201040489 六、發明說明： 【發明所屬之技術領域】 本發明涉及一種零件檢測系統及方法，尤其涉及一種 ; 零件形位公差檢測系統及方法。 【先前技術】 影像量測是目前精密量測領域中最廣泛使用的量測 方法，該方法不僅精度高，而且量測速度快。影像量測主 要用於零件的尺寸誤差和形位誤差的測量，對保證產品品 ® 質起著重要的作用。做法一般是使用影像量測機台分別獲 取標準零件和待測零件的點雲（即由多個三維離散點組成 的點的集合），而後將點雲資料登錄電腦，執行相應軟體對 點雲資料進行各種處理，獲取檢測結果。 其中，對零件進行形位公差檢測是上述各種處理所需 要使用的重要技術之一，也是上述各種處理所需要解決的 一個關鍵問題。傳統的方法採用將待測零件和標準零件放 Q 置在一起，然後手動測量兩者之間的形位公差，這種檢測 方法很不方便，不但費時費力，更主要的是這種檢測方法 不能提供精確的貨料。 【發明内容】 鑒於以上内容，有必要提供一種零件形位公差檢測系 統及方法，其可自動檢測零件的形位公差。 一種零件形位公差檢測系統，該系統包括：資料獲取 模組，用於從影像量測機台獲取待測零件的圖檔及用戶從 該待測零件圖檔中選擇的待測特徵元素；網格化模組，用 201040489 於對待測零件圖檔進行三角網格化，獲取該待測零件圖檔 的點雲資料；所述資料獲取模組，還用於從標準零件圖檔 ；·- 中獲取相對該待測特徵元素的標準特徵元素；點雲提取模 ;' 組，用於從待測零件圖檔的點雲資料中提取擬合成特徵元 素的點雲；點雲擬合模組，用於將點雲提取模組所提取的 點雲擬合成特徵元素；形位公差計算模組，用於計算點雲 擬合模組擬合的特徵元素與標準特徵元素之間的形位公 差；報表生成模組，用於輸出形位公差分析表，顯示在顯 ®示螢幕上。 一種零件形位公差檢測方法，該方法包括如下步驟： (a)獲取待測零件的圖檔及用戶從該待測零件圖檔中選擇 的待測特徵元素；（b)對待測零件圖檔進行三角網格化，獲 取該待測零件圖檔的點雲資料；（c)從標準零件圖檔中獲取 相對該待測特徵元素的標準特徵元素；（d)從待測零件圖檔 的點雲資料中提取擬合成特徵元素的點雲；（e)將所提取的 ^ 點雲擬合成特徵元素；（f)計算擬合的特徵元素與標準特徵 元素之間的形位公差；（g)輸出形位公差分析表，顯示在顯 示榮幕上。 相較於習知技術，所述的零件形位公差檢測系統及方 法，其可自動檢測零件的形位公差，極大地提高了檢測速 度和精度，並減少了誤差的產生。 【實施方式】 參閱圖1所示，係本發明零件形位公差檢測系統較佳 實施方式的系統架構圖。該系統主要包括顯示設備1、主 5 201040489 機2、影像量測機台3和輸入設備4。所述主機2包括儲存 體20和形位公差檢測單元21。 • 其中，所述影像量測機台3用於獲取標準零件和待測 ♦ 零件的圖檔，並將攝取的圖檔資料傳送到測試主機2，該 標準零件和待測零件的圖檔由點雲組成，所述點雲是指由 多個三維離散點組成的點的集合。 所述儲存體20可以是主機2中的硬碟等，用於儲存 點雲資料22。所述點雲資料22包括標準零件圖檔的點雲 〇 和待測零件圖檔的點雲等。 所述主機2連接有顯示設備1，用於顯示影像量測機 台3傳送給主機2的圖檔等。所述輸入設備4可以是鍵盤 和滑鼠等，用於進行資料登錄。 所述形位公差檢測單元21用於計算待測零件圖檔31 的特徵元素與標準零件圖檔30的特徵元素之間的形位公 差（參閱圖2所示），並輸出形位公差分析表，顯示在顯示 Q 螢幕上。所述形位公差是指機械加工後零件的實際元素相 對於標準零件元素之間的誤差，包括形狀公差和位置公 差。任何零件都是由點、線、面等構成的，這些點、線、 面稱為零件的元素。 形狀公差：被測元素的實際形狀對理想形狀允許的變 動量。形狀公差包括直線度、平面度、圓度、圓柱度、線 輪廓度和麵輪廓度等。位置公差：被測元素的實際位置對 理想位置允許的變動量。位置公差包括平行度、垂直度、 傾斜度、同軸度、對稱度和位置度等。 6 201040489 其中，該形位公差檢測單元 210、網袼化模組211、點雲提广括資料獲取模組 213、形仅公差計算模組214和報二212、點雲擬合模組 所稱的—是完成-特定功能的本發明 合於描述軟體在電腦中的執行過程，因比程式更適 軟體描述都以模組描述。 本發明以下對 Ο Ο 所述資料獲取模組21〇用於莽 用戶從待測零件圖檔31中選擇_取相零件圖檔31及 徵元素用於進行形位公差分析。在本元素，該待測特 徵元素包括：待測零件圖檔31的線、圓式中’待測特 所述網格化触211料對待測、圓柱或球等。 角網格化，獲取該待測零件圖構^點雲=31進行三 所述資料獲取模組21〇_ 貝料° 件圖檔30中獲取相對該待測特徵元體則標準零 榡準特徵元料為計料卿徵元切^素值該 所述點雲提取模組212用於從=的參考值。 雲資才种提取擬合成特徵元素的點雲，檔31的點 所述。 〃體過程參見圖4 所述點雲擬合模組213用於將點雲 取的點錄合成特Μ素，具體過程ϋ4組212所提 所述擬合成的特徵元素包括：擬合線^和圖6所述。 擬合圓枉和擬合球等。 α圓、擬合面、 所述形位公差計算模組214用 213擬合的特徵元素與標準特徵元素之間的=;= 7 201040489 述形位公差包括形狀公差和位置公差，形狀公差包括直線 度、平面度、圓度、圓柱度、線輪廓度和麵輪廓度等，位 置公差包括平行度、垂直度、傾斜度、同滅、對稱度和 位置度等。 其中，直線度為擬合線上的點雲到標準零件圖檔 的標準線的距離和。 ❹ 〇 平面度為擬合面上的點雲到標準零件圖檔30的禪準 面的距離和。 + 的距離和 圓度為擬合圓上的點雲到標準零件圖檔30的標準圓 圖槽30的標 圓柱度為擬合圓柱上的點雲到標準零件 準圓柱的距離和。 圖檔30的標 線輪廓度為擬合線的輪廓度與標準零件 準線的輪廓度誤差。 準面合面的輪廊度與標準零軸3。的標 距離===標準零件圖播3。的標準面的最大 垂直度：卜卜，)+¥卜 元素的長度，l2代表待測钟=M’Ll代表標準特徵 素的兩個位置測量待測特/70素的長度，在待測特徵元 和m2及相應的轴經 ' 徵70素與直角座標系的距離Ml 1 年口（^2 0 8 201040489 傾斜度： h 同軸度：，其中 、 特徵元素的中心點座標’（X2，y2)代表標準特徵測 點座標。 U I的中心 對稱度：Ηα「《χ，其中，卜 對稱的兩條邊之間的距離。 相特徵元素中 ❹ Ο 位置度：卜2/^，其中，f m rh τγ hh ^ 、 x 待測特徵元素盘p 準特徵TO素的中心點在x軸方向上的偏差 徵元素與標準特徵元素的中心點在Yy代表待測特 郎·、+、相主XL _L 孕由方向上的偏差。 所述報表生成模組215用於輸出形位公 示在顯示螢幕上。 差刀析表’顯 實施係本發明零件形位公差檢測方法較佳 步驟Si ’資料獲取模組210獲取待 及用戶從該待測零件„31中選擇的待 測特徵元素用於進行形位公差分析。在本 素5亥待 測特徵元素包括：待測零件圖檔31的 方式中’待 球等。 圓、面、圓柱或 步驟S2’網格化模經如對待测零件圖槽 角網格化，獲取該待測零件圖檔31的點雲資；進仃一 步驟S3，資料獲取模組21〇從儲=。 圖播30帽取㈣該待測特徵元素的的標準零件 準特徵元素作為計算待測特徵元素形位心、徵70素’δ亥標 Α差的參考值。 9 201040489 步驟S4，點雲提取模組212從待測零件圖檔31的點 雲資料中提取擬合成特徵元素的點雲，具體過程參見圖4 •所述。 ； 步驟S5,點雲擬合模組213將點雲提取模組212所提 取的點雲擬合成特徵元素，具體過程參見圖5和圖6所述。 所述擬合成的特徵元素包括：擬合線、擬合圓、擬合面、 擬合圓柱和擬合球等。 步驟S6，形位公差計算模組214計算點雲擬合模組 〇 213擬合的特徵元素與標準特徵元素之間的形位公差。 步驟S7，報表生成模組215輸出形位公差分析表，顯 示在顯示榮幕上。 參閱圖4所示，係圖3中步驟S4的具體流程圖。 步驟S41，點雲提取模組212繪製多義線，該多義線 將用戶選擇的待測特徵元素包圍。 步驟S42,點雲提取模組212從待測零件圖檔31的點 0 雲資料中提取位於該多義線内的點雲。 步驟S43，點雲提取模組212從多義線内的點雲中提 取位於螢幕最上層的點雲。 步驟S44，點雲提取模組212從螢幕最上層的點雲中 提取出待測零件圖檔31的邊界點作為擬合成特徵元素的 點雲。 參閱圖5所示，係圖3中步驟S5的具體流程圖。 步驟S50，點雲擬合模組213根據待測特徵元素的類 型獲取該待測特徵元素的擬合類型。其中，所述擬合類型 201040489 包括：線、圓、面、圓柱和球等。 步驟S51，判斷所述擬合類型是否為線或圓，如果該 擬合類型為線或圓，則執行步驟S52和步驟S53後再執行 :步驟S54，如果該擬合類型不是線或圓，則直接執行步驟 S54 ° 步驟S52,點雲擬合模組213將步驟S4中提取的點雲 擬合成面。 步驟S53,點雲擬合模組213將步驟S4中提取的點雲 〇 投影到該擬合面上獲得所述提取點雲在該擬合面上的投影 點。 步驟S54，根據擬合類型獲取相應的迭代方程式進行 迭代計算，所述迭代方程式包括擬合線的迭代方程式、擬 合圓的迭代方程式、擬合面的迭代方程式、擬合圓柱的迭 代方程式和擬合球的迭代方程式等。其中，如果擬合類型 為線或圓，則使用步驟S53中獲取的投影點進行迭代計 q 算，如果擬合類型不是線或圓，則直接使用步驟S4中提 取的點雲進行迭代計算。 具體而言，如果擬合類型為線，則迭代方程式為擬合 線的方程式，如方程式（1)所示：201040489 VI. Description of the Invention: [Technical Field] The present invention relates to a part detecting system and method, and more particularly to a part shape tolerance detecting system and method. [Prior Art] Image measurement is currently the most widely used measurement method in the field of precision measurement. This method is not only highly accurate, but also has a fast measurement speed. Image measurement is mainly used for the measurement of dimensional error and geometrical error of parts, which plays an important role in ensuring the quality of products. The method generally uses an image measuring machine to respectively acquire a point cloud of a standard part and a part to be tested (ie, a set of points consisting of a plurality of three-dimensional discrete points), and then log the point cloud data to the computer to execute the corresponding software pair point cloud data. Various processes are performed to obtain test results. Among them, the geometrical tolerance detection of parts is one of the important technologies required for the above various treatments, and it is also a key problem to be solved by the above various treatments. The traditional method uses the parts to be tested and the standard parts to be placed together, and then manually measures the geometrical tolerance between the two. This detection method is inconvenient, not only time-consuming and laborious, but more importantly, this detection method cannot Provide accurate materials. SUMMARY OF THE INVENTION In view of the above, it is necessary to provide a part shape tolerance detecting system and method which can automatically detect the geometrical tolerance of a part. A part shape tolerance detecting system, the system comprising: a data acquisition module, configured to acquire an image file of a part to be tested and a character to be tested selected by the user from the image file to be tested from the image measuring machine; The grid module is used to perform triangle meshing on the part file to be tested with 201040489, and obtain the point cloud data of the part file to be tested; the data acquisition module is also used for the standard part drawing file; Obtaining a standard feature element relative to the feature element to be tested; a point cloud extraction module; a group for extracting a point cloud fitted into a feature element from a point cloud data of the part image to be tested; a point cloud fitting module, The point cloud extracted by the point cloud extraction module is fitted into a feature element; the geometrical tolerance calculation module is used to calculate the geometric tolerance between the feature element and the standard feature element fitted by the point cloud fitting module; The generation module is used to output the geometric tolerance analysis table and is displayed on the display screen. A method for detecting a position and shape tolerance of a part, the method comprising the following steps: (a) acquiring an image file of the part to be tested and a feature element to be tested selected by the user from the image file of the part to be tested; (b) performing the image of the part to be tested Triangular meshing, obtaining point cloud data of the part file to be tested; (c) obtaining standard feature elements relative to the feature element to be tested from the standard part drawing file; (d) point cloud from the part file to be tested Extracting a point cloud fitted to a feature element in the data; (e) fitting the extracted point cloud into a feature element; (f) calculating a geometrical tolerance between the fitted feature element and the standard feature element; (g) output The geometric tolerance analysis table is displayed on the display screen. Compared with the prior art, the part shape tolerance detection system and method can automatically detect the geometrical tolerance of the part, greatly improve the detection speed and accuracy, and reduce the error. [Embodiment] Referring to Figure 1, there is shown a system architecture diagram of a preferred embodiment of a part shape tolerance detecting system of the present invention. The system mainly includes a display device 1, a main 5 201040489 machine 2, an image measuring machine 3 and an input device 4. The main body 2 includes a storage body 20 and a geometrical position tolerance detecting unit 21. The image measuring machine 3 is configured to acquire the image of the standard part and the part to be tested, and transmit the ingested image data to the test host 2, and the image of the standard part and the part to be tested is determined by the point Cloud composition, the point cloud refers to a collection of points consisting of multiple three-dimensional discrete points. The storage body 20 may be a hard disk or the like in the host 2 for storing the point cloud data 22. The point cloud data 22 includes a point cloud of a standard part image file and a point cloud of the part image to be tested. The host 2 is connected to the display device 1 for displaying an image file transmitted by the image measuring machine 3 to the host 2 and the like. The input device 4 can be a keyboard, a mouse, or the like for data registration. The geometrical position tolerance detecting unit 21 is configured to calculate a geometrical tolerance between the characteristic element of the part image 31 to be tested and the characteristic element of the standard part drawing file 30 (refer to FIG. 2), and output the geometrical tolerance analysis table. , displayed on the display Q screen. The geometric tolerance refers to the error between the actual element of the machined part and the standard part element, including shape tolerance and position tolerance. Any part is made up of points, lines, faces, etc. These points, lines, and faces are called elements of the part. Shape Tolerance: The amount of change allowed by the actual shape of the measured element to the ideal shape. Shape tolerances include straightness, flatness, roundness, cylindricity, line profile, and face profile. Position tolerance: The amount of variation allowed by the actual position of the measured element versus the ideal position. Position tolerances include parallelism, perpendicularity, tilt, concentricity, symmetry, and position. 6 201040489 wherein the geometric position tolerance detecting unit 210, the meshing module 211, the point cloud lifting and data acquisition module 213, the shape tolerance calculation module 214, the report two 212, and the point cloud fitting module are called The present invention, which is a completion-specific function, is described in the description of the execution process of the software in the computer, and the software description is more described by the module than the program. In the following, the data acquisition module 21 is used by the user to select the _phase component image file 31 and the element for the geometrical tolerance analysis from the part file 31 to be tested. In this element, the element to be tested includes: a line of the part file 31 to be tested, a circle in the circle, and a grid-shaped touch 211 material to be measured, a cylinder or a ball, and the like. Angle meshing, obtaining the part to be tested, the point cloud = 31, and the data acquisition module 21 〇 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ The elementary material is used to calculate the value of the elementary cloud extraction module 212 for the reference value from =. The cloud resource is extracted from a point cloud fitted to a feature element, as described at point 31 of the file. The sacral process is described in FIG. 4, and the point cloud fitting module 213 is used to record the points taken by the point cloud into the special elements. The specific elements of the group process 212 are: the fitting line ^ and Figure 6 is described. Fit the circle and fit the ball, etc. α circle, fitting surface, the geometrical tolerance calculation module 214 uses 213 to fit the characteristic element with the standard feature element =; = 7 201040489 The geometrical tolerance includes shape tolerance and position tolerance, and the shape tolerance includes a straight line Degree, flatness, roundness, cylindricity, line profile and face profile, etc. Positional tolerances include parallelism, perpendicularity, inclination, homogenization, symmetry, and position. Among them, the straightness is the distance from the point cloud on the fitted line to the standard line of the standard part drawing file. ❹ 〇 The flatness is the distance from the point cloud on the fitted surface to the zen surface of the standard part image 30. The distance and roundness of + are the standard circle on the fitted circle to the standard circle of the standard part file 30. The sizing of the groove 30 is the distance from the point cloud on the fitted cylinder to the quasi-cylindrical part of the standard part. The line profile of Figure 30 is the profile of the fitted line and the profile of the standard part. The standard of the joint surface and the standard zero axis 3. The standard distance === standard part drawing 3 . The maximum perpendicularity of the standard surface: Bub, the length of the +¥b element, l2 represents the clock to be measured = M'Ll represents the two positions of the standard eigenvalues, and the length of the element to be measured/70 is measured. Yuan and m2 and the corresponding axis are the distance between the 70-symbol and the right-angle coordinate system. Ml 1 year mouth (^2 0 8 201040489 inclination: h coaxiality: where, the center point coordinate of the characteristic element' (X2, y2) Represents the coordinate of the standard feature point. The central symmetry of the UI: Ηα ""χ, where the distance between the two sides of the symmetry. The 特征 位置 position of the phase feature element: Bu 2/^, where fm rh τγ hh ^ , x The characteristic element of the test element disk p The quasi-feature of the TO element is offset in the x-axis direction and the center point of the standard feature element at Yy represents the Trang, +, and the phase main XL _L The report generation module 215 is configured to output the shape information on the display screen. The difference tool analysis table is a preferred step of the method for detecting the shape and position tolerance of the present invention. And the user to select the special part to be tested from the part to be tested „31 The element is used for geometrical tolerance analysis. The characteristic elements to be tested in the 5th floor include: the method of the part to be tested 31, 'waiting for the ball, etc. Circle, face, cylinder or step S2' meshing mode is treated as The slot angle of the part map is gridded, and the point cloud of the part file 31 to be tested is obtained; in step S3, the data acquisition module 21 is stored in the storage unit. The standard part quasi-feature element is used as a reference value for calculating the shape element center of the feature to be tested, and the sign of the element is 70. '201040489 Step S4, the point cloud extraction module 212 is from the point cloud of the part file 31 to be tested. The point cloud fitted to the feature element is extracted from the data, and the specific process is as described in FIG. 4 • In step S5, the point cloud fitting module 213 fits the point cloud extracted by the point cloud extraction module 212 into a feature element, and the specific process 5 and 6. The fitted feature elements include: a fitted line, a fitted circle, a fitted surface, a fitted cylinder, a fitted ball, etc. Step S6, the geometrical tolerance calculation module 214 calculates Point cloud fitting module 〇213 between the feature element and the standard feature element In step S7, the report generation module 215 outputs a shape tolerance analysis table, which is displayed on the display screen. Referring to FIG. 4, it is a specific flowchart of step S4 in FIG. 3. Step S41, point cloud extraction The module 212 draws a polyline that surrounds the user-selected feature element to be tested. Step S42, the point cloud extraction module 212 extracts a point located in the polyline from the point 0 cloud data of the part image 31 to be tested. Step S43, the point cloud extraction module 212 extracts a point cloud located at the top layer of the screen from the point cloud in the polyline. In step S44, the point cloud extraction module 212 extracts the part to be tested from the point cloud of the uppermost layer of the screen. The boundary point of the map file 31 serves as a point cloud fitted to the feature element. Referring to FIG. 5, it is a specific flowchart of step S5 in FIG. In step S50, the point cloud fitting module 213 obtains the fitting type of the feature element to be tested according to the type of the feature element to be tested. Wherein, the type of fit 201040489 includes: lines, circles, faces, cylinders, balls, and the like. In step S51, it is determined whether the type of the fit is a line or a circle. If the type of the fit is a line or a circle, step S52 and step S53 are performed: step S54, if the type of the fit is not a line or a circle, Directly executing step S54 ° step S52, the point cloud fitting module 213 fits the point cloud extracted in step S4 into a surface. In step S53, the point cloud fitting module 213 projects the point cloud 提取 extracted in step S4 onto the fitting surface to obtain a projection point of the extracted point cloud on the fitting surface. Step S54: Perform an iterative calculation according to the fitting type to obtain a corresponding iterative equation, the iterative equation of the fitted line, the iterative equation of the fitted circle, the iterative equation of the fitting surface, the iterative equation of the fitting cylinder, and the fitting The iterative equation of the ball and so on. Wherein, if the fitting type is a line or a circle, the projection point acquired in step S53 is used for iterative calculation. If the fitting type is not a line or a circle, the point cloud extracted in step S4 is directly used for iterative calculation. Specifically, if the fit type is a line, the iterative equation is the equation for the fitted line, as shown in equation (1):
方程式（1)為：點雲到擬合線的距離平均平方和的最小 值，其中，Xi為當前點雲的X軸座標，yi為當前點雲的Y 轴座標，Zi為當前點雲的Z軸座標，x〇為擬合線第一點的 11 201040489 X軸座標，y〇為擬合線第一點的Y轴座標，z〇為擬合線第 一點的Z軸座標，α為當前點與擬合線第一點的連線與該 擬合線的夾角。 如果擬合類型為圓，則迭代方程式為擬合圓的方程 式，如方程式（2)所示： /(x) = min) ^ (V(xi - xf+(yt - yf+(zi -zf -r^ n (2)Equation (1) is the minimum of the sum of the squares of the distance from the point cloud to the fitted line, where Xi is the X-axis coordinate of the current point cloud, yi is the Y-axis coordinate of the current point cloud, and Zi is the Z of the current point cloud. The axis coordinate, x〇 is the first point of the fitted line 11 201040489 X-axis coordinate, y〇 is the Y-axis coordinate of the first point of the fitted line, z〇 is the Z-axis coordinate of the first point of the fitted line, α is the current The angle between the point and the line connecting the first point of the line to the fitted line. If the fit type is a circle, the iterative equation is the equation for fitting the circle, as shown in equation (2): /(x) = min) ^ (V(xi - xf+(yt - yf+(zi -zf -r^ n (2)
方程式（2)為.點雲到圓心的距離減去半徑的平均平方 和的最小值，其中，Xi為當前點雲的X軸座標，yi為當前 點雲的Y軸座標，Zi為當前點雲的Z軸座標，X為圓心的 X軸座標，y為圓心的Y軸座標，z為圓心的Z軸座標，R 為擬合圓的半徑。 如果擬合類型為面，則迭代方程式為擬合面的方程 式，如方程式（3)所示：Equation (2) is the distance from the point cloud to the center of the circle minus the minimum sum of the squares of the radius, where Xi is the X-axis coordinate of the current point cloud, yi is the Y-axis coordinate of the current point cloud, and Zi is the current point cloud The Z-axis coordinate, X is the X-axis coordinate of the center, y is the Y-axis coordinate of the center, z is the Z-axis coordinate of the center, and R is the radius of the fitted circle. If the fit type is a face, the iterative equation is the equation for the fit face, as shown in equation (3):
/(x) = min| (Axt + By{ + Czi + D)' ^a2+b2+c2 n (3) 方程式（3)為：點雲到面的距離平均平方和的最小值， 其中，Xi為當前點雲的X軸座標，yi為當前點雲的Y軸座 標，Zi為當如點雲的Z轴座標，Axi+Byi+Czi+D=0為平面 的一般方程式。 如果擬合類型為圓柱，則迭代方程式為擬合圓柱的方 程式，如方程式（4)所示： 12 (4)201040489 /(x) = min ^{^xi ~x〇f +(yi~y〇f +(zi ~z〇f *sin(a)-i?j n 方程式(4)為：點雲到圓柱中心轴的距離減去半徑的平 均平方和的最小值，其中，Xi為當前點雲的X轴座標，yi 為當前點雲的Y軸座標，Zi為當前點雲的Z軸座標，x〇為 圓柱中心軸第一點的X軸座標，y0為圓柱中心軸第一點的 Y軸座標，z0為圓柱中心軸第一點的Z軸座標，α為當前/(x) = min| (Axt + By{ + Czi + D)' ^a2+b2+c2 n (3) Equation (3) is the minimum of the sum of squared distances from the point cloud to the surface, where Xi For the current X-axis coordinate of the point cloud, yi is the Y-axis coordinate of the current point cloud, and Zi is a general equation for the plane axis, such as the Z-axis coordinate of the point cloud, Axi+Byi+Czi+D=0. If the fit type is a cylinder, the iterative equation is the equation for fitting the cylinder, as shown in equation (4): 12 (4)201040489 /(x) = min ^{^xi ~x〇f +(yi~y〇 f +(zi ~z〇f *sin(a)-i?jn Equation (4) is: the distance from the point cloud to the central axis of the cylinder minus the minimum of the mean square sum of the radii, where Xi is the current point cloud The X-axis coordinate, yi is the Y-axis coordinate of the current point cloud, Zi is the Z-axis coordinate of the current point cloud, x〇 is the X-axis coordinate of the first point of the central axis of the cylinder, and y0 is the Y-axis coordinate of the first point of the central axis of the cylinder , z0 is the Z-axis coordinate of the first point of the central axis of the cylinder, α is the current
點與圓柱中心軸第一點的連線與該中心軸的夾角，R為圓 柱的半徑。 如果擬合類型為球，則迭代方程式為擬合球的方程 式，如方程式（5)所示： /(X) = min 飞 ~x〇f +(yi-y〇f+(ζ： ~z〇)2The angle between the point and the first point of the central axis of the cylinder and the central axis, and R is the radius of the cylinder. If the fit type is a sphere, the iterative equation is the equation for fitting the sphere, as shown in equation (5): /(X) = min fly ~x〇f +(yi-y〇f+(ζ: ~z〇) 2
RR
方程式（5)為：點雲到球心的距離減去半徑的平均平方 和的最小值，其中，Xi為當前點雲的X軸座標，yi為當前 點雲的Y軸座標，Zi為當前點雲的Z軸座標，X為球心的 X軸座標，y為球心的Y軸座標，z為球心的Z軸座標，R 為球的半徑。 步驟S55，點雲擬合模組213判斷是否到達預先設定 的迭代總次數，如果到達迭代總次數，則執行步驟S58， 如果沒有到達迭代總次數，執行步驟S56。在本較佳實施 方式中，用m表示迭代總次數，用i表示迭代次數即第幾 次迭代（i從0開始計數），假設m=3。 步驟S56，點雲擬合模組213根據迭代次數和待測特 13 201040489 徵元素的點雲總數得到該次迭代中的點雲數目η。 在本實施方式中，當i=0，即第1次迭代時，從待測 特徵元素的點雲總數中按10 : 1的比例等間距均勻取點， ； 即此時η等於待測特徵元素的點雲總數的十分之一。其 中，從待測特徵元素的點雲總數中按10 : 1的比例等間距 均勻取點的具體步驟為：首先沿X軸、Υ軸、Ζ軸方向對 待測特徵元素的點雲包圍盒進行等間距分割，使該包圍盒 被均勻分成10個小包圍盒，而後分別求得所述10個小包 〇 圍盒的中心，最後分別取出離所述10個小包圍盒的中心最 近的點。 當i=l，即第2次迭代時，從待測特徵元素的點雲總 數中按10 : 5的比例等間距均勻取點，即此時η等於待測 特徵元素的點雲總數的二分之一。 當i=2，即第3次迭代時，從待測特徵元素的點雲總 數中按1 : 1的比例等間距均勻取點，即此時η等於待測特 ❹ 徵元素的點雲總數。 步驟S57，點雲擬合模組213根據本次迭代的點雲數 目η和相應的迭代方程式擬合特徵元素，然後流程轉到步 驟S55，進行下一次迭代直至到達迭代總次數。當i=0，即 第1次迭代時，運用最小二乘法進行擬合，當i不為零時， 即第2次和第3次迭代時，運用擬牛頓演算法進行擬合。 其中，運用擬牛頓演算法擬合特徵元素的具體流程圖參見 圖6的描述。 步驟S58，點雲擬合模組213輸出最後一次迭代擬合 14 201040489 成的特徵元素。 參閱圖6所示，係圖5中步驟S57中運用擬牛頓演算 法擬合特徵元素的具體流程圖。 步驟S570，計算步驟S54中獲取的迭代方程式的值 f(x) 〇Equation (5) is: the distance from the point cloud to the center of the sphere minus the minimum of the average square sum of the radii, where Xi is the X-axis coordinate of the current point cloud, yi is the Y-axis coordinate of the current point cloud, and Zi is the current point The Z-axis coordinate of the cloud, X is the X-axis coordinate of the center of the sphere, y is the Y-axis coordinate of the center of the sphere, z is the Z-axis coordinate of the center of the sphere, and R is the radius of the sphere. In step S55, the point cloud fitting module 213 determines whether the preset total number of iterations is reached. If the total number of iterations is reached, step S58 is performed. If the total number of iterations is not reached, step S56 is performed. In the preferred embodiment, m is used to represent the total number of iterations, i is used to represent the number of iterations, i.e., the iterations (i counts from 0), assuming m=3. In step S56, the point cloud fitting module 213 obtains the number of point clouds η in the iteration according to the number of iterations and the total number of point clouds of the elements to be measured. In the present embodiment, when i=0, that is, the first iteration, the points are evenly spaced from the total number of point clouds of the feature elements to be tested by a ratio of 10:1; that is, η is equal to the feature element to be tested. One tenth of the total number of point clouds. The specific steps of uniformly taking points from the total number of point clouds of the feature elements to be tested at a ratio of 10:1 are: first, the point cloud bounding box of the feature element to be measured along the X axis, the Υ axis, and the Ζ axis direction, etc. The spacing is divided so that the bounding box is evenly divided into 10 small bounding boxes, and then the centers of the 10 small packets are respectively determined, and finally the points closest to the center of the 10 small bounding boxes are respectively taken out. When i=l, that is, the second iteration, the points are evenly spaced from the total number of point clouds of the feature elements to be tested by a ratio of 10:5, that is, η is equal to the total number of point clouds of the feature elements to be tested. one. When i=2, that is, the third iteration, the point is evenly spaced from the total number of point clouds of the feature element to be tested by a ratio of 1:1, that is, η is equal to the total number of point clouds of the element to be measured. In step S57, the point cloud fitting module 213 fits the feature elements according to the point cloud number η of the iteration and the corresponding iterative equation, and then the flow goes to step S55 to perform the next iteration until the total number of iterations is reached. When i=0, the first iteration, the least squares method is used for fitting. When i is not zero, that is, the second and third iterations, the quasi-Newton algorithm is used for fitting. The specific flow chart for fitting the feature elements using the quasi-Newton algorithm is described in the description of FIG. In step S58, the point cloud fitting module 213 outputs the feature element of the last iteration fitting 14 201040489. Referring to Fig. 6, a specific flow chart for fitting feature elements by using the quasi-Newton algorithm in step S57 of Fig. 5 is shown. Step S570, calculating the value of the iterative equation f(x) obtained in step S54.
步驟S571 ’判斷上述計算得到的f(x)是否小於預先設 疋的擬合精度FunX ’如果f(x)小於擬合精度FunX，則結 束擬合過程’執行步驟S576，如果f(x)大於等於擬合精度 FU 則執行步驟S572。所述擬合精度指擬合標準特徵 元素和待測特徵元素要達到的程度。 iY奶2 ’計算⑽的下降方向。所述下降方向 f(X)的值轡，丨, ^圖持的彳向，即使待測零件圖檔Μ的點雲到 +虽3〇的點雲的距離變小的方向。 Υ驟S573，判斷是否存在所述 所述下降方向，則結束擬合過程，執如果不 在所p降方向，則執行步驟S5,步驟S576, 步驟S574,計算待測特徵元 擬合步長以Μ鮮特徵元一沿下降方 擬合步長指以標準特徵元素的點疮雲的距離f(x_r 徵兀素的點雲和標準特徵元 ^基準’為擬合 徵兀素的點雲的距離。具體而二、’每次移動該 :ΐ :點雲沿下降方向移動‘认首先計算得到祠 :件到該待測特徵元素的點’而後利用 距離知），即㈣，+ =榡舉特徵元素的 其中，所述f(x 15 201040489 驟S570中f(x)的計算方法完全相同，僅僅使用的參數不 同，可參考步驟S570完成計算。 步驟S575，判斷步驟S574中計算得到的f(x-l)是否 小於f(x)。若f(x-l)小於f(x)，則返回步驟S572 ;若f(x-l) 不小於f(x)，則返回步驟S574，並以f(x-l)的值作為新的 f(x)值。 步驟S576，輸出擬合成的特徵元素。 最後應說明的是，以上實施方式僅用以說明本發明的 ® 技術方案而非限制，儘管參照較佳實施方式對本發明進行 了詳細說明，本領域的普通技術人員應當理解，可以對本 發明的技術方案進行修改或等同替換，而不脫離本發明技 術方案的精神和範圍。 【圖式簡單說明】 圖1係本發明零件形位公差檢測系統較佳實施方式的 系統架構圖。 p 圖2係形位公差檢測示意圖。 圖3係本發明零件形位公差檢測方法較佳實施方式的 流程圖。 圖4係圖3中步驟S4的具體流程圖。 圖5係圖3中步驟S5的具體流程圖。 圖6係圖5中步驟S57運用擬牛頓演算法擬合特徵元 素的具體流程圖。 【主要元件符號說明】 顯示設備 1 16 201040489 2 主機 影像量測機台 3 輸入設備 4 儲存體 20 形位公差檢測單元 21 點雲資料 22 資料獲取模組 210 網格化模組 211Step S571 'determine whether the above calculated f(x) is smaller than the fitting precision FunX of the preset ' If the f(x) is smaller than the fitting precision FunX, the fitting process is ended 'execution step S576, if f(x) is larger than Equal to the fitting accuracy FU, step S572 is performed. The fitting accuracy refers to the degree to which the standard feature element and the feature element to be tested are to be reached. iY milk 2 'calculates the downward direction of (10). The value of the descending direction f(X) 辔, 丨, ^ is the direction of the image, even if the point cloud of the part image to be tested is + the distance of the point cloud of 3 变 becomes smaller. Step S573, determining whether the falling direction exists, ending the fitting process, if not in the direction of the p-down, executing step S5, step S576, step S574, calculating the fitting step of the feature to be tested. The fresh feature element along the descending square fitting step refers to the distance f of the point cloud of the standard feature element (x_r 兀 的 的 和 和 和 和 和 和 和 和 和 和 和 和 和 和 和 和 和 。 。 。 。 。 。 。 。 。 。 。 。 。 。 。 。 Specifically, second, 'Every time you move: ΐ: point cloud moves in the direction of descent'. First, calculate the 祠: piece to the point of the feature element to be tested and then use the distance to know, ie (4), + = 特征 特征 特征The calculation method of f(x) in the f(x 15 201040489 step S570 is exactly the same, and only the used parameters are different, the calculation can be completed by referring to step S570. Step S575, determining the f(xl) calculated in step S574. Whether it is less than f(x). If f(xl) is less than f(x), it returns to step S572; if f(xl) is not less than f(x), it returns to step S574 and takes the value of f(xl) as new The value of f(x). Step S576, outputting the fitted feature elements. Finally, the above implementation The present invention has been described with reference to the preferred embodiments, and it will be understood by those skilled in the art that the present invention may be modified or substituted instead of BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a system architecture diagram of a preferred embodiment of a part shape tolerance detecting system of the present invention. p FIG. 2 is a schematic diagram of a dimensional tolerance detection. Figure 4 is a specific flow chart of step S4 in Figure 3. Figure 5 is a specific flow chart of step S5 in Figure 3. Figure 6 is the operation of step S57 in Figure 5. A detailed flow chart of the proposed Newton algorithm to fit the feature elements. [Main component symbol description] Display device 1 16 201040489 2 Host image measuring machine 3 Input device 4 Storage body 20 Shape and position tolerance detecting unit 21 Point cloud data 22 Data acquisition Module 210 meshing module 211
點雲提取模組 212 點雲擬合模組 213 形位公差計算模組 214 報表生成模組 215 獲取待測零件圖檔及用戶選擇的待測特徵元素S1 對待測零件圖檔進行三角網格化，獲取該待測零件圖 檔的點雲資料 S2 獲取標準特徵元素 S3 從待測零件圖檔的點雲資料中提取擬合成特徵元素 的點雲 S4 將提取的點雲擬合成特徵元素 S5 計算擬合的特徵元素與標準特徵元素之間的形位公 差 S6 輸出形位公差分析表 S7 17Point cloud extraction module 212 point cloud fitting module 213 shape tolerance calculation module 214 report generation module 215 acquires the part image to be tested and the selected feature element S1 selected by the user, and triangulates the part image to be tested , acquiring the point cloud data of the part file to be tested S2, acquiring the standard feature element S3, extracting the point cloud S4 fitted to the feature element from the point cloud data of the part image to be tested, and fitting the extracted point cloud into the feature element S5 Geometric Tolerance between Combined Feature Elements and Standard Feature Elements S6 Output Geometric Tolerance Analysis Table S7 17