JPH0634564A - Weld part inspection method of steel plate - Google Patents
Weld part inspection method of steel plateInfo
- Publication number
- JPH0634564A JPH0634564A JP18680692A JP18680692A JPH0634564A JP H0634564 A JPH0634564 A JP H0634564A JP 18680692 A JP18680692 A JP 18680692A JP 18680692 A JP18680692 A JP 18680692A JP H0634564 A JPH0634564 A JP H0634564A
- Authority
- JP
- Japan
- Prior art keywords
- image data
- weld part
- welded portion
- welded
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、鋼板の突合せ溶接部，
すみ肉溶接部その他各種の溶接部の検査に利用される鋼
板の溶接部検査方法に係わり、特に種々の溶接部の溶接
状態を適切に検査する鋼板の溶接部検査方法に関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a butt weld portion of a steel plate,
The present invention relates to a welded portion inspection method for steel sheets used for inspection of fillet welded portions and other various welded portions, and particularly relates to a welded portion inspection method for steel sheets that appropriately inspects the welded state of various welded portions.
【０００２】[0002]
【従来の技術】この種の溶接部検査方法として、オフラ
イン検査とオンライン検査とが上げられるが、そのう
ち、前者のオフライン検査では、溶接部およびその溶接
部周辺部の一部を切り取ってサンプルを取り出し、当該
サンプルに対して次のような検査方法を実施している。2. Description of the Related Art Off-line inspections and online inspections are available as welding type inspection methods of this type. Among them, in the former offline inspection, a sample is taken out by cutting off a weld and a part around the weld. The following inspection method is applied to the sample.
【０００３】具体的には、サンプルの溶接部分を油圧装
置で押し破り、その破れ方から良否を判定するエリクセ
ンテスト、メタルフロー写真や金属組織写真などから良
否を判定する顕鏡テスト、引張り強度の試験により良否
を判定する引張試験、さらに曲げ強度の試験により良否
を判定する曲げ試験などが上げられ、これらは何れもサ
ンプルを固定した状態で試験するために微細な欠陥の状
態を検査できる。Specifically, a welded portion of a sample is smashed by a hydraulic device, and an Erichsen test for judging the quality based on the breaking method, a microscopic test for judging the quality based on a metal flow photograph or a metal structure photograph, and a tensile strength test. Tensile tests for determining pass / fail by tests, bending tests for determining pass / fail by bending strength tests, and the like are included. In all of these tests, the state of fine defects can be inspected because the sample is tested in a fixed state.
【０００４】しかし、オフラインの検査方法は、事後評
価であることから迅速な対応ができないこと、また溶接
部個所ごとに溶接品質が変化するが、その状況を適切に
把握できない問題がある。そこで、以上のような不具合
に問題を改善するためには、オンラインによる検査方法
が最適であると言える。従来、かかるオンラインによる
検査方法は、溶接部の目視検査，ハンマリングテストお
よび溶接時の溶接電流チャートの確認などにより行われ
ている。However, since the off-line inspection method is an ex-post evaluation, it is not possible to respond promptly, and the welding quality changes depending on the welded portion, but there is a problem that the situation cannot be properly grasped. Therefore, it can be said that the online inspection method is the most suitable for resolving the above problems. Conventionally, such an online inspection method is performed by visual inspection of a welded portion, a hammering test, confirmation of a welding current chart during welding, and the like.
【０００５】[0005]
【発明が解決しようとする課題】しかしながら、以上の
ような検査方法のうち、溶接部の目視検査による場合に
は溶接部の内部品質を判定するのが非常に難しいこと、
その検査員の経験，熟練度によって判定結果に相当なバ
ラツキが出ることである。However, of the above inspection methods, it is very difficult to judge the internal quality of the welded portion by visual inspection of the welded portion.
There is considerable variation in the judgment results depending on the experience and skill of the inspector.
【０００６】一方、ハンマリングテストでは、その振動
信号などから溶接部の状況を判定するが正確化に欠ける
ばかりでなく、その判定結果後に目視による確認を行う
必要があり、同様に微細な欠陥を発見できない可能性が
ある。さらに、溶接電流チャートの場合には、溶接部の
明確な変化を認識できるものの、微小な欠陥の違いを発
見できない問題がある。従って、以上のような理由から
オンラインにて溶接部内部の品質を含む溶接部の検査に
は目視による定性的判定によるのが主流となっている。On the other hand, in the hammering test, the condition of the welded portion is judged from the vibration signal and the like, but not only is it lacking in accuracy, but it is necessary to perform visual confirmation after the judgment result, and in the same way, fine defects are similarly detected. It may not be found. Furthermore, in the case of the welding current chart, there is a problem in that although a clear change in the welded portion can be recognized, a difference in minute defects cannot be found. Therefore, for the reasons described above, the mainstream of the inspection of the welded part including the quality inside the welded part online is visual qualitative judgment.
【０００７】本発明は上記実情に鑑みてなされたもの
で、オンラインにて迅速に溶接部の良否を判定可能であ
り、目視検査を行うことなく溶接部の内部品質を含む品
質検査を適正に判定する鋼板の溶接部検査方法を提供す
ることを目的とする。The present invention has been made in view of the above-mentioned circumstances, and it is possible to quickly determine the quality of a welded portion online, and properly determine a quality inspection including the internal quality of the welded portion without performing a visual inspection. It is an object of the present invention to provide a method for inspecting a welded part of a steel sheet.
【０００８】また、本発明の他の目的は、種々の欠陥の
状態をきめ細かく判定可能であり、その判定結果に基づ
いて溶接作業ないしは溶接制御に対して適切な対策をと
りうる鋼板の溶接部検査方法を提供することにある。Another object of the present invention is to inspect the state of various defects finely and to inspect the welded portion of the steel sheet which can take appropriate measures for welding work or welding control based on the result of the determination. To provide a method.
【０００９】[0009]
【課題を解決するための手段】先ず、請求項１に対応す
る発明は上記課題を解決するために、鋼板の溶接部を検
査する鋼板の溶接部検査方法において、First, in order to solve the above-mentioned problems, the invention corresponding to claim 1 provides a method for inspecting a welded portion of a steel sheet, comprising the steps of:
【００１０】赤外センサを用いて前記溶接部およびその
溶接部近傍から輻射される赤外線を検出するとともに、
当該赤外センサから得られる溶接部およびその溶接部近
傍の温度分布データを画像処理して特徴画像データを抽
出し、この特徴画像データと予め記憶されている既知標
準画像データとを比較し、溶接部の良否を判定する鋼板
の溶接部検査方法である。An infrared sensor is used to detect infrared rays radiated from the weld and the vicinity of the weld, and
Image processing is performed on the temperature distribution data of the weld and the vicinity of the weld obtained from the infrared sensor to extract characteristic image data, and the characteristic image data is compared with known standard image data stored in advance, and welding is performed. This is a method for inspecting a welded part of a steel plate for determining the quality of a part.
【００１１】次に、請求項２に対応する発明は、この赤
外センサから得られる溶接部およびその溶接部近傍の温
度分布データを画像処理して温度差，面積およびパター
ンの１種類以上の特徴画像データを抽出し、この抽出さ
れた１種類以上の特徴画像データと予め記憶されている
温度差，面積およびパターンの１種類以上の既知標準画
像データ、複数の既知不良画像データとを比較し、特徴
画像データが最も近似する既知不良画像データに基づい
て欠陥の種類を判定する方法である。Next, the invention according to claim 2 is characterized in that one or more kinds of temperature difference, area and pattern are subjected to image processing of the temperature distribution data of the weld and the vicinity of the weld obtained from the infrared sensor. Image data is extracted, and the extracted one or more types of characteristic image data are compared with one or more types of known standard image data of temperature difference, area, and pattern that are stored in advance, and a plurality of known defective image data, This is a method of determining the type of defect based on known defective image data to which the characteristic image data is most similar.
【００１２】[0012]
【作用】従って、請求項１に対応する発明は以上のよう
な手段を講じたことにより、赤外センサを用いて溶接部
およびその溶接部近傍から輻射される赤外線を検出する
ので、溶接部およびその溶接部近傍の画像データを確実
に取得できるとともに、この測定画像データから特徴画
像データを抽出し、この特徴画像データと標準画像デー
タとを比較して溶接部の良否を判定するので、全く目視
検査を行うことなく溶接部の内部品質を含む品質検査を
適正に判定できる。Therefore, in the invention according to claim 1, by taking the above-mentioned means, the infrared ray radiated from the welded portion and the vicinity of the welded portion is detected by using the infrared sensor. Image data in the vicinity of the welded portion can be reliably acquired, and characteristic image data is extracted from this measured image data, and the quality of the welded portion is determined by comparing this characteristic image data with standard image data. The quality inspection including the internal quality of the welded portion can be properly determined without performing the inspection.
【００１３】次に、請求項２に対応する発明は、温度
差，面積およびパターンの１種類以上の特徴画像データ
と予め記憶されている温度差，面積およびパターンの１
種類以上の既知標準画像データ、複数の既知不良画像デ
ータとを比較し、特徴画像データが最も近似する既知不
良画像データに基づいて溶接部の欠陥を判定するので、
種々の欠陥の状態をきめ細かく判定でき、かつ、その判
定結果，特に欠陥判定に基づいて溶接作業ないしは溶接
制御に対して適切な対策を講ずることができる。Next, the invention according to claim 2 relates to one or more types of characteristic image data of temperature difference, area and pattern and temperature difference, area and pattern stored in advance.
Compared with known standard image data of more than one kind, a plurality of known defective image data, because the characteristic image data determines the defect of the welded portion based on the known defective image data that is most approximate,
The state of various defects can be finely determined, and appropriate measures can be taken for welding work or welding control based on the determination result, particularly the defect determination.
【００１４】[0014]
【実施例】以下、本発明方法の実施例について図面を参
照して説明する。Embodiments of the method of the present invention will be described below with reference to the drawings.
【００１５】図１は本発明方法を適用してなる検査装置
の全体構成を示す機能ブロック図である。同図において
１は例えば突合せ溶接部２を有する鋼板であって、これ
は紙面に向かって例えば図示右側から図示左側方向に走
行しているが、このとき溶接作業終了直後の鋼板溶接部
２の入熱状態を、赤外波長を検知する赤外センサ３を用
いて測定する。この溶接部２の入熱状態を測定するのは
溶接部２の内部品質と密接な関係を持っているためであ
る。FIG. 1 is a functional block diagram showing the overall structure of an inspection apparatus to which the method of the present invention is applied. In the figure, reference numeral 1 denotes a steel plate having, for example, a butt weld portion 2, which is running from the right side to the left side in the figure toward the paper surface. The thermal state is measured using the infrared sensor 3 which detects an infrared wavelength. The heat input state of the welded portion 2 is measured because it has a close relationship with the internal quality of the welded portion 2.
【００１６】この赤外センサ３は、赤外線用固体撮像装
置ないしは赤外線テレビカメラなどと呼ばれるセンサが
用いられ、溶接機本体（図示せず）の側面または上部な
どの所要位置に溶接部２およびその溶接部近傍全体が測
定視野に入るように設置され、これら溶接部２および溶
接部近傍から発する赤線波長を検知することにより、当
該溶接部２および溶接部近傍全体の温度分布を測定し、
その測定画像データを画像処理装置４に送出する。As the infrared sensor 3, a sensor called an infrared solid-state image pickup device or an infrared television camera is used, and the welded portion 2 and the welded portion are welded to a required position such as a side surface or an upper portion of a welding machine body (not shown). The entire area near the welded portion is installed so as to be in the measurement field of view, and the red line wavelength emitted from the welded portion 2 and the vicinity of the welded portion is detected to measure the temperature distribution of the welded portion 2 and the entire vicinity of the welded portion.
The measurement image data is sent to the image processing device 4.
【００１７】この画像処理装置４は、いかなる特徴デー
タを抽出するかによって異なるが、例えば低域通過フィ
ルタなどの雑音除去用フイルタ５を用いて測定画像デー
タのサンプリング時に生ずる疑似データや雑音成分，微
細変化を抑制した後、階調変換手段６を用いて画像の部
分ごとの熱温度に応じた濃度レベルに変換し、さらに画
像処理記憶手段７にて画像処理を実行する。The image processing device 4 differs depending on what kind of characteristic data is extracted. For example, a pseudo data, noise component, fine component, etc. generated at the time of sampling the measurement image data using a noise removing filter 5 such as a low pass filter. After suppressing the change, the gradation conversion unit 6 is used to convert the density level according to the heat temperature of each part of the image, and the image processing storage unit 7 executes the image processing.
【００１８】８は特徴抽出処理手段であって、ここでは
いかなる特徴を抽出するかによって異なるが、例えば階
調変換された画像データそのまま，つまり階調差そのも
のである温度差データを抽出する他、当該階調差をもっ
た画像データから鋼板の溶接部２の面積データを抽出
し、さらに専らパータンを重視したパターンデータなど
を抽出し、得られたこれらの温度差データ，面積データ
およびパターンデータをそれぞれ特徴画像データ記憶手
段９に領域分けを行って記憶する。なお、面積データは
あるしきい値レベルの下に単に２値化データをもって表
せばよく、一方、パターンデータの場合には例えば２値
化データに変換後、領域の構造的テクスチャーや統計的
テクスチャー処理によって特徴データを再現する。な
お、この特徴抽出処理手段８での各特徴画像データの抽
出は、前記温度差データ，面積データおよびパターンデ
ータの特徴画像データについて一定のフローに従って順
次求めていく方式でもよく、或いは同時並列的に特徴画
像データを抽出していく方式でもよい。Reference numeral 8 denotes a feature extraction processing means, which varies depending on what features are extracted here. For example, in addition to extracting the tone-converted image data as it is, that is, the temperature difference data which is the tone difference itself, The area data of the welded portion 2 of the steel plate is extracted from the image data having the gradation difference, and the pattern data and the like, which emphasizes the pattern exclusively, are extracted, and the obtained temperature difference data, area data and pattern data are obtained. The characteristic image data storage means 9 divides the areas and stores them. It should be noted that the area data may be expressed simply as binarized data below a certain threshold level, while in the case of pattern data, for example, after being converted into binarized data, structural texture or statistical texture processing of the region is performed. Reproduce the feature data by. The extraction of each characteristic image data by the characteristic extraction processing means 8 may be a method of sequentially obtaining the characteristic image data of the temperature difference data, the area data and the pattern data according to a constant flow, or simultaneously in parallel. A method of extracting characteristic image data may be used.
【００１９】１０は比較すべき基準となる画像データを
記憶する既知画像データ記憶手段であって、この記憶手
段１０には例えば溶接事後に良と判定された温度差，面
積およびパターンに係わる標準画像データ、さらに必要
に応じて良と判定された温度差，面積およびパターンに
係わる標準画像データの他、過去の判定結果に基づいて
不良と判定された温度差，面積およびパターンごとの多
数の既知不良画像データが記憶されている。これら標準
画像データの記憶にあっては、特徴画像データ記憶手段
９と同様に例えば温度差，面積およびパターンごとに標
準画像データおよび多数の既知不良画像データが領域分
けを行って記憶されている。Reference numeral 10 is a known image data storage means for storing image data as a reference to be compared, and the storage means 10 stores, for example, a standard image relating to a temperature difference, an area and a pattern determined to be good after welding. In addition to data, standard image data related to the temperature difference, area, and pattern determined to be good as necessary, a number of known defects for each temperature difference, area, and pattern determined to be defective based on past determination results. Image data is stored. In the storage of these standard image data, similar to the characteristic image data storage means 9, the standard image data and a large number of known defective image data are divided into regions and stored for each temperature difference, area and pattern.
【００２０】従って、以上のようにして鋼板１の例えば
突合せ溶接ごとに、当該溶接部２および溶接部近傍の特
徴画像データを抽出して特徴画像データ記憶手段９に記
憶されるが、このとき良否判定手段１１では、先ず最初
に例えば温度差に着目して特徴画像データ記憶手段９に
記憶されている特徴画像データと既に標準画像データ記
憶手段１０に記憶されている温度差に係わる標準画像デ
ータとを比較し、ここで特徴画像データが標準画像デー
タとほぼ等しければ溶接部２の内部状態が良好であると
判定し、仮に特徴画像データが標準画像データと異なる
場合には必要に応じて特徴画像データと多数の既知不良
画像データとを順次比較し、特徴画像データと既知不良
画像データの間に非常に近似するものがあれば、当該特
徴画像データがその最も近い既知不良画像データに基づ
いて温度差に関する欠陥信号を出力する。Therefore, as described above, for each butt welding of the steel sheet 1, the characteristic image data of the welded portion 2 and the vicinity of the welded portion are extracted and stored in the characteristic image data storage means 9. In the determination means 11, first, for example, focusing on the temperature difference, the characteristic image data stored in the characteristic image data storage means 9 and the standard image data relating to the temperature difference already stored in the standard image data storage means 10 are detected. If the characteristic image data is substantially equal to the standard image data, it is determined that the internal state of the welded portion 2 is good. If the characteristic image data is different from the standard image data, the characteristic image is determined as necessary. The data and a large number of known defective image data are sequentially compared, and if there is a very close approximation between the characteristic image data and the known defective image data, the characteristic image data is found. Outputting a defect signal relating to the temperature difference based on the closest known bad image data.
【００２１】なお、良否判定手段１１では、引き続き、
面積に着目して前述同様に特徴画像データ記憶手段９に
記憶された特徴画像データと既に標準画像データ記憶手
段１０に記憶されている面積に係わる標準画像データと
を比較し、ここで特徴画像データが標準画像データとほ
ぼ等しければ溶接部２の形状が良好であると判定し、仮
に特徴画像データが標準画像データと異なる場合には必
要に応じて特徴画像データと多数の既知不良画像データ
とを順次比較し、前述と同様に必要に応じて最も近い既
知不良画像データに基づいて面積に関する欠陥信号を出
力する。また、パターンについても前述と同様な処理に
より良否および必要に応じて欠陥状態を検査する。The pass / fail judgment means 11 continues to
Focusing on the area, the characteristic image data stored in the characteristic image data storage means 9 and the standard image data relating to the area already stored in the standard image data storage means 10 are compared with each other in the same manner as described above. Is approximately equal to the standard image data, it is determined that the shape of the welded portion 2 is good, and if the characteristic image data is different from the standard image data, the characteristic image data and a large number of known defective image data are determined as necessary. Successive comparisons are performed, and a defect signal related to the area is output based on the closest known defective image data as needed, as described above. In addition, the pattern is also inspected for defect and defect state as necessary by the same processing as described above.
【００２２】従って、以上のような実施例の検査方法に
よれば、赤外センサ３を用いて溶接部２の入熱状態，つ
まり溶接部２およびその溶接部近傍の温度分布を測定す
るようにしたので、オンラインにて温度分布を正確に測
定できるばかりか、この測定画像データから階調変換を
行って例えば温度差，面積およびパターンの何れかに係
わる特徴画像データを抽出し、この特徴画像データと予
め溶接事後の良とされた標準画像データとを比較するの
で、迅速、かつ、確実に溶接部２の良否を判定すること
ができ、しかも最終結果の製品について目視による観察
を行うことなく溶接部内部の品質を含む溶接部の欠陥検
査を行うことができる。Therefore, according to the inspection method of the above embodiment, the heat input state of the welded portion 2, that is, the temperature distribution of the welded portion 2 and its vicinity is measured by using the infrared sensor 3. Therefore, not only the temperature distribution can be accurately measured online, but also the characteristic image data relating to any of the temperature difference, the area and the pattern is extracted by performing gradation conversion from the measured image data. Is compared with standard image data which is determined to be good after welding in advance, so that the quality of the welded portion 2 can be determined quickly and reliably, and welding is performed without visually observing the final product. It is possible to perform a defect inspection of a welded part including quality inside the part.
【００２３】また、温度差，面積およびパターンに関す
る特徴画像データを抽出した後、各温度差，面積および
パターンに関する標準画像データとを比較するので、種
々の方面から溶接部の欠陥検査を判定することができ
る。Further, after the characteristic image data relating to the temperature difference, area and pattern is extracted and compared with the standard image data relating to each temperature difference, area and pattern, it is possible to judge the defect inspection of the welded portion from various directions. You can
【００２４】さらに、上記実施例の検査方法は、測定画
像データから階調変換を行って得られた特徴画像データ
と予め既知の温度差，面積およびパターンに関する標準
画像データの他、同じく温度差，面積およびパターンに
関する多数の既知不良画像データとを比較し、特徴画像
データに基づいて最も近似する既知不良画像データから
欠陥の状態を判定するので、種々の欠陥の状態を決め細
かく判定でき、これによって例えばある同一の欠陥が多
い場合にはそれに対する溶接作業ないしは溶接制御の対
策を容易に講じることができ、より良い溶接品質の改良
にも役立つものである。Further, in the inspection method of the above embodiment, in addition to the characteristic image data obtained by performing the gradation conversion from the measured image data and the standard image data regarding the temperature difference, the area and the pattern known in advance, the same temperature difference, Compared with a large number of known defective image data related to area and pattern, the defect state is determined from the most similar known defective image data based on the characteristic image data, so various defect states can be determined and determined in detail, and For example, when there are many identical defects, it is possible to easily take measures for welding work or welding control for them, which is also useful for improving better welding quality.
【００２５】なお、上記実施例では、測定画像データを
階調変換するようにしたが、例えば測定画像データから
面積データだけを抽出する場合には２値化変換を行い、
この２値化変換によって測定画像データを得るようにし
ても良い。また、雑音除去用フィルタ５は、階調変換手
段６の入力側に設けたが、階調変換手段６の出力側に設
けてもよい。その他、本発明はその要旨を逸脱しない範
囲で種々変形して実施できる。In the above embodiment, the measurement image data is subjected to gradation conversion. However, for example, when only area data is extracted from the measurement image data, binarization conversion is performed,
The measurement image data may be obtained by this binarization conversion. Further, although the noise removing filter 5 is provided on the input side of the gradation converting means 6, it may be provided on the output side of the gradation converting means 6. In addition, the present invention can be modified in various ways without departing from the scope of the invention.
【００２６】[0026]
【発明の効果】以上説明したように本発明によれば、次
のような種々の効果を奏する。請求項１の発明は、オン
ラインにて迅速に溶接部の良否を判定でき、目視検査を
行うことなく溶接部の内部品質を含む品質検査を適正に
行うことができる。次に、請求項２の発明は、種々の欠
陥の状態を決め細かく判定でき、溶接作業ないしは溶接
制御に対して適切な対策を講ずることができる。As described above, according to the present invention, the following various effects are exhibited. According to the first aspect of the present invention, the quality of the welded portion can be quickly determined online, and the quality inspection including the internal quality of the welded portion can be properly performed without performing the visual inspection. Next, in the invention of claim 2, various defect states can be determined and finely determined, and appropriate measures can be taken for welding work or welding control.
【図１】 本発明に係わる鋼板の溶接部検査方法の一実
施例としての検査装置の基本構成を示す図。FIG. 1 is a diagram showing a basic configuration of an inspection apparatus as an embodiment of a method for inspecting a welded portion of a steel sheet according to the present invention.
１…鋼板、２…溶接部、３…赤外センサ、４…画像処理
装置、５…雑音除去用フィルタ、６…階調変換手段、７
…画像処理記憶手段、８…特徴抽出処理手段、９…特徴
画像データ記憶手段、１０…既知画像データ記憶手段、
１１…良否判定手段。DESCRIPTION OF SYMBOLS 1 ... Steel plate, 2 ... Welding part, 3 ... Infrared sensor, 4 ... Image processing device, 5 ... Noise removal filter, 6 ... Gradation conversion means, 7
... image processing storage means, 8 ... feature extraction processing means, 9 ... feature image data storage means, 10 ... known image data storage means,
11 ... Pass / fail judgment means.
Claims (2)
査方法において、 赤外センサを用いて前記溶接部およびその溶接部近傍か
ら輻射される赤外線を検出するとともに、当該赤外セン
サから得られる溶接部およびその溶接部近傍の温度分布
データを画像処理して特徴画像データを抽出し、この特
徴画像データと予め記憶されている既知標準画像データ
とを比較し、溶接部の良否を判定することを特徴する鋼
板の溶接部検査方法。1. A method for inspecting a welded part of a steel sheet, comprising: detecting an infrared ray radiated from the welded part and the vicinity of the welded part by using an infrared sensor, and obtaining the infrared ray from the infrared sensor. Image processing is performed on the temperature distribution data of the welded part and the vicinity of the welded part to extract characteristic image data, and the characteristic image data is compared with the known standard image data stored in advance to determine the quality of the welded part. A method for inspecting a welded part of a steel sheet, which is characterized in that
査方法において、 赤外センサを用いて前記溶接部およびその溶接部近傍か
ら輻射される赤外線を検出し、この赤外センサから得ら
れる溶接部およびその溶接部近傍の温度分布データを画
像処理して温度差，面積およびパターンの１種類以上の
特徴画像データを抽出し、この抽出された１種類以上の
特徴画像データと予め記憶されている温度差，面積およ
びパターンの１種類以上の既知標準画像データ、複数の
既知不良画像データとを比較し、特徴画像データが最も
近似する既知不良画像データに基づいて欠陥の種類を判
定することを特徴とする鋼板の溶接部検査方法。2. A method for inspecting a welded portion of a steel sheet, comprising: detecting an infrared ray radiated from the welded portion and the vicinity of the welded portion by using an infrared sensor, and obtaining the infrared ray from the infrared sensor. Image processing is performed on the temperature distribution data of the welded portion and the vicinity of the welded portion to extract one or more types of characteristic image data of temperature difference, area, and pattern, and the extracted one or more types of characteristic image data are stored in advance. It is possible to compare one or more types of known standard image data of a temperature difference, an area, and a pattern, and a plurality of known defective image data, and determine the type of defect based on the known defective image data to which the characteristic image data is most similar. Characteristic steel sheet weld inspection method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4186806A JP2861649B2 (en) | 1992-07-14 | 1992-07-14 | Steel plate weld inspection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4186806A JP2861649B2 (en) | 1992-07-14 | 1992-07-14 | Steel plate weld inspection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH0634564A true JPH0634564A (en) | 1994-02-08 |
| JP2861649B2 JP2861649B2 (en) | 1999-02-24 |
Family
ID=16194916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP4186806A Expired - Fee Related JP2861649B2 (en) | 1992-07-14 | 1992-07-14 | Steel plate weld inspection method |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP2861649B2 (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003275890A (en) * | 2002-03-18 | 2003-09-30 | Nippon Steel Corp | Treating method for prolonging fatigue life and welded joint having long service life with this method |
| JP2005030891A (en) * | 2003-07-11 | 2005-02-03 | Toshiba Corp | Surface non-destructive inspection apparatus and surface non-destructive inspection method |
| CN105234599A (en) * | 2015-10-20 | 2016-01-13 | 沈阳富创精密设备有限公司 | Welding temperature field control system and method |
| WO2018087821A1 (en) * | 2016-11-09 | 2018-05-17 | 株式会社オプティム | Inspection system, inspection method, inspection device, and program |
| JP2021181104A (en) * | 2020-05-18 | 2021-11-25 | 東芝三菱電機産業システム株式会社 | Weld quality determination assisting system |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113302017A (en) * | 2018-10-08 | 2021-08-24 | 海斯坦服务公司 | Method for detecting welding defects in arc welding and arc welding system |
| KR102514450B1 (en) * | 2019-11-27 | 2023-03-24 | 고려대학교 산학협력단 | Method of detecting defect in welded joint using thermal imaging camera |
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| JPS5232387A (en) * | 1975-09-05 | 1977-03-11 | Nippon Steel Corp | Method of detecting defects of weld zones of steel pipes |
| JPH01216246A (en) * | 1988-02-24 | 1989-08-30 | Tanaka Kikinzoku Kogyo Kk | Quality deciding method for resistance welded article |
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| JPH0483152A (en) * | 1990-07-25 | 1992-03-17 | Shuji Nakada | Method and device for inspecting junction of electronic part |
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|---|---|---|---|---|
| JPS5232387A (en) * | 1975-09-05 | 1977-03-11 | Nippon Steel Corp | Method of detecting defects of weld zones of steel pipes |
| JPH01216246A (en) * | 1988-02-24 | 1989-08-30 | Tanaka Kikinzoku Kogyo Kk | Quality deciding method for resistance welded article |
| JPH0265257A (en) * | 1988-08-31 | 1990-03-05 | Nec Kyushu Ltd | Seam welder |
| JPH0360882A (en) * | 1989-07-28 | 1991-03-15 | Honda Motor Co Ltd | Method for deciding quality of welding state |
| JPH0483152A (en) * | 1990-07-25 | 1992-03-17 | Shuji Nakada | Method and device for inspecting junction of electronic part |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003275890A (en) * | 2002-03-18 | 2003-09-30 | Nippon Steel Corp | Treating method for prolonging fatigue life and welded joint having long service life with this method |
| JP2005030891A (en) * | 2003-07-11 | 2005-02-03 | Toshiba Corp | Surface non-destructive inspection apparatus and surface non-destructive inspection method |
| CN105234599A (en) * | 2015-10-20 | 2016-01-13 | 沈阳富创精密设备有限公司 | Welding temperature field control system and method |
| WO2017067241A1 (en) * | 2015-10-20 | 2017-04-27 | 沈阳富创精密设备有限公司 | Welding temperature field control system and method |
| CN105234599B (en) * | 2015-10-20 | 2018-06-12 | 沈阳富创精密设备有限公司 | Welding temperature station control system and method |
| WO2018087821A1 (en) * | 2016-11-09 | 2018-05-17 | 株式会社オプティム | Inspection system, inspection method, inspection device, and program |
| JP2021181104A (en) * | 2020-05-18 | 2021-11-25 | 東芝三菱電機産業システム株式会社 | Weld quality determination assisting system |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2861649B2 (en) | 1999-02-24 |
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