WO2015115499A1 - Tool inspection method and tool inspection device - Google Patents
Tool inspection method and tool inspection device Download PDFInfo
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- WO2015115499A1 WO2015115499A1 PCT/JP2015/052396 JP2015052396W WO2015115499A1 WO 2015115499 A1 WO2015115499 A1 WO 2015115499A1 JP 2015052396 W JP2015052396 W JP 2015052396W WO 2015115499 A1 WO2015115499 A1 WO 2015115499A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20152—Watershed segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
Definitions
- the present invention relates to a tool inspection method and a tool inspection apparatus.
- JP 1997-21080A discloses a method for detecting a shape defect of a tool by scanning a line entering a minute amount from the outer peripheral shape drawn by a ridge line with a laser focus displacement meter and detecting irregularities on the scanning line. Yes.
- JP1998-96616A as a method of inspecting a chip of a tool tip, a tool of a milling machine is transferred by a traveling robot and fixed to a jig on a chip inspection / exchange work table, and a slit is projected from a projection window of a structure light unit. It is disclosed that light is projected onto a tool tip, a camera shot of the tool tip is taken and analyzed by an image processing device, and index data representing the size of the tool tip defect is obtained to determine the necessity for replacement. .
- the Y-axis direction and Z-axis direction spread of the recess formed by wear progressing from the edge portion of the tool tip is recognized as index data, and the index data is a preset allowable value. It is determined that there is a need for replacement when exceeding
- JP1997-21830A is for inspecting a tool for defects by scanning with a laser beam, so that the apparatus becomes large and the time required for the inspection becomes longer.
- JP1998-96616A is a method that pays attention to a change in the wear region and is a method that indirectly measures the wear region, and it is difficult to say that the state of the tool can be inspected with high accuracy.
- An object of the present invention is to provide a tool inspection method and a tool inspection apparatus capable of inspecting a tool with a simple method with high accuracy.
- a tool inspection method for imaging a target tool to be inspected, generating a grayscale image, and processing the grayscale image with a first filter.
- a terrain image generation step for generating a terrain image showing a luminance gradient, and processing the grayscale image with a second filter to extract a feature portion in the wear region, and a marker is placed on the extracted feature portion
- a marker image generation step for setting and generating a marker image
- a wear region extraction step for extracting a wear region by region division by a Maker Based Watershed method using the topographic image and the marker image
- the wear region extraction step A determination step of determining a state of the target tool based on the extracted area of the wear region.
- a tool inspection apparatus that captures a target tool to be inspected and generates a grayscale image, and a grayscale image generated by the first filter.
- a terrain image generation unit for processing to generate a terrain image showing a luminance gradient; and processing the grayscale image with a second filter to extract a feature portion in a wear region; and marking the extracted feature portion with a marker
- a marker image generation unit that generates a marker image by setting the wear region extraction unit that extracts a wear region by region division using a Maker Based Watershed method using the topographic image and the marker image, and the wear region extraction step
- a determination unit that determines a state of the target tool based on the extracted area of the wear region.
- FIG. 1 is a schematic configuration diagram of a tool inspection apparatus according to a first embodiment of the present invention.
- FIG. 2 is a flowchart showing the procedure of the tool inspection method according to the first embodiment of the present invention.
- FIG. 3 shows an inspection target image.
- FIG. 4 shows a grayscale image.
- FIG. 5 is a diagram for explaining the Watershed method.
- FIG. 6 shows a topographic image.
- FIG. 7 shows a marker image.
- FIG. 8A shows an image obtained by extracting a wear region.
- FIG. 8B is a partially enlarged view of FIG. 8A.
- FIG. 9 is a graph showing a change between the automatically extracted wear area and the manually extracted wear area.
- FIG. 10 shows a topographic image in the second embodiment of the present invention.
- the tool inspection apparatus 100 is an apparatus that inspects the state of a tool.
- the inspection target is a throw-away tip of various tools.
- the tool inspection apparatus 100 includes a camera 1 as an image acquisition unit that acquires an inspection target image by photographing a cutting edge of a throw-away tip mounted on a processing apparatus, and an image acquired by the camera 1. And a computer 2 that performs image processing of data and determines the state of the throw-away chip.
- the camera 1 is attached to a processing apparatus.
- the computer 2 may be provided adjacent to the processing device, or may be provided at a location away from the processing device.
- the computer 2 includes a display 21 as a display unit capable of displaying image data, and a keyboard 22 and a mouse 23 as input units capable of inputting an instruction from a user.
- step 11 the cutting edge of the throw-away tip mounted on the processing apparatus is photographed using the camera 1, and an inspection target image (FIG. 3) is acquired (image acquisition process).
- an inspection target image shown in FIG. 3, a wear portion is displayed at the lower left of the image.
- the inspection target image is acquired as a color image.
- a gray scale image (FIG. 4) is generated from the inspection target image acquired in step 11 (gray scale image generation step). Instead of generating a grayscale image from the color inspection target image acquired in step 11, the grayscale image may be generated directly by the camera 11.
- step 13 the gray scale image acquired in step 12 is positioned. Specifically, the upper linear portion 41 of the edge contour is detected, and the gray scale image is positioned using affine transformation so that the linear portion 41 is horizontal. In this way, all the inspection target images acquired in step 11 are positioned at the same angle.
- the wear area of the blade tip of the throw-away tip is extracted using the Watershed method.
- the watershed method is a method for dividing an image using a density gradient. Region division is performed using the region growth method with the obtained minimum value of the absolute value of the gradient as a seed. As a result, since the region grows from a portion with a small gradient to a portion with a large gradient, a boundary of the region is generated at the edge portion. More specifically, with reference to FIG. 5, the luminance gradient of the pixel of the image is regarded as a topographic structure, and the minimum value and the maximum value of the luminance value are regarded as a valley and a ridge, respectively. First, the minimum value of the luminance value (minimum values 1 and 2 in FIG.
- the watershed method has a problem that the image is divided into the number of seeds. Therefore, in the present embodiment, region division is performed using a Maker Based Watershed method that solves this problem by providing a seed marker in advance.
- a marker is automatically generated using a Gabor filter to be described later, and the wear area of the tip of the throw-away tip is extracted automatically. . This will be described in detail below.
- step 14 the grayscale image acquired in step 12 is processed by the Gabor filter as the first filter and then the luminance gradient is calculated to generate a terrain image (FIG. 6) indicating the luminance gradient (terrain image generation step). ).
- the topographic image indicates the brightness gradient of an image that can be regarded as a topographic structure.
- the Gabor filter is defined by the product of a Gaussian function and a sine wave, and emphasizes those that match the filter characteristics and smoothes those that do not match.
- the filter characteristics are set by adjusting the width of the Gaussian function and the phase and amplitude of the sine wave.
- the worn portion is displayed white (high luminance value), and the non-weared portion is displayed black (low luminance value). Therefore, in step 14, the edge portion of the grayscale image is emphasized using a Gabor filter to enhance the white portion.
- the boundary of the wear region is displayed in white in the topographic image.
- the portion displayed in white in the topographic image is regarded as a ridge when the Maker Based Watershed method is applied, so by generating a topographic image that emphasizes the white part of the grayscale image using the Gabor filter, Maker Based The wear area extraction accuracy is improved when the watershed method is applied.
- the Gabor filter emphasizes the filter that matches the filter characteristics and smoothes the filter that does not match
- the Gabor filter emphasizes the white portion corresponding to the wear area and wears small scratches and the like. A portion (noise) that is not related to the noise can be smoothed and removed.
- step 15 the grayscale image acquired in step 12 is processed with a Gabor filter as a second filter to emphasize features in the wear region, and further binarized to extract features in the wear region.
- the marker 71 is set to the extracted feature part and a marker image (FIG. 7) is produced
- the features in the wear region are extracted using a plurality of Gabor filters having different characteristics. Specifically, a portion corresponding to the maximum value is extracted from the filter values calculated by the filter processing using a plurality of Gabor filters.
- the characteristic portion extracted by the Gabor filter is an edge portion or a region having a high luminance value having a luminance value larger than a certain value.
- the feature region in the wear region is also set as a marker. there is a possibility.
- a restriction process for restricting the marker is performed (marker restriction process). Specifically, weighting is performed according to the distance from the cutting edge contour of the throw-away tip to the marker, and the marker is limited based on the weighting. That is, the marker set in a portion far from the edge contour is deleted.
- step 17 as shown in FIG. 7, the area restriction marker 72 is set so as to surround a certain area around the marker set in the marker image (restriction marker setting step).
- the marker is limited according to the distance from the edge contour to the marker, and by setting the region limiting marker 72 in step 17, the wear region is extracted by region division by the Maker ⁇ ⁇ ⁇ Based Watershed method in the next step. The extraction accuracy when doing so can be improved.
- step 18 the wear area is extracted by area division by the Maker Based Watershed method using the topographic image (FIG. 6) and the marker image (FIG. 7) (wear area extraction step). Specifically, the region is expanded by applying the Maker ⁇ ⁇ ⁇ Based Watershed method to the topographic image using the markers 71 and 72 set in the marker image, and the wear region is extracted.
- FIG. 8A shows an image obtained by extracting the wear region.
- FIG. 8B is a partially enlarged view of FIG. 8A. 8A and 8B, the boundary of the extraction region is indicated by reference numeral 81. In FIG. 8B, the marker 71 is indicated by hatching.
- FIG. 8A shows an image whose orientation has been restored to the same angle as the inspection target image shown in FIG.
- the wear region extracted by the Maker81Based Watershed method (the region surrounded by the boundary indicated by reference numeral 81) is almost coincident with the wear portion displayed in white, and the wear region is accurately extracted. I was able to confirm that it was possible.
- step 19 the area of the wear region extracted in step 18 is calculated, and the state of the tip of the throw-away tip is determined based on the area (determination step). For example, the state of the blade tip of the throw-away tip is determined by the following method. When the area of the wear region extracted in step 18 reaches a predetermined area, the throw-away tip is determined to have a lifetime. Further, deterioration levels 1 to 5 are set in advance according to the size of the area of the wear region, and when the deterioration level 5 is reached, the throw-away tip is determined to have a lifetime.
- the correlation between the area of the wear area and the number of times of use of the throw-away tip is set in advance, and the number of times of use is estimated according to the area of the wear area extracted in step 18 to determine the state of the cutting edge of the throw-away tip. Determine.
- the area of the wear region extracted in step 18 changes abruptly at a predetermined rate or more, it is determined that the throw-away tip is abnormal.
- steps 12 to 19 described above are automatically executed by software stored in the computer 2. As a result, if it is determined that the throw-away tip has reached the end of its life, a notification that prompts replacement is issued.
- the side surface of the tip of the throw-away tip was imaged to obtain the inspection target image.
- the image size of the inspection target image is 1024 ⁇ 960 pixels.
- the obtained image to be inspected is subjected to image processing in steps 12 to 18 in FIG. 2 to automatically extract the wear region, and the area of the extracted wear region is calculated. Further, as a comparative example, every time machining was performed, the side surface of the blade tip of the throw-away tip was imaged, the wear region was manually extracted, and the area of the extracted wear region was calculated.
- Fig. 9 shows the change between the automatically extracted wear area and the manually extracted wear area.
- the area of the automatically extracted wear region is indicated by a solid line
- the area of the manually extracted wear region is indicated by a broken line.
- the change in the area of the automatically extracted wear region generally coincided with the change in the area of the manually extracted wear region. From this, it was confirmed that the wear region can be extracted with high accuracy by using the image processing in steps 12 to 18. Therefore, it can be said that the state of the blade tip of the throw-away tip can be accurately determined by performing image processing in steps 12 to 18 to automatically extract the wear region and evaluating the area of the extracted wear region.
- the state of the target tool is automatically determined based on the area of the wear area extracted by area division by the Maker Based Watershed method using the topographic image (FIG. 6) and the marker image (FIG. 7),
- the tool can be inspected with high accuracy by a simple method.
- a Gabor filter is used to emphasize features in the wear region of the grayscale image, and markers used in the Maker Based Watershed method are set there. In this way, markers can be set automatically instead of manually.
- the marker is limited according to the distance from the edge contour to the marker, and the area restriction marker 72 is set so as to surround a certain area around the marker, so the wear area is extracted by dividing the area by the Maker Based Watershed method. The extraction accuracy when doing so can be improved.
- the topographic image obtained in step 14 in FIG. 2 is different from the topographic image obtained in the first embodiment.
- a topographic image (FIG. 10) showing a luminance gradient is obtained by black-and-white inversion processing of a grayscale image (FIG. 4) with a Gabor filter.
- the worn portion is displayed in black (low luminance value) and the non-weared portion is displayed in white (high luminance value).
- the marker of the marker image (FIG. 7) is set corresponding to a low luminance value in the terrain image. Therefore, when the region is divided by applying the Watershed method, the region grows from the valley (low luminance value) to the ridge (high luminance value), so that the wear region extraction accuracy is improved.
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Abstract
This tool inspection method is provided with a grayscale image generation step for imaging a tool under inspection and generating a grayscale image; a topographical image generation step for processing the grayscale image using a first filter and generating a topographical image indicating a brightness gradient; a marker image generation step for processing the grayscale image using a second filter, extracting a characteristic portion within a wear area, setting a marker for the extracted portion, and generating a marker image; a wear area extraction step for using the topographical image and marker image to extract a wear area through maker-based watershed area segmentation; and a determination step for determining the state of the tool under inspection on the basis of the surface area of the wear area extracted in the wear area extraction step.
Description
本発明は、工具検査方法及び工具検査装置に関するものである。
The present invention relates to a tool inspection method and a tool inspection apparatus.
JP1997-218030Aには、工具の形状欠損を検出する方法として、稜線が描く外周形状から微小量内側に入った線上をレーザーフォーカス変位計で走査し、走査線上の凹凸を検知する方法が開示されている。
JP 1997-21080A discloses a method for detecting a shape defect of a tool by scanning a line entering a minute amount from the outer peripheral shape drawn by a ridge line with a laser focus displacement meter and detecting irregularities on the scanning line. Yes.
また、JP1998-96616Aには、工具チップの欠損を検査する方法として、フライス盤の工具を走行ロボットによって移送してチップ検査交換作業台上の治具に固定し、ストラクチャライトユニットの投光窓からスリット光を工具チップに投光し、工具チップのカメラ撮影を行って画像処理装置で解析し、工具チップの欠損の大きさを表わす指標データを求めて交換必要性を判定することが開示されている。工具チップの交換必要性については、工具チップのエッジ部から進行する摩耗によって形成される凹部のY軸方向とZ軸方向の拡がりを指標データとして認識し、その指標データが予め設定された許容値を超えた場合に交換必要性ありと判定される。
In JP1998-96616A, as a method of inspecting a chip of a tool tip, a tool of a milling machine is transferred by a traveling robot and fixed to a jig on a chip inspection / exchange work table, and a slit is projected from a projection window of a structure light unit. It is disclosed that light is projected onto a tool tip, a camera shot of the tool tip is taken and analyzed by an image processing device, and index data representing the size of the tool tip defect is obtained to determine the necessity for replacement. . Regarding the necessity of tool tip replacement, the Y-axis direction and Z-axis direction spread of the recess formed by wear progressing from the edge portion of the tool tip is recognized as index data, and the index data is a preset allowable value. It is determined that there is a need for replacement when exceeding
JP1997-218030Aに記載の方法は、レーザ光の走査によって工具の欠損を検査するものであるため、装置が大掛かりとなり、また、検査に要する時間も長くなる。
The method described in JP1997-21830A is for inspecting a tool for defects by scanning with a laser beam, so that the apparatus becomes large and the time required for the inspection becomes longer.
JP1998-96616Aに記載の方法は、摩耗領域の変化に着目した手法であって摩耗領域を間接的に測定する方法であるため、工具の状態を精度良く検査できるとは言い難い。
The method described in JP1998-96616A is a method that pays attention to a change in the wear region and is a method that indirectly measures the wear region, and it is difficult to say that the state of the tool can be inspected with high accuracy.
本発明は、簡単な方法で精度良く工具を検査することが可能な工具検査方法及び工具検査装置を提供することを目的とする。
An object of the present invention is to provide a tool inspection method and a tool inspection apparatus capable of inspecting a tool with a simple method with high accuracy.
本発明のある態様によれば、工具検査方法であって、検査対象となる対象工具を撮像し、グレースケール画像を生成するグレースケール画像生成工程と、前記グレースケール画像を第1のフィルタで処理して輝度勾配を示す地形画像を生成する地形画像生成工程と、前記グレースケール画像を第2のフィルタで処理して摩耗領域内の特徴部を抽出し、当該抽出された前記特徴部にマーカを設定してマーカ画像を生成するマーカ画像生成工程と、前記地形画像と前記マーカ画像を用い、Maker Based Watershed法による領域分割によって摩耗領域を抽出する摩耗領域抽出工程と、前記摩耗領域抽出工程にて抽出された前記摩耗領域の面積に基づいて前記対象工具の状態を判定する判定工程と、を備える。
According to an aspect of the present invention, there is provided a tool inspection method for imaging a target tool to be inspected, generating a grayscale image, and processing the grayscale image with a first filter. A terrain image generation step for generating a terrain image showing a luminance gradient, and processing the grayscale image with a second filter to extract a feature portion in the wear region, and a marker is placed on the extracted feature portion A marker image generation step for setting and generating a marker image, a wear region extraction step for extracting a wear region by region division by a Maker Based Watershed method using the topographic image and the marker image, and the wear region extraction step A determination step of determining a state of the target tool based on the extracted area of the wear region.
本発明の別の態様によれば、工具検査装置であって、検査対象となる対象工具を撮像し、グレースケール画像を生成するグレースケール画像生成部と、前記グレースケール画像を第1のフィルタで処理して輝度勾配を示す地形画像を生成する地形画像生成部と、前記グレースケール画像を第2のフィルタで処理して摩耗領域内の特徴部を抽出し、当該抽出された前記特徴部にマーカを設定してマーカ画像を生成するマーカ画像生成部と、前記地形画像と前記マーカ画像を用いMaker Based Watershed法による領域分割によって摩耗領域を抽出する摩耗領域抽出部と、前記摩耗領域抽出工程にて抽出された前記摩耗領域の面積に基づいて前記対象工具の状態を判定する判定部と、を備える。
According to another aspect of the present invention, there is provided a tool inspection apparatus that captures a target tool to be inspected and generates a grayscale image, and a grayscale image generated by the first filter. A terrain image generation unit for processing to generate a terrain image showing a luminance gradient; and processing the grayscale image with a second filter to extract a feature portion in a wear region; and marking the extracted feature portion with a marker A marker image generation unit that generates a marker image by setting the wear region extraction unit that extracts a wear region by region division using a Maker Based Watershed method using the topographic image and the marker image, and the wear region extraction step A determination unit that determines a state of the target tool based on the extracted area of the wear region.
<第1実施形態>
まず、本発明の第1実施形態について説明する。 <First Embodiment>
First, a first embodiment of the present invention will be described.
まず、本発明の第1実施形態について説明する。 <First Embodiment>
First, a first embodiment of the present invention will be described.
本発明の第1実施形態に係る工具検査装置100は、工具の状態を検査する装置である。本実施形態では、検査対象が各種工具のスローアウェイチップである場合について説明する。
The tool inspection apparatus 100 according to the first embodiment of the present invention is an apparatus that inspects the state of a tool. In this embodiment, a case where the inspection target is a throw-away tip of various tools will be described.
図1に示すように、工具検査装置100は、加工装置に実装されたスローアウェイチップの刃先を撮影して検査対象画像を取得する画像取得部としてのカメラ1と、カメラ1にて取得した画像データの画像処理を行い、スローアウェイチップの状態を判定するコンピュータ2と、を備える。カメラ1は加工装置に取り付けられる。コンピュータ2は加工装置に隣接して設けてもよいし、加工装置と離れた場所に設けてもよい。
As shown in FIG. 1, the tool inspection apparatus 100 includes a camera 1 as an image acquisition unit that acquires an inspection target image by photographing a cutting edge of a throw-away tip mounted on a processing apparatus, and an image acquired by the camera 1. And a computer 2 that performs image processing of data and determines the state of the throw-away chip. The camera 1 is attached to a processing apparatus. The computer 2 may be provided adjacent to the processing device, or may be provided at a location away from the processing device.
コンピュータ2は、画像データを表示可能な表示部としてのディスプレイ21と、ユーザからの指示が入力可能な入力部としてのキーボード22及びマウス23と、を備える。
The computer 2 includes a display 21 as a display unit capable of displaying image data, and a keyboard 22 and a mouse 23 as input units capable of inputting an instruction from a user.
次に、図2を参照して、スローアウェイチップの検査方法について詳しく説明する。
Next, the method for inspecting the throw-away chip will be described in detail with reference to FIG.
ステップ11では、カメラ1を用いて加工装置に実装されたスローアウェイチップの刃先を撮影し、検査対象画像(図3)を取得する(画像取得工程)。図3に示す検査対象画像では、画像左下に摩耗部分が表示されている。検査対象画像は、カラー画像として取得される。
In step 11, the cutting edge of the throw-away tip mounted on the processing apparatus is photographed using the camera 1, and an inspection target image (FIG. 3) is acquired (image acquisition process). In the inspection target image shown in FIG. 3, a wear portion is displayed at the lower left of the image. The inspection target image is acquired as a color image.
ステップ12では、ステップ11にて取得した検査対象画像からグレースケール画像(図4)を生成する(グレースケール画像生成工程)。なお、ステップ11にて取得したカラーの検査対象画像からグレースケール画像を生成するのではなく、カメラ11で直接グレースケール画像を生成するようにしてもよい。
In step 12, a gray scale image (FIG. 4) is generated from the inspection target image acquired in step 11 (gray scale image generation step). Instead of generating a grayscale image from the color inspection target image acquired in step 11, the grayscale image may be generated directly by the camera 11.
ステップ13では、図4に示すように、ステップ12で取得したグレースケール画像の位置決めを行う。具体的には、刃先の輪郭の上部直線部41を検出し、その直線部41が水平となるようにアフィン変換を用いてグレースケール画像の位置決めを行う。このように、ステップ11で取得された全ての検査対象画像は、同じ角度に位置決めされる。
In step 13, as shown in FIG. 4, the gray scale image acquired in step 12 is positioned. Specifically, the upper linear portion 41 of the edge contour is detected, and the gray scale image is positioned using affine transformation so that the linear portion 41 is horizontal. In this way, all the inspection target images acquired in step 11 are positioned at the same angle.
ステップ14~18では、Watershed法を用いてスローアウェイチップの刃先の摩耗領域を抽出する。Watershed法とは、濃度勾配を用いて画像の領域分割を行う手法である。得られた勾配の絶対値の極小値をシードとして領域成長法を用いて領域分割を行う。これにより、勾配の小さい部分から大きい部分へと領域が成長するためエッジの部分に領域の境界が生成される。図5を参照して具体的に説明すると、画像の画素の輝度勾配を地形的構造と見なし、輝度値の極小値、極大値をそれぞれ谷、尾根と見なす。まず、輝度値の極小値(図5では極小値1及び2)を探索し、極小値1及び2の谷に注水して極大値(図5では極大値1,2,及び3)となる箇所が見付かるまで注水する。極小値を挟む極大値が見付かったら、極小値を挟む極大値の間が1つの領域となる、図5では2つの領域(領域1及び2)に分割される。
In Steps 14 to 18, the wear area of the blade tip of the throw-away tip is extracted using the Watershed method. The watershed method is a method for dividing an image using a density gradient. Region division is performed using the region growth method with the obtained minimum value of the absolute value of the gradient as a seed. As a result, since the region grows from a portion with a small gradient to a portion with a large gradient, a boundary of the region is generated at the edge portion. More specifically, with reference to FIG. 5, the luminance gradient of the pixel of the image is regarded as a topographic structure, and the minimum value and the maximum value of the luminance value are regarded as a valley and a ridge, respectively. First, the minimum value of the luminance value ( minimum values 1 and 2 in FIG. 5) is searched, and water is poured into the valleys of the minimum values 1 and 2 to become maximum values ( maximum values 1, 2, and 3 in FIG. 5). Water until water is found. When a local maximum value sandwiching the local minimum value is found, the region between the local maximum values sandwiching the local minimum value becomes one region, which is divided into two regions (regions 1 and 2) in FIG.
Watershed法では、画像がシードの数の領域に分割されてしまうという問題がある。そこで、本実施形態では、事前にシードとなるマーカを与えることでこの問題を解決するMaker Based Watershed法を用いて領域分割を行う。Maker Based Watershed法では、マーカを手動で与える必要があるが、本実施形態では、後述するガボールフィルタを用いて自動的にマーカを生成して全自動でスローアウェイチップの刃先の摩耗領域を抽出する。以下に詳しく説明する。
The watershed method has a problem that the image is divided into the number of seeds. Therefore, in the present embodiment, region division is performed using a Maker Based Watershed method that solves this problem by providing a seed marker in advance. In the Maker Based Watershed method, it is necessary to manually provide a marker, but in this embodiment, a marker is automatically generated using a Gabor filter to be described later, and the wear area of the tip of the throw-away tip is extracted automatically. . This will be described in detail below.
ステップ14では、ステップ12で取得したグレースケール画像を第1のフィルタとしてのガボールフィルタで処理した後に輝度勾配を計算することによって輝度勾配を示す地形画像(図6)を生成する(地形画像生成工程)。地形画像は、地形的構造と見なすことができる画像の輝度勾配を示すものである。
In step 14, the grayscale image acquired in step 12 is processed by the Gabor filter as the first filter and then the luminance gradient is calculated to generate a terrain image (FIG. 6) indicating the luminance gradient (terrain image generation step). ). The topographic image indicates the brightness gradient of an image that can be regarded as a topographic structure.
ガボールフィルタは、ガウス関数と正弦波の積で定義されるものであり、フィルタ特性に合うものを強調し、合わないものは平滑化するものである。フィルタ特性は、ガウス関数の幅や正弦波の位相と振幅を調整することによって設定される。
The Gabor filter is defined by the product of a Gaussian function and a sine wave, and emphasizes those that match the filter characteristics and smoothes those that do not match. The filter characteristics are set by adjusting the width of the Gaussian function and the phase and amplitude of the sine wave.
図4に示すように、グレースケール画像では、摩耗部分が白く(高い輝度値)表示され、摩耗していない箇所は黒く(低い輝度値)表示されている。そこで、ステップ14では、白部分を強調するべくガボールフィルタを用いてグレースケール画像のエッジの部分を強調する。これにより、図6に示すように、地形画像では摩耗領域の境界が白く表示される。地形画像で白く表示される箇所は、Maker Based Watershed法を適用した際には尾根と見なされるため、ガボールフィルタを用いてグレースケール画像の白部分を強調した地形画像を生成することによって、Maker Based Watershed法を適用した際の摩耗領域の抽出精度が向上する。
As shown in FIG. 4, in the gray scale image, the worn portion is displayed white (high luminance value), and the non-weared portion is displayed black (low luminance value). Therefore, in step 14, the edge portion of the grayscale image is emphasized using a Gabor filter to enhance the white portion. Thereby, as shown in FIG. 6, the boundary of the wear region is displayed in white in the topographic image. The portion displayed in white in the topographic image is regarded as a ridge when the Maker Based Watershed method is applied, so by generating a topographic image that emphasizes the white part of the grayscale image using the Gabor filter, Maker Based The wear area extraction accuracy is improved when the watershed method is applied.
また、ガボールフィルタは、フィルタ特性に合うものを強調し、合わないものは平滑化するものであるため、ガボールフィルタを用いれば、摩耗領域に対応する白部分を強調しつつ、小さい傷等の摩耗とは関係のない箇所(ノイズ)は平滑化して取り除くことができる。
In addition, since the Gabor filter emphasizes the filter that matches the filter characteristics and smoothes the filter that does not match, the Gabor filter emphasizes the white portion corresponding to the wear area and wears small scratches and the like. A portion (noise) that is not related to the noise can be smoothed and removed.
ステップ15では、ステップ12で取得したグレースケール画像を第2のフィルタとしてのガボールフィルタで処理して摩耗領域内の特徴部を強調し、さらに2値化処理して摩耗領域内の特徴部を抽出する。そして、抽出された特徴部にマーカ71を設定してマーカ画像(図7)を生成する(マーカ画像生成工程)。摩耗領域内の特徴部は、特性の異なる複数のガボールフィルタを用いて抽出する。具体的には、複数のガボールフィルタによるフィルタ処理にて算出される各フィルタ値のなかから最大値に対応する箇所を抽出する。ガボールフィルタにて抽出される特徴部としては、エッジの部分や輝度値が一定以上の大きさを有する輝度値の高い領域である。
In step 15, the grayscale image acquired in step 12 is processed with a Gabor filter as a second filter to emphasize features in the wear region, and further binarized to extract features in the wear region. To do. And the marker 71 is set to the extracted feature part and a marker image (FIG. 7) is produced | generated (marker image production | generation process). The features in the wear region are extracted using a plurality of Gabor filters having different characteristics. Specifically, a portion corresponding to the maximum value is extracted from the filter values calculated by the filter processing using a plurality of Gabor filters. The characteristic portion extracted by the Gabor filter is an edge portion or a region having a high luminance value having a luminance value larger than a certain value.
摩耗領域内の特徴部は、複数のガボールフィルタによるフィルタ処理にて算出される各フィルタ値のなかから最大値に対応する箇所が抽出されるため、摩耗されていない領域もマーカとして設定されてしまう可能性がある。
Since the portion corresponding to the maximum value is extracted from the filter values calculated by the filter processing using a plurality of Gabor filters, the feature region in the wear region is also set as a marker. there is a possibility.
そこで、ステップ16では、マーカを制限する制限処理が行われる(マーカ制限工程)。具体的には、スローアウェイチップの刃先輪郭からマーカまでの距離に応じた重み付けを行い、重み付けに基づいてマーカを制限する。つまり、刃先輪郭から遠い部分に設定されるマーカは消去される。
Therefore, in step 16, a restriction process for restricting the marker is performed (marker restriction process). Specifically, weighting is performed according to the distance from the cutting edge contour of the throw-away tip to the marker, and the marker is limited based on the weighting. That is, the marker set in a portion far from the edge contour is deleted.
ステップ17では、図7に示すように、マーカ画像に設定されたマーカの周囲の一定領域を囲むように領域制限マーカ72を設定する(制限マーカ設定工程)。ステップ16にて刃先輪郭からマーカまでの距離に応じてマーカを制限すると共に、ステップ17にて領域制限マーカ72を設定することによって、次のステップにおいてMaker Based Watershed法による領域分割によって摩耗領域を抽出する際の抽出精度を向上させることができる。
In step 17, as shown in FIG. 7, the area restriction marker 72 is set so as to surround a certain area around the marker set in the marker image (restriction marker setting step). In step 16, the marker is limited according to the distance from the edge contour to the marker, and by setting the region limiting marker 72 in step 17, the wear region is extracted by region division by the Maker に よ る Based Watershed method in the next step. The extraction accuracy when doing so can be improved.
ステップ18では、地形画像(図6)とマーカ画像(図7)を用い、Maker Based Watershed法による領域分割によって摩耗領域を抽出する(摩耗領域抽出工程)。具体的には、マーカ画像に設定されたマーカ71,72を用いて地形画像に対してMaker Based Watershed法を適用して領域拡張することによって領域分割を行い、摩耗領域を抽出する。
In step 18, the wear area is extracted by area division by the Maker Based Watershed method using the topographic image (FIG. 6) and the marker image (FIG. 7) (wear area extraction step). Specifically, the region is expanded by applying the Maker マ ー カ Based Watershed method to the topographic image using the markers 71 and 72 set in the marker image, and the wear region is extracted.
摩耗領域を抽出した画像を図8Aに示す。図8Bは図8Aの部分拡大図である。図8A及び8B中、抽出領域の境界は符号81で示す。また、図8Bにおいてマーカ71は斜線で示す。なお、図8Aでは、図3に示す検査対象画像と同じ角度となるように、向きを元に戻した画像を示す。図8Bからわかるように、Maker Based Watershed法によって抽出された摩耗領域(符号81で示す境界で囲まれた領域)は、白く表示された摩耗部分とほぼ一致しており、精度良く摩耗領域を抽出できることが確認できた。
FIG. 8A shows an image obtained by extracting the wear region. FIG. 8B is a partially enlarged view of FIG. 8A. 8A and 8B, the boundary of the extraction region is indicated by reference numeral 81. In FIG. 8B, the marker 71 is indicated by hatching. Note that FIG. 8A shows an image whose orientation has been restored to the same angle as the inspection target image shown in FIG. As can be seen from FIG. 8B, the wear region extracted by the Maker81Based Watershed method (the region surrounded by the boundary indicated by reference numeral 81) is almost coincident with the wear portion displayed in white, and the wear region is accurately extracted. I was able to confirm that it was possible.
ステップ19では、ステップ18にて抽出された摩耗領域の面積を算出し、その面積に基づいてスローアウェイチップの刃先の状態を判定する(判定工程)。例えば、以下のような方法でスローアウェイチップの刃先の状態を判定する。ステップ18にて抽出された摩耗領域の面積が予め定められた面積に達した場合には、スローアウェイチップが寿命と判定する。また、摩耗領域の面積の大きさ応じて劣化レベル1~5を予め設定しておき、劣化レベル5に達した場合にはスローアウェイチップが寿命と判定する。また、予め摩耗領域の面積とスローアウェイチップの使用回数との相関を設定しておき、ステップ18にて抽出された摩耗領域の面積に応じて使用回数を推定してスローアウェイチップの刃先の状態を判定する。また、ステップ18にて抽出された摩耗領域の面積が所定の割合以上で急激に変化した場合には、スローアウェイチップの異常と判定する。
In step 19, the area of the wear region extracted in step 18 is calculated, and the state of the tip of the throw-away tip is determined based on the area (determination step). For example, the state of the blade tip of the throw-away tip is determined by the following method. When the area of the wear region extracted in step 18 reaches a predetermined area, the throw-away tip is determined to have a lifetime. Further, deterioration levels 1 to 5 are set in advance according to the size of the area of the wear region, and when the deterioration level 5 is reached, the throw-away tip is determined to have a lifetime. In addition, the correlation between the area of the wear area and the number of times of use of the throw-away tip is set in advance, and the number of times of use is estimated according to the area of the wear area extracted in step 18 to determine the state of the cutting edge of the throw-away tip. Determine. In addition, when the area of the wear region extracted in step 18 changes abruptly at a predetermined rate or more, it is determined that the throw-away tip is abnormal.
以上で説明したステップ12~19の処理は、コンピュータ2に記憶されたソフトウエアによって自動で実行される。その結果、スローアウェイチップが寿命と判定されれば、交換を促す通知が発せられる。
The processes in steps 12 to 19 described above are automatically executed by software stored in the computer 2. As a result, if it is determined that the throw-away tip has reached the end of its life, a notification that prompts replacement is issued.
次に、実施例について説明する。
Next, examples will be described.
1回加工する毎にスローアウェイチップの刃先の側面を撮像して検査対象画像を取得した。検査対象画像の画像サイズは、1024×960ピクセルである。取得した検査対象画像に対して図2のステップ12~18の画像処理を施して摩耗領域を自動で抽出し、その抽出した摩耗領域の面積を算出した。また、比較例として、10回加工する毎にスローアウェイチップの刃先の側面を撮像し、摩耗領域を手動で抽出し、その抽出した摩耗領域の面積を算出した。
Each time it was processed, the side surface of the tip of the throw-away tip was imaged to obtain the inspection target image. The image size of the inspection target image is 1024 × 960 pixels. The obtained image to be inspected is subjected to image processing in steps 12 to 18 in FIG. 2 to automatically extract the wear region, and the area of the extracted wear region is calculated. Further, as a comparative example, every time machining was performed, the side surface of the blade tip of the throw-away tip was imaged, the wear region was manually extracted, and the area of the extracted wear region was calculated.
図9に、自動抽出した摩耗領域の面積と手動抽出した摩耗領域の面積との変化を示す。図9では、自動抽出した摩耗領域の面積を実線で示し、手動抽出した摩耗領域の面積を破線で示す。図9からわかるように、自動抽出した摩耗領域の面積の変化は、手動抽出した摩耗領域の面積の変化と概ね一致した。このことから、ステップ12~18の画像処理を用いれば、摩耗領域を高い精度で抽出できることが確認できた。したがって、ステップ12~18の画像処理を行って自動で摩耗領域を抽出し、その抽出した摩耗領域の面積を評価すれば、スローアウェイチップの刃先の状態を精度良く判定することができると言える。
Fig. 9 shows the change between the automatically extracted wear area and the manually extracted wear area. In FIG. 9, the area of the automatically extracted wear region is indicated by a solid line, and the area of the manually extracted wear region is indicated by a broken line. As can be seen from FIG. 9, the change in the area of the automatically extracted wear region generally coincided with the change in the area of the manually extracted wear region. From this, it was confirmed that the wear region can be extracted with high accuracy by using the image processing in steps 12 to 18. Therefore, it can be said that the state of the blade tip of the throw-away tip can be accurately determined by performing image processing in steps 12 to 18 to automatically extract the wear region and evaluating the area of the extracted wear region.
以上の第1実施形態によれば、以下に示す効果を奏する。
According to the above 1st Embodiment, there exists the effect shown below.
本実施形態では、地形画像(図6)とマーカ画像(図7)を用いMaker Based Watershed法による領域分割によって抽出された摩耗領域の面積に基づいて対象工具の状態が自動で判定されるため、簡単な方法で精度良く工具を検査することができる。
In the present embodiment, since the state of the target tool is automatically determined based on the area of the wear area extracted by area division by the Maker Based Watershed method using the topographic image (FIG. 6) and the marker image (FIG. 7), The tool can be inspected with high accuracy by a simple method.
また、本実施形態では、ガボールフィルタを用いてグレースケール画像の摩耗領域内の特徴部を強調し、そこにMaker Based Watershed法で用いられるマーカを設定する。このようにマーカを手動ではなく自動で設定することができる。
In this embodiment, a Gabor filter is used to emphasize features in the wear region of the grayscale image, and markers used in the Maker Based Watershed method are set there. In this way, markers can be set automatically instead of manually.
また、刃先輪郭からマーカまでの距離に応じてマーカを制限すると共に、マーカの周囲の一定領域を囲むように領域制限マーカ72が設定されるため、Maker Based Watershed法による領域分割によって摩耗領域を抽出する際の抽出精度を向上させることができる。
In addition, the marker is limited according to the distance from the edge contour to the marker, and the area restriction marker 72 is set so as to surround a certain area around the marker, so the wear area is extracted by dividing the area by the Maker Based Watershed method. The extraction accuracy when doing so can be improved.
<第2実施形態>
次に、本発明の第2実施形態について説明する。以下では、上記第1実施形態と異なる点のみについて説明する。 Second Embodiment
Next, a second embodiment of the present invention will be described. Hereinafter, only differences from the first embodiment will be described.
次に、本発明の第2実施形態について説明する。以下では、上記第1実施形態と異なる点のみについて説明する。 Second Embodiment
Next, a second embodiment of the present invention will be described. Hereinafter, only differences from the first embodiment will be described.
本第2実施形態では、図2のステップ14で得られる地形画像が、上記第1実施形態で得られる地形画像と相違する。
In the second embodiment, the topographic image obtained in step 14 in FIG. 2 is different from the topographic image obtained in the first embodiment.
輝度勾配を示す地形画像(図10)は、グレースケール画像(図4)をガボールフィルタで白黒反転処理することによって得られる。
A topographic image (FIG. 10) showing a luminance gradient is obtained by black-and-white inversion processing of a grayscale image (FIG. 4) with a Gabor filter.
白黒反転処理が施された地形画像(図10)では、摩耗部分が黒く(低い輝度値)表示され、摩耗していない箇所は白く(高い輝度値)表示されるため、ステップ15で設定されたマーカ画像(図7)のマーカは地形画像では低い輝度値に対応して設定されることになる。したがって、Watershed法を適用して領域分割する際には、谷(低い輝度値)から尾根(高い輝度値)へと向かって領域が成長するため、摩耗領域の抽出精度が向上する。
In the topographic image (FIG. 10) subjected to the black-and-white reversal process, the worn portion is displayed in black (low luminance value) and the non-weared portion is displayed in white (high luminance value). The marker of the marker image (FIG. 7) is set corresponding to a low luminance value in the terrain image. Therefore, when the region is divided by applying the Watershed method, the region grows from the valley (low luminance value) to the ridge (high luminance value), so that the wear region extraction accuracy is improved.
以上の第2実施形態においても、上記第1実施形態と同様の作用効果を奏する。
Also in the second embodiment described above, the same operational effects as in the first embodiment are obtained.
以上、本発明の実施形態について説明したが、上記実施形態は本発明の適用例の一部を示したに過ぎず、本発明の技術的範囲を上記実施形態の具体的構成に限定する趣旨ではない。
The embodiment of the present invention has been described above. However, the above embodiment only shows a part of application examples of the present invention, and the technical scope of the present invention is limited to the specific configuration of the above embodiment. Absent.
本願は2014年2月3日に日本国特許庁に出願された特願2014-018512に基づく優先権を主張し、この出願の全ての内容は参照により本明細書に組み込まれる。
This application claims priority based on Japanese Patent Application No. 2014-018512 filed with the Japan Patent Office on February 3, 2014, the entire contents of which are incorporated herein by reference.
Claims (6)
- 工具検査方法であって、
検査対象となる対象工具を撮像し、グレースケール画像を生成するグレースケール画像生成工程と、
前記グレースケール画像を第1のフィルタで処理して輝度勾配を示す地形画像を生成する地形画像生成工程と、
前記グレースケール画像を第2のフィルタで処理して摩耗領域内の特徴部を抽出し、当該抽出された前記特徴部にマーカを設定してマーカ画像を生成するマーカ画像生成工程と、
前記地形画像と前記マーカ画像を用い、Maker Based Watershed法による領域分割によって摩耗領域を抽出する摩耗領域抽出工程と、
前記摩耗領域抽出工程にて抽出された前記摩耗領域の面積に基づいて前記対象工具の状態を判定する判定工程と、
を備える工具検査方法。 A tool inspection method,
A gray scale image generation step of imaging a target tool to be inspected and generating a gray scale image;
A terrain image generation step of processing the grayscale image with a first filter to generate a terrain image indicating a luminance gradient;
A marker image generation step of processing the grayscale image with a second filter to extract a feature portion in a wear region, setting a marker on the extracted feature portion, and generating a marker image;
Using the terrain image and the marker image, a wear region extraction step of extracting a wear region by region division by the Maker Based Watershed method,
A determination step of determining the state of the target tool based on the area of the wear region extracted in the wear region extraction step;
A tool inspection method comprising: - 請求項1に記載の工具検査方法であって、
前記第1のフィルタ及び前記第2のフィルタは、ガボールフィルタである工具検査方法。 The tool inspection method according to claim 1,
The tool inspection method, wherein the first filter and the second filter are Gabor filters. - 請求項1に記載の工具検査方法であって、
前記対象工具の刃先輪郭から前記マーカまでの距離に応じた重み付けを行い、前記重み付けに基づいて前記マーカを制限するマーカ制限工程をさらに備えることを特徴とする請求項1又は2に記載の工具検査方法。 The tool inspection method according to claim 1,
The tool inspection according to claim 1, further comprising a marker limiting step of performing weighting according to a distance from a cutting edge contour of the target tool to the marker, and limiting the marker based on the weighting. Method. - 請求項3に記載の工具検査方法であって、
前記マーカ画像に設定された前記マーカの周囲を囲むように領域制限マーカを設定する制限マーカ設定工程をさらに備える工具検査方法。 The tool inspection method according to claim 3,
A tool inspection method further comprising a restriction marker setting step of setting an area restriction marker so as to surround the marker set in the marker image. - 請求項1に記載の工具検査方法であって、
前記地形画像生成工程にて生成される前記地形画像は、前記グレースケール画像を前記第1のフィルタで白黒反転処理することによって得られる工具検査方法。 The tool inspection method according to claim 1,
The terrain image generated in the terrain image generation step is a tool inspection method obtained by performing black-and-white reversal processing on the grayscale image with the first filter. - 工具検査装置であって、
検査対象となる対象工具を撮像し、グレースケール画像を生成するグレースケール画像生成部と、
前記グレースケール画像を第1のフィルタで処理して輝度勾配を示す地形画像を生成する地形画像生成部と、
前記グレースケール画像を第2のフィルタで処理して摩耗領域内の特徴部を抽出し、当該抽出された前記特徴部にマーカを設定してマーカ画像を生成するマーカ画像生成部と、
前記地形画像と前記マーカ画像を用いMaker Based Watershed法による領域分割によって摩耗領域を抽出する摩耗領域抽出部と、
前記摩耗領域抽出工程にて抽出された前記摩耗領域の面積に基づいて前記対象工具の状態を判定する判定部と、
を備える工具検査装置。 A tool inspection device,
A grayscale image generation unit that images a target tool to be inspected and generates a grayscale image;
A terrain image generation unit for processing the grayscale image with a first filter to generate a terrain image indicating a luminance gradient;
A marker image generating unit that processes the grayscale image with a second filter to extract a feature portion in a wear region, sets a marker in the extracted feature portion, and generates a marker image;
A wear region extraction unit that extracts a wear region by region division by the Maker Based Watershed method using the terrain image and the marker image;
A determination unit that determines the state of the target tool based on the area of the wear region extracted in the wear region extraction step;
A tool inspection device comprising:
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