CN105868498B - Covering boundary characteristic reconstructing method based on scan line point cloud - Google Patents
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Abstract
A kind of covering boundary characteristic reconstructing method based on scan line point cloud, it is characterized in that extracting characteristic point in every scan line, straight line or B-spline curves are constructed using characteristic point to reconstruct boundary characteristic, and the extraction of characteristic point includes neighborhood search, the identification of measurement point position, straight line fitting and seeks three steps of friendship.Scanning line ordering in point edge, by point and chamfering point on curvature identification face, is distinguished fitting a straight line using the point on surface and side, use the intersection point of straight line as characteristic point using the orderly point set of B-spline curves interpolation in neighborhood search.Characteristic point is finally fitted to straight line or B-spline curves are used to express the boundary characteristic of covering.The present invention can improve the precision and level of application of numerical control processing, to improve aircraft skin assembly efficiency and assembling quality.
Description
Technical Field
The invention relates to an aircraft manufacturing technology, in particular to a manufacturing method of an aircraft skin, and specifically relates to a skin boundary feature reconstruction method based on scanning line point cloud.
Background
At present, in the field of aircraft manufacturing, manual trimming of boundaries is required during assembly of aircraft skins, so that fit gaps among the skins are guaranteed to be within an allowable error range, and the skins are not interchangeable. The reasons for this phenomenon are: firstly, the skin is formed by stretching, the boundary cutting basis is a cutting sample, firstly, allowance is left for rough cutting, and then the allowance is left for fine trimming of the skin boundary; secondly, in order to realize accurate and efficient edge cutting of the skin, an airplane manufacturer adopts a numerical control edge cutting process, namely an edge cleaning manufacturing process, but the process is generally carried out only by carrying out allowance-free processing on part of edges at present, and small allowance still needs to be left on the rest edges; thirdly, in the aircraft skin, the specific gravity of the composite material is increased more and more, the dimensional stability of the composite material is good, but the boundary of the composite material is not repaired generally, and when the metal skin is matched with the composite material skin, the boundary of the metal skin needs to be repaired to complete the matching with the composite material skin.
Gaps are not negligible in skin assembly, and the presence of gaps seriously affects the aerodynamic profile and stealth performance of the aircraft. In order to solve the problem that the assembly clearance of the aircraft skin affects the aerodynamic appearance and the stealth performance of the aircraft, aircraft manufacturers adopt an assembly process that the unassembled skin is assembled based on the assembled skin boundary, the metal skin is assembled based on the composite skin boundary, and a small margin is left in part of a clean edge part during numerical control edge cutting of the skin.
The net edge manufacturing process reduces the trimming amount of the boundary when the skin is assembled to a certain extent, and the important reason that the full-edge net edge cannot be realized is that the actual boundary data of the skin is lacked, and the actual boundary of the skin plays the role of inspection and processing basis: the actually measured boundary of the composite material skin is the cutting basis of the matched metal skin boundary; different from a common machining piece, the thin-wall structure of the skin causes the skin to be easy to deform during machining, and the machining precision can be guaranteed only through on-machine detection.
Disclosure of Invention
The invention aims to solve the problems that the assembly quality is influenced and the reconstruction efficiency is low because the full-edge-cleaning processing cannot be realized due to the lack of skin actual boundary data during the current skin assembly, and provides a skin boundary characteristic reconstruction method based on scanning line point cloud for providing a basis for realizing automatic and efficient edge-cleaning processing.
The technical scheme of the invention is as follows:
a skin boundary feature reconstruction method based on scanning line point cloud is characterized by comprising the following steps:
(1) obtaining scanning line point cloud of the skin through measurement;
(2) searching adjacent points on a scanning line where the sampling point is located to obtain a neighborhood point set;
(3) determining whether the sampling point is positioned on the surface or near the boundary according to the geometric shape parameters of the neighborhood of the sampling point;
(4) performing data preprocessing on the neighborhood of the sampling point near the boundary to enable the neighborhood to be parallel to an XY coordinate plane;
(5) dividing the neighborhood of the sampling points near the boundary into three parts, namely a surface point, a side point and an inverted corner point;
(6) fitting two straight lines by using the point subsets on the surface and the side surface, solving the intersection point of the straight lines, taking the intersection point as a boundary characteristic point, and carrying out posture reduction on the characteristic point;
(7) and constructing a straight line or a B-spline curve by using the characteristic points to obtain the boundary of the skin to be measured.
The neighborhood searching method of the sampling points in the step (2) is as follows:
(2-1) excluding the sampling point as the end point of the scanning line, and recording the two points with the minimum P Euclidean distance to the sampling point as P1 lAnd P1 rIf, ifAndis less than a set threshold value thetacThen it is considered as an endpoint;
(2-2) for the line interior point, independently searching the adjacent points at the left side and the right side of the line interior point, and then combining the adjacent points into a neighborhood, known as P and PThenCan be obtained by the following method: computingEuclidean distance rsAll the points in the interior are sequentially taken out from small to large according to the distanceiIf, ifAndis greater than a set threshold value thetavThen point PiIs thatReuse ofAndto obtainThe points can be searched in sequence by gradually advancing in sequenceThereby obtaining a P right side neighboring point set; the acquisition method of the left side neighboring point set is the same, and the neighborhood of P is obtained after splicing:
and (2-3) in the searching process of the two sides, when the distance between the latest obtained adjacent point and the sampling point is more than or equal to r, ending the searching of the side, and entering the searching of the other side or finishing the searching.
According to the method, a greedy snake model is adopted for neighborhood searching, unidirectional searching is carried out along the left direction and the right direction, and the conditions of uneven data intervals and crossing are considered when a searching model is designed. The search process itself is centered on the sample point and adjacent points are acquired along the scan line, so the search process and the sorting process are performed simultaneously. In the step (2-2), when the distribution uniformity of the points on the scanning line is good, rsThe average dot spacing is set to be 2-3 times, and when the uniformity is poor, the average dot spacing is set to be a typical maximum dot spacing.
In the step (3), the boundary point between the sampling point P and the neighborhood thereof is calculatedAndconstructed vectorAndjudging whether the sampling point is positioned at the boundary of the skin according to whether the included angle is smaller than a threshold value theta;
the data preprocessing operation in the step (4) is as follows:
(4-1) fitting the scanning plane equation a with neighborhood points1x+b1y+c1z + d is 0, wherein a1、b1、c1D is a constant, and the established error objective function is as follows:
where N is the number of points in the neighborhood, di1Is the projected distance d from point to planei1＝a1xi+b1yi+c1zi+d；
(4-2) obtaining a rotation matrix C required for coordinate transformation using the formula k ═ Cn, where k ═ 0,0,1]TIs a Z-axis unit direction vector, n ═ a1,b1,c1]TObtaining a plane unit normal vector for fitting;
(4-3) according to the formulaRotate neighborhood points where [ xi,yi,zi]TIn the form of the original coordinates, the coordinates of the original coordinates,the coordinates after rotation;
(4-4) projecting the rotated points, the projected coordinates beingWherein,
through pose transformation, the neighborhood is aligned with the XY coordinate plane, so that the three-dimensional problem is converted into a planar two-dimensional problem, a straight line fitting model is simplified, and the B spline fitting calculation amount is reduced.
In the step (5), the neighborhood of the sampling points near the boundary is divided into three subsets according to the following method:
(5-1) adopting cubic non-uniform B-spline curve interpolation neighborhood points, and parameterizing the type value points by using a normative accumulated chord length parameterization method, wherein the node vector U is [ U ═ U0,u1,…,un+k+1]Adopting a heavy node end point condition that nodes at two ends of a defined domain are k +1 weight, namely u0＝u1＝…＝uk0 and un+1＝un+2＝…＝un+k+11, the boundary condition during interpolation is a tangent condition;
(5-2) Using the formulaCalculated value point PiCurvature of wherein rj(u) is the j-th derivative vector of the spline curve at parameter u;
(5-3) sequentially taking out points from the neighborhood, comparing the curvature value with a set curvature threshold value, if the curvature value is larger than the curvature threshold value, determining the points as chamfering points, and determining the points on two sides of the chamfering point as a surface point and a side point. The construction of the B spline curve can adopt two methods of interpolation and fitting, compared with the fitting method, the node vector calculation of the interpolation method is simple, and the control vertex is obtained by solving a linear equation set.
The step (6) adopts the following method to obtain the characteristic points:
(6-1) fitting two straight lines by using the point subsets on the two surfaces, wherein the equation of the straight line is ax + by + c is 0, and the established target error function is as follows:
where N is the number of points in the neighborhood, diIs the projected distance d from point to linei＝|axi+byi+c|；
(6-2) obtaining a straight line intersection, i.e., a boundary feature point on the scanning line, and using the formula P ═ CT·PsReducing the obtained characteristic point coordinates to a measurement coordinate system, wherein CTAs a transpose of matrix C, PsAnd P are the coordinates before and after the feature point reduction, respectively.
The invention has the beneficial effects that:
the skin boundary characteristic reconstruction method based on the scanning line point cloud is used for acquiring the actual boundary of the skin. The actually measured boundary of the skin is important input information of the numerical control trimming process, and the precision and the application degree of numerical control machining can be improved, so that the assembly efficiency and the assembly quality of the aircraft skin are improved. The whole process of the method is completed by a computer, the laser scanner for non-contact measurement is adopted to obtain measurement data, and the skin with weak rigidity is not contacted in the measurement process, so that the measurement reliability is guaranteed.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a measured scan line point cloud.
FIG. 3 is a reconstructed skin boundary feature.
Detailed Description
The invention is further described below with reference to the figures and examples.
As shown in fig. 1-3.
A skin boundary feature reconstruction method based on scanning line point cloud comprises the following steps:
(1) measuring to obtain scanning line point cloud of the skin;
(2) searching adjacent points on a scanning line where the sampling point is located to obtain a neighborhood point set;
(3) determining whether the sampling point is positioned on the surface or near the boundary according to the geometric shape parameters of the neighborhood of the sampling point;
(4) performing data preprocessing on the neighborhood of the sampling point near the boundary to enable the neighborhood to be parallel to an XY coordinate plane;
(5) dividing the neighborhood of the sampling points near the boundary into three subsets, namely surface points, side points and chamfer points;
(6) fitting two straight lines by using the point subsets on the two surfaces, solving the intersection point of the straight lines, taking the intersection point as a boundary characteristic point, and carrying out posture reduction on the characteristic point;
(7) and constructing a straight line or a B-spline curve by using the characteristic points to obtain the boundary of the skin to be measured.
The details are as follows:
as shown in fig. 1, the operation flow of extracting skin boundary features from the scanning line point cloud mainly includes: firstly, obtaining a scanning line point cloud of a skin boundary by using laser scanning equipment; then searching the neighborhood of the sampling point; then, determining whether the sampling point is positioned on the surface or near the boundary according to the geometric shape parameters of the neighborhood of the sampling point; performing data preprocessing on the neighborhood of the sampling point near the boundary to enable the neighborhood to be parallel to an XY coordinate plane; then, interpolating through the neighborhood points by using a plane B spline curve, and calculating the curvature of each point; dividing the midpoint of the neighborhood into three subsets, namely a surface point, a side point and a chamfer point according to the curvature of the midpoint; then fitting two straight lines by using the point subsets on the two surfaces, and solving the intersection point of the straight lines; then restoring the intersection point to obtain a characteristic point on the scanning line; and finally, constructing a straight line or a B-spline curve by using all the obtained characteristic points, thereby obtaining the boundary line of the skin to be measured.
FIG. 2 shows the point cloud of the boundary scan line of the skin test piece, the point spacing and the line spacing are 0.075mm and 1.5mm, and the skin thickness is 3 mm.
The neighborhood searching method of the sampling points in the step (2) is as follows:
(2-1) excluding the sampling point as the end point of the scanning line, and recording the two points with the minimum P Euclidean distance to the sampling point asAndif it isAndis less than a set threshold value thetacThen it is considered as an endpoint;
(2-2) for the line interior point, independently searching the adjacent points at the left side and the right side of the line interior point, and then combining the adjacent points into a neighborhood, known as P and PThenCan be obtained by the following method: computingEuclidean distance rsAll the points in the interior are sequentially taken out from small to large according to the distanceiIf, ifAndis greater than a set threshold value thetavThen point PiIs thatReuse ofAndto obtainThe points can be searched in sequence by gradually advancing in sequenceResulting in a set of P right neighbors. The acquisition method of the left side neighboring point set is the same, and the neighborhood of P is obtained after splicing:
and (2-3) in the searching process of the two sides, when the distance between the latest obtained adjacent point and the sampling point is more than or equal to r, ending the searching of the side, and entering the searching of the other side or finishing the searching.
In this embodiment, the angle threshold θ for determining whether the sampling point is an end pointc120 DEG, radius r in search on both sidess0.3mm, angle threshold θv45 deg., and the neighborhood radius r 2 mm.
In the step (3), the boundary point between the sampling point P and the neighborhood thereof is calculatedAndconstructed vectorAndjudging whether the sampling point is positioned at the boundary of the skin according to whether the included angle is smaller than a threshold value theta;
in the present embodiment, the angle threshold θ for identifying the sampling point position is 110 °.
The data preprocessing operation in the step (4) is as follows:
(4-1) fitting the scanning plane equation a with neighborhood points1x+b1y+c1z + d is 0, wherein a1、b1、c1D is a constant, and the established error objective function is as follows:
where N is the number of points in the neighborhood, di1Is the projected distance d from point to planei1＝a1xi+b1yi+c1zi+d；
(4-2) obtaining a rotation matrix C required for coordinate transformation using the formula k ═ Cn, where k ═ 0,0,1]TIs a Z-axis unit direction vector, n ═ a1,b1,c1]TObtaining a plane unit normal vector for fitting;
(4-3) according to the formulaRotate neighborhood points where [ xi,yi,zi]TIn the form of the original coordinates, the coordinates of the original coordinates,the coordinates after rotation;
(4-4) projecting the rotated points, the projected coordinates beingWherein,
in the step (5), the neighborhood of the sampling points near the boundary is divided into three subsets according to the following method:
(5-1) adopting cubic non-uniform B-spline curve interpolation neighborhood points, and parameterizing the type value points by using a normative accumulated chord length parameterization method, wherein the node vector U is [ U ═ U0,u1,…,un+k+1]Adopting a heavy node end point condition that nodes at two ends of a defined domain are k +1 weight, namely u0＝u1＝…＝uk0 and un+1＝un+2＝…＝un+k+11, the boundary condition during interpolation is a tangent condition;
(5-2) Using the formulaCalculated value point PiCurvature of wherein rj(u) is the j-th derivative vector of the spline curve at parameter u;
(5-3) sequentially taking out points from the neighborhood, comparing the curvature value with a set curvature threshold value, if the curvature value is larger than the curvature threshold value, determining the points as chamfering points, and determining the points on two sides of the chamfering point as a surface point and a side point.
The step (6) adopts the following method to obtain the characteristic points:
(6-1) fitting two straight lines by using the point subsets on the two surfaces, wherein the equation of the straight line is ax + by + c is 0, and the established target error function is
Where N is the number of points in the neighborhood, diIs the projected distance d from point to linei＝|axi+byi+c|；
(6-2) obtaining a straight line intersection, i.e., a boundary feature point on the scanning line, and using the formula P ═ CT·PsReducing the obtained characteristic point coordinates to a measurement coordinate system, wherein C is a rotation matrix, and P issAnd P are the coordinates before and after the feature point reduction, respectively.
The resulting skin boundary features are shown in FIG. 3.
The skin scanning line point cloud can clearly identify each scanning line, the distance between the scanning lines is the line spacing, and the distance between sampling points on the scanning lines is called the point spacing. And scanning the scanning line point cloud along the skin boundary by a handheld or machine tool-supported laser scanner.
The aircraft skin belongs to a thin-wall structural member, and aiming at the problem that the boundary characteristic reconstruction efficiency is low, the skin boundary characteristic reconstruction method based on the scanning line point cloud considers the influences of uneven distribution of measuring points and micro chamfering. The method extracts characteristic points on each scanning line, and uses the characteristic points to construct a straight line or a B-spline curve to reconstruct boundary characteristics. The extraction of the feature points comprises three steps of neighborhood searching, measurement point position identification, straight line fitting and intersection. The method comprises the steps of sorting points along a scanning line in neighborhood search, interpolating an ordered point set by using a B spline curve, identifying points on a surface and chamfering points through curvature, respectively fitting straight lines by using the points on the surface and the side surface, and using intersection points of the straight lines as feature points. The characteristic points are used as discrete points of the actual boundary line, and the actual boundary of the skin is reconstructed by fitting a straight line or a spline curve with the characteristic points.
The invention does not search the characteristic points in the existing measuring points, reduces the point cloud density required for guaranteeing the accuracy of the characteristic points and conforms to the design intention. The measured boundary line is used as a processing basis and a detection basis of numerical control trimming, and the full clean edge of the skin can be realized, so that the assembly efficiency and the assembly quality of the skin are greatly improved.
The above-mentioned preferred embodiments, further illustrating the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned are only preferred embodiments of the present invention and should not be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (4)
1. A skin boundary feature reconstruction method based on scanning line point cloud is characterized by comprising the following steps:
(1) obtaining scanning line point cloud of the skin through measurement;
(2) searching adjacent points on a scanning line where the sampling point is located to obtain a neighborhood point set;
(3) determining whether the sampling point is positioned on the surface or near the boundary according to the geometric shape parameters of the neighborhood of the sampling point;
(4) performing data preprocessing on the neighborhood of the sampling point near the boundary to enable the neighborhood to be parallel to an XY coordinate plane;
(5) dividing the neighborhood of the sampling points near the boundary into three parts, namely a surface point, a side point and an inverted corner point; and dividing the neighborhood of the sampling points near the boundary into three subsets according to the following method:
(5-1) adopting cubic non-uniform B-spline curve interpolation neighborhood points, and parameterizing the type value points by using a normative accumulated chord length parameterization method, wherein the node vector U is [ U ═ U0,u1,…,un+k+1]Adopting a heavy node end point condition that nodes at two ends of a defined domain are k +1 weight, namely u0＝u1＝…＝uk0 and un+1＝un+2＝…＝un+k+11, the boundary condition during interpolation is a tangent condition;
(5-2) Using the formulaCalculated value point PiCurvature of wherein rj(u) is the j-th derivative vector of the spline curve at parameter u;
(5-3) sequentially taking out points from the neighborhood, comparing the curvature value with a set curvature threshold value, if the curvature value is greater than the curvature threshold value, determining the points as chamfering points, and determining points on two sides of each chamfering point as surface points and side points;
(6) fitting two straight lines by using the point subsets on the surface and the side surface, solving the intersection point of the straight lines, taking the intersection point as a boundary characteristic point, and carrying out posture reduction on the characteristic point; the characteristic point acquisition method comprises the following steps:
(6-1) fitting two straight lines by using the point subsets on the two surfaces, wherein the equation of the straight line is ax + by + c is 0, and the established target error function is as follows:
where N is the number of points in the neighborhood, diIs the projected distance d from point to linei＝|axi+byi+c|；
(6-2) obtaining a straight line intersection, i.e., a boundary feature point on the scanning line, and using the formula P ═ CT·PsRestoring the obtained characteristic point coordinates to measurementIn a coordinate system of which CTAs a transpose of matrix C, PsAnd P is the coordinates before and after the characteristic point is restored respectively;
(7) and constructing a straight line or a B-spline curve by using the characteristic points to obtain the boundary of the skin to be measured.
2. The method of claim 1, wherein the neighborhood searching method of the sampling point in the step (2) is as follows:
(2-1) excluding the sampling point as the end point of the scanning line, and recording the two points with the minimum P Euclidean distance to the sampling point as P1 lAnd P1 rIf, ifAndis less than a set threshold value thetacThen it is considered as an endpoint;
(2-2) for the line interior point, independently searching the adjacent points at the left side and the right side of the line interior point, and then combining the adjacent points into a neighborhood, known as P and PThenCan be obtained by the following method: computingEuclidean distance rsAll the points in the interior are sequentially taken out from small to large according to the distanceiIf, ifAndis greater than a set threshold value thetavThen point PiIs thatReuse ofAndto obtainThe points can be searched in sequence by gradually advancing in sequenceThereby obtaining a P right side neighboring point set; the acquisition method of the left side neighboring point set is the same, and the neighborhood of P is obtained after splicing:
and (2-3) in the searching process of the two sides, when the distance between the latest obtained adjacent point and the sampling point is more than or equal to r, ending the searching of the side, and entering the searching of the other side or finishing the searching.
3. The method as claimed in claim 1, wherein in step (3), the sample point P and its neighborhood boundary point are calculatedAndconstructed vectorAndis inserted into the hollow cavityAnd judging whether the sampling point is positioned at the skin boundary according to whether the included angle is smaller than a threshold value theta, if so, positioning the sampling point at the skin boundary, and if so, positioning the sampling point outside the skin boundary.
4. The method of claim 1, wherein the data preprocessing operation in step (4) is as follows:
(4-1) fitting the scanning plane equation a with neighborhood points1x+b1y+c1z + d is 0, wherein a1、b1、c1D is a constant, and the established error objective function is as follows:
where N is the number of points in the neighborhood, di1Is the projected distance d from point to planei1＝|a1xi+b1yi+c1zi+d|；
(4-2) obtaining a rotation matrix C required for coordinate transformation using the formula k ═ Cn, where k ═ 0,0,1]TIs a Z-axis unit direction vector, n ═ a1,b1,c1]TObtaining a plane unit normal vector for fitting;
(4-3) according to the formulaRotate neighborhood points where [ xi,yi,zi]TIn the form of the original coordinates, the coordinates of the original coordinates,the coordinates after rotation;
(4-4) projecting the rotated points, the projected coordinates beingWherein,
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| CN111062960A (en) * | 2019-12-11 | 2020-04-24 | 南京航空航天大学 | Aircraft skin butt joint characteristic line extraction method based on scattered point cloud |
| CN111583318B (en) * | 2020-05-09 | 2020-12-15 | 南京航空航天大学 | Rectifying skin repairing method based on virtual butt joint of measured data of wing body |
| CN111806720B (en) * | 2020-06-24 | 2021-12-07 | 成都飞机工业（集团）有限责任公司 | Rectification skin construction method based on measured data of wing body butt joint |
| CN111814888A (en) * | 2020-07-14 | 2020-10-23 | 南京航空航天大学苏州研究院 | Three-dimensional scanning line point cloud gap step extraction method for aircraft skin butt joint |
| CN114593699B (en) * | 2022-03-07 | 2023-06-23 | 中国石油大学(华东) | Structural dimension measuring method based on reconstruction model spiral line |
| CN117494534B (en) * | 2024-01-03 | 2024-03-22 | 南京航空航天大学 | Aircraft skin repairing method based on point cloud and finite element analysis |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102944174A (en) * | 2012-11-28 | 2013-02-27 | 北京矿冶研究总院 | Point cloud data processing method and system |
| CN104008257A (en) * | 2014-06-16 | 2014-08-27 | 北京航空航天大学 | Reversal design method for component with complex curved surface |
-
2016
- 2016-04-20 CN CN201610247304.7A patent/CN105868498B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102944174A (en) * | 2012-11-28 | 2013-02-27 | 北京矿冶研究总院 | Point cloud data processing method and system |
| CN104008257A (en) * | 2014-06-16 | 2014-08-27 | 北京航空航天大学 | Reversal design method for component with complex curved surface |
Non-Patent Citations (4)
| Title |
|---|
| Trajectory Planning Algorithm Based on Quaternion for 6-DOF Aircraft Wing Automatic Position and Pose Adjustment Method;Zhu Yongguo等;《Chinese Journal of Aeronautics》;20101231;第23卷(第6期);第707-714页 * |
| 基于模板的点云边特征提取技术;张旭 等;《计算机集成制造***》;20080630;第14卷(第6期);第1175-1181页 * |
| 复杂曲面截面特征点云数据处理技术;杜长江;《中国优秀硕士学位论文全文数据库信息科技辑》;20121015(第10期);论文第1-80页 * |
| 面向数字化装配的大型蒙皮精确成形技术;李西宁 等;《大飞机关键制造技术》;20151231(第1/2期);第81-86页 * |
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