A Method for Slicing CAD Models in Binary STL Format

Transcription

1 6 th International Advanced Technologies Symposium (IATS 11), May 2011, Elazığ, Turkey A Method for Slicing CAD Models in Binary STL Format O. Topçu 1, Y. Taşcıoğlu 2 and H. Ö. Ünver 3 1 TOBB University of Economics and Technology, Ankara, Turkey, otopcu@etu.edu.tr 2 TOBB University of Economics and Technology, Ankara, Turkey, ytascioglu@etu.edu.tr 3 TOBB University of Economics and Technology, Ankara, Turkey, hounver@etu.edu.tr A Method for Slicing CAD Models in Binary STL Format Abstract Additive manufacturing (AM) is a technique that fabricates parts layer by layer. As a result of this capability it offers numerous advantages over conventional manufacturing. This study concentrates on the process planning stage of the AM and presents a STL slicing algorithm with the ability of G-Code generation.. This work is part of a wider range effort concentrated on the development of an open-source AM system therefore a special emphasis is given that all the software packages used in this study are freely available open source or freeware applications. The results and the capability of the code are tested by simulating several complex 3D geometries on a CNC milling simulator software using the generated G-Codes. Keywords direct slicing, additive manufacturing, rapid prototyping, STL format. A I. INTRODUCTION dditive manufacturing (AM) is defined by a range of technologies in which solid objects are built, directly from 3D CAD models, by adding material in thin layers. Physical parts which are technically impossible to produce with conventional manufacturing methods are produced in hours instead of days and at a relatively low cost. AM is commonly used for producing prototypes of consumer products, patterns for molds, and medical implants. Research on the AM technology, which is also called as Rapid Prototyping (RP), Layered Manufacturing (LM), and Solid Freeform Fabrication (SFF), has been carried out in various universities and companies for over 20 years. A small list of some known commercial AM technologies includes: Stereolithography (SLA) by 3D Systems, Selective Laser Sintering (SLS) by DTM Corp., Fused Deposition Modeling (FDM) by Stratasys Corp., Solid Ground Curing (SGC) by Cubital and Laminated Object Manufacturing (LOM) by Helisys. In addition to known commercial solutions, quite a few AM processes are under development at various universities such as University of Texas, University of Michigan, University of Dayton, MIT, Stanford and Carnegie Melon University [1]. The focus of the research is not only on production methods but also on data exchange standard and process planning. The STL (STereoLithography) file format is considered to be the de-facto data exchange standard for AM processes. Most, if not all, of the major 3D CAD software packages provide tools for STL conversion. The conversion is the execution of a surface triangulation algorithm which is used frequently in finite element analysis since 1970s. Shortcomings of the STL format in terms of processing time and precision set a barrier for demanding engineering projects [1-4]. While some researchers suggest improvements to the STL, others adapt existing CAD formats to AM or propose new formats. Jacob et al. [4] reduce the file size of current STL format by eliminating the repeating vertices data. Zhang et al. [5] use models in IGES format in their welding-based AM system. In [6], a new format PLY (Polygon File Format) is described. In applications where high quality surfaces and dimensional tolerances are less important, the STL format is well researched and is appropriate for professional and nonprofessional users of the area. Process planning includes the steps for slicing the part s triangulated surfaces, tool path generation for each layer and conversion of the tool path data to a suitable CNC data (i.e G- codes). The latter two steps are well established and are almost identical to CNC milling. Different part slicing algorithms for various AM processes exist in literature. Adaptive slicing [7] is one of the approaches to resolve the inevitable staircase effect by using variable layer thickness. The algorithm proposed by Asiabanpour and Khoshnevis [8] saves each layer data on a separate file in order to ease path generation process and free computer memory. In [9], a slicing method suitable for Color Rapid Prototyping (CRP) is presented. There is presently no industrial or formal standard for slice data. As a result the developer or the manufacturer is free to decide on or to develop its own slice data to create the required AM machine code. Some proposals made in this area are listed below. Common Layer Interface (CLI) represents each layer by its thickness and a set of contours. These contours define the boundaries of the solid material; Layer Exchange ASCII Format (LEAF) provides each contour as a non-self-intersecting polygon described in terms of 2D primitives, the polyline and the circular arc; SLC Formats represents several slice formats that use a polyline approximation to represent the contours of a slice. 141

2 O. Topçu, Y. Taşcıoğlu, H. Ö. Ünver The AM process is mainly composed of three stages. First stage includes 3D CAD modelling and STL conversion. Nowadays this stage can be performed by using the freely available 3D modeling software such as SketchUp 3D, CoCreate Modeling Personal Edition, PowerShape-e and etc. Second stage is the aforementioned process planning and the final stage is the production of the physical part, which depends entirely on the AM machine. This study concentrates on the second stage of the AM process and presents a STL slicing algorithm with the ability of G-Code generation. Special emphasis is given that all the software packages used in this study are freely available open source or freeware applications. This work is part of a wider range effort concentrated on the development of an opensource AM system. II. SLICE ALGORITHM z x y imported STL data of the solid body. Given the oriented model, part slicing is a purely geometrical process. Figure 2 exemplifies possible relations between the slicing plane and a facet of the model. From this point on the mentioned facet will be labeled as F, its vertices as V 1, 2, 3 (x 1, 2, 3, y 1, 2, 3, z 1, 2, 3 ) and its normal vector as N (x n, y n, z n ). Normal vectors are required for graphical visualization of the solid body on a computer screen hence they are omitted in the slicing algorithm. According to slicing plane s z-axis value, z, there are five kinds of different positional relationships between the slicing plane and the corresponding facet, as shown in Fig.2 and listed below: 1) z 1 =z & z 2 =z & z 3 =z. 2) z 1 =z & z 2 =z & (z 3 <z z 3 >z). 3) z 1 =z & ((z 2 <z & z 3 >z) (z 2 >z & z 3 <z)). 4) ((z 1 <z) & ((z 2 >z & z 3 >z)) ((z 1 >z) & ((z 2 <z & z 3 <z)). 5) z 1 =z & ((z 2 >z & z 3 >z) (z 2 <z & z 3 <z)). When the numbering is different from the listed above, the algorithm that is given in Fig.3 is capable of handling facets, which are randomly numbered as a circumstance. z y x Figure 1: (a) a tessellated cube with twelve facets (b) a sliced cube Fig.1(a) shows triangulated STL representation of the surfaces of a simple cube shape and Fig.1(b) shows the sliced cube. The algorithm is mainly developed to obtain the contour data of the desired slicing level. As a result it does not have the capacity to slice some facets which are positioned between two slicing planes where no intersections occur. This problem can be solved easily by reducing the gap between two adjacent slices. The gap between two adjacent slices defines the layer thickness which is also a variable inside the code. Also, the software code is developed based on the assumption that the STL representation of the geometry is error-free and is normally assigned in the z-direction. Intentionally, the developed code has divided into separate sections which carry on the calculations based on the results of the previous section. This approach has simplified the debugging process and made the code more flexible when needed. A. Intersection Point Detection Figure 2: Possible intersection cases. The first section of the slicing algorithm finds intersection points between equally set apart slicing planes and the Figure 3: Intersection detection algorithm. In CASE I, F is parallel to the base and there is no need to calculate the intersection points because all three vertices of the F represent the intersection points. In CASE II, F touches the slicing plane with its two vertices and these two points are the intersection locations. The stored information are values of these vertices and the z value of the remaining vertex which will be used to determine whether to keep or dispose of the data. In CASE III, one of the F s vertices touches the slicing plane. The other intersection must be calculated using the 142

3 A Method for Slicing CAD Models in Binary STL Format common form of the well-known line equation (1), where the slope is m and the two variables are x and y. y mx b (1) Also in CASE III, z values of the corresponding points are both stored as 1. This coding enables the algorithm to identify the obtained line data in further parts of the code. In CASE IV, none of the F s vertices touch the slicing plane but one of the vertices is on the other side of the plane when observed from x- or y-axis. As expected, this case creates two intersection points. Obtained line data is also labeled with 1. In CASE V, F touches the slicing plane at a single point which is redundant information for the rest of the code. The outputs of the intersection point detection algorithm are all the points that are located on the slicing plane with the appointed classification reference values. B. Line Creation Fig.4 shows the algorithm that is designed to create lines of the slice data with mentioned z values. Line creation algorithm is similar to the intersection detection. For simplicity and future improvement possibilities, it is kept apart as another process. Figure 4: Line generation algorithm. The outputs of the line creation algorithm are lines that are obtained from the outputs of the aforementioned intersection point detection algorithm with the appointed classification reference value. Tab.1 show the column labels of the output data arrays where the number of points and the number of lines represent the row count of the array. The z values are unchanged and the two associated points are grouped to create a line. Table 1: Output data of intersection point detection and line creation algorithms. Output Data Point Array Line Array Column labels of the array x, y, z ref x 1, y 1, x 2, y 2, z ref C. Repeating Lines Fig.5 represents the overlapping lines which happen when two facets share an edge at the slicing plane. Figure 5: Repeating line cases. As an example, top of a cube can have 10 lines after line creation algorithm. However, only a polygon with 4 lines is enough to draw the boundary of the cube. In order to define interior and exterior of the contour the repeating lines must be removed or merged into a single line. At this point, previously stored z values 2, 1 and the reference values play important role. Comparison between these values and the z values of the overlapping lines z Line1 and z Line2 are listed below: 1) z Line1 = 2 & z Line2 = 2. 2) (z Line1 = 2 & z Line2 2) (z Line1 2 & z Line2 = 2). 3) ((z Line1 < z & z Line2 > z) (z Line1 > z & z Line2 < z)). 4) ((z Line1 <z) & (z Line2 <z)) ((z Line1 >z) & (z Line2 >z)). In the first case (Fig.5 CASE A), neither line represent any boundary. Therefore these two lines are removed from the data that is going to be used to obtain the edge contour at that slicing plane. In the second case (Fig.5 CASE B), both lines reside in the same edge and one of them is removed from the data. As a fact, which line is removed does not matter; because the obtained contour data does not need any reference information. In the third case (Fig.5 CASE C), both lines share the same edge that is actually a boundary for the solid body. Consequently one of the lines is removed. In the fourth case (Fig.5 CASE D), the edge does not represent any boundary of the solid body when the slicing plane is considered. In other words, this data do not create a closed area which is going to be built in the AM process at further points. As a result both lines are removed from the contour data. The outputs of the repeating lines algorithm are the lines that represent the boundary of the solid body on the slicing plane. D. Line Grouping Line grouping is rearranging of the vertices so that they form a closed loop. This closed loop defines the hatch path boundary. Hatch path generation is necessary when additive or subtractive building processes are used to shorten production time and improve surface quality. In this study, line grouping is omitted because the aim of this study is to understand slicing procedure and use this knowledge as a base for future AM process developments. 143

4 O. Topçu, Y. Taşcıoğlu, H. Ö. Ünver E. G-Code Generation This section of the algorithm uses the standard milling G- Code. Only a few machine codes are sufficient to create the required slice as a layer of the whole artifact. The algorithm traces the whole slice data along the x-axis of the slicing plane for every y-axis value and then marks the required locations. In other words, it detects the locations of start and end points of the building process. If the STL file is error free, there exists an even number of marking points. The schematic illustration of G-Code generation obtained from a hollow cube solid is given in Fig.6 The dark, thick lines symbolize the solid and the light, thin lines symbolize the empty space. difference between the figures is due to difference in the step size of the G-Code. The step size is determined from the dimensions of the workpiece. Fig.8 shows one of the slices of the whole part obtained from a 100mm x 100mm x 1mm dimensioned rectangular solid. Fig.9 shows one of the slices produced from a 1m x 1m x 1mm in size solid rectangular workpiece. Figure 8: CNC milling simulation result of logo 1. Figure 6: (a) Schematic illustration of G-Code generation and (b) resultant code of the algorithm. III. CASE STUDIES Examined geometries, which are specially chosen for the purpose of this study, are shown in Fig.7. Single layers of these geometries are tested on CNCSimulator [10], a freeware CNC milling simulation software, by using the G-codes generated with the algorithm described at the previous section. Hollow cube and Kepler s rhombic dodecahedron solid bodies provide all the cases mentioned in the intersection point detection algorithm. For this reason these models are frequently used to detect bugs in the coding phase. Figure 7: Studied geometries to test the competence of the algorithm. Fig.8 and Fig.9 depict the simulation results of the two studied geometries. As it can be seen, slices of relatively complex 3D parts have been simulated using the developed slicing and G-Code generation algorithms. The resolution Figure 9: CNC milling simulation result of logo 2. IV. CONCLUSION In this study a new STL slicing and G-Code generation algorithm was explained. This software algorithm shapes the foundation for future studies and generates slice data of any given 3D model. Performance of the algorithm is evaluated by simulating several complex 3D geometries on a CNC milling simulator using the generated G-Codes. There is currently a limitation on the STL file size but this limitation can be handled by a modifying the code to study with one facet data at a time. Moreover, considering that the operation of integers is faster than that of floating point numbers, the floating point coordinates of the facets in a model may be transformed to 32 bits integers to speed up the process. ACKNOWLEDGMENT The authors appreciate İ. Ethem Bağcı s guidance for NetBeans IDE and Java s memory heap problem. 144

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