Dexterity index based dexterity analysis method for five-axis nc machine tool workspace

Abstract

The application discloses a dexterity analysis method for a five-axis numerical control machine tool workspace based on a dexterity index, which comprises the following steps: generating a random point cloud workspace uniformly according to five-axis machine tool layout information; determining the actual workspace point cloud under the layout according to the actual allowable motion range of each axis; calculating the density of each point in the actual workspace point cloud and dividing the dexterous area and the non-dexterous area of the workspace; verifying the posture reachability of the machine tool in the dexterous area and correcting the average density of the dexterous area; analyzing the dexterity change law in the non-dexterous area; and obtaining the workspace dexterity analysis result of the five-axis machine tool based on the distribution of the dexterous area and the non-dexterous area and the dexterity change trend of the non-dexterous area. The application is free from the dependence of dexterity analysis on empirical conclusions and has no special requirements for the layout configuration of the machine tool, and can analyze any layout five-axis machine tool in a universal scheme and provide detailed dexterity analysis results for reference of machine tool designers.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of dexterity analysis of five-axis machine tool workspace, and particularly relates to a five-axis NC machine tool workspace dexterity analysis method based on dexterity index. BACKGROUND

[0002] The structure of a five-axis machine tool generally comprises a main rotating shaft, a secondary rotating shaft and three moving shafts, such as the vertical five-axis linkage machine tool disclosed in Chinese patent document CN111390579A, which comprises a machine tool bed, a base, an X-axis moving assembly, a Y-axis moving assembly, a Z-axis moving assembly, a cradle type worktable and a buffer anti-collision assembly. The small double-turntable five-axis linkage NC milling machine disclosed in Chinese patent document CN105500037A comprises a base, wherein the base is provided with an X-axis device, a Y-axis device and an electric spindle, the Y-axis device is combined with the base, the X-axis device is combined with the Y-axis device, the electric spindle is combined with the X-axis device, one side of the base is provided with a side vertical plate combined with a Z-axis device, the Z-axis device is combined with a rotating table device, and the rotating table device is combined with a workpiece rotating table device.

[0003] The workspace of a five-axis machine tool refers to the range of motion allowed by the tool tip when the machine tool is working normally; dexterity mainly summarizes how many poses can be completed by the machine tool (i.e. the tool tip) at a certain position, also known as pose reachability; if the tool can work in any pose at a certain position, then the point has dexterity; and the dexterity of the workspace specifically refers to the distribution of the pose reachability of the tool tip when working in the workspace. When designing a five-axis machine tool, in order to meet the actual work requirements, attention is often paid to the dexterity of the workspace, and therefore the actual design range of the five movement axes is often appropriately expanded to ensure this.

[0004] The existing five-axis machine tools need to customize the arrangement scheme and range of variation of the five movement axes according to the actual workspace use requirements when designing, which often depends on the experience of the designer; although the design result can basically meet the actual use requirements, it often causes redundant design of movement axes and waste of effective workspace to some extent. SUMMARY

[0005] The present application discloses a five-axis NC machine tool workspace dexterity analysis method based on dexterity index, which can analyze the workspace dexterity distribution under the ideal state according to the layout information of each machine tool, so that the design distribution of the movement axes can be more in line with the use requirements, reducing the redundant movement range of the axes and the waste of effective space.

[0006] A five-axis NC machine tool workspace dexterity analysis method based on dexterity index, comprising the following steps:

[0007] (1) According to the five-axis machine tool layout information, a random point cloud workspace is uniformly generated;

[0008] (2) According to the actual allowable motion range of each axis, the actual workspace point cloud under the layout is determined;

[0009] (3) The density of each point in the actual workspace point cloud is calculated, and the dexterous area and non-dexterous area of the workspace are divided;

[0010] (4) The posture reachability of the machine tool in the dexterous area is verified, and the average density of the dexterous area is corrected;

[0011] (5) The dexterity change law in the non-dexterous area is analyzed;

[0012] (6) Based on the results of steps (3) and (5), the dexterity analysis result of the five-axis machine tool workspace is finally obtained.

[0013] The present application generates random point cloud by using five-axis machine tool abstract motion chain model, and can quickly analyze the dexterity of the machine tool in the workspace by analyzing and calculating the density distribution of the point cloud. According to the result of dexterity analysis, suggestions for the design of five-axis machine tool are further given.

[0014] Further, in step (1), the random point cloud workspace is generated based on the motion chain structure of the five-axis machine tool layout information, and the specific process is as follows:

[0015] Uniformly arrange the coordinates of the five motion axes, randomly combine the coordinate data, calculate the pose of the machine tool end point corresponding to each combination through the motion chain structure, that is, the pose information of a point in the point cloud; repeat several times, and the generated points are the random point cloud workspace under the layout of the five-axis machine tool.

[0016] In the present application, the layout information of the five-axis machine tool is simplified as the arrangement information of the five motion axis joints, that is, the main rotary axis, the secondary rotary axis and the three moving axes, and the attitude change transmission relationship from the workpiece coordinate system to the tool coordinate system. The axis coordinates of the main rotary axis are defined as α, the axis coordinates of the secondary rotary axis are defined as β, and the axis coordinates of the three moving axes are defined as X, Y and Z. Therefore, (α, β, X, Y, Z) is a set of axis coordinate combinations. Through the calculation of the motion chain model, each axis coordinate combination can uniquely determine a set of tool end pose coordinates (x, y, z, i, j, k) under the motion chain of the machine tool, wherein x, y and z represent the spatial position, and i, j and k represent the projection components of the tool axis unit vector in the three directions of the space coordinate system.

[0017] A set of n axis coordinate combinations (α m , βm , X m , Y m , Z m ), where m = 1, 2, 3, …, n, each axis coordinate data is uniform and random, thus a set of tool end pose coordinates (x m , y m , z m , i m , j m , k m ) can be generated, and the set of tool end poses constitutes a point cloud containing position and attitude information. When n is large enough, the point cloud can completely cover the entire machine tool workspace, i.e. a random point cloud workspace is generated.

[0018] Further, the specific process of step (2) is as follows:

[0019] According to the actual design requirements of the five-axis machine tool, the actual variation ranges of the five movement axes are limited, and then a limited range of actual workspace point cloud is generated by the method in step (1); by fitting the envelope surface of the actual workspace point cloud, the region information of the actual reachable workspace is obtained.

[0020] The actual workspace point cloud in step (2) is determined by limiting the range on the basis of the random point cloud workspace in step (1). According to the actual design requirements of the five-axis machine tool, the actual variation ranges of the five movement axes can be limited, such as the variation range of α is [α l , α h ], the variation range of β is [β l , β h ], the variation range of x is [x l , x h ], the variation range of y is [y l , y h ], and the variation range of z is [z l , z h ]. Then, a limited range of actual workspace point cloud can be generated according to the method in step (1).

[0021] Although the axis coordinates are uniform within their respective variation ranges, different densities of point clouds can be generated due to different kinematic chain structures, and the calculation methods are different. The greater the density of the region, the greater the probability of the tool end reaching this region, and the more attitudes that can be achieved at this region, and the better the dexterity. This principle can be used for subsequent analysis of dexterity.

[0022] Furthermore, this actual workspace point cloud represents the actual reachable workspace under this machine tool layout, that is, the area that the end point of the machine tool can reach and its corresponding orientation. By fitting and solving the envelope surface of the actual workspace point cloud, the region information of the actual reachable workspace can be obtained.

[0023] Further, in step (3), the density of each point in the actual workspace point cloud is calculated. The specific process is as follows:

[0024] Read the location information from the point cloud data, i.e. (x m y m , z m ), where m = 1, 2, 3, ..., n;

[0025] Calculate the search radius t step Based on the principle that density data must be considered in all three dimensions, the calculation formula is as follows:

[0026]

[0027] Where, d max d represents the maximum value of each of the point cloud data x, y, and z. min This represents the minimum value among the point cloud data x, y, and z, and n represents the number of points in the point cloud.

[0028] Record the search radius t for each point step The number of midpoints is the initial density data ρ for that point. m .

[0029] In step (3), the dexterous zone is defined as the area that the tool can reach relative to the workpiece in the most possible orientations. Conversely, the non-dexterous zone is the remaining workspace outside the dexterous zone, where the tool cannot reach some of the orientations achievable in the dexterous zone. The specific process for dividing the workspace into dexterous and non-dexterous zones is as follows:

[0030] Calculate the initial average density ρ of the smart region c The calculation formula is as follows:

[0031]

[0032] Where, ρ i This represents the density values ​​of the 100 points with the highest density among all points; generally, the points with the highest density are located in the dexterity zone; to avoid randomness, the 100 points with the highest density are selected for calculation.

[0033] Set the tolerance threshold ε. The lower the tolerance threshold ε, the larger the point cloud data required. Based on the 95% confidence interval in statistics, it can generally be set to 0.05. Designers can modify it according to their design requirements.

[0034] The density ρ at each point is calculated sequentially. m and the initial average density ρ of the dexterity zone c To make a comparison, if ρ m <(1-ε)ρ c If a point is not found in the smart area, it is placed in the non-smart area; otherwise, it is placed in the smart area, until all points are divided. At this point, the envelope of all smart area points is the smart area, and the rest of the workspace is the non-smart area.

[0035] Furthermore, in step (4), the orientation reachability of the machine tool in the dexterity zone is verified. The specific process is as follows:

[0036] Calculate the dexterity index r of the points within the dexterity zone by randomly sampling several points within the dexterity zone. m The formula is as follows:

[0037]

[0038] The range of variation of the principal rotation axis α during design is [α l α h The range of variation of the secondary rotation axis β is [β l ,β h ], ΔS X ΔS Y ΔS Z They are respectively at the position (x) of this point m y m , z m The range of motion of the two rotational axes that allows the corresponding X, Y, and Z motion axes to have solutions within the design range;

[0039] If all the calculation results are 1, it means that the dexterity region has complete attitude reachability, and the dexterity is verified; if they are not all 1, the tolerance threshold ε in step (3) is changed and the calculation is repeated.

[0040] Based on the final division of the skill areas, the average density ρ of the skill areas was recalculated. c The calculation formula is as follows:

[0041]

[0042] Where w represents the number of points in the dexterity zone, ρ i This represents the density value of each point in the dexterity zone.

[0043] Furthermore, the specific process of step (5) is as follows:

[0044] For the non-smart region, the search radius t calculated in step (3) is used. stepResample the point cloud, that is, sample the density data once for each search radius, draw a three-dimensional density distribution graph of the point cloud according to the color depth corresponding to the density size of the sampled data result, and analyze the dexterity change trend through the density change trend of the non-dexterous area.

[0045] Further, the specific process of step (6) is:

[0046] According to the distribution of the dexterous area and the non-dexterous area in step (3) and the dexterity change trend of the non-dexterous area in step (5), a comprehensive analysis result of the dexterity of the machine tool configuration is given, and the dexterity analysis of the working space of the five-axis numerical control machine tool is completed.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] 1. The present application uses the statistical characteristics of uniform random point cloud to analyze the working space of the five-axis machine tool cutter relative to the workpiece, replaces the complex and difficult to calculate dexterity performance data with density data which is simple and easy to calculate, and makes the result have sufficient value and credibility.

[0049] 2. The present application gets rid of the dependence of dexterity analysis on empirical conclusions, has no special requirements for machine tool layout configuration, can analyze any layout five-axis machine tool with a general scheme, and gives detailed dexterity analysis results for reference for machine tool designers. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A five-axis numerical control machine tool working space dexterity analysis method flowchart based on the dexterity index is used for the embodiment of the present application;

[0051] Figure 2 A typical double-turntable five-axis machine tool simplified model structure diagram is used for the embodiment of the present application;

[0052] Figure 3a A double-turntable five-axis machine tool actual working space point cloud diagram is used for the embodiment of the present application;

[0053] Figure 3b An envelope surface working space diagram of a double-turntable five-axis machine tool is used for the embodiment of the present application;

[0054] Figure 4 A density distribution diagram of the actual working space point cloud of a double-turntable five-axis machine tool is used for the embodiment of the present application;

[0055] Figure 5 A two-dimensional surface diagram of the X, Y and Z movement axes of the double-turntable five-axis machine tool at the sampling point in the dexterous area changing with the two rotating axes C and A is used for the embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. The embodiments described in the following exemplary examples do not represent all the embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0057] The present application will be further described in detail below in combination with the drawings and examples. It should be noted that the following examples are intended to facilitate the understanding of the present application and do not limit it in any way.

[0058] As shown in Figure 1 , a dexterity index-based five-axis NC machine tool workspace dexterity analysis method includes the following steps:

[0059] (1) According to the five-axis machine tool layout information, a random point cloud workspace is uniformly generated, specifically:

[0060] The layout information of the five-axis machine tool is simplified as the arrangement information of the five motion axis joints, i.e. the main rotary axis, the secondary rotary axis and the three moving axes, by the posture change transmission relationship from the workpiece coordinate system to the tool coordinate system. Define the axis coordinates of the main rotary axis as α, the axis coordinates of the secondary rotary axis as β, and the axis coordinates of the three moving axes as X, Y and Z, respectively. Then (α, β, X, Y, Z) is a set of axis coordinate combinations. Through the calculation of the kinematic chain model, each axis coordinate combination can uniquely determine a set of tool end pose coordinates (x, y, z, i, j, k) under the kinematic chain of the machine tool, where x, y and z represent the spatial position, and i, j and k represent the projection components of the tool axis unit vector in the three directions of the spatial coordinate system.

[0061] This embodiment uses a typical dual-rotary table five-axis machine tool simplified model as shown in Figure 2 as the analysis. The simplified kinematic chain information of the machine tool is CAYXZ, where the C-axis initially rotates around the spatial Z-axis, which is the main rotary axis; the A-axis initially rotates around the spatial X-axis, which is the secondary rotary axis; and the remaining Y, X and Z axes correspond to the three moving axes.

[0062] A set of coordinate data is uniformly generated for each axis coordinate, and a set of n axis coordinate combinations (α m , β m , X m , Y m , Z m ) can be obtained by randomly arranging and combining the five-axis data, where m = 1, 2, 3, …, n, and each axis coordinate data is uniform and random. Thus, a set of tool end pose coordinates (x m , y m , zm , i m , j m , k m ), this set of tool end poses constitutes a point cloud containing position and pose information. When n is large enough, this point cloud can completely cover the entire machine tool workspace, i.e. a random point cloud workspace is generated. In this embodiment, n = 10 6 .

[0063] (2) According to the actual allowable motion range of each axis, the actual workspace point cloud under this layout is determined, specifically:

[0064] According to the actual design requirements of the five-axis machine tool, the actual variation range of the five motion axes can be limited. In this embodiment, the variation range of a is [-180°, 180°], the variation range of β is [-120°, 120°], the variation range of x is [-100, 100], the variation range of y is [-100, 100], and the variation range of z is [-100, 100]. Then, according to the method of step (1), a limited range of actual workspace point cloud can be generated.

[0065] Figure 3a is the actual workspace point cloud generated under this double-turntable five-axis machine tool embodiment. After preliminary observation, the central region of the point cloud is relatively dense, and the edge region is relatively sparse. Subsequently, according to the density division, further analysis of the dexterity of the workspace can be carried out. Figure 3b is the solution result of the envelope surface of the actual workspace point cloud. The inside of the envelope surface is the actual workspace of this double-turntable five-axis machine tool embodiment.

[0066] (3) Calculate the density of each point in the actual workspace point cloud, and divide the workspace into dexterous and non-dexterous regions, specifically:

[0067] Read the position information in the point cloud data, i.e. (x m , y m , z m ), where m = 1, 2, 3, …, n;

[0068] Calculate the search radius t step , and the density data in three-dimensional direction should be considered as the principle, the calculation formula is as follows:

[0069]

[0070] where d max represents the maximum value of the point cloud data x, y, z, d min represents the minimum value of the point cloud data x, y, z, and n represents the number of points in the point cloud.

[0071] Substitute the above data in this embodiment into the formula, the search radius result is as follows:

[0072]

[0073] Record the number of points in the search radius t step , which is the preliminary density data p m of the point.

[0074] The dexterous region is defined as the region that the tool can reach in the most pose directions relative to the workpiece. Conversely, the non-dexterous region is the remaining workspace region outside the dexterous region, and the tool cannot reach some poses in the dexterous region when working in this region. The method for dividing the dexterous region and the non-dexterous region is as follows:

[0075] Calculate the initial average density p c of the dexterous region. Generally, the point with the highest density is located in the dexterous region. To avoid randomness, the 100 points with the highest density are taken for calculation, that is, the formula for calculating the initial average density p c of the dexterous region is as follows:

[0076]

[0077] where p i represents the density value of the 100 points with the highest density among all points.

[0078] Set the tolerance threshold e. The lower the tolerance threshold e, the larger the point cloud data required. According to the 95% confidence interval of statistics, the tolerance threshold e is set to 0.05 in this embodiment, and the designer can modify it according to the design requirements.

[0079] Compare the density p m of each point with the initial average density p c of the dexterous region. If p m < (1-e) p c , it is classified into the non-dexterous region; otherwise, it is classified into the dexterous region, until all points are classified. At this time, the envelope region of all dexterous region points is the dexterous region, and the remaining region of the workspace is the non-dexterous region.

[0080] Figure 4 The density distribution diagram of the actual workspace point cloud of this double-turntable five-axis machine tool embodiment is shown. For ease of display, the central cross-sectional diagram in three directions is shown. The brightest and most consistent part of the central color region is the dexterous region divided this time, and the remaining external region is the non-dexterous region.

[0081] (4) Verify the pose reachability of the machine tool in the dexterous region, which is as follows:

[0082] Calculate the dexterity index rm , the calculation formula is as follows:

[0083]

[0084] wherein the variation range of the main rotation axis a is [a l , a h ], the variation range of the secondary rotation axis b is [b l , b h ], and AS X , AS Y , and AS Z are the motion ranges of the two rotation axes at the position (x m , y m , z m ) that can make the corresponding X, Y, and Z motion axes have solutions in the design range.

[0085] In this embodiment, the point (-28.7558171126383, 75.1558119805253, -49.4124839739242) in the dexterous region is used for calculation, and according to the transmission relationship of the double-rotary table motion chain structure, the changes of the X, Y, and Z motion axes are inversely solved with the two rotation axes as independent variables.

[0086] Figure 5 The two-dimensional surface graphs of the X, Y, and Z motion axes with the changes of the two rotation axes C and A at the sampling points in the dexterous region of this double-rotary table five-axis machine tool are shown in the figure. When C and A change within their allowable ranges, X, Y, and Z always change within the [-100, 100] interval, i.e., they are all within the design ranges of the three motion axes, so the relationship AS X = AS Y = AS Z = (a h -a l )(b h -b l ) is established, the dexterity index r m = 1, and the pose accessibility of this point is verified.

[0087] However, the calculation needs to traverse the entire design variation range at this point, which is relatively complex and slow, so only a few points in the dexterous region are randomly sampled for verification. The calculation results vary within the range of 0 to 1. Generally speaking, if the calculation results are all 1, it means that the dexterous region has complete pose accessibility; if they are not all 1 or the calculation results differ greatly, the tolerance threshold e in step (3) needs to be changed for repeated calculation.

[0088] In this embodiment, the sampled points all satisfy the dexterity index r m=1, therefore there is no need to modify the tolerance threshold ε, and the workspace of this dual rotary table five-axis machine tool embodiment has complete attitude accessibility.

[0089] Based on the final division results of the smart zone points, the average density ρ of the smart zone is recalculated. c The calculation formula is as follows:

[0090]

[0091] Where w represents the number of points in the dexterity zone, ρ i This represents the density value of each point in the dexterity zone.

[0092] (5) Analyze the dexterity variation patterns in the non-dexterity zone, specifically:

[0093] For ease of display and drawing, the search radius t calculated in step (3) is used for the non-smart region. step The point cloud is resampled, meaning density data is sampled once for each search radius. Based on the sampled data, a 3D density distribution map of the point cloud is plotted according to the density magnitude and corresponding color intensity. The trend of dexterity variation can be analyzed by observing the density variation trend in non-dexterity areas.

[0094] Figure 4 This is a density distribution diagram of the point cloud in the actual workspace of this dual-rotor five-axis machine tool embodiment. Except for the central dexterity region, the density of the non-dexterity region generally shows a trend of gradually decreasing from the center of the workspace outwards. That is, the closer to the periphery, the lower the dexterity, and the more uniform the rate of change.

[0095] (6) Based on the regional data obtained in steps (3) and (5), the workspace dexterity analysis results of the five-axis machine tool are given.

[0096] In this embodiment of the dual rotary table five-axis machine tool, the analysis results have been roughly described above. Specifically, considering practical design, since the workspace is roughly ellipsoidal, it is more suitable for machining workpieces of similar shapes, as it can better save the travel of the five motion axes. Furthermore, having a clear analysis of the workspace makes it easier for designers to further design the machine tool and its related parameters.

[0097] The embodiments described above provide a detailed explanation of the technical solutions and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dexterity index based analysis method for workspace dexterity of a 5-axis CNC machine tool, characterized in that, The method comprises the following steps: (1) generating a random point cloud workspace according to five-axis machine tool layout information; (2) determining the actual workspace point cloud under the layout according to the actual allowable motion range of each axis; (3) calculating the density of each point in the actual workspace point cloud, and dividing the dexterous area and the non-dexterous area of the workspace; the specific process is as follows: Read the position information in the point cloud data, that is, (x m , y m , z m ), where m = 1, 2, 3, …, n; Computing search radius t step The formula is as follows: where d max denotes the maximum value in the point cloud data x, y, z respectively, d min denotes the minimum value in the point cloud data x, y, z respectively, n denotes the number of points in the point cloud; Record the number of points within search radius t step The number of points in the midpoint, i.e. the preliminary density data p m ; The specific process of dividing the dexterous area and the non-dexterous area of the workspace is as follows: calculating the initial average density p of the smart cut zone c The calculation formula is as follows: wherein p i represents the density value of the 100 points with the highest density among all points; A tolerance threshold ε is set, and the density p of each point is sequentially compared with the initial average density p of the dexterous region m and the initial average density p of the dexterous region c If p m <(1-ε) p c , the point is classified into the non-dexterous region; otherwise, it is classified into the dexterous region, until all points are classified; at this time, the envelope region of all dexterous region points is the dexterous region, and the remaining region of the work space is the non-dexterous region; (4) verifying the posture reachability of the machine tool in the dexterous area and correcting the average density of the dexterous area; the specific process is as follows: Randomly sample several points in the dexterous region to calculate the dexterity index r of the points in the dexterous region m , as follows: wherein the variation range of the primary rotation axis a is [a l , a h ], the variation range of the secondary rotation axis β is [β l , β h ], and ΔS X , ΔS Y , ΔS Z are the motion ranges of the two rotation axes at the position (x m , y m , z m ) that can make the corresponding X, Y, Z motion axes have solutions in the design range, respectively. If the calculation results are all 1, it indicates that the dexterous area has complete posture reachability, and the dexterity is verified; if not, change the tolerance threshold ε in step (3) to repeat the calculation again; According to the final division result of the dexterous area, the average density p of the dexterous area is recalculated c The calculation formula is as follows: where w represents the number of the midpoint of the dexterous region, p i represents the density value of each midpoint of the dexterous region; (5) analyzing the dexterity change law in the non-dexterous area; (6) based on the results of steps (3) and (5), finally obtaining the workspace dexterity analysis result of the five-axis machine tool.

2. The method of analyzing the dexterity of a workspace of a five-axis CNC machine tool based on the index of ingenuity according to claim 1, characterized in that, In step (1), the random point cloud workspace is generated based on the motion chain structure of the five-axis machine tool layout information, and the specific process is as follows: Uniformly arrange the coordinates of the five motion axes, randomly combine the coordinate data, calculate the pose of the machine tool end point corresponding to each combination through the motion chain structure, that is, the pose information of a point in the point cloud; repeat several times, and the generated points are the random point cloud workspace under the layout of the five-axis machine tool.

3. The method of analyzing the dexterity of a workspace of a five-axis CNC machine tool based on the index of ingenuity according to claim 1, wherein The specific process of step (2) is as follows: According to the actual design requirements of the five-axis machine tool, the actual change range of the five motion axes is limited, and then a limited range of actual workspace point cloud is generated by the method in step (1); by fitting the envelope surface of the actual workspace point cloud, the area information of the actual reachable workspace is obtained.

4. The method of analyzing the dexterity of a workspace of a five-axis CNC machine tool based on the index of ingenuity according to claim 1, wherein, The specific process of step (5) is as follows: For the non-dexterous area, the search radius t calculated in step (3) step The point cloud is resampled, that is, the density data is sampled once for each search radius. Based on the sampled data results, the three-dimensional density distribution diagram of the point cloud is drawn according to the color depth corresponding to the density size, and the dexterity trend is analyzed through the density change trend of the non-dexterous area.

5. The method of analyzing the dexterity of a workspace of a five-axis CNC machine tool based on the index of ingenuity as claimed in claim 1, wherein, The specific process of step (6) is as follows: According to the distribution of the dexterous area and the non-dexterous area in step (3) and the dexterity change trend of the non-dexterous area in step (5), the dexterity of the machine tool configuration is given a comprehensive analysis result, and the dexterity analysis of the workspace of the five-axis numerical control machine tool is completed.

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