Computer Aided Visualization Tool for Part Quality ...

Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE 2016 August 21-24, 2016, Charlotte, North Carolina

DETC2016-60019

COMPUTER AIDED VISUALIZATION TOOL FOR PART QUALITY ANALYSIS OF ADDITIVE MANUFACTURING PROCESS Mohammad Taufik PDPM Indian Institute of Information Technology, Design & Manufacturing Jabalpur, Jabalpur, 482005, Madhya Pradesh, India

Prashant K. Jain PDPM Indian Institute of Information Technology, Design & Manufacturing Jabalpur, Jabalpur, 482005, Madhya Pradesh, India

ABSTRACT This paper presents a novel Computer Aided Visualization (CAV) tool to visualize and perform part quality analysis for Additive Manufacturing (AM) before actual fabrication of the part. An algorithm has been developed to extrude the 2D sliced contours to turn them into 3D layered model in the CAD environment. Extrude feature has been used to maintain the high level of precision during virtual fabrication stage. The part quality related issues were identified layer-by-layer by superposition of base model on its virtual layered model. The performance of the proposed automated modelling system is evaluated on different prototype surfaces related to part quality issues. With the proposed Computer Aided Visualization (CAV) tool, users have the facility to modify locally digital product models. The analysis of digital models can be used to generate a report on issues induced in Additive Manufacturing (AM). As a result, of this, the pre-processing and post preparation stage can be altered for optimal results. The proposed tool can significantly decrease the manufacturing lead time and cost needed for quality product development. Furthermore, the CAV tool would be very efficient for custom analysis and rapid product understanding purpose.

many issues, such as containment situations, volumetric error, poor surface finish, strength and accuracy of manufacturing prototype, etc., [1-11]. Many eminent researchers have studied different techniques to eliminate the slicing issues and this section presents some of the related work carried out in this research area. Various researchers found that the shape deviation and form errors compensation for AM manufactured part was indirectly controlled by correcting process parameters and slice files. Different compensation method has been previously studied in the literature by Tong et al. [12-13] for compensation of AM machine errors for SLA and FDM processes. In their first attempt Tong et al. [12] studied the effect of a constant “shrinkage compensation factor” which is changed for the different axis to overcome the shrinkage effect. The results presented in this work demonstrate the feasibility of such an approach is limited to 2D case. The purpose of their next research paper [13] is to extend the first approach to software error compensation of FDM machines and explores the approach to apply for compensation by correcting slice files. In the presented work compensation on sliced data has been proven successful by considering correction of both STL and SSL file formats. Senthilkumaran et al. [14] developed a software to automate compensation of an STL and a layered file. They suggested that the factors affecting the shrinkage in LS processes are shrinkage of RP material, process parameters, and the geometries of prototype manufactured. They have also presented a dexel based method for minimization of part error caused by shrinkage in which they account part geometry as well as beam offset [15]. They compensate shrinkage at every layer and every hatch length, and the new contour lists were rebuilt with its new vertices and a corresponding new compensated CLI file used for the part building process. Lee et al. [16] developed a methodology for generating a deformed

INTRODUCTION Additive Manufacturing (AM) is a unique class of manufacturing processes; it builds the solid volume from a CAD model by successively adding material layer by layer [1]. AM processes can be broadly divided into nine stages for manufacturing a part, namely generation of the geometric model, STL format translation, verification of STL, determination of optimal orientation, slicing, support generation, toolpath generation, additive fabrication and part removal/ cleaning (support removal) [1]. Slicing is an extremely critical stage for all AM processes. The slicing stage is prone to

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of 3D objects. Fig. 2 shows the discretization of a sphere in the integer space. Their developed technique used several algorithmic and based on voxel set and certain integer intervals.

model from an STL model based on a user defined error criteria. Developed program modifies an STL model nonuniformly and uses UG NX as a CAD system and C++ as a programming language. A new Data structure of an STL model that includes searching and splitting the facets by using Euler operators is presented. In this work the difficult work of creating solid models to build non-uniformly deformed STL models were avoided. The shortcomings of STL-related to free form fabrication process has been identified by Stroud and Xirouchakis [17]. They used three methodologies to improve the accuracy of an AM process. One of them leads to improvement in the approximation of the CAD model by the interaction of various STL approximation control parameters such as the triangle side length, the chord length, normal vector tolerance, etc. Ragunath and Pandey [18] studied the effect of various process parameters, namely laser power, beam speed, hatch spacing, part bed temperature and scan length on the process and material shrinkage and developed empirical relations between the shrinkage and various process parameters. It is reported in this research that scan length effects shrinkage in the X direction and the scan length can have a linear relationship with scaling factors. McClurkin and Rosen [19] developed Computer Aided Build Style Selection (CABSS) method to render decision support for the building of an SLA part using response surface methodology and multi-objective optimization. They presented the effect of four process parameters i.e. orientation, layer thickness, hatch spacing and build style on the three goals to be controlled i.e. accuracy, build time and surface finish. To make a higher order model each variable was considered at three equally spaced levels. Their results show that due to overcure, print through, and build quantization errors; SLA is more accurate in the X–Y laser scanning direction (length and width measurement) than in the Z- height direction (depth measurement). For analyzing the effect of process uncertainty and irregular prototype geometry, simple models and closed-loop algorithms have been presented and implemented in the previous works, such as those in [20-23]. Song and Mazumder [23] showed a novel work in which the compensation for the lack of deposition with the generalized predictive controller was demonstrated by cladding on a stepped surface. They monitored the melt pool temperature of a laser cladding process through the radiation measured by a dual-color pyrometer using the Planck radiation law and controlling the process with a PC. Authors showed that generalized predictive controller successfully compensated for the lack of deposition by adjusting the laser power during laser cladding process. Choi and Chan [24] developed a dexel-based Virtual Prototype (VP) Fabrication system. Figure 1 shows the rectangular finite strip of solid built around a dexel. By using a dexel or rectangular strips-based virtual prototyping system, the designer can analyze shape deviation more easily and take prompt action for product development. Biswas and Bhowmick [25] discussed the importance of voxelation in rapid prototyping. They presented the use of voxelation discretization and representation

Fig. 1 Rectangular finite strip of solid built around a dexel [24]

Fig. 2 A snapshot of the layering algorithm in action [25] Some attempts [26-30] have been made to improve surface quality by staircase machining during the post-treatment processes, which use different post finishing techniques instead of parameter optimization during the pre-fabrication phase. Although, several researchers have studied the shape deviation and form errors generated in AM prototypes. However, part quality related issues are more efficiently control if it can be detected with a high precision technique in the preprocessing stage. Due to the model discretization in the virtual modelling process, virtual prototypes are not very exact on the x-y plane and have deviation effect. However, part quality related issues control more precisely if it can provide exact contour modelling procedure in the x-y plane for each layer. One way to exact contour modelling on the x-y plane for each existing layer in the pre-processing stage is by extruding the current 2D sliced contour into a 3D layered model as shown in Fig. 3. Hence, by using the extrude feature it is possible to prevent the loss of information in x-y plan and provide a high level of precision during the virtual modelling process. This approach also precisely resembles the pattern of AM process. Therefore, to developed an efficient tool an extrude feature is used in this research study to convert a 2D sliced contour into a

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In brief, this section focuses on the overall methodology for the implementation of the proposed algorithm of the CAV tool. In particular, it addresses a new method of obtaining information from the CAD environment for analysis and digital fabrication of AM products. The following sections describe each step of proposed algorithm and their implementation procedure for analysis of part quality in AM build parts.

3D layered model. For this purpose, MATLAB R2014a and SOLIDWORKS 2012 packages are coupled with a set of modules. The proposed CAV tool, mainly use the two software packages to product design in CAD environment to analysis various part quality issues. nth contour 1st contour

2nd contour Start

Read STL file of the part and bound on layer thickness value

STL with high precision

Find Zmin and Zmax

Set Z=Zmin and lt = layer thickness

3D layered model is very exact on the x-y plane

Data transfer in coupled operations

Fig. 3 An Extrude feature based methodology to convert a 2D sliced contour into a 3D layered model THE PROPOSED COMPUTER AIDED VISUALIZATION TOOL Figure 4 shows the flow diagram of the CAV tool. Firstly, an STL format of the geometric model is created by CAD software. The developed program find the intersection between the slice plane and the triangles at initial slice height and construct contour from these intersection points. This process step is repeated for different slice heights to generate the list of contours. Then in the next step, for the analysis of part quality, 3D contour modelling and visualization process has been developed in a CAD environment. The proposed 3D contour extrusion system is being designed with macros (a standard CAD package). It helps the user in the generation of the corresponding extrusion-based layered model and corresponding STL. The overall steps involved in the development of a CAD software assisted 3D layered modelling for the part quality analysis are as follows: (1) Write the 2D contour information in macros file (.SWP file) format; this step has been discussed in detail in the next section. (2) These 2D contour information in the form of the Macros file format are transferred to a Macros module of standard CAD software which provides a facility for direct modelling of the contour data in the CAD environment. (3) After the 2D contour information is transferred in the coupled operations, run the macros for automatic construction of 3D digital models. This step initiates the 3D extrusion of each 2D contour data, and hence, it resembles the fabrication pattern of AM processes, which allows the user to perform effective validation of the desired part quality checks.

Slicing of STL file

Contour construction

Resembles the pattern of AM process

Z = Z+lt

Z ≤ Zmax Save new STL file

Print contour data in macros file format Stop

Fig. 4 Flowchart: overall methodology SLICING OF A TESSELLATED CAD MODEL This is the initial step of the proposed part quality analysis algorithm in a CAD environment. Initially, the slicing algorithm finds intersection points of a facet with a cutting plane at different slice heights (based on the user specified constant increments (lt) in slicing plane height). The possibilities of intersection between slicing plane and facet can be grouped into following five possible groups [30-34], as shown in Fig. 5 and listed below: In group I, if all three vertices have a Z-coordinate value exactly equal to the Z value of slicing plane then the facet is parallel to the slicing plane. As shown in Fig. 5, in this case, the Z-coordinate value of each vertex is equal to the slice height, i.e., ZS = ZP, ZS =ZQ and ZS = ZR.

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are ignored in the slicing algorithm. However, normal vectors are more useful in displaying orientation/graphics of the facets. In group II, two vertices of facet touch the slicing plane as shown in Fig. 5 and the developed program stores these vertices as an intersection point. As shown in Fig. 5 the necessary information for this condition is ZS = ZQ = ZR and ZP > ZS. In this case, a mathematical equation is not used to determine the intersection points, and the third vertex could be situated above or below the slicing plane. In group III, one edge vertex of the triangle touches the slicing plane and hence the intersection results must be calculated using two remaining vertices which are on the different sides of the slicing plane. In group IV, the slicing plane (Z = ZS) does not touch any vertex of facets, and one edge vertex of the triangle can be above or below the slice plane. As shown in Fig. 5, if one edge vertex of the triangle is below the slice plane, two intersection points, say B (XB,YB, ZB) and H (XH,YH, ZH) will be obtained. Therefore, end points of facet edge must satisfy the following two conditions (ZR < ZS < ZP) & (ZR < ZS < ZQ) for the calculation of first and second intersection points, B and H respectively. The fifth group exists if one vertex of facet touches the slicing plane and the remaining facet vertices lie above or below the slicing plane as shown in Fig. 5. It provides redundant information [35] since the program will obtain necessary slicing information from neighbouring facets. When all the possibilities of intersection between the slicing plane and the corresponding facets are checked, and corresponding intersection points are computed, we must connect the points in each facet to generate the straight-line. This slicing list array stores the lines which intersect with the slicing plane in a random order. Based on the slicing list array the closed contour is generated by the contour generation algorithm. CONTOUR GENERATION Firstly, the algorithm sorting the first segment from the slicing list array to a new file then finds its neighbor or a follow line. When the required follow line has discovered by the program, it will be recorded in a new file, but it might be possible to get an unordered list as shown in Fig. 6. For these straight-line data, there will be two points. The start point is called as head of straight-line and end point is termed as the tail of the straight-line. In general, the fully closed contour is generated only if the head of one straight-line lies on the tail of neighboring straight-line. According to head-to-tail search as shown in Fig. 6, we can find out the correct arrangement of head and tail status of neighbor straight-line to connect the straight-lines. For each slice height, this procedure is repeated until all lines are connected. Using this comparison process for the whole slicing list, we can generate the fully closed contour [30-34].

Fig. 5 Five possible cases of facet-plane slicing Therefore, it can be quickly discovered that all three vertices represent the intersection points. Here, it is important to discuss that the normal vectors are not required for calculation of intersection points. Therefore, data of the unit normal vectors

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Set skSegment = Part.SketchManager.CreateLine(P1, P2, P3, P4,…Pj…,Pm) Lastly, the complete file structure of 3D layered model from 2D contour lists in the form of .SWP file format is generated through MATLAB program and is used in the data transfer in coupled operations from the source system (i.e. MATLAB R2014a) to target system (i.e. SOLIDWORKS 2012).

Fig. 6 Illustration of contour generation procedures (a) Slicing list (b) After sorting neighbor list (c) after arrangement of the head and tail status of list (d) final contour list

Fig. 7 .SWP format for extrusion of 2D sliced contour into 3D layered model OBTAINING PART QUALITY INFORMATION FROM A CAD ENVIRONMENT Examples are now offered to show how the CAV tool helps part quality analysis for AM product design, analysis and fabrication. For the proper investigation purpose, prototypes that induced part quality issues were taken for the case study.

EXTRUSION OF SLICED CONTOUR IN A CAD ENVIRONMENT FOR PROTOTYPE DEVELOPMENT For the Data transfer in coupled operations, MATLAB 14 programming language is used as a source system to write the contour lists file. The contour list data with correct data structure should be constructed for the successful coupling along with the CAD environment. Here, the target system is SOLIDWORKS macros. Therefore, contour list data will be written in a macros files format (.SWP files) as required in SOLIDWORKS. The process of writing of .SWP file data structure for the contour points and the corresponding height of extrusion or layer thickness includes following steps. Firstly, the contour lists are generated. Then, the vertices of each contour and corresponding constant extrusion height are written according to the .SWP file structure as shown in Fig. 7. Figure 7 displays the data format of .SWP file based on sliced contours lists. Set Part syntax opens a new part document in SOLIDWORKS software; Set skSegment syntax is used for modelling the contours and Set myFeature syntax is used for extrusion-purpose. e.g., if a first 2D contour has n points (i.e., P1, P2, P3, P4,…Pi…,Pn) the second contour has m points (i.e., P1, P2, P3, P4,…Pj…,Pm) at two different heights then according to .SWP file structure these should be written as Set skSegment = Part.SketchManager.CreateLine(P1, P2, P3, P4,…Pi…,Pn)

EXAMPLE 1

(a) CAD model

(b) virtual prototype

Fig. 8 CAD model and a 3D layered based virtual AM model

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Ra

OSD

VE

No FLV

FLP

ESS MSD

USD

Fig. 9 Superposition view of CAD model on its virtual AM model in the CAD environment

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EXAMPLE 2

The proposed CAV tool is being developed within SOLIDWORKS through its macro programming interface. Three typical examples were presented in Figs. 8-9, 10-11 & 12-13. Typical prototypes geometry used for this study is either composed of planer surfaces or free-form shapes and planer surfaces at different orientations. The contour data is generated in slicing stage through MATLAB. Then it is transformed into a macro file format for part quality analysis in a CAD environment. The example part 1 is shown in Fig. 8a and Fig. 8b in CAD model and layered model, respectively. The layer thickness of the 3D layered model is 0.254mm. The family of 2D contours is extruded into a 3D layered model in a CAD environment to detect the part quality issue for AM prototypes layer to layer. The part quality issued associated with this model are Shape Deviation (SD), Volumetric Error (VE), the Surface Roughness (Ra) and Feature Loss (FL). Each of the issues is identified individually through CAV tool for part quality analysis purpose. The layer model is shown in Fig. 8(b) with a higher shape deviation issues. The shape deviation of situations is automatically generated during resembling the pattern of AM process in a CAD environment as shown in Figs. 8b&9. For this example, layers with oversize, undersize, equal size and the combination of oversize and undersize situations are found over the virtual AM prototype surface. The nomenclature of the shape deviation situations are OSD, USD and MSD for the oversize, undersize and the combination of oversize and undersize situations, respectively. Equal size situation (ESS) is a very desirable because the corresponding shape deviation is low for AM prototypes. The feature loss while resembling the process of AM in a CAD environment are also found and termed as a Features Loss as Peaks (FLP), Features Loss as Valleys (FLV) and Features Loss as Flat Areas (FLFA) for the 3D extruded layered model, respectively. Features loss are associated with a small number of layers, and their accurate value of dimensional deviations are also possible to estimate by CAV tool as shown in Fig. 9. It is also found that if there are one or more than one planer surface at inclined situations or curve surfaces present along with CAD model, the corresponding loss of information is higher and induced more Volumetric Error (VE) and surface roughness (Ra). Maximum deviation for above-mentioned factors is also easy to identify with the CAV tool which is not possible to predict with general measuring instruments. As a result, of the present analysis of the example parts with CAV tool the pre-processing and post preparation stage can be altered for optimal results. For example, during posttreatment operation, the shape deviation situation of the example parts 1 and 3 indicates the deficient and over material filling post treatments are required over the same contour. To control this situation, proper allowances can be incorporated through CAV tool in the preprocessing stage. These allowances are easily provided by locally altering the 2D contour information in a CAD environment. Another technique to control these issue by changing the model orientation or layer thickness. Deferent issues and their presentation strategy can be

(a) CAD model (b) virtual prototype Fig. 10 CAD model and a 3D layered based virtual AM model OSD

Maximum VE

Fig. 11 Superposition of CAD model on its virtual AM model EXAMPLE 3

(a) CAD model (b) virtual prototype Fig. 12 CAD model and a 3D layered based virtual AM model

FLP MSD

ESS

FLFA MSD Maximum VE

Fig. 13 Superposition of CAD model on its virtual AM model

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easily analyzed and understand with the developed CAV tool. Apart from part surface quality issues, with CAV tool different types of analysis are also possible, e.g., CAV tool can be applied qualitatively to assess the impact of slicing method to material properties, e.g. ultimate strength or Young’s modulus. Because literature [2] reveals that there is a link between the slicing step size and the mechanical properties e.g., tensile strength of the specimen decreases with increasing of slicing step size or layer thickness. In order to predict the functional assembly without actually assembling the parts, the present CAV tool can be used. In AM parts tolerances variation is mainly dependent upon the part orientation and layer thickness. Therefore, the proposed technique can be used to perform different iteration in virtual environment to choose the suitable part orientation and layer thickness in order to control the tolerances variation. Therefore, the use of CAV tool can be applied to know that the AM part dimensions will satisfy the form fit and functionality without actually fabricating the parts. The whole analysis of digital models can be used to generate a report on issues induced in 3D AM model. For this report, the detailed analysis of part quality can be directly conducted for actual manufacturing uses during pre-processing stage.

Selection of contours for required feature

Calculating deviation for the contour based on the superimposition of original model with layered model

Inclusion of points within the layered model

Adding feature not belonging to the contour

Fig. 14 Feature Inclusion Approach (FIA)

FEATURE INCLUSION APPROACH (FIA): THE LOCAL CONTROL APPROACH The traditional approaches for minimizing the deviation issues are either to change the model orientation in the base CAD modelling system or the STL file format or to reduce the layer thickness or the process parameters. For prevention of deviations, layer thickness variation is applied by most of the researchers using adaptive slicing procedures. The adaptive slicing technique is paramount in comparison to most of the techniques to control the prototype deviations. Although it is difficult to the practical implementation of adaptive slicing technique during the layer deposition process with most of the hardware and software currently available along with AM/3D printing systems. For example, FLASHFORGE Creator Pro (available at CAD/CAM Lab at IIITDMJ MP INDIA) has an only constant slicing facility. Generally, with AM/3D printing systems, commercial slicing software and also hardware are not capable of changing the nozzle tip diameter during layered manufacturing process. Therefore, in practical cases where a prototype has surfaces with deviation issues, the model orientation has to change to prevent the deviations on all the surfaces. However, selection of the different prototype orientation is not always useful. One orientation may result in the desired features with a higher volumetric error and surface roughness values. In other words, preventing the feature loss may adversely affect some other objective. Therefore, the method of varying the model orientation is applied globally to the entire part independent of the other feature relations on the individual layers. To address the issues associated with previously developed global control shape deviation strategies. In the present work, a CAD environment is used to convert each 2D contour into 3D layer model through extruding feature.

(a) Example 1: FIA for single layer

(b) Example 2: FIA for two layers Fig. 15 Feature inclusion approach (FIA): including features locally in the CAD environment Due to separately extruding each 2D contour it is possible to edit the 2D contour data of each layer in a CAD environment and hence by doing so only a part of the contour can be

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technique is more efficient. From the present study it is also observed that there is relation behind the feature loss and facet normal. Moreover, some guideline presented here that can be employed to utilize this knowledge for best-fit pre-processing techniques. If the angle between facet normal and slice direct is 00 or 1800 then there is a possibility to feature loss as flat area. If the angle between facet normal and slice direct changing its magnitude from less than 900 to more than 900 , or vice versa. Then there is a possibility to feature loss as peak. In order to validate FIA accuracy, the current study compares the AM based Fused Deposition Modelling (FDM) printed specimens with their virtual prototype. The results presented in Fig. 16 shows printed specimens before and after implementation of FIA. The FIA accuracy are validated layerby-layer by comparison of FIA printed specimens on its virtual layered model as shown in Figs. 15&16. Results of this comparison support the accuracy of FIA, such as the inclusion of local control in order to regain lost features.

modified. Thus, a CAD environment provides a better way of including the features without unnecessarily affecting the whole model geometry. Thus, the local feature inclusion is possible only for those contours or layers which have feature loss. To apply the feature inclusion approach only on the required contours or layers, the superimposition of the original model with a layered model representing feature lost have to be first identified. For this purpose, the feature inclusion approach has been developed in this study. This method is explained in detail in the next paragraphs. The first step in the feature inclusion approach is to identify the contours or layers of the virtual 3D layered prototype for which the feature relation have to be included. Next, from the superimposition of the original model with the layered model, the deviation points between them are calculated, and the points which lie outside the layered model are included within the layered model. FLP

Peak feature included in an AM part through FIA

CONCLUSIONS In this paper, an algorithm is developed and implemented to convert a 2D sliced contour into a 3D layered model in the CAD environment. Thus, the CAV tool is mainly used for part quality analysis and control purpose. Shape Deviation (SD), Volumetric Error (VE) and Feature Loss (FL) are accurately predicted and analyzed. Quality issues are further simulated through the CAV tool with the superimposition of the original model with layered models. Part quality issues are then measured in a CAD environment. Finally, a Feature inclusion approach (FIA) is implemented to include features in a CAD environment in a layered model. The proposed CAV tool provide local control on each 2D sliced contour. Thus, CAV tool is used for data interaction and have potential to alter the prototype features in a virtual environment and provide virtual design facility to users. The advanced CAV tool provides a precise description of 3D layered model, and it can be used for actual manufacturing conditions. The methodology developed can also be transferred to existing commercial CAD packages as an AM part quality analysis module.

Before FIA After FIA (a) Example 1: FIA for single layer FLP

FLFA

Before FIA

Before FIA Peak feature included in an AM part through FIA

Flat Area feature included in an AM part through FIA

REFERENCES [1] Taufik, M. and Jain, P.K., 2013. Role of build orientation in layered manufacturing: a review. International Journal of Manufacturing Technology and Management, 27(1-3), pp.4773. [2] Li, H., Wang, T., Sun, J., and Yu, Z., 2016. The adaptive slicing algorithm and its impact on the mechanical property and surface roughness of freeform extrusion parts. Virtual and Physical Prototyping, 11(1), pp.27-39. [3] Jain, P.K., Pandey, P.M. and Rao, P.V.M., 2010. Tailoring material properties in layered manufacturing. Materials & Design, 31(7), pp.3490-3498. [4] Kumar, N., Shaikh, S., Jain, P.K. and Tandon, P., 2015. Effect of fractal curve based toolpath on part strength in fused

After FIA After FIA (b) Example 2: FIA for two layers Fig. 16 Printed specimens for validation of FIA results The final step is to extrude that contours which correspond to the inclusion of new points. The remaining contours are the previous ones which correspond to the initially sliced model. The schematic of the methodology for a new feature inclusion approach is shown in Fig. 14. Figure 15 shows the results for FIA implementations. The CAD model is locally altered from the sliced contours data and the features loss are prevented through FIA. Therefore, in respect of previous developed global approaches, the proposed

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