Automating mechanical drafting workflows with geometry quantization
12536717 ยท 2026-01-27
Assignee
Inventors
Cpc classification
G06F30/12
PHYSICS
International classification
G06F30/12
PHYSICS
Abstract
A method and system provide for automating drawing. A drawing of two or more entities is obtained and a resolution is determined. Based on the resolution, the drawing is quantized into a cell map. The cell map is a collection of multiple cells stored in a contiguous memory. Each of the multiple cells is quantized geometry data for and provides a smallest tangible unit of information about a corresponding entity. Each of the multiple cells is a number that represents domain specific information about the corresponding entity. The quantizing rounds off of values beyond a specified threshold to a resolution tolerance of the resolution. The cell map is utilized to automate modifications to the drawing.
Claims
1. A computer-implemented method for automating drawing, comprising: (a) obtaining a drawing comprised of two or more entities; (b) determining a resolution; (c) based on the resolution, quantizing the drawing into a cell map stored in a contiguous memory, wherein: (i) the cell map comprises a collection of multiple cells stored in the contiguous memory; (ii) each of the multiple cells comprises an encoded value representing quantized geometry data and domain-specific information for a corresponding entity of the two or more entities; (iii) each of the multiple cells comprises a smallest tangible unit of information about the corresponding entity; (iv) the quantizing comprises a rounding off of values beyond a specified threshold to a resolution tolerance of the resolution; and (d) utilizing the cell map to automate modifications to the drawing, wherein the utilizing the cell map stored in contiguous memory enables automated modifications to the drawing without requiring a conventional boundary representation.
2. The computer-implemented method of claim 1, wherein the determining the resolution comprises: obtaining a bounding box of the drawing; obtaining a cell size flag value that specifies a level of desired granularity for the cell map; determining a maximum number of rows or columns for the cell map; and based on the maximum number of rows or columns, the cell size flag, and the bounding box, calculating the resolution as:
resolution=max(bounding-box-length,bounding-box-width)/maximum-rows-or-columns.
3. The computer-implemented method of claim 1, wherein the utilizing the cell map to automate modifications comprises: collecting a view chain and determining whether an entity is to be broken horizontally or vertically; collecting views of the two or more entities to break; isolating geometry in the cell map; merging the collected views together; and identifying, based on the isolated geometry in the cell map, unique sections of the two to more entities on all of the merged collected views.
4. The computer-implemented method of claim 3, wherein the identifying the unique sections comprises: confirming that a defined wavelength is correct with respect to the cell map; confirming that a defined tolerance is correct with respect to the cell map; traversing the cell map while comparing columns based on the defined wavelength and defined tolerance to determine a number of consecutive matching values; and determining and returning a final value for the defined wavelength based on a maximum number of consecutive matching values.
5. The computer-implemented method of claim 1, wherein: the quantizing the drawing comprises: creating the cell map based on a view of the drawing and one or more annotations or dimensions; masking the cell map to remove a set of details; creating a filled cell map out of the masked cell map; iterating over each of the one or more annotations or dimensions, wherein during the iterating: one or more of the annotations or dimensions are cloned; creating a line cell map comprised of potential candidate locations for the one or more annotations or dimensions; the utilizing the cell map to automate modifications comprises, during the iterating: determining one or more intersections of the line cell map and the filled cell map, wherein the intersections identify a subset of the candidate locations for the one or more annotations or dimensions; determining for each candidate location in the subset whether there is overlap; and identifying the candidate locations in the subset without any overlap as the locations for the one or more annotation or dimensions.
6. The computer-implemented method of claim 1, wherein: the quantizing the drawing comprises: collecting one or more views from the drawing; determining a padding for the one or more views; applying the padding to the one or more views to generate padded views; generating, from the padded views, padded cell maps, wherein the cell map comprises a combination of the padded cell maps; merging the padded cell maps together into a merged padded cell map; the utilizing the cell map to automate modifications comprises: finding a sheet cell map without the one or more views; and identifying an empty space in the sheet cell map where the merged padded cell map fits without overlapping anything.
7. The computer-implemented method of claim 1, wherein the utilizing the cell map to automate modifications comprises: masking the cell map for each view of the drawing; grouping views of the drawing into horizontal and vertical groups; determining, based on the masked cell maps, that one or more of the views in the horizontal and vertical groups are fungible; and removing the fungible views.
8. The computer-implemented method of claim 7, wherein the determining that one or more of the views are fungible comprises: comparing two cell maps for two of the views, wherein the comparing comprises: comparing cell counts for the two cell maps to determine whether the cell counts are different, wherein different cell counts indicates that the two views are not fungible; generating a union of the two cell maps; determining whether the union matches either of the two views, wherein a match indicates of the two cell maps is contained in the other of the two cell maps; and comparing rotated versions of the two cell maps to determine if they match, wherein matching rotated versions indicates fungibility.
9. The computer-implemented method of claim 1, wherein the utilizing the cell map to automate modifications comprises: identifying a single body in the drawing; determining that the cell map has two or more distinct loops; determining that the two or more distinct loops are symmetric across a same axis; grouping same x-horizontally symmetric loops together and same y-vertically symmetric loops together; creating groups using unique cells of the cell map; determining that same x-horizontally symmetric loops or same y-vertically symmetric loops have a same center; and based on the same center, generating a section view.
10. The computer-implemented method of claim 1, wherein: the quantizing the drawing comprises: creating the cell map based on a view of the drawing; the utilizing the cell map to automate modifications comprises: detecting, based on the cell map, one or more loops of the view; exploding each of the one or more loops to separate the cell map; determining whether each non-fungible point is present in the exploded one or more loops, wherein at least one of the non-fungible points is present; and for each non-fungible point present in the exploded one or more loops, mapping the non-fungible point to the exploded loop in which it is present.
11. The computer-implemented method of claim 10, further comprising: identifying loops that are similar based on the cell map; and storing information about similar loops.
12. A computer-implemented system for automating drawing, comprising: (a) a computer having a contiguous memory; (b) a processor executing on the computer; (c) the memory storing a set of instructions, wherein the set of instructions, when executed by the processor cause the processor to perform operations comprising: (i) obtaining a drawing comprised of two or more entities; (ii) determining a resolution; (iii) based on the resolution, quantizing the drawing into a cell map stored in the contiguous memory, wherein: (A) the cell map comprises a collection of multiple cells stored in the contiguous memory; (B) each of the multiple cells comprises an encoded value representing quantized geometry data and domain specific information for a corresponding entity of the two or more entities; (C) each of the multiple cells comprises a smallest tangible unit of information about the corresponding entity; (D) the quantizing comprises a rounding off of values beyond a specified threshold to a resolution tolerance of the resolution; and (iv) utilizing the cell map to automate modifications to the drawing, wherein the utilizing the cell map stored in contiguous memory enables automated modifications to the drawing without requiring a conventional boundary representation.
13. The computer-implemented system of claim 12, wherein the operations for determining the resolution comprise: obtaining a bounding box of the drawing; obtaining a cell size flag value that specifies a level of desired granularity for the cell map; determining a maximum number of rows or columns for the cell map; and based on the maximum number of rows or columns, the cell size flag, and the bounding box, calculating the resolution as:
resolution=max(bounding-box-length,bounding-box-width)/maximum-rows-or-columns.
14. The computer-implemented system of claim 12, wherein the operations for utilizing the cell map to automate modifications comprise: collecting a view chain and determining whether an entity is to be broken horizontally or vertically; collecting views of the two or more entities to break; isolating geometry in the cell map; merging the collected views together; and identifying, based on the isolated geometry in the cell map, unique sections of the two to more entities on all of the merged collected views.
15. The computer-implemented system of claim 14, wherein the identifying the unique sections comprises: confirming that a defined wavelength is correct with respect to the cell map; confirming that a defined tolerance is correct with respect to the cell map; traversing the cell map while comparing columns based on the defined wavelength and defined tolerance to determine a number of consecutive matching values; and determining and returning a final value for the defined wavelength based on a maximum number of consecutive matching values.
16. The computer-implemented system of claim 12, wherein: the operations for quantizing the drawing comprise: creating the cell map based on a view of the drawing and one or more annotations or dimensions; masking the cell map to remove a set of details; creating a filled cell map out of the masked cell map; iterating over each of the one or more annotations or dimensions, wherein during the iterating: one or more of the annotations or dimensions are cloned; creating a line cell map comprised of potential candidate locations for the one or more annotations or dimensions; the operations for utilizing the cell map to automate modifications comprise, during the iterating: determining one or more intersections of the line cell map and the filled cell map, wherein the intersections identify a subset of the candidate locations for the one or more annotations or dimensions; determining for each candidate location in the subset whether there is overlap; and identifying the candidate locations in the subset without any overlap as the locations for the one or more annotation or dimensions.
17. The computer-implemented system of claim 12, wherein: the operations for quantizing the drawing comprise: collecting one or more views from the drawing; determining a padding for the one or more views; applying the padding to the one or more views to generate padded views; generating, from the padded views, padded cell maps, wherein the cell map comprises a combination of the padded cell maps; merging the padded cell maps together into a merged padded cell map; the operations for utilizing the cell map to automate modifications comprise: finding a sheet cell map without the one or more views; and identifying an empty space in the sheet cell map where the merged padded cell map fits without overlapping anything.
18. The computer-implemented system of claim 12, wherein the operations for utilizing the cell map to automate modifications comprise: masking the cell map for each view of the drawing; grouping views of the drawing into horizontal and vertical groups; determining, based on the masked cell maps, that one or more of the views in the horizontal and vertical groups are fungible; and removing the fungible views.
19. The computer-implemented system of claim 18, wherein the determining that one or more of the views are fungible comprises: comparing two cell maps for two of the views, wherein the comparing comprises: comparing cell counts for the two cell maps to determine whether the cell counts are different, wherein different cell counts indicates that the two views are not fungible; generating a union of the two cell maps; determining whether the union matches either of the two views, wherein a match indicates of the two cell maps is contained in the other of the two cell maps; and comparing rotated versions of the two cell maps to determine if they match, wherein matching rotated versions indicates fungibility.
20. The computer-implemented system of claim 12, wherein the operations for utilizing the cell map to automate modifications comprise: identifying a single body in the drawing; determining that the cell map has two or more distinct loops; determining that the two or more distinct loops are symmetric across a same axis; grouping same x-horizontally symmetric loops together and same y-vertically symmetric loops together; creating groups using unique cells of the cell map; determining that same x-horizontally symmetric loops or same y-vertically symmetric loops have a same center; and based on the same center, generating a section view.
21. The computer-implemented system of claim 12, wherein: the operations for quantizing the drawing comprise: creating the cell map based on a view of the drawing; the operations for utilizing the cell map to automate modifications comprise: detecting, based on the cell map, one or more loops of the view; exploding each of the one or more loops to separate the cell map; determining whether each non-fungible point is present in the exploded one or more loops, wherein at least one of the non-fungible points is present; and for each non-fungible point present in the exploded one or more loops, mapping the non-fungible point to the exploded loop in which it is present.
22. The computer-implemented system of claim 21, wherein the operations further comprise: identifying loops that are similar based on the cell map; and storing information about similar loops.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
(1) Referring now to the drawings in which like reference numbers represent corresponding parts throughout:
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DETAILED DESCRIPTION OF THE INVENTION
(31) In the following description, reference is made to the accompanying drawings which form a part hereof, and which is shown, by way of illustration, several embodiments of the present invention. It is understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention.
Overview
(32) Understanding morphology will help build human intuition which is necessary to automate complicated workflows. Accordingly, embodiments of the invention attempt to understand the morphology of a part in a way that a draftsman understands it. Existing data structures that store geometry information like BREP/Viewport/BlockTableRecords are better in providing drafting features. While these data structures are information rich, they are not suitable for automation. Accordingly, embodiments of the invention augment existing geometry data structures with something more suitable for automation. Quantization is the process where the geometry data is dumbed down in a way that makes the geometry understandable as a human would. The quantized geometry provides faster and efficient data representation needed for automation.
(33) To provide such quantization, embodiments of the invention provide cell maps. It may be noted that the quantization of geometry happens on paper space. Such paper space is where values are rounded off with some resolution factor. In this regard, the resolution is a function of paper size and/or user preference. In quantization, the lineage and other domain-specific information about geometry entities are kept intact. The smallest tangible unit of the information is called the cell and the collection of such cells is called the cell map.
(34) Cell maps that are utilized to approximate drafting specific tasks and inferring/building a draftsman's intuition. Capabilities resulting from the use of such cell maps include automation, the building of human intuition and judgment, and aesthetics.
(35) Automation: Once the morphology of a part is understood, the automation of the workflow may be easier. For example, the number of orthographic views needed to fully define a part may be determined. As humans, we can easily understand and answer the question by just looking at a part, because we look at the part, not at its edges, faces, and vertices. Instead, humans look at a part as a whole and understand what shape it is and which views would make sense to convey full information about the part. Once the morphological features of a part are understood, it becomes clearer what views are needed.
(36) Building Human Intuition and Judgment: The final consumers of a 2D (two-dimensional) drawing are humans (e.g., other engineers). The human brain acts as an interpreter that can read and decode a mechanical drawing. While creating a drawing, draftsman implicitly understand this fact and take some creative shortcuts. For example, if features-to-detail are located at the end of a long part, a draftsman usually breaks the part where nothing is happening and a reader would still understand the geometry of the part. The same is true if the long part has repetitive patternsbreaking such a part also would make sense. Accordingly, embodiments of the invention, as software, build human intuition that can mimic what a draftsman would do. Morphological processing and analysis of quantized data help build such human intuition.
(37) Aesthetics: As established, mechanical drawings are ultimately read by humans. Aesthetics and readability are important aspects. Draftsmen typically have to spend considerable time in moving and arranging the views and annotations to make drawings look good on the paper. Embodiments of the invention help make the drawings more aesthetically pleasing and may even take client's preferences into account during this process. For example, embodiments of the invention may auto-arrange dimensions without them overlapping each other or any part geometry.
(38) In view of the above, exemplary capabilities of embodiments of the invention may include (1) actions inside of the drawing workflow such as auto views, auto arrangement of annotations, and autobreak; and (2) actions outside of the drawing such as faster nesting algorithms, and/or the arrangement of dimensions inside the modeling workspace. The following provide further details regarding some of the exemplary capabilities that may be provided in accordance with one or more embodiments of the invention:
(39) (1) AutoBreak: with the help of cell maps, embodiments of the invention can find information entropy and redundancy. A cell map may be thought of as a geometrical-image-snapshot, where the information is condensed in 64 bits rather than a conventional image-pixel (picture-cell) 24 bits. Redundancy may be identified in a long part, and embodiments may break the long part if the information redundancy extends beyond a threshold.
(40) (2) Overlap Detection and Automatic Arrangement: As the information is laid out in contiguous memory, it is extremely efficient to do some Boolean operations on this data. Embodiments can quickly find out if some objects overlap with one another and after detecting such overlap, embodiments can quickly find a better place for these objects where they don't overlap. This Quantized Geometry may be used to automatically arrange the dimensions, hole and thread notes, and other annotations. The cell map may also be used to automatically nest the views on a sheet and scale them up and down if they overlap.
(41) (3) Dimensioning algorithms and finding unique sections, loops, symmetry, and intersections. In the dimensioning algorithms, which help with auto-dimensioning, the geometric cell maps are used to explode loops and start dimensions per loop instead of the entire view. Per loop dimension mimics what draftsmen do while manually dimensioning. At a loop level, embodiments of the invention can quickly identify/find the symmetry of these loops, and such an identification may help reduce the redundant dimensions on symmetric geometry. Embodiments of the invention may also overlay one drawing view on top of another drawing view of the same component. With such capability, embodiments can identify if the geometry is identical and figure out the portion of geometry which is distinct. Such an identification can help dimension the Boolean intersection of the view mimicking what draftsmen do.
Additional Description of the Problem
(42) Rich-geometry data powers all the user facing functionality of drawings. Data like face ID, feature ID, entity specific information like coordinates of end points, control points, center points, arc points, transformations, etc. are the backbone of all the drawing functionality. However, for automation, geometry information particularly in the current form is not optimal. It is extremely difficult or sometimes impossible to build human centric logic required in the drawing domain with the current data. For example, it may be difficult, if not impossible to determine if an annotation overlaps with a view, or how to determine if breaks can be applied to a part automatically.
(43) To understand the problems, it may be useful to differentiate videos and drawings. With videos: (1) humans are the consumers; (2) the recording of a video could happen at a higher frame-rate, but (3) human constraints existan image stays on the retina for 1/20.sup.th of a second, thus videos could be streamed at 24 FPS (frames per second); i.e., (4) a video is streamed temporally quantizing or sampling a second with 24 images. With drawings: (1) humans are the consumers; (2) the actual model may contain information at the 10.sup.6 tolerance, but (3) human constraintusers cannot see less than line thickness, thus, excess information can be removed; and (4) that is why embodiments of the invention can spatially sample/quantize the view/sheet by some resolution factor.
(44) Quantization
(45) Geometry quantization is a way of representing the mechanical-drawing data in a quantized form. As used herein, this form is referred to as a geometric cell map or cell map. Cell maps maintain geometry and domain-specific information in the form of a contiguous array of cells (also referred to as geometry cells). Cell maps are highly efficient in approximating drafting-specific tasks and building a draftsman's intuition. With the quantization process, spatial information may be lost but does not cause a problem as humans cannot comprehend the much higher spatial information in 10.sup.6 tolerance on a piece of paper.
(46) The terminology used herein may have the following meanings:
(47) Resolution: A resolution of a cell map defines the dimension of a single unit of the sample. Resolution is generally a function of a final memory footprint that one might have after sampling the data performance characteristics required by that particular application (resolution factor and performance are inversely proportional). Application requirements may differ in that some cell map applications may require smaller resolution while other applications may work well without more data.
(48) Geometry Cell: A geometry cell is quantized geometry data representing information in a single square block of resolution unit. It is the smallest tangible unit of information about a certain entity. A cell is generated from quantizing/sampling/discretizing geometry entities. Further, a geometry cell may consist of a 64-bit value/number that represents the domain-specific information about the entity.
(49) Geometry cell map/cell map: Cell maps are a collection of geometry cells stored in a contiguous array. Collectively, cell maps define an object in a quantized/discrete form. Further, a cell map is a function of a resolution factor.
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(53) Logical Flow
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(55) At step 502, a drawing is obtained and consists of two or more entities.
(56) At step 504, a resolution is determined (e.g., based on paper space).
(57) At step 506, based on the resolution, the drawing is quantized into a cell map. The cell map is a collection of multiple cells stored in a contiguous memory. Each of the multiple cells is quantized geometry data for a corresponding entity of the two or more entities. Each of the multiple cells is a smallest tangible unit of information about the corresponding entity. Each of the multiple cells is a number that represents domain specific information about the corresponding entity. In addition, the quantizing rounds off values beyond a specified threshold to a resolution tolerance of the resolution.
(58) At step 508, the cell map is utilized to automate modifications to the drawing.
(59) Resolution Calculation
(60) Resolution is an important factor that decides the size of the cell-map. When the cell-map is too huge (which can happen when the resolution is small), the memory consumption increases and the performance degrades. When the cell-map is too small (which can happen when the resolution is large), accurate results may not be acquired. To solve this, embodiments of the invention utilize a method for calculating resolution.
(61) Internally, based on the cell-size flag, embodiments may cap the maximum rows or columns that can be in a cell-map. Accordingly, at step 606, a maximum number of rows or columns for the cell map is determined.
(62) Based on the maximum rows or columns value and the bounding box, the resolution can be back calculated (at step 608) as:
resolution=max(bounding-box-length,bounding-box-width)/maximum-rows-or-columns
(63) In an example, assume that the size of the part is 250 mm18 mm with the resolution set for the optimal sized cell-map. With a scale of 1:2, the size of the view in the paper space will be 125 mm9 mm, the number of cells in the cell-map is 51238 and the resolution can be back-calculated as 0.24. With a scale of 1:1, the size of the view in the paper space will be 250 mm18 mm, the number of cells in the cell-map is 51238, and the resolution can be back-calculated as 0.49. If the scale is 2:1, the size of the view in the paper space will be 500 mm36 mm, the number cells in the cell map is 51238, and the resolution can be back-calculated as 0.97.
(64) Cell Map Utilization
(65) Once a cell map has been generated (i.e., the geometry has been quantized), there are multiple different ways to automate the drafting/drawing process (i.e., based on/utilizing the cell map [step 508 of
(66) Auto Break
(67) The Auto Break functionality is the ability to find redundancy in a long part, and break the part if the information redundancy exceeds a defined threshold.
(68) However, it is not desirable to remove all of the redundancy as doing so would result in a part that does not look aesthetically correct.
(69) When determining whether to apply a break to a part or not, or how many breaks to apply, embodiments of the invention may attempt to not overuse the break mechanism. For example, a setting may ensure that the total % of a part removed with auto-break is more than a minimum total break percentage.
(70) The minimum total break percentage sets a minimum percentage limit on the total redundancy. However, because of this, there may be some undesirable results if the total extends beyond this minimum but the individual portions are small, which creates too many breaks rendering the entire functionality useless. Embodiments of the invention may prevent such an anomaly by setting a minimum individual break percentage which places a limit on how big the individual redundant portions need to be before the auto break is applied.
(71) In
(72) Further to the above,
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(74) At step 1004, views are collected to break. The breaks are inherited from a projected view so embodiments of the invention may look at the projected view (when identifying the breaks) to make sure that anything important in another view is not being removed.
(75) Embodiments of the invention may only compute for vertical breaks. Accordingly, at step 1006, if a part is found that has more height than width, the part may be rotated by 90 degrees.
(76) At step 1008, the geometry is isolated in the cell map. Cell maps contain a lot more information than just geometry. Accordingly, embodiments of the invention may run a mask through the cell map isolating just the geometry components in it.
(77) At step 1010, the collected view cell maps are merged together.
(78) At step 1012, unique sections in the views are identified via the cell map.
(79) The output of the process are the broken view parameters. These parameters define what percentage of parts needs to be removed because of redundancy for the part that is to be broken (e.g., it may also return the parameters of the unique sections in the part).
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(81) Before reviewing the details of
(82) (1) Finding out where to apply a break: Referring again to
(83) (2) Finding out where to put dimensions: Please refer again to
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(85) As the cell map is traversed horizontally, it is traversed incrementally. The process starts by comparing the nth Column to the n+1th Column. Then the process tries with (n) & (n+1)th column with (n+2)&(n+3) column. The process then continues increasing. Thus, the process starts with n to (n+m)th column and compares that bunch with (n+m+1) to (n+m+m) ie (n+2m) columns. In other words, the process starts by comparing the 1.sup.st and 2.sup.nd columns and increments up comparing groups of columns (e.g., the first 6 columns with the next 6 columns). As used hereinthe m value is a constant referred to as the maxWavelengthToTry.
(86) As the comparison is conducted, the system takes into account the inherent approximation that happens once the cell map world is entered. In this regard, embodiments of the invention may sometimes check not just the intended cell, but also the adjacent cell. A tolerance value gives clients a way to modify the value of how many adjacent cells need to be checked. Embodiments of the invention may set a default value of 1, which is moderate checking, where precedence is given to the exact cell, but if that fails, 8 adjacent cells are checked (i.e., three in the above row, three in the below row, and two cells on the side). In this regard, a tolerance level for comparing values in the cell map may be strict (e.g., 0), moderate (e.g., 1), or relaxed (e.g., 2).
(87) At step 1102, the system checks to determine the inputs are correct. More specifically, embodiments confirm that a defined wavelength is correct with respect to the cell map. For example, if 1 (i.e., the defaults value) is the value for maxWavelengthToTry, attempts are made to set the maxWavelengthToTry to the number of columns in the cell map divided by 4. Four (4) is picked because that is the maximum value that could ever be the wavelength of the unique-section, any more than that is logically not possible or useful. A check may also be made to ensure that the tolerance is not too high for smaller cell maps (i.e., confirming that a defined tolerance is correct with respect to the cell map).
(88) At step 1104, the cell map is traversed while comparing columns (up to the maxWaveLengthToTry column). The comparison is based on the defined tolerance (e.g., strict, moderate, or relaxed) to determine a number of consecutive matching values. In this regard, a comparison is made incrementally across the cell map columns from 1-1 columns to maxWavelengthToTry and the results are stored in a data structure count (e.g., Consecutive Count Values). This data structure count stores the values in the form of how many matches it saw consecutively while traversing the columns. In other words, the process finds out how data varies column by column at step 1106.
(89) At step 1108, once all the columns have been processed (one by one), the chunks/groups of columns are checked to see if they are same. The maximum chuck of columns may be defined via a constant value (e.g., maxWavelengthToTry).
(90) Once all such traversals are done the location of the maximum consecutive matches is identified/determined and the final value of the defined wavelength (corresponding to that location) is retrieved and returned at step 1110.
(91) Overlap Detection and Automatic Arrangement
(92) Arranging dimensions is a time-consuming and multi-click operation. Arranging dimensions should also take care of the annotations like hole and thread notes, wield symbols, and notes. Further, when arranging dimensions, the arrangement should take care of overlaps. Embodiments of the invention provide for overlap detection and automatic arrangement which provides the capability to determine where objects overlap with one another and quickly finding a better place for the objects where they don't overlap. Such capabilities may be used to automatically arrange dimensions, hole and thread notes, and/or other annotations. Further, views may be nested on a sheet and scaled up/down if they overlap.
(93) As described above, arranging dimension is more time consuming than actually creating dimensions. Arranging requires a precise drag and a click and repeated moves if another dimension is added that conflicts or just moves the views around. Thus, it is important to automate this task. There are various types of dimensions (also referred to as dims): (a) linear dims that can only be moved in vertical or horizontal direction; (b) aligned dims that can be moved in a certain angle; and (c) radial and diametric dims that can be moved in 360 degrees. The goal however for the movement is simple: the dimension should be closer to the entity that it is measuring, it should be compact but as per standard and it should not overlap with any other dimension, annotation or view. Abiding by these simple rules provides an algorithm to arrange the dimensions.
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(95) At step 1202, the input (i.e., the view in question, the annotations, and dimensions to be arranged) are processed to create a cell map.
(96) At step 1204, the cell map 1302 is masked to remove a set of (unnecessary) details (e.g., that the system does not want to see from it).
(97) As it is not desirable to have dimensions and annotations inside of the view boundary, at step 1206, a filled cell map is created out of the masked cell map 1304 and the filled area is excluded form putting any dimension/annotation in.
(98) At step 1208, the process iterates over each and every dimension to try and find a place for it on the sheet. While iterating, the dimension to be nested is cloned. This way, the dimension cell map is at the location where it is at and the text length required is known. Before the process proceeds to nesting the dimension, a check may be made to determine if the nesting has been overridden for the auto-arranging. Steps 1210-1216 provide the details for the iterating step 1208.
(99) At step 1210, once the dimension to be nested has been identified, the potential candidate locations for the dimension are identified. In addition, in step 1210, a line of the potential points (for the potential candidate locations) is created and called a line cell map. The line cell map is then superimposed on the view itself.
(100) Once this is superimposed, step 1212 identifies the intersections of these two cell maps 1308 and 1310.
(101) Once these sorted locations are found, step 1214 tries each location (in the subset of sorted locations) to find/identify a candidate location where there isn't any overlap.
(102) At step 1216, the candidate locations (in the subset) without any overlap are found (e.g., such as that in
(103) Automatic Nesting
(104) Placing views aesthetically is important for drawings, and most of the user clicks happen to achieve this. Nesting is the process of placing the views on the sheet in a compact way that does not overlap. Automatic Nesting (or Auto-Nest) automates the view placement workflow and tidies up the drawing. However, it may be noted that Auto-Nest may not be useful unless it is paired with auto arrange as described herein because auto-arrange is the process where as a user we get to know how many dimensions are getting created for a drawing view, until we know that, placing the views will not make sense. In this regard, when scaling or nesting, embodiments of the invention provide for an area (i.e., empty space) kept for annotation and dimensions around the view (referred to as padding). The more details that are in a view, the more space that is required and thus, more space to be left around the part/view. Further, some parts may have details on just one side of a part and another side might just be plain. Accordingly, embodiments of the invention dynamically calculate the area around a part looking at the region and looking at the details in that region.
(105) The Auto-Nest algorithm takes all of the base views in the sheet as input. With all of the base views collected in a list, the first order of business is to sort these views according to the area they need. Here, embodiments of the invention consider the entire area of the view-chain (i.e., base view along with its orthographic views). In this regard, when dealing with model views, they usually come in as a bunch (i.e., base view+its projected views). Embodiments of the invention may maintain the relative position of the model views from one another. Accordingly, views are not nested. Instead, view-chains are nested.
(106) Once all of the base views are sorted, the first base view is selected and the others in the list may be ignored and/or removed from the sheet itself. For the selected base view, the Auto-Nest operation begins. All the child views from the base view are collected and the process begins generating the cell maps. At the same time, the cell map for the sheet is collected. For the first view the sheet cell map will be empty.
(107)
(108) At step 1402, one or more views are collected from the drawing.
(109) At step 1404, paddings for the one or more views are determined/computed and a tuple is created. Depending on the setting, the padding may be computed for the view and stored in a data structure. As used herein, a padding defines a measure of how much distance is desired between two views.
(110) At step 1406, depending on where the view is, the paddings are applied to the view's cellmap. In this regard, all of the views that are vertical and horizontal are collected (e.g., the top, front, and bottom view are collected). Right front and left view may be collected into separate lists. Depending on the maximum padding, the same padding may be generated across all of the views. Step 1404 outputs a combined cell map (i.e., a combination of the padded cell maps) which is a good way of visualizing the nesting.
(111) At step 1406, the padded cell maps are merged/condensed/squished together to form a constellation (also referred to as a merged padded cell map). The function to merge the cell maps together takes a base view as input and makes other views latch onto the sides of it.
(112) At step 1408, a sheet cell map without the (one or more) views is identified/found.
(113) At step 1410, empty spaces in the sheet cell map where the merged padded cell map/view fits (without overlapping anything) are identified/found. In one or more embodiments, white space within a sheet cell map may be used to identify where the cell map can fit that won't overlap with anything.
(114) Automatic Views, Dimensioning Algorithms and Finding Unique Sections, Loops, Symmetry, and Intersections
(115) Automatic views enable the ability to determine if two views are fungible in nature, and if so, only keeps one of the views. The dimensioning algorithms enable the auto-dimensioning (described above). In one or more embodiments, the dimensioning algorithms provide for the cell map exploding loops and starting dimensions per loop instead of over an entire view. Further, symmetry in the loops can be identified to reduce dimensions for symmetric geometry. In addition, a drawing view may be overlaid on top of another drawing view of the same component in order to identify if the geometry is identical. Once distinct (non-identical) geometry is identified, dimensioning may be limited to the Boolean intersection of the view.
(116) Automatic Views
(117) In a template-based workflow, which views need to be stamped for a particular component are defined. However, this is not efficient considering that it is not known before hand how many views are required for a certain component. Auto View functionality solves this problem by automatically figuring out which views are needed.
(118) As an example,
(119) The auto view algorithm runs on already-created views and takes two views as inputsa base view and a projected view array.
(120) At step 1704, the views are grouped in horizontal and vertical groups. The base view is made common to both of the groups.
(121) At step 1706, a check is conducted to determine if any of the view representations in each group are fungible and can be removed.
(122) At step 1708, any views that are identified as fungible are removed. The process is complete at step 1710.
(123)
(124) At step 1802, the cell maps are compared to see if they have a different cell count. If they have a different cell count, then they are not the samei.e., they are not fungible and the process exits early at step 1804.
(125) If the cell count is the same, a union of the two inputs is conducted at step 1806.
(126) At step 1808, a determination is made if the union matches a superset or a subset. As used herein, the superset and subset are the two input cell maps.
(127) If there is a match, then it is like one cell map is contained in another as indicated at step 1810.
(128) If there is no match, then rotated versions of the input cell maps are compared at step 1812 to see if they match. In other words, a horizontally mirrored version and vertically mirrored versions are compared. In addition, step 1810 may include checking if two cell maps match with 90 degree rotated versions of one another. If no matches are found the views are not fungible as indicated at step 1804. If matches are found the views are determined to be fungible at 1814.
(129)
(130) In addition, auto views does one more level of checking. In particular, with the front view, it checks if the winner (that view that is not removed) OR both views (where neither view is removed) from the left view and the right view are fungible with the front view or the mirrored version of the front view. In such cases where fungibility is found, the left or right view may be removed giving priority to the front view. In one or more embodiments, auto view never removes the front view considering that view to be the base view which means all other views are referring to that view (i.e., it tries to maintain the view chain intact).
(131) Autocut Views (Detail and Section Views)
(132) With auto views, orthographic views can be identified and generated. However, for some parts, orthographic views alone are insufficient to fully define a part. In particular, a detailed view and/or section may be needed. Embodiments of the invention identify if a detailed view is needed and where it should be placed. The general idea is that if a part has a lot of entities squished together in a small area, it should require a detailed view as an orthographic view of the entire part may make these details hard to dimension. A default minimum dimension length may be used to identify which length should be considered small. To identify the need of detailed view, embodiments of the invention may use HeatMaps. Heatmaps are generated while the cellmap is generated and are embedded inside the cellmap. The first four (4) values of a cell encodes this. A heatmap is a measure of how many times a certain cell has been hit with distinct information. As an example, if the same cell contains an end point of a line and a start point of an arc, the cell has heat count of 2. Similarly if there is another edge close to it with the distance less than the resolution, it may also contribute in increasing the heat-count. If in the view, it is observed that a lot of nearby cells have higher-heat maps, then it means that there is a concentration of information in that small area. Such a concentration suggests the need for a detailed view. Further, one or more embodiments may determine that a section view is needed if a part is circular and turned and symmetric.
(133) The input for a section view algorithm are the view identifications (IDs) and the output is the sectioned views.
(134) At step 2002, it is determined if the number of bodies is greater than one (1) (i.e., the number of bodies should be just one). In other words, the algorithm identifies one body in the drawing.
(135) At step 2004, a determination is made whether the cell map has at least two distinct loops. In this regard, the cell map should have multiple loops considering that if there is just one single loop, the section view will not show anything the users don't already know.
(136) At step 2006, a determination is made regarding whether the loops are symmetric across the same axis.
(137) At step 2008, the same x-horizontally symmetric loops are grouped together and the same y-vertically symmetric loops are grouped together.
(138) At step 2010, groups are created using unique cells of the cell map.
(139) At step 2012, a determination is made regarding whether the loops have the same center.
(140) If the loops have the same center (i.e., they are concentric), the part is identified as a turned component and needs a section view at step 2014. If the various determinations fail, no section view is required as set forth at step 2016. In one or more embodiments, the process learns from a user's previous data whether a user desires to use a detailed view and/or whether squished details are important/not important to them. Accordingly, embodiments of the invention may utilize machine learning to identify over time whether or not to generate a section and/or a detailed view.
(141) Cell Map Based Loop Generator Algorithm
(142) Step 2004 determines whether there are multiple distinct loops. In this regard, embodiments of the invention must first identify loops in a drawing (e.g., so that the dimension generator algorithms can generate dimensions per loop). The loop detection is especially useful for point based dimension generation algorithms (e.g., horizontal/vertical chain/baseline dimensions). Loop detection avoids the number of dimensions between the loops. This section provides an algorithm for detecting loops.
(143) As an example, take a part such as that illustrated in
(144)
(145) Same Loop Checker Algorithm
(146) Referring again to
(147)
(148) Symmetric Dimension Fungibility Algorithm
(149) This is a type of dimension priority algorithm. It looks for the symmetric dimensions and makes them fungible.
(150)
(151)
(152) Symmetric with Baseline Dimension Generator Algorithm
(153) This algorithm is a type of dimension generator algorithm and runs on all of the symmetric contours about the x or y-axis.
(154) In
(155) Unique Section Generator Algorithm
(156) This algorithm finds the unique section and stores it in a data structure. the algorithm runs on a per loop basis and finds unique sections in that loop and populates the data structure accordingly. There are two types of geometrycircular and linear. Cell maps are used to find whether a loop is circular or linear. A sequence of cell map operations are used to find the unique section.
(157)
(158) If a loop is circular, the circular cell map is unwrapped to give the linear cell map at step 2618. The unique section is then identified at step 2620 (i.e., via steps 2604, 2606, and 2610-2614). If originally circular, the stored unique section is then wrapped to get the circular cell map at step 2622. Thereafter, the data is stored at step 2624.
(159) Each unique section may have periodicity information and in one or more embodiments, only the first unique section may be stored.
(160)
(161)
(162) Hardware Embodiments
(163)
(164) In one embodiment, the computer 2902 operates by the hardware processor 2904A performing instructions defined by the computer program 2910 (e.g., a computer-aided design [CAD] application) under control of an operating system 2908. The computer program 2910 and/or the operating system 2908 may be stored in the memory 2906 and may interface with the user and/or other devices to accept input and commands and, based on such input and commands and the instructions defined by the computer program 2910 and operating system 2908, to provide output and results.
(165) Output/results may be presented on the display 2922 or provided to another device for presentation or further processing or action. In one embodiment, the display 2922 comprises a liquid crystal display (LCD) having a plurality of separately addressable liquid crystals. Alternatively, the display 2922 may comprise a light emitting diode (LED) display having clusters of red, green and blue diodes driven together to form full-color pixels. Each liquid crystal or pixel of the display 2922 changes to an opaque or translucent state to form a part of the image on the display in response to the data or information generated by the processor 2904 from the application of the instructions of the computer program 2910 and/or operating system 2908 to the input and commands. The image may be provided through a graphical user interface (GUI) module 2918. Although the GUI module 2918 is depicted as a separate module, the instructions performing the GUI functions can be resident or distributed in the operating system 2908, the computer program 2910, or implemented with special purpose memory and processors.
(166) In one or more embodiments, the display 2922 is integrated with/into the computer 2902 and comprises a multi-touch device having a touch sensing surface (e.g., track pod or touch screen) with the ability to recognize the presence of two or more points of contact with the surface. Examples of multi-touch devices include mobile devices (e.g., IPHONE, NEXUS S, DROID devices, etc.), tablet computers (e.g., IPAD, HP TOUCHPAD, SURFACE Devices, etc.), portable/handheld game/music/video player/console devices (e.g., IPOD TOUCH, MP3 players, NINTENDO SWITCH, PLAYSTATION PORTABLE, etc.), touch tables, and walls (e.g., where an image is projected through acrylic and/or glass, and the image is then backlit with LEDs).
(167) Some or all of the operations performed by the computer 2902 according to the computer program 2910 instructions may be implemented in a special purpose processor 2904B. In this embodiment, some or all of the computer program 2910 instructions may be implemented via firmware instructions stored in a read only memory (ROM), a programmable read only memory (PROM) or flash memory within the special purpose processor 2904B or in memory 2906. The special purpose processor 2904B may also be hardwired through circuit design to perform some or all of the operations to implement the present invention. Further, the special purpose processor 2904B may be a hybrid processor, which includes dedicated circuitry for performing a subset of functions, and other circuits for performing more general functions such as responding to computer program 2910 instructions. In one embodiment, the special purpose processor 2904B is an application specific integrated circuit (ASIC).
(168) The computer 2902 may also implement a compiler 2912 that allows an application or computer program 2910 written in a programming language such as C, C++, Assembly, SQL, PYTHON, PROLOG, MATLAB, RUBY, RAILS, HASKELL, or other language to be translated into processor 2904 readable code. Alternatively, the compiler 2912 may be an interpreter that executes instructions/source code directly, translates source code into an intermediate representation that is executed, or that executes stored precompiled code. Such source code may be written in a variety of programming languages such as JAVA, JAVASCRIPT, PERL, BASIC, etc. After completion, the application or computer program 2910 accesses and manipulates data accepted from I/O devices and stored in the memory 2906 of the computer 2902 using the relationships and logic that were generated using the compiler 2912.
(169) The computer 2902 also optionally comprises an external communication device such as a modem, satellite link, Ethernet card, or other device for accepting input from, and providing output to, other computers 2902.
(170) In one embodiment, instructions implementing the operating system 2908, the computer program 2910, and the compiler 2912 are tangibly embodied in a non-transitory computer-readable medium, e.g., data storage device 2920, which could include one or more fixed or removable data storage devices, such as a zip drive, floppy disc drive 2924, hard drive, CD-ROM drive, tape drive, etc. Further, the operating system 2908 and the computer program 2910 are comprised of computer program 2910 instructions which, when accessed, read and executed by the computer 2902, cause the computer 2902 to perform the steps necessary to implement and/or use the present invention or to load the program of instructions into a memory 2906, thus creating a special purpose data structure causing the computer 2902 to operate as a specially programmed computer executing the method steps described herein. Computer program 2910 and/or operating instructions may also be tangibly embodied in memory 2906 and/or data communications devices 2930, thereby making a computer program product or article of manufacture according to the invention. As such, the terms article of manufacture, program storage device, and computer program product, as used herein, are intended to encompass a computer program accessible from any computer readable device or media.
(171) Of course, those skilled in the art will recognize that any combination of the above components, or any number of different components, peripherals, and other devices, may be used with the computer 2902.
(172)
(173) A network 3004 such as the Internet connects clients 3002 to server computers 3006. Network 3004 may utilize ethernet, coaxial cable, wireless communications, radio frequency (RF), etc. to connect and provide the communication between clients 3002 and servers 3006. Further, in a cloud-based computing system, resources (e.g., storage, processors, applications, memory, infrastructure, etc.) in clients 3002 and server computers 3006 may be shared by clients 3002, server computers 3006, and users across one or more networks. Resources may be shared by multiple users and can be dynamically reallocated per demand. In this regard, cloud computing may be referred to as a model for enabling access to a shared pool of configurable computing resources.
(174) Clients 3002 may execute a client application or web browser and communicate with server computers 3006 executing web servers 3010. Such a web browser is typically a program such as MICROSOFT INTERNET EXPLORER/EDGE, MOZILLA FIREFOX, OPERA, APPLE SAFARI, GOOGLE CHROME, etc. Further, the software executing on clients 3002 may be downloaded from server computer 3006 to client computers 3002 and installed as a plug-in or ACTIVEX control of a web browser. Accordingly, clients 3002 may utilize ACTIVEX components/component object model (COM) or distributed COM (DCOM) components to provide a user interface on a display of client 3002. The web server 3010 is typically a program such as MICROSOFT'S INTERNET INFORMATION SERVER.
(175) Web server 3010 may host an Active Server Page (ASP) or Internet Server Application Programming Interface (ISAPI) application 3012, which may be executing scripts. The scripts invoke objects that execute business logic (referred to as business objects). The business objects then manipulate data in database 3016 through a database management system (DBMS) 3014. Alternatively, database 3016 may be part of, or connected directly to, client 3002 instead of communicating/obtaining the information from database 3016 across network 3004. When a developer encapsulates the business functionality into objects, the system may be referred to as a component object model (COM) system. Accordingly, the scripts executing on web server 3010 (and/or application 3012) invoke COM objects that implement the business logic. Further, server 3006 may utilize MICROSOFT'S TRANSACTION SERVER (MTS) to access required data stored in database 3016 via an interface such as ADO (Active Data Objects), OLE DB (Object Linking and Embedding DataBase), or ODBC (Open DataBase Connectivity).
(176) Generally, these components 3000-3016 all comprise logic and/or data that is embodied in/or retrievable from device, medium, signal, or carrier, e.g., a data storage device, a data communications device, a remote computer or device coupled to the computer via a network or via another data communications device, etc. Moreover, this logic and/or data, when read, executed, and/or interpreted, results in the steps necessary to implement and/or use the present invention being performed.
(177) Although the terms user computer, client computer, and/or server computer are referred to herein, it is understood that such computers 3002 and 3006 may be interchangeable and may further include thin client devices with limited or full processing capabilities, portable devices such as cell phones, notebook computers, pocket computers, multi-touch devices, and/or any other devices with suitable processing, communication, and input/output capability.
(178) Of course, those skilled in the art will recognize that any combination of the above components, or any number of different components, peripherals, and other devices, may be used with computers 3002 and 3006. Embodiments of the invention are implemented as a software/CAD application on a client 3002 or server computer 3006. Further, as described above, the client 3002 or server computer 3006 may comprise a thin client device or a portable device that has a multi-touch-based display.
CONCLUSION
(179) This concludes the description of the preferred embodiment of the invention. The following describes some alternative embodiments for accomplishing the present invention. For example, any type of computer, such as a mainframe, minicomputer, or personal computer, or computer configuration, such as a timesharing mainframe, local area network, or standalone personal computer, could be used with the present invention.
(180) In view of the above, embodiments of the invention quantize the geometry and represent such quantization in a unique form (referred to as a geometric cell map). Geometry data is usually represented as conventional geometry data structures like lines, arcs, and circles. The quantized form for the drawing domain specifically is unique. The determination of human intuition is achievable via the use of cell maps which is a concept that is platform, application, and language agnostic. Further, cell maps may be improved over time, are flexible, operations can be performed in parallel, and multiple algorithms can run simultaneously while providing end users with choices to select a preferred option. In addition, the manner in which embodiments of the invention identify how to break a part is unique. Further, finding out/identifying the unique section within a part is also unique. Such capabilities may same almost 40-60% of the time users may have to spend to perform the same operations manually.
(181) The foregoing description of the preferred embodiment of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the invention be limited not by this detailed description, but rather by the claims appended hereto.