Elbow supporting column forming quality evaluation method

Abstract

The invention discloses an elbow supporting column forming quality evaluation method, and belongs to the field of structure quality evaluation, and the method comprises the steps: building a three-dimensional model of an elbow supporting column based on the design size of the elbow supporting column, and fitting an outer side curve and an inner side curve corresponding to the inner side and the outer side of an upper curved surface of an elbow section; acquiring an image of a maximum curved surface after the bent pipe supporting column is bent and formed by using image acquisition equipment; fitting an image outer side curve and an image inner side curve based on image recognition according to the contour of the elbow supporting column on the image; forming quality coefficients of the outer curved surface and the inner curved surface of the bent pipe section are calculated; and calculating the integral forming quality coefficient of the bent pipe section according to the forming quality coefficient, and evaluating the integral forming quality of the bent pipe section. The method can be used for evaluating the forming quality of the bent pipe supporting columns with various shapes and size parameters, enough guarantee is provided for the production quality of the bent pipe supporting columns, and the production efficiency and the production quality are improved.

Description

Technical Field

[0001] The invention relates to the field of structural quality assessment, and in particular to a method for assessing the forming quality of a bent pipe support column. Background Art

[0002] With the development of the construction industry, more and more buildings are using steel structures. Compared with traditional concrete buildings, steel structures use steel plates or steel sections instead of reinforced concrete, which has higher strength and better earthquake resistance. And because the components can be factory-made and installed on-site, the construction period is greatly reduced. Because steel can be reused, it can greatly reduce construction waste and is more environmentally friendly. Therefore, it is widely used in countries around the world and applied in industrial and civil buildings.

[0003] Due to the complexity of building steel structures, steel support columns of various shapes are required. However, the molding quality of bent tube support columns is difficult to control during the factory assembly line production process. Moreover, as the core component of the building structure, the production molding quality of bent tube support columns often affects the stability of later assembly and use. Existing molding quality assessment methods for bent tube support columns mostly rely on manual measurement and observation. This method is highly subjective and has low precision. It is difficult to meet the needs of modern high-precision production. In addition, when dealing with the production of a large number of bent tube support columns with different bending shapes, the evaluation efficiency is low and the evaluation rules are cumbersome. Therefore, it is urgent to propose a new molding quality assessment method for bent tube support columns. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for evaluating the forming quality of a bent tube support column, which uses digital processing technology and image processing technology to comprehensively evaluate the forming quality of the bent tube support column.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for evaluating the forming quality of a bent tube support column is provided, comprising: Step S1: constructing a three-dimensional model of the curved pipe support column based on the design dimensions of the curved pipe support column, splitting the three-dimensional model into a straight pipe section and a curved pipe section, and fitting the outer curve and the inner curve corresponding to the inner and outer sides of the upper curved surface of the curved pipe section; Step S2: using an image acquisition device to acquire an image of the maximum curved surface of the curved tube support column after bending; fitting an image outer curve and an image inner curve based on image recognition according to the contour of the curved tube support column on the image; Step S3: Move the fitted outer curve and inner surface curve, the outer curve of the image and the inner curve of the image into a two-dimensional space, establish a two-dimensional coordinate system in the two-dimensional space, compare the differences between the outer curve of the image and the outer curve, and the inner curve of the image and the inner curve in the two-dimensional coordinate system, and calculate the forming quality coefficients of the outer and inner surfaces of the curved pipe segment , ; Step S4: According to the molding quality coefficient , Calculate the forming quality coefficient of the entire bending section , based on the molding quality coefficient Evaluate the overall forming quality of the bent pipe section.

[0006] Further, the method of fitting the outer curve and the inner curve corresponding to the inner side and the outer side of the upper curved surface of the curved pipe section is: Step S11: determining the central axis curve of the curved pipe segment on the three-dimensional model, and fitting M outer surface curves equidistant from the central axis curve on the outer surface of the curved pipe segment; Step S12: Construct a three-dimensional coordinate system in the space of the three-dimensional model and evenly take n reference points, based on the three-dimensional coordinates of two adjacent reference points , , and calculate the average span between two adjacent reference points on each outer surface curve : ; in, m is the number of the outer surface curve, i Number the reference points on the outer surface curve; Step S13: Obtain the average span data set of the reference points on the M outer surface curves , For the M The average span of reference points on the outer surface curves, filter the average span data set The maximum value in and minimum value ; Step S14: Return to step S11, refit M different outer surface curves equidistant from the central axis curve on the outer surface of the curved pipe segment, repeat steps S11-S13, and calculate the maximum and minimum values ​​of the average span after each outer surface curve fitting process; Step S15: After V After repeated fitting of the outer surface curve, we get V The maximum value of the average span after fitting and minimum value dataset ; Step S16: Set the maximum value data set The outer surface curve corresponding to the maximum value in is used as the outer curve and the minimum value data set The outer surface curve corresponding to the minimum value in is taken as the inner curve.

[0007] Furthermore, fitting the outer curve of the image and the inner curve of the image based on image recognition specifically includes: Step S21: grayscale the image of the maximum curved surface of the curved tube support column after bending to obtain a grayscale image, and set the grayscale standard value of the surface pixel of the curved tube support column , and combine any two adjacent pixels in the grayscale image as the pixel combination for screening the contour boundary, and obtain the grayscale value of the pixels in each pixel combination ; , are the numbers of two adjacent pixels respectively; Step S22: Setting the threshold of the grayscale value difference of pixels on both sides of the contour boundary , and according to the allowable difference of the gray value of the pixel on the surface of the bent pipe support column , filter the pixel combinations located on the contour of the curved support column, and the screening process satisfies the constraints: ; Step S23: Constructing a pixel combination data set based on the selected pixel combinations ; ; in, w Pixel Binning Dataset The number of pixel combinations within For the selected w Pixel combination, They are two pixels in a pixel combination respectively; Step S24: Calculate pixel combination data set Pixel center coordinates of the inner pixel group ; ; in, For the w Pixels in a pixel group The pixel coordinates of For the w Pixels in a pixel group The pixel coordinates of Step S25: Combining data sets according to pixels The pixel center coordinates of each pixel combination in the , and the straight-line distance between the pixel center of one pixel combination and the pixel centers of the remaining pixel combinations is calculated l ; ; in, For the w -1 pixel center coordinates of the pixel group; Step S26: Get pixel combination w The straight-line distance dataset between the pixel center of and the pixel centers of the rest of the pixel combinations , set the evaluation pixel combination w Whether to discretize the straight line distance threshold outside the contour boundary , traverse the straight-line distance dataset All straight-line distance data within; If there is a straight line distance , then determine the pixel combination w Located on the contour boundary; otherwise, determine the pixel combination w Not located on the contour boundary and as discrete pixel combinations; Step S27: until the pixel combination data set is screened out After all the discrete pixels in the image are combined, the discrete pixels are combined from the pixel combination dataset Delete the inner pixel to get a new pixel combination data set ; ; in, a Pixel Binning Dataset The number of pixel combinations within; Step S28: Combine pixels into a dataset The pixel center coordinates corresponding to the inner pixel combination are connected end to end in sequence and smoothed to fit the contour curve of the bent pipe support column after bending based on image recognition; Step S29: split the contour curve to obtain an image outer curve and an image inner curve based on image recognition.

[0008] Further, step S3 includes: Step S31: moving the fitted outer curve and inner surface curve, the image outer curve and the image inner curve into a two-dimensional space, establishing a two-dimensional coordinate system in the two-dimensional space, and aligning the image outer curve with the outer curve, and the image inner curve with one end of the inner curve in the two-dimensional coordinate system; Step S32: uniformly selecting a number of control point groups on the outer curve and the outer curve of the image, each control point group includes two control points, one control point is located on the outer curve of the image, and the other control point is located on the outer curve; Step S33: Calculate the distance between the control points in the control point group according to the coordinates of the control points in the two-dimensional coordinate system. ; ; in, is the number of the control point group, Control group The coordinates of one of the reference points, Control group Coordinates of another reference point in Step S34: Calculate the forming quality coefficient of the outer surface of the curved pipe section using the distance value between the reference points in each reference point group ; ; in, is the number of control point groups, is the allowable value of forming deviation of the bending section; Step S35: Repeat steps S32-S34, and evenly select a number of control point groups on the inner curve and the inner curve of the image to calculate the forming quality coefficient of the inner surface of the curved pipe segment. .

[0009] Further, step S4 includes: Step S41: Based on the model quality coefficient and molding quality coefficient Calculate the forming quality coefficient of the entire bending section : ;in, are the weight coefficients of the influence of the forming quality of the outer and inner surfaces of the curved pipe segment on the overall forming quality, and they satisfy ; Step S42: Setting the threshold of the molding quality coefficient , comparison threshold and molding quality coefficient If , then the forming quality of the bent tube support column is good. , it is judged that the forming quality of the bent tube support column is poor.

[0010] The beneficial effects of the present invention are as follows: the present solution is used to evaluate the molding quality of curved tube support columns for construction, and is suitable for application in the production process of factory assembly lines. First, the standard outer surface curve and inner surface curve are fitted based on the design dimensions of the curved tube support column, and then the image outer curve and the image inner curve after the curved tube support column is formed are identified and fitted using image recognition technology, and the two are compared to output a molding quality coefficient based on the difference in contour after molding, which is used for a comprehensive evaluation of the molding quality of the curved tube support column. The present invention can cope with the molding quality evaluation of curved tube support columns of various shapes and size parameters, provide sufficient guarantees for the production quality of curved tube support columns, and improve production efficiency and production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Flow chart of the forming quality assessment method for bent tube support columns.

[0012] Figure 2 Schematic diagram of fitting the outer curve and the inner curve.

[0013] Figure 3 Schematic diagram of the control points on the outer curve and the inner curve of the image. DETAILED DESCRIPTION

[0014] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0015] like Figure 1 As shown, a method for evaluating the forming quality of a bent tube support column comprises: Step S1: construct a three-dimensional model of the curved pipe support column based on the design dimensions of the curved pipe support column, split the three-dimensional model into a straight pipe section and a curved pipe section, and fit the outer curve and inner curve corresponding to the inner and outer sides of the upper curved surface of the curved pipe section.

[0016] The method of fitting the outer curve and inner curve corresponding to the inner and outer sides of the curved surface of the curved pipe segment is: Step S11: determining the central axis curve of the curved pipe segment on the three-dimensional model, and fitting M outer surface curves equidistant from the central axis curve on the outer surface of the curved pipe segment; in the fitted equidistant outer surface curves, the outer surface curve is always perpendicular to the cross section of the curved pipe segment, the curve located at the outermost side of the curved surface has the largest span, and the curve located at the innermost side of the curved surface has the smallest span; Step S12: Construct a three-dimensional coordinate system in the space of the three-dimensional model and evenly take n Reference points, such as Figure 2 As shown, according to the three-dimensional coordinates of two adjacent reference points , , and calculate the average span between two adjacent reference points on each outer surface curve : ; in, m is the number of the outer surface curve, i Number the reference points on the outer surface curve; Step S13: Obtain the average span data set of the reference points on the M outer surface curves , For the M The average span of reference points on the outer surface curves, filter the average span data set The maximum value in and minimum value ; Step S14: Return to step S11, refit M different outer surface curves equidistant from the central axis curve on the outer surface of the curved pipe segment, repeat steps S11-S13, and calculate the maximum and minimum values ​​of the average span after each outer surface curve fitting process; Step S15: After V After repeated fitting of the outer surface curve, we get V The maximum value of the average span after fitting and minimum value dataset ; Step S16: Set the maximum value data set The outer surface curve corresponding to the maximum value in is used as the outer curve and the minimum value data set The outer surface curve corresponding to the minimum value in is taken as the inner curve. Through a finite number of outer surface curve and inner surface curve fitting processes, the optimal outer and inner curves fitted are used as the comparison standard of the bending profile after the curved tube support column is formed.

[0017] Step S2: Use an image acquisition device to acquire an image of the maximum bending surface of the bent tube support column after bending. During the image acquisition process, place the formed bent tube support column on a workbench to ensure that a complete bending contour image of the bent tube support column can be obtained, and fit the outer curve and inner curve of the image based on image recognition according to the contour of the bent tube support column on the image.

[0018] Fitting the outer curve and inner curve of an image based on image recognition specifically includes: Step S21: grayscale the image of the maximum curved surface of the curved tube support column after bending to obtain a grayscale image, and set the grayscale standard value of the surface pixel of the curved tube support column , and combine any two adjacent pixels in the grayscale image as the pixel combination for screening the contour boundary, and obtain the grayscale value of the pixels in each pixel combination ; , are the numbers of two adjacent pixels respectively; Step S22: Setting the threshold of the grayscale value difference of pixels on both sides of the contour boundary , and according to the allowable difference of the gray value of the pixel on the surface of the bent pipe support column , filter the pixel combinations located on the contour of the curved support column, and the screening process satisfies the constraints: ; Step S23: Constructing a pixel combination data set based on the selected pixel combinations ; ; in, w Pixel Binning Dataset The number of pixel combinations within For the filtered w Pixel combination, They are two pixels in a pixel combination respectively; Step S24: Calculate pixel combination data set Pixel center coordinates of the inner pixel group ; ; in, For the w Pixels in a pixel group The pixel coordinates of For the w Pixels in a pixel group The pixel coordinates of Step S25: Combining data sets according to pixels The pixel center coordinates of each pixel combination in the , and the straight-line distance between the pixel center of one pixel combination and the pixel centers of the remaining pixel combinations is calculated l ; ; in, For the w -1 pixel center coordinates of the pixel group; Step S26: Get pixel combination w The straight-line distance dataset between the pixel center of and the pixel centers of the rest of the pixel combinations , set the evaluation pixel combination w Whether to discretize the straight line distance threshold outside the contour boundary , traverse the straight-line distance dataset All straight-line distance data within; If there is a straight line distance , then determine the pixel combination w Located on the contour boundary; otherwise, determine the pixel combination w Not located on the contour boundary and as discrete pixel combinations; When any pixel combination is not on the contour boundary, its distance from every other pixel combination will be greater than the straight-line distance threshold. ; When it is on the contour boundary, there is at least one pixel combination nearby, so it is judged that the distance to at least one pixel combination is less than or equal to the straight-line distance threshold Therefore, through the above steps, discrete pixel combinations can be accurately selected, the error of contour fitting can be reduced, and the accuracy of the fitted contour boundary can be improved.

[0019] Step S27: until the pixel combination data set is screened out After all the discrete pixels in the image are combined, the discrete pixels are combined from the pixel combination dataset Delete the inner pixel to get a new pixel combination data set ; ; in, a Pixel Binning Dataset The number of pixel combinations within; Step S28: Combine pixels into a dataset The pixel center coordinates corresponding to the inner pixel combination are connected end to end in sequence and smoothed to fit the contour curve of the bent pipe support column after bending based on image recognition; Step S29: split the contour curve to obtain an image outer curve and an image inner curve based on image recognition.

[0020] Step S3: Move the fitted outer curve and inner surface curve, the outer curve of the image and the inner curve of the image into a two-dimensional space, establish a two-dimensional coordinate system in the two-dimensional space, compare the differences between the outer curve of the image and the outer curve, and the inner curve of the image and the inner curve in the two-dimensional coordinate system, and calculate the forming quality coefficients of the outer and inner surfaces of the curved pipe segment , .

[0021] Step S3 specifically includes: Step S31: moving the fitted outer curve and inner surface curve, the image outer curve and the image inner curve into a two-dimensional space, establishing a two-dimensional coordinate system in the two-dimensional space, and aligning the image outer curve with the outer curve, and the image inner curve with one end of the inner curve in the two-dimensional coordinate system; Step S32: uniformly select a number of control point groups on the outer curve and the outer curve of the image, each control point group includes two control points, one control point is located on the outer curve of the image, and the other control point is located on the outer curve, such as Figure 3 As shown; Step S33: Calculate the distance between the control points in the control point group according to the coordinates of the control points in the two-dimensional coordinate system. ; ; in, is the number of the control point group, Control group The coordinates of one of the reference points, Control group Coordinates of another reference point in Step S34: Calculate the forming quality coefficient of the outer surface of the curved pipe section using the distance value between the reference points in each reference point group ; ; in, is the number of control point groups, is the allowable value of forming deviation of the bending section; Step S35: Repeat steps S32-S34, and evenly select a number of control point groups on the inner curve and the inner curve of the image to calculate the forming quality coefficient of the inner surface of the curved pipe segment. .

[0022] Step S4: According to the molding quality coefficient , Calculate the forming quality coefficient of the entire bending section , based on the molding quality coefficient Evaluate the overall forming quality of the bent pipe section.

[0023] Step S4 specifically includes: Step S41: Based on the model quality coefficient and molding quality coefficient Calculate the forming quality coefficient of the entire bending section : ;in, are the weight coefficients of the influence of the forming quality of the outer and inner surfaces of the curved pipe segment on the overall forming quality, and they satisfy , generally take , ,Since the inner curved surface of the bent pipe section is more difficult to form than the outer curved surface, its influence on the overall forming quality is greater; Step S42: Setting the threshold of the molding quality coefficient , comparison threshold and molding quality coefficient If , then the forming quality of the bent tube support column is good. , it is judged that the forming quality of the bent tube support column is poor.

[0024] The present invention is used to evaluate the molding quality of curved pipe support columns for construction, and is suitable for application in the production process of factory assembly lines. First, the standard outer surface curve and inner surface curve are fitted based on the design size of the curved pipe support column, and then the image outer curve and the image inner curve after the curved pipe support column is formed are identified and fitted using image recognition technology, and the two are compared to output a molding quality coefficient based on the difference in contour after molding, which is used for comprehensive evaluation of the molding quality of the curved pipe support column. The present invention can cope with the molding quality evaluation of curved pipe support columns of various shapes and size parameters, provide sufficient guarantee for the production quality of curved pipe support columns, and improve production efficiency and production quality.

Claims

1. A method for evaluating the forming quality of a bent tube support column, characterized in that: include: Step S1: constructing a three-dimensional model of the curved pipe support column based on the design dimensions of the curved pipe support column, splitting the three-dimensional model into a straight pipe section and a curved pipe section, and fitting the outer curve and the inner curve corresponding to the inner and outer sides of the upper curved surface of the curved pipe section; Step S2: using an image acquisition device to acquire an image of the maximum curved surface of the curved tube support column after bending; fitting an image outer curve and an image inner curve based on image recognition according to the contour of the curved tube support column on the image; Step S3: Move the fitted outer curve and inner surface curve, the outer curve of the image and the inner curve of the image into a two-dimensional space, establish a two-dimensional coordinate system in the two-dimensional space, compare the differences between the outer curve of the image and the outer curve, and the inner curve of the image and the inner curve in the two-dimensional coordinate system, and calculate the forming quality coefficients of the outer and inner surfaces of the curved pipe segment , ; Step S4: According to the molding quality coefficient , Calculate the forming quality coefficient of the entire bending section , based on the molding quality coefficient Evaluate the overall forming quality of the bent pipe section.

2. The method for evaluating the forming quality of a bent tube support column according to claim 1, characterized in that: The method for fitting the outer curve and the inner curve corresponding to the inner side and the outer side of the upper curved surface of the curved pipe section is: Step S11: determining the central axis curve of the curved pipe segment on the three-dimensional model, and fitting M outer surface curves equidistant from the central axis curve on the outer surface of the curved pipe segment; Step S12: Construct a three-dimensional coordinate system in the space of the three-dimensional model and evenly take n reference points, based on the three-dimensional coordinates of two adjacent reference points , , and calculate the average span between two adjacent reference points on each outer surface curve : ； in, m is the number of the outer surface curve, i Number the reference points on the outer surface curve; Step S13: Obtain the average span data set of the reference points on the M outer surface curves , For the M The average span of reference points on the outer surface curves, filter the average span data set The maximum value in and minimum value ; Step S14: Return to step S11, refit M different outer surface curves equidistant from the central axis curve on the outer surface of the curved pipe segment, repeat steps S11-S13, and calculate the maximum and minimum values ​​of the average span after each outer surface curve fitting process; Step S15: After V After repeated fitting of the outer surface curve, we get V The maximum value of the average span after fitting and minimum value dataset ; Step S16: Set the maximum value data set The outer surface curve corresponding to the maximum value in is used as the outer curve and the minimum value data set The outer surface curve corresponding to the minimum value in is taken as the inner curve.

3. The method for evaluating the forming quality of a bent tube support column according to claim 2, characterized in that: The step of fitting the outer curve of the image and the inner curve of the image based on image recognition specifically includes: Step S21: grayscale the image of the maximum curved surface of the curved tube support column after bending to obtain a grayscale image, and set the grayscale standard value of the surface pixel of the curved tube support column , and combine any two adjacent pixels in the grayscale image as the pixel combination for screening the contour boundary, and obtain the grayscale value of the pixels in each pixel combination ; , are the numbers of two adjacent pixels respectively; Step S22: Setting the threshold of the grayscale value difference of pixels on both sides of the contour boundary , and according to the allowable difference of the gray value of the pixel on the surface of the bent pipe support column , filter the pixel combinations located on the contour of the curved support column, and the screening process satisfies the constraints: ； Step S23: Constructing a pixel combination data set based on the selected pixel combinations ; ； in, w Pixel Binning Dataset The number of pixel combinations within For the filtered w Pixel combination, They are two pixels in a pixel combination respectively; Step S24: Calculate pixel combination data set Pixel center coordinates of the inner pixel group ; ； in, For the w Pixels in a pixel group The pixel coordinates of For the w Pixels in a pixel group The pixel coordinates of Step S25: Combining data sets according to pixels The pixel center coordinates of each pixel combination in the , and the straight-line distance between the pixel center of one pixel combination and the pixel centers of the remaining pixel combinations is calculated l ; ； in, For the w -1 pixel center coordinates of the pixel group; Step S26: Get pixel combination w The straight-line distance dataset between the pixel center of and the pixel centers of the rest of the pixel combinations , set the evaluation pixel combination w Whether to discretize the straight line distance threshold outside the contour boundary , traverse the straight-line distance dataset All straight-line distance data within; If there is a straight line distance , then determine the pixel combination w Located on the contour boundary; otherwise, determine the pixel combination w Not located on the contour boundary and as discrete pixel combinations; Step S27: until the pixel combination data set is screened out After all the discrete pixels in the image are combined, the discrete pixels are combined from the pixel combination dataset Delete the inner pixel to get a new pixel combination data set ; ； in, a Pixel Binning Dataset The number of pixel combinations within; Step S28: Combine pixels into a dataset The pixel center coordinates corresponding to the inner pixel combination are connected end to end in sequence and smoothed to fit the contour curve of the bent pipe support column after bending based on image recognition; Step S29: split the contour curve to obtain an image outer curve and an image inner curve based on image recognition.

4. The method for evaluating the forming quality of a bent tube support column according to claim 3, characterized in that: The step S3 comprises: Step S31: moving the fitted outer curve and inner surface curve, the image outer curve and the image inner curve into a two-dimensional space, establishing a two-dimensional coordinate system in the two-dimensional space, and aligning the image outer curve with the outer curve, and the image inner curve with one end of the inner curve in the two-dimensional coordinate system; Step S32: uniformly selecting a number of control point groups on the outer curve and the outer curve of the image, each control point group includes two control points, one control point is located on the outer curve of the image, and the other control point is located on the outer curve; Step S33: Calculate the distance between the control points in the control point group according to the coordinates of the control points in the two-dimensional coordinate system. ; ； in, is the number of the control point group, Control group The coordinates of one of the reference points, Control group Coordinates of another reference point in Step S34: Calculate the forming quality coefficient of the outer surface of the curved pipe section using the distance value between the reference points in each reference point group ; ； in, is the number of control point groups, is the allowable value of forming deviation of the bending section; Step S35: Repeat steps S32-S34, and evenly select a number of control point groups on the inner curve and the inner curve of the image to calculate the forming quality coefficient of the inner surface of the curved pipe segment. .

5. The method for evaluating the forming quality of a bent tube support column according to claim 4, characterized in that: The step S4 comprises: Step S41: Based on the model quality coefficient and molding quality coefficient Calculate the forming quality coefficient of the entire bending section : ;in, are the weight coefficients of the influence of the forming quality of the outer and inner surfaces of the curved pipe segment on the overall forming quality, and they satisfy ; Step S42: Setting the threshold of the molding quality coefficient , comparison threshold and molding quality coefficient If , then the forming quality of the bent tube support column is good. , it is judged that the forming quality of the bent tube support column is poor.

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