JP7635931B2 - How to detect product defects - Google Patents
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- 230000007547 defect Effects 0.000 title claims description 62
- 238000001514 detection method Methods 0.000 claims description 53
- 238000005266 casting Methods 0.000 claims description 39
- 238000000034 method Methods 0.000 claims description 21
- 238000012935 Averaging Methods 0.000 claims description 6
- 238000002591 computed tomography Methods 0.000 description 23
- 230000005484 gravity Effects 0.000 description 5
- 238000012937 correction Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 239000002184 metal Substances 0.000 description 4
- 238000013519 translation Methods 0.000 description 3
- 230000002950 deficient Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 229910052755 nonmetal Inorganic materials 0.000 description 1
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Description
本発明は製品の欠陥検出方法に関し、特に鋳造品に生じる内部欠陥である鋳巣を検出するのに適した方法に関するものである。 The present invention relates to a method for detecting defects in products, and in particular to a method suitable for detecting blowholes, which are internal defects that occur in castings.
製品の欠陥、例えば鋳造品に生じる鋳巣を検出するために輪切り断面画像が取得できるＣＴ（computed tomography）を使用する試みがなされているが（特許文献１）、ＣＴ画像にはアーチファクトという特有のノイズが生じるため、これによる欠陥検出精度の低下が問題となる。そこで例えば特許文献２には医用ＣＴ画像においてこれに生じる金属アーチファクトを除去する方法が示されており、ここでは、金属領域のピクセル点を中心とする円形検出領域を設定し、当該円形検出領域内の上記金属領域と非金属領域の比がプリセット値以上となった場合に上記円形検出領域を境界領域として境界平滑化処理を実行することにより金属アーチファクトを除去している。 Attempts have been made to use CT (computed tomography), which can obtain cross-sectional images, to detect product defects, such as blowholes that occur in castings (Patent Document 1), but CT images produce a unique noise called artifacts, which causes a problem of reduced defect detection accuracy. For example, Patent Document 2 shows a method for removing metal artifacts that occur in medical CT images, in which a circular detection area is set with the pixel point of the metal area at its center, and when the ratio of the metal area to the non-metal area in the circular detection area exceeds a preset value, the circular detection area is used as the boundary area and boundary smoothing processing is performed to remove the metal artifacts.
しかし、上記従来のアーチファクト除去方法では複雑な演算を必要とするため、製品の欠陥検出を簡易に行うことができないという問題があった。 However, the conventional artifact removal methods described above require complex calculations, which means that product defect detection cannot be easily performed.
そこで、本発明はこのような課題を解決するもので、ＣＴ画像からのアーチファクト除去の演算負担を軽減して、簡易に製品の欠陥検出を行うことが可能な欠陥検出方法を提供することを目的とする。 Therefore, the present invention aims to solve these problems by providing a defect detection method that reduces the computational burden of removing artifacts from CT images and enables easy detection of product defects.
上記目的を達成するために、本第１発明では、所定数の同形製品を同一位置で輪切りした断面画像を取得し、これら断面画像の、対応する各画像領域の輝度を平均化した平均輝度を算出して、当該平均輝度を付与した前記各画像領域よりなる基準断面画像を生成し、一方、欠陥検出対象となる前記製品と同形の製品を前記同一位置で輪切りした検出断面画像を取得し、当該検出断面画像の各画像領域と当該各画像領域に相当する前記基準断面画像の各画像領域の輝度の差分をとった差分輝度を算出して、当該差分輝度を付与した画像領域よりなる欠陥検出用断面画像を生成し、前記欠陥検出用断面画像を前記製品の異なる輪切り位置で必要数生成して、これら欠陥検出用断面画像より欠陥検出用三次元画像を生成し、当該欠陥検出用三次元画像から欠陥を特定することを特徴とする。なお「画像領域」は一画素の領域でも良いし、所定の複数画素の領域でも良い。 In order to achieve the above object, the first invention is characterized in that: a predetermined number of cross-sectional images of identical products are obtained by cutting the products at the same position, an average brightness is calculated by averaging the brightness of the corresponding image areas of these cross-sectional images, and a reference cross-sectional image is generated from the image areas to which the average brightness is added; a detection cross-sectional image of a product of the same shape as the product to be detected is obtained by cutting the product at the same position, a difference brightness is calculated by taking the difference between the brightness of each image area of the detection cross-sectional image and each image area of the reference cross-sectional image corresponding to each image area, and a defect detection cross-sectional image is generated from the image areas to which the difference brightness is added; a required number of the defect detection cross-sectional images are generated at different cross-sectional positions of the product, a defect detection three-dimensional image is generated from these defect detection cross-sectional images, and defects are identified from the defect detection three-dimensional image . Note that the "image area" may be an area of one pixel or an area of a predetermined number of pixels.
本第１発明において、基準断面画像ではその画像領域に、所定数の同形製品を同一位置で輪切りした断面画像の対応する各画像領域の輝度を平均化した平均輝度が付与されているから、上記各製品の内部に局部的な欠陥があってこの部分で輝度が大きく変化していても、平均化によって欠陥による輝度変化は薄められて十分小さくなり、欠陥画像部分の無い基準断面画像が得られる。この際、上記各断面画像上のアーチファクトは、上記同形製品の設置位置・姿勢が同一であれば、常に同一位置に同一輝度で現れるから、上記のように輝度を平均化しても基準断面画像上にそのまま残存する。一方、検出断面画像上には、欠陥画像部分とこれに重畳して上記基準断面画像上と同様のアーチファクトが現れる。そこで、検出断面画像の各画像領域と当該各画像領域に相当する前記基準断面画像の各画像領域の輝度の差分をとった差分輝度を算出して、当該差分輝度を付与した画像領域よりなる欠陥検出用断面画像を生成すると、欠陥検出用断面画像ではアーチファクトは除去されて欠陥の画像のみが残るから、欠陥が容易かつ確実に特定できる。このようにして、本第１発明によれば、複雑な演算を要することなく輝度の平均化演算と輝度の差分演算のみの簡易な演算で、製品の欠陥検出を簡易に行うことができる。加えて、欠陥検出用三次元画像が生成されるから、欠陥の大きさ（体積）や存在位置をさらに正確に検出でき、欠陥の特定をより確実に行うことができる。 In the first invention, the image area of the reference cross-sectional image is given an average brightness obtained by averaging the brightness of each corresponding image area of the cross-sectional images obtained by cutting a predetermined number of identical products at the same position. Therefore, even if there is a local defect inside each product and the brightness changes significantly at this part, the brightness change due to the defect is diluted and becomes sufficiently small by averaging, and a reference cross-sectional image without a defective image part is obtained. In this case, if the installation position and posture of the identical products are the same, the artifacts on each cross-sectional image always appear at the same position with the same brightness, so they remain on the reference cross-sectional image even if the brightness is averaged as described above. On the other hand, the detection cross-sectional image appears with the defect image part and the same artifacts superimposed thereon. Therefore, by calculating the difference in brightness between each image area of the detection cross-sectional image and each image area of the reference cross-sectional image corresponding to each image area, a defect detection cross-sectional image consisting of image areas to which the difference brightness is given is generated, and the artifacts are removed from the defect detection cross-sectional image and only the image of the defect remains, so that the defect can be easily and reliably identified. In this way, according to the first invention, product defect detection can be easily performed by simple calculations of averaging brightness and calculating brightness differences without requiring complex calculations. In addition, since a three-dimensional image for defect detection is generated, the size (volume) and location of the defect can be detected more accurately, and the defect can be identified more reliably.
本第２発明では、前記所定数取得する各断面画像中から、明らかな欠陥を予め除いておく。 In the second invention , obvious defects are removed in advance from the predetermined number of acquired cross-sectional images.
本第２発明によれば、自動あるいは目視等によって各断面画像中から明らかな欠陥が予め除かれるから、残る欠陥の特定の効率が向上する。 According to the second invention , obvious defects are removed beforehand from each cross-sectional image automatically or visually, etc., so that the efficiency of identifying remaining defects is improved.
本第３発明では、前記製品は鋳物であり、前記欠陥は鋳巣である。 In the third invention , the product is a casting, and the defect is a blowhole.
以上のように、本発明の欠陥検出方法によれば、ＣＴ画像からのアーチファクト除去の演算負担が軽減されて、簡易に製品の欠陥検出を行うことができる。 As described above, the defect detection method of the present invention reduces the computational burden of removing artifacts from CT images, making it possible to easily detect defects in products.
なお、以下に説明する実施形態はあくまで一例であり、本発明の要旨を逸脱しない範囲で当業者が行う種々の設計的改良も本発明の範囲に含まれる。 The embodiment described below is merely an example, and various design improvements made by those skilled in the art that do not deviate from the gist of the present invention are also included in the scope of the present invention.
図１には本発明方法を実施するための装置の一例を示す。製品として例えば鋳造品ＷがＸ－Ｙ平面内で回転するテーブル（図示略）上に載置される。Ｘ－Ｙ平面に平行な面内で、鋳造品Ｗを挟んで対向するようにＣＴ（computed tomography）を構成するＸ線照射器１とラインセンサ２が位置しており、Ｘ線照射器１からコリメータ等によってビームを扁平に狭められた扇状に拡がるＸ線Ｒが鋳造品Ｗを輪切り状に透過してラインセンサ２に入力している。 Figure 1 shows an example of an apparatus for carrying out the method of the present invention. A product, for example a casting W, is placed on a table (not shown) that rotates in the X-Y plane. An X-ray irradiator 1 and a line sensor 2 that constitute a CT (computed tomography) are positioned facing each other on either side of the casting W in a plane parallel to the X-Y plane, and X-rays R that spread out in a fan shape, with the beam narrowed flat by a collimator or the like, are emitted from the X-ray irradiator 1 and penetrate the casting W in a cross-sectional manner before being input to the line sensor 2.
ラインセンサ２からの出力はデジタルデータとして制御装置内のコンピュータ３に入力される。鋳造品Ｗをテーブル上で回転させることによって全方位からの透過Ｘ線がラインセンサ２に入力し、ラインセンサ２からのデジタルデータを入力したコンピュータ３で公知の方法によって、Ｚ方向の所定位置での鋳造品Ｗの輪切り断面画像が得られる。 The output from the line sensor 2 is input as digital data to the computer 3 in the control device. By rotating the casting W on the table, transmitted X-rays from all directions are input to the line sensor 2, and the computer 3 inputs the digital data from the line sensor 2 and obtains a cross-sectional image of the casting W at a predetermined position in the Z direction using a known method.
本実施形態では、鋳造品Ｗを載置したテーブルと、Ｘ線照射器１およびラインセンサ２は、Ｚ方向へ相対移動可能であり、コンピュータ３内の演算によって、Ｚ方向の複数位置で得られた断面画像から鋳造品Ｗの三次元画像を得ることができる。Ｘ線照射器１やテーブルの回転、Ｚ方向への相対移動等はコンピュータ３からの出力信号で適宜行われる。なお、以下にフローチャートで説明する各手順は、コンピュータ３のプログラムによって実行される。 In this embodiment, the table on which the casting W is placed, the X-ray irradiator 1, and the line sensor 2 are capable of relative movement in the Z direction, and a three-dimensional image of the casting W can be obtained from cross-sectional images obtained at multiple positions in the Z direction through calculations within the computer 3. Rotation of the X-ray irradiator 1 and the table, relative movement in the Z direction, etc. are appropriately performed by output signals from the computer 3. Each procedure described below in the flowchart is executed by a program on the computer 3.
Ａ．基準断面画像の生成
（断面画像の取得）
図２には基準断面画像を生成するための手順を示す。最初にステップ１０１で、鋳造品の所定のＺ方向位置での断面画像を取得する。本実施形態では、一の鋳造品についてＺ方向で複数位置の断面画像を取得する。これを図３に示し、図３のＦ１，Ｆ２，Ｆ３はそれぞれＺ方向の３位置Ｚ１，Ｚ２，Ｚ３で得られた鋳造品Ｗの断面画像の一例である。本実施形態では、一の鋳造品についてＺ方向の複数（後述する三次元画像を得るのに必要な所定数）位置での断面画像を取得した後、同形の必要数の他の鋳造品についてもＺ方向の同一位置でそれぞれ断面画像を取得する。
A. Generation of reference cross-sectional images (acquisition of cross-sectional images)
Fig. 2 shows a procedure for generating a reference cross-sectional image. First, in step 101, a cross-sectional image of a casting at a predetermined Z-direction position is acquired. In this embodiment, cross-sectional images of one casting at multiple positions in the Z direction are acquired. This is shown in Fig. 3, where F1, F2, and F3 are examples of cross-sectional images of a casting W acquired at three positions Z1, Z2, and Z3 in the Z direction, respectively. In this embodiment, after cross-sectional images of one casting at multiple positions in the Z direction (a predetermined number required to obtain a three-dimensional image described later) are acquired, cross-sectional images are acquired at the same positions in the Z direction for a required number of other castings of the same shape.
(断面画像の位置補正)
続いて図２のステップ１０２では、上記断面画像Ｆ１～Ｆ３の位置を補正する。これはテーブル上に載置される鋳造品の位置がその都度僅かにズレるからである。このズレは本実施形態では鋳造品がテーブル上に置かれていることによりＺ方向には生じず、Ｘ－Ｙ平面内でのみ生じる。なお、鋳造品が置かれる際の位置ズレが問題の無い程度に小さい場合には特に位置補正の必要はない。
(Correction of cross-sectional image position)
Next, in step 102 in Fig. 2, the positions of the cross-sectional images F1 to F3 are corrected. This is because the position of the casting placed on the table shifts slightly each time. In this embodiment, this shift does not occur in the Z direction because the casting is placed on the table, but occurs only in the XY plane. Note that if the position shift when the casting is placed is small enough to not cause any problems, there is no need to correct the position.
断面画像の上記位置補正は本実施形態では、別に用意された当該鋳造品のＣＡＤ三次元画像を利用して行う。図４にはＣＡＤ三次元画像Ｗ´の一例を示し、図中のＦ１´，Ｆ２´，Ｆ３´は、ＣＡＤ三次元画像Ｗ´を上記３位置Ｚ１，Ｚ２，Ｚ３と同一のＺ方向位置Ｚ１´，Ｚ２´，Ｚ３´で輪切りにしたＣＡＤ断面画像である。 In this embodiment, the above-mentioned position correction of the cross-sectional image is performed by using a separately prepared CAD three-dimensional image of the casting. Figure 4 shows an example of a CAD three-dimensional image W', where F1', F2', and F3' are CAD cross-sectional images obtained by slicing the CAD three-dimensional image W' at Z-direction positions Z1', Z2', and Z3', which are the same as the above-mentioned three positions Z1, Z2, and Z3.
位置補正ステップ１０２の詳細を図５に示す。図５において、ステップ２０１では、ＣＴ断面画像データ（断面画像に対応）とこれに対応するＣＡＤ断面画像データ（ＣＡＤ断面画像に対応）を切り出す。ステップ２０２ではＣＡＤ断面画像データをスキャンして四隅のコーナを検出する。ＣＡＤ断面画像Ｆ１´で検出されたコーナＣ１´，Ｃ２´，Ｃ３´，Ｃ４´を図６（１）に示す。 Details of the position correction step 102 are shown in FIG. 5. In FIG. 5, in step 201, the CT cross-sectional image data (corresponding to the cross-sectional image) and the corresponding CAD cross-sectional image data (corresponding to the CAD cross-sectional image) are extracted. In step 202, the CAD cross-sectional image data is scanned to detect the four corners. The corners C1', C2', C3', and C4' detected in the CAD cross-sectional image F1' are shown in FIG. 6 (1).
図５のステップ２０３では検出したコーナＣ１´～Ｃ４´の周辺に対応するＣＴ断面画像データの縦方向及び横方向の微分値を算出し、微分値が一定量以上の重心を算出する（ステップ２０４）。図６（２）は、算出された重心Ｃ１，Ｃ２，Ｃ３，Ｃ４を断面画像Ｆ１上に示したものである。 In step 203 of FIG. 5, the vertical and horizontal differential values of the CT cross-sectional image data corresponding to the periphery of the detected corners C1' to C4' are calculated, and the centers of gravity where the differential values are equal to or greater than a certain amount are calculated (step 204). FIG. 6 (2) shows the calculated centers of gravity C1, C2, C3, and C4 on the cross-sectional image F1.
図５のステップ２０５では、コーナＣ１´～Ｃ４´と重心Ｃ１～Ｃ４の相対位置関係よりＣＡＤ断面画像データに対するＣＴ断面画像データの平行移動量と回転量を算出する。この算出は例えば、鋳造品のＺ方向の複数位置Ｚ１～Ｚ３でそれぞれ検出ないし算出されたコーナＣ１´～Ｃ４´と重心Ｃ１～Ｃ４の相対位置関係を使って例えばその最小二乗誤差が最小になるような適当な平行移動量および回転角を求めることにより行う。ステップ２０６ではＣＴ断面画像データを、算出された上記平行移動量および回転角だけ移動させて断面画像Ｆ１～Ｆ３の画像位置を補正する。 In step 205 of FIG. 5, the amount of parallel translation and rotation of the CT cross-sectional image data relative to the CAD cross-sectional image data is calculated based on the relative positional relationship between the corners C1'-C4' and the centers of gravity C1-C4. This calculation is performed, for example, by determining an appropriate amount of parallel translation and rotation angle that minimizes the least square error using the relative positional relationship between the corners C1'-C4' and the centers of gravity C1-C4 detected or calculated at multiple positions Z1-Z3 in the Z direction of the casting. In step 206, the CT cross-sectional image data is moved by the calculated amount of parallel translation and rotation angle to correct the image positions of the cross-sectional images F1-F3.
（平均輝度の算出、基準断面画像の生成）
図２のステップ１０３では、位置補正された断面画像Ｆ１～Ｆ３について、各画像領域（本実施形態では画素）の平均輝度を算出する。この平均輝度の算出は、必要数の同形の鋳造品Ｗについて、Ｚ方向の同一位置で輪切りにされた各断面画像の各画像領域の輝度の平均を算出するものである。このように平均を算出することによって、鋳造品の内部に局部的な欠陥としての鋳巣があってこの部分で輝度が大きく変化していても、この平均化によって鋳巣による輝度変化は薄められて十分小さくなる。
(Calculation of average brightness, generation of reference cross section image)
2, the average brightness of each image region (pixel in this embodiment) is calculated for the position-corrected cross-sectional images F1 to F3. This calculation of the average brightness is carried out by calculating the average brightness of each image region of each cross-sectional image sliced at the same position in the Z direction for a required number of identically shaped castings W. By calculating the average in this manner, even if there is a blown cavity as a local defect inside the casting and the brightness changes significantly in this area, the averaging dilutes the brightness change due to the blown cavity and makes it sufficiently small.
一方、各断面画像のアーチファクトは常に同一位置に同一輝度で現れるから、上記のような輝度の平均を算出してもそのまま残存する。したがって、各画像領域の輝度を平均化し、このような平均輝度が付与された画像領域からなる基準断面画像を生成すると（ステップ１０４）、基準断面画像は、鋳巣が解消された鋳造品の断面画像にアーチファクトが重畳したものとなる。本実施形態では、このような基準断面画像を、鋳造品についてＺ方向の複数（後述する三次元画像を得るのに必要な数）位置で生成しておく。 However, because artifacts in each cross-sectional image always appear at the same position with the same brightness, they remain as they are even if the average brightness is calculated as described above. Therefore, when the brightness of each image region is averaged and a reference cross-sectional image is generated consisting of image regions to which this average brightness has been assigned (step 104), the reference cross-sectional image is one in which artifacts are superimposed on a cross-sectional image of the casting from which the blowholes have been eliminated. In this embodiment, such reference cross-sectional images are generated at multiple positions in the Z direction for the casting (the number required to obtain a three-dimensional image, described later).
平均輝度算出ステップ１０３（図２）の詳細を図７に示す。図７のステップ３０１では基準断面画像データのＣＴ値（輝度値に相当）を０にリセットしておく。続いてＣＴ画像データのＣＴ値を上記基準断面画像データに加算し（ステップ３０２）、この加算を必要数のＣＴ断面画像データについて繰り返し（ステップ３０３）、ステップ３０４で基準断面画像データの全ての画素のＣＴ値をＣＴ断面画像データの個数（必要数）で除算する。 Details of the average brightness calculation step 103 (FIG. 2) are shown in FIG. 7. In step 301 in FIG. 7, the CT value (corresponding to the brightness value) of the reference cross-sectional image data is reset to 0. Next, the CT value of the CT image data is added to the reference cross-sectional image data (step 302), and this addition is repeated for the required number of CT cross-sectional image data (step 303). In step 304, the CT values of all pixels of the reference cross-sectional image data are divided by the number of CT cross-sectional image data (required number).
なお、平均輝度の算出は必ずしも上述のような算術平均には限られず、必要に応じて加重平均等によっても良い。 The calculation of the average brightness is not necessarily limited to the arithmetic mean as described above, but may be a weighted average, etc., if necessary.
Ｂ．欠陥検出用断面画像の生成
（検出断面画像の取得）
図８には欠陥検出用断面画像を生成するための手順を示す。最初にステップ４０１で検出断面画像を取得する。検出断面画像は、欠陥検出の対象となる鋳造品をテーブル上に載置して前述したＣＴによって得られるＺ方向の所定位置での輪切り断面画像である。欠陥検出の対象となる鋳造品は、基準断面画像を生成してある鋳造品と同形のものである。
B. Generation of cross-sectional images for defect detection (acquisition of detection cross-sectional images)
8 shows a procedure for generating a cross-sectional image for defect detection. First, in step 401, a detection cross-sectional image is acquired. The detection cross-sectional image is a cross-sectional image cut in a predetermined position in the Z direction obtained by placing a casting that is to be subjected to defect detection on a table and using the above-mentioned CT. The casting that is to be subjected to defect detection has the same shape as the casting for which the reference cross-sectional image has been generated.
図８のステップ４０２では前述したと同様の手順（ステップ１０２、ステップ２０１～２０６参照）で検出断面画像の位置補正を行う。 In step 402 of FIG. 8, the position of the detected cross-sectional image is corrected using the same procedure as described above (see step 102 and steps 201 to 206).
（差分輝度の算出）
続いて図８のステップ４０３では、検出断面画像と基準断面画像の差分輝度を算出する。この場合の基準断面画像は、検出断面画像とＺ方向の同一位置で鋳造品を輪切りして得られたものを使用する。ここで、検出断面画像は、鋳造品の断面画像にアーチファクトと鋳巣の画像（鋳巣がある場合には）が重畳したものとなっている。これに対して基準断面画像は、前述したように、鋳造品の断面画像部分にアーチファクトが重畳したものである。したがって、検出断面画像と基準断面画像の差分輝度を算出すれば、アーチファクトと、鋳造品の断面画像部分は除去されて、鋳巣の画像部分だけが残った欠陥検出用断面画像が得られることになる。
(Calculation of Differential Luminance)
Next, in step 403 of FIG. 8, the difference in brightness between the detected cross-sectional image and the reference cross-sectional image is calculated. In this case, the reference cross-sectional image is obtained by cutting the casting in a cross-sectional view at the same position in the Z direction as the detected cross-sectional image. Here, the detected cross-sectional image is a cross-sectional image of the casting on which an artifact and an image of a blowhole (if a blowhole exists) are superimposed. In contrast, the reference cross-sectional image is, as described above, a cross-sectional image of the casting on which an artifact is superimposed. Therefore, by calculating the difference in brightness between the detected cross-sectional image and the reference cross-sectional image, a defect detection cross-sectional image can be obtained in which the artifact and the cross-sectional image portion of the casting are removed and only the image portion of the blowhole remains.
差分輝度算出ステップ４０３の詳細を図９に示す。図９のステップ５０１ではＣＴ断面画像データ（検出断面画像に対応）のＣＴ値から基準断面画像データのＣＴ値を減算し、減算結果が正値である場合はこれを０に設定し（ステップ５０２）、減算結果が負値である場合は－１を乗算して正値にする（ステップ５０３）。鋳巣がある場合にはその部分の輝度は低下するから減算値は大きな負値になる。そこで、これを正値に変換しておく。 Details of the differential brightness calculation step 403 are shown in Figure 9. In step 501 in Figure 9, the CT value of the reference cross-sectional image data is subtracted from the CT value of the CT cross-sectional image data (corresponding to the detected cross-sectional image), and if the result of the subtraction is a positive value, it is set to 0 (step 502), and if the result of the subtraction is a negative value, it is multiplied by -1 to make it a positive value (step 503). If there is a cast cavity, the brightness of that area will decrease, so the subtraction value will be a large negative value. Therefore, this is converted to a positive value.
（低差分輝度領域の除去）
図８のステップ４０４では、ステップ４０３で得られた欠陥検出用断面画像中の、輝度が閾値よりも小さい部分をノイズとして除去する。
(Removal of low brightness difference areas)
In step 404 in FIG. 8, any portion of the defect detection cross-sectional image obtained in step 403 that has a brightness lower than a threshold value is removed as noise.
（エッジノイズの除去、欠陥検出用断面画像の生成）
図８のステップ４０５では、鋳造品の輪郭に沿って生じる欠陥検出用断面画像中の高輝度部分をエッジノイズとして除去して欠陥検出用断面画像を生成する（ステップ４０６）。図１０には、エッジノイズ除去ステップ４０５の詳細を示す。図１０において、ステップ６０１で境界距離を設定し、ステップ６０２では、当該欠陥検出断面画像データに対応するＣＡＤ断面画像データで認識できる鋳造品の輪郭部から境界距離以下の領域内に零でない差分データ（差分輝度値）がある場合にこれを０に設定する。
(Removal of edge noise, generation of cross-sectional images for defect detection)
In step 405 in Fig. 8, high luminance parts occurring along the contour of the casting are removed as edge noise in the defect detection cross-sectional image to generate a defect detection cross-sectional image (step 406). Fig. 10 shows the details of the edge noise removal step 405. In Fig. 10, a boundary distance is set in step 601, and in step 602, if there is non-zero difference data (difference luminance value) within an area less than the boundary distance from the contour of the casting that can be recognized in the CAD cross-sectional image data corresponding to the defect detection cross-sectional image data, this is set to 0.
（欠陥検出用三次元画像の生成、鋳巣の特定）
図８のステップ４０７では、上記欠陥検出用断面画像を、鋳造品のＺ方向の異なる輪切り位置で必要数取得して、欠陥検出用三次元画像を生成する。この欠陥検出用三次元画像は鋳巣部分（候補）のみが三次元空間に配置されたものとなる。そこで、続くステップ４０８では上記欠陥検出用三次元画像中の鋳巣候補から鋳巣を特定する。
(Generating 3D images for defect detection, identifying blowholes)
In step 407 in Fig. 8, a required number of cross-sectional images for defect detection are obtained at different cross-sectional positions in the Z direction of the casting to generate a three-dimensional image for defect detection. This three-dimensional image for defect detection is one in which only the cast cavity portions (candidates) are arranged in three-dimensional space. In the following step 408, the cast cavity is identified from the candidate cast cavity in the three-dimensional image for defect detection.
鋳巣特定ステップ４０８の詳細を図１１に示す。図１１のステップ７０１では、鋳巣判定基準（体積、長さ、存在位置）を設定する。ステップ７０２では、欠陥検出用三次元画像データにおいて連続した非零領域である鋳巣候補を検出する。続くステップ７０３では、検出された鋳巣候補の体積、長さ、存在位置を算出し、鋳巣判定基準を満たす場合は当該鋳巣候補を鋳巣と特定する（ステップ７０４からステップ７０５）。この手順を全ての鋳巣候補について行う（ステップ７０６）。 Details of the blowhole identification step 408 are shown in Figure 11. In step 701 of Figure 11, the blowhole identification criteria (volume, length, location) are set. In step 702, blowhole candidates that are continuous non-zero areas in the 3D image data for defect detection are detected. In the following step 703, the volume, length, and location of the detected blowhole candidates are calculated, and if the blowhole identification criteria are met, the blowhole candidate is identified as a blowhole (steps 704 to 705). This procedure is performed for all blowhole candidates (step 706).
（その他の実施形態）
上記実施形態では欠陥検出用三次元画像で鋳巣を特定するようにしたが、二次元の欠陥検出用断面画像で特定するようにしても良い。
上記実施形態において、各断面画像中の明らかな欠陥を目視等によって予め除いておくようにすれば、残る欠陥の特定を効率的に行うことができる。
上記実施形態では鋳造品の鋳巣を検出するものとしたが、本発明は製品の欠陥の検出に広く適用できるものである。
Other Embodiments
In the above embodiment, a cast cavity is identified using a three-dimensional image for defect detection, but it may be identified using a two-dimensional cross-sectional image for defect detection.
In the above embodiment, if obvious defects in each cross-sectional image are removed in advance by visual inspection or the like, the remaining defects can be efficiently identified.
Although the above embodiment is directed to detecting blowholes in a casting, the present invention can be widely applied to detecting defects in products.
１…Ｘ線照射器、２…ラインセンサ、３…コンピュータ、Ｒ…Ｘ線、Ｗ…鋳造品（製品）。 1...X-ray irradiator, 2...line sensor, 3...computer, R...X-ray, W...casting (product).
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