708895A (en) * | 2015-06-11 | 2018-02-16 | 瑞尼斯豪公司 | Increasing material manufacturing apparatus and method |
-
2020
- 2020-07-22 TW TW109124749A patent/TWI761892B/en active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107708895A (en) * | 2015-06-11 | 2018-02-16 | 瑞尼斯豪公司 | Increasing material manufacturing apparatus and method |
CN105619818A (en) * | 2015-12-31 | 2016-06-01 | 浙江大学 | Fused deposition modeling 3D printing monitoring system based on acoustic emission |
Also Published As
Publication number | Publication date |
---|---|
TW202204890A (en) | 2022-02-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
You et al. | Review of laser welding monitoring | |
Shao et al. | Review of techniques for on-line monitoring and inspection of laser welding | |
CN113231651B (en) | Additive manufacturing apparatus and method | |
Fan et al. | Research and prospect of welding monitoring technology based on machine vision | |
Cai et al. | Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature | |
CN101328584A (en) | Laser cladding real time monitoring system | |
CN109477737B (en) | Method and device for in-situ and real-time quality control in additive manufacturing process | |
Sun et al. | Sensor systems for real-time monitoring of laser weld quality | |
TWI761892B (en) | Defect detection mechanism and defect identification method for metal lamination manufacturing | |
Chen et al. | A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification | |
CN109269985B (en) | High-frequency ultrasonic online monitoring method for internal defects of metal moving molten pool | |
Huang et al. | Feasibility study of using acoustic signals for online monitoring of the depth of weld in the laser welding of high-strength steels | |
Luo et al. | Monitoring of laser welding using source localization and tracking processing by microphone array | |
Mathivanan et al. | Artificial neural network to predict the weld status in laser welding of copper to aluminum | |
CN108375581B (en) | Double-beam laser welding process defect control method based on acousto-optic signal monitoring | |
CN110702686A (en) | Directional energy deposition process nondestructive testing equipment and method based on coherent imaging | |
Chen et al. | Experimental study of quality monitoring system integrated with a microphone array in laser microlap welding | |
Zhao et al. | GMAW metal transfer mode identification from welding sound | |
CN112098520A (en) | Detection system and method for detecting internal defect shape of material based on laser ultrasonic | |
CN115406973A (en) | Online monitoring device and method for internal defects of electric arc additive forming metal component | |
Lv et al. | Research on welding penetration state recognition based on BP-Adaboost model for pulse GTAW welding dynamic process | |
CN112371995A (en) | Selective laser melting 3D printing crack detection method and device and storage medium | |
KR102565300B1 (en) | Testing method of secondary battery and manufacturing method of secondary battery including the same | |
CN218099027U (en) | System for monitoring internal defects of selective laser melting process in real time on line | |
Grabmann et al. | Analysis of photodiode signals for monitoring the laser beam welding process of cell-internal contacts in lithium-ion batteries |