Center for Global Design and Manufacturing Research

The areas of research and teaching are Computer-Aided Design and Manufacturing with emphasis on Feature Based Design, Optimization in Design and Manufacturing & Product Lifecycle Management. The objective of research in the Advanced Manufacturing Simulation Lab(AMSL) is the simulation of virtual manufacturing processes models based on computational algorithms for improving part accuracy and energy efficiency. AMSL is committed to develop new algorithms which will provide reliable solutions to predict and provide remediation for both additive and subtractive manufacturing processes. 

Modeling, Simulation and Optimization of Manufacturing Processes

The Center for Global Design and Manufacturing (CGDM) is a research lab leading state-of-the-art research in areas of Advanced Manufacturing and Intelligent Product Design. Advanced Manufacturing research in CGDM is primarily focused on Additive Manufacturing (AM) with the motivation of improving the efficiency of the AM processes. The center is committed to develop novel computational geometry algorithms that will provide reliable solutions in design and process optimization. The research explores AM processes to develop design for additive manufacturing tools and pre-process optimization tools for optimum part designs that are lightweight and are of good quality. Such novel designs are finding tremendous applications in major aerospace and automotive industries as well as bio-medical industries who are looking for lighter, reliable and sustainable design solutions. The center’s research has been funded through various federal grants and industrial collaborations.

Additive Manufacturing

Additive Manufacturing (AM) is the process of part building by depositing material in a layer wise manner under computer control. AM can be used to easily create complex parts of almost any shape or geometry which would otherwise be difficult to achieve by traditional manufacturing processes. 

Design Rules for Additive Manufacturing

As additive manufacturing takes a completely new approach to create parts, the conventional design for manufacturing rules are not applicable to AM. To develop and explore the new design paradigm for AM, a novel Design for AM (DFAM) approach has been developed to integrate manufacturability constraints of Additive manufacturing processes with design techniques. The design rules are formulated by studying the relationship between input part geometry and AM process parameters. Also, a proprietary Producibility Index (PI) is proposed in order to compare different designs for manufacturability.

Topology Optimization in Additive Manufacturing

Tools such as topology optimization (TopOpt) are gaining wide recognition in the design process. Topology optimization is the computational method for finding minimum material distribution scheme for a defined loading condition within a design space without any preconceived notion for part geometry. Although, AM is capable of manufacturing non-conventional and organic geometric designs, it is important to consider the inherent manufacturing constraints associated with the additive processes. Novel algorithms have been developed at CGDM to incorporate the DFAM constraints within TopOpt.

As additive manufacturing(AM) takes a completely new approach to create parts, the conventional design for manufacturing rules are not applicable to AM. To develop and explore the new design paradigm for AM, the research team at CGDM is working on Design for Additive Manufacturing (DFAM) guidelines and tools that will assist engineers in creating lightweight and functional part designs. Based on the DFAM guidelines, design evaluation tools are developed for AM. The tools help engineers assess the producibility of a part and identify the design issues ahead of time and make necessary corrections in order to mitigate manufacturability issues. 
The students have developed the algorithms and tools which take STL files as an input and perform design analysis within Matlab. Students have also developed the tools for CAD environment such as Siemens NX to directly process CAD geometries.

Design of variable-density structures for additive manufacturing using gyroid structure

This research presents an approach to design gyroid structures of uniformly varying density based on previously established relationship between the parametric equation of gyroids and the volume fraction. The method helps to achieve control over location based variation of the cellular density as well as unit cell size without losing any surface continuity of the gyroids. A topology optimization based methodology is used as the basis for generating variable-density gyroid lattice structures within a given design space under predefined loading conditions.

Novel design approaches using various types of cellular lattice structures for AM

The research team is also exploring the field of cellular structures which will help create intelligent designs tailored for applications under various structural and thermal conditions. The center’s objective is to combine the capabilities of AM processes with optimum designs using cellular and bionic structures.

Optimum Support Structure Generation for Additive Manufacturing using Unit Cell Structures

Due to the inherent nature of the process, support structures are required to support overhanging features while building a part by AM. Support Structures increase the build time and cost of manufacturing and also have an adverse effect on the surface finish of the part. A new approach is presented for minimizing support structures using space filling cellular structures in conjunction with Dijkstra’s shortest path algorithm to generate optimized support structures. This not only reduces a amount of material used during manufacturing but also saves build time and sintering energy.

Optimal Part Orientation to Minimize the Support Structures

This research work analyzes the effect of part orientation on two types of form errors, namely, cylindricity and flatness errors. 
An algorithm to calculate the optimal orientation for minimizing flatness and cylindricity errors is developed and tested with the help of two test cases. However, an optimal orientation for minimum form errors may result in a greater utilization of support structures which increases the material consumption in LM processes and therefore should be avoided. 
A voxel-based approach for calculating support structures has been developed in this paper which is then applied to minimize the volume of support structures while minimizing the cylindricity and flatness errors of the part features.

Optimal Part Orientation based on Support Structure Removal Accessibility

This study presents a methodology to identify the best part build orientation for a part such that, the need of support structure is minimized while ensuring maximum removal of support. A hierarchical octree data structure has been used to perform accessibility analysis to analyze maximum removal of support. A novel octree traversal lookup table has been developed for this purpose. A multi-objective optimization routine identifies the optimal build orientation.

Support Structure Accessibility using Ray-Tracing Approach

A novel ray-tracing based approach is developed to determine the accessibility of the support structure for removal during post processing. This algorithm can be useful in identifying the optimum process parameters ahead of time. A tool is developed in Matlab based on this approach to calculate support accessibility of any part at a given build orientation.

Part Shrinkage and Deformation Compensation using Neural Networks

Parts manufactured by Selective Laser Sintering (SLS) process undergo shrinkage and deformations due to the thermal nature of the process.

In this research, the surface of a SLS manufactured part is scanned using a laser scanner. An Artificial Neural Network(ANN) is then used to study the part shrinkage and deformation from the scanned data. The ANN then implements the appropriate compensation to the original CAD design so that the compensated design yields a part that complies with the original design.

Vertex Translation Algorithm

This is a novel approach to reduce the CAD to STL translation error. This approach, referred to as Vertex Translation Algorithm (VTA), compares an STL facet to its corresponding CAD surface, computes the chordal error at multiple points on the STL surface, and translates the point with the maximum chordal error until it lies on the design surface. This translation results in the reduction of the chordal error locally without unnecessarily increasing the size of the STL file. In addition, a facet isolation algorithm (FIA) has also been developed. This isolation algorithm extracts the STL facets corresponding to the surfaces and features of the part that have to be modified by the translation algorithm.

Steiner patch based file format for Additive Manufacturing processes

The Steiner representation has been used to approximate the surfaces of two test parts and the chordal errors in the surfaces are calculated. The chordal errors in the Steiner format are compared with the STL and AMF formats of the test surfaces and the results have been presented. Further, an error based adaptive tessellation algorithm is developed for generating the Steiner representation which reduces the number of curved facets while still improving the accuracy of the Steiner format. The test parts are virtually manufactured using the adaptive Steiner, STL and AMF format representations and the GD&T errors of the manufactured parts are calculated and compared. The results demonstrate that the modified Steiner format is able to significantly reduce the chordal and profile errors as compared to the STL and AMF formats.

Additive Manufacturing File Format using Bezier Patches

In this research, a methodology for developing a new file format using curved bi-quadratic Bezier patches which can approximate a CAD model with higher accuracy as compared to the STL file is proposed. Two new Bezier based formats are developed in this research: the first format uses curved Bezier patches with linear edges and the second format uses curved Bezier patches with curved edges. In the first format named as the linear edge format, the patches are modeled such that the edges remain coincident with those of the original STL facets. In the second format called the curved edge format, the Bezier patch edges are modeled as quadratic Bezier curves.

Surface Based Modification Algorithm

Additive Manufacturing (AM) machines use the Stereo lithography (STL) file as standard input file format to build parts. STL model is a triangular faceted approximation of a CAD model which represents a part with less accuracy than the CAD model.
The Surface-based Modification Algorithm (SMA) can adaptively and locally increase the facet density of an STL model. SMA is an error minimization approach to modify the STL facets locally based on chordal error, cusp height and cylindricity error for cylindrical features. Final results show a distinct improvement in the part error of the STL model using SMA when compared to the original STL file.

Slice Contour Modification

This research attempts to minimize STL approximation error by modifying each 2D slice of the STL file such that the new modified slice satisfies a chordal error criteria. First, the STL file is sliced at different levels. Using STL triangle chord points obtained after slicing, new points are found on the NURBS surface corresponding to the chord points and a new contour is generated. This new contour is formed by recursively modifying the original STL contour until the chordal error in each contour is below a specified threshold. This is performed for all slices and the part is then virtually manufactured using both the original and the modified contours.

Direct CAD slicing using Image Processing algorithm: IPSlicer

During the conversion of a complex CAD geometry to an STL file, geometric errors are introduced in the model. These drawbacks associated with the STL file may translate into a faulty or inaccurate final manufactured part. This paper presents a novel Image Processing (IP) based Direct CAD Slicer, IPSlicer, which can be used to manufacture components directly from CAD geometry (without converting to STL file). Using sectional image snapshots of a part, captured normal to the build direction and sectional 2D bounding box data, contour points for each section are identified by performing boundary tracing operation followed by application of Contour Mapping Algorithm (CMA). The method slices the actual NURBS geometry and thus parts manufactured by this method have reduced GD&T errors such as flatness, cylindricity, and profile error.

A K-D Tree Based Clustered Adaptive Layering Approach to Improve Part Accuracy by Varying Slice Thickness

A new approach of determining the variable slices using a 3D k-d tree method has been proposed in this research. The proposed approach is validated for three test parts and by comparing the volumetric, cylindricity, sphericity, and profile errors obtained from this approach with those obtained using a uniform slicing method. Since current AM machines are incapable of handling adaptive slicing approach directly, a “pseudo” grouped adaptive layering approach is also proposed here. This “clustered slicing” technique will enable the fabrication of a part in bands of varying slice thicknesses with each band having clusters of uniform slice thicknesses.

Adaptive Slicing in Additive Manufacturing Process Using a Modified Boundary Octree Data Structure (MBODS)

This method, termed as modified boundary octree data structure (MBODS) algorithm, is used to convert the stereolithography (STL) file of an object to an octree data structure based on the part’s geometry, the machine parameters, and a user defined tolerance value. A subsequent algorithm computes the variable slice thicknesses using the MBODS representation of the part and virtually manufactures the part using these calculated slice thicknesses. Points sampled from the virtually manufactured part are inspected to evaluate the volumetric, profile, and cylindricity part errors.

Optimal Part Orientation to Achieve Geometric Tolerances

Siemens PLM NX API is used to extract the GD&T callouts and associated geometric information of the CAD model. The mathematical relationships between build orientation and GD&T Tolerances are developed as part of a combined optimization model to identify best build orientations for minimizing support structures while meeting the design tolerances. The feasible build orientations along with the corresponding support structures are depicted using a visual model. The regions requiring support structures are identified and a Quadtree decomposition is used to find the volume of support structures. The tolerances studied in this research are: Perpendicularity, Parallelism, Angularity, Conicity, Total Runout and Circular Runout.

Process energy analysis and optimization in selective laser sintering

The laser energy expenditure of SLS process and its correlation to the geometry of the manufactured part and the SLS process parameters, however, have not received much attention from AM/SLS researchers. This research presents a mathematical analysis of the laser energy required for manufacturing simple parts using the SLS process. The total energy expended is calculated as a function of the total area of sintering (TAS) using a convex hull based approach and is correlated to the part geometry, slice thickness and the build orientation. The TAS and laser energy are calculated for three sample parts and the results are provided in the paper. Finally, an optimization model is presented which computes the minimal TAS and energy required for manufacturing a part using the SLS process.

Support Parameter Tool

Support Parameters module is used to visualize the presence of support structures needed to build the part for a user-input build orientation. This tool provides quick feedback on support structures as well as calculates parameters such as support volume, support contact area, build height, and down-facing areas (areas that do not need support structures). Because this module is developed to visualize the support structures for a particular orientation, the support structures are represented as lines for faster calculations.

Support Generation Tool

Support Generation module is used to generate support structures needed to build the part for a user-input build orientation. Different types of support structures can be generated using this module, details of which will be explained in the instruction manual.

Different types of supports including solid supports, hollow supports, conformal supports and honeycomb supports.

Support Structure Generation and Visualization

Build Time Estimation Tool

Build Time is the sum of time taken by the laser to sinter each layer and the change over time between successive layers. This tool slices the body in to several layers based on the specified layer thickness. The laser path in each layer is traced and the total sintering area, sintering time across all the layers is computed. The user can specify different build direction, slice thickness, laser parameters (diameter, speed, overlap) and hatch pattern to perform analysis and identify the build parameters which result in minimum build time for a given part.

Cusp Error Estimation Tool

Cusp Error is a measure used to quantify the deviation between the designed and manufactured part. The cusp error has an effect on the surface finish of the manufactured part. This tool calculates the cusp error with respect to every facet of the CAD body and the average cusp error. All the facets satisfying the specified threshold cusp height are highlighted in green and the rest are highlighted in red. The user can specify different input parameters such as orientation of the part, layer thickness and threshold cusp height and perform analysis to determine the suitable build parameters for good surface finish.

DFAM - Small Opening Detection Tool

Small Opening Detection module is used to detect and highlight small holes and gaps in the geometry that are difficult to manufacture in AM Process. The tool can Highlight critical small opening features in the geometry that may be fused during the laser sintering process. Depends on laser diameter or nozzle diameter. This tool can rotate a part and highlight critical small opening features in the geometry and calculate the areas of the small openings in each layer.

DFAM - Thin Feature Detection Tool

Thin Feature Detection module is used to detect and highlight thin features in the geometry that are difficult to manufacture in AM Process. Thin features are prone to increased thermal deformations due to build up of residual stresses. The thin feature dimensions depend on laser diameter or nozzle diameter. This tool can rotate a part and highlight critical thin sections in the geometry and calculate the areas of thin section region in each layer.

DFAM - Sharp Corner Detection Tool

Sharp Corner Detection Tool is used to detect and highlight sharp corners in the geometry that are difficult to manufacture in powder bed AM process. Sharp corners are tips within each layer where the angle of the tip is less than threshold value of the AM machine. If the angle is too small, these sharp corners in the geometry may be difficult to manufacture during the laser sintering process. This tool can rotate a part and highlights critical sharp corners in the geometry and calculate the number of the sharp corners in each layer.

DFAM - Thin to Thick Transition Detection Tool

Thin to Thick Transition Detection module is used to detect and highlight thin to thick transition areas in the geometry that could cause high thermal distortion in AM Process. Thin to thick transitions are transition areas from a low area layer to a high area layer along the build direction. In the DMLS process, the laser melts the metal powders and fuses them together. The heat energy in the current layer is transferred to the substrate through the layers beneath it. If the transition area between any consecutive layers is too small, it may cause thermal distortion due to impediments to heat transfer during DMLS process.

DFAM - Thin Walls Detection Tool

Thin Walls Detection module is used to detect and highlight thin walls in the geometry that are difficult to manufacture in AM Process. Thin wall is vertical wall structure where the thickness of the wall is too small. Thin walls are very challenge to manufacture duo to the laser spot diameter or FDM nozzle size. Moreover, thin walls could lead to failure or high thermal distortion due to the thermal stress with in the thin wall region.

DFAM - Recoater Arm Collision Detection Tool

Recoater Arm Collision Detection Tool is used to detect and highlight the potential deformation areas or sharp edges of the parts that may collide with the recoater arm and damage the part and/or recoater. Recoater arm spread the powder from one side of the build platform to the other. If there is a long-edge parallel to the recoater, it will be more difficult for the recoater to pass over it in case of deformation. This tool can rotate a part and specify the recoater direction and identify areas of the part that may be prone to recoater damage.

Detection of DFAM features such as small openings, thin regions, sharp corners, thin to thick transition, recoater arm collision and thin walls.

Detection of DFAM Features in Early Design

Accessibility and Setups Analysis Tool

Accessibility and Setups Analysis Tool is used to analyze effect of build orientation on removability of supports as well as the need for setup analysis. The tool can identify support structures that will be accessible/ inaccessible based on user specified directions of tool approach for support removal. In addition, the tool can also optimize sequence of tool approach directions to optimum number of setups for removing maximum support structures.

Support structure accessibility.

Support Structure Accessibility

Producibility Index Calculation Tool

Producibility Index (PI) Calculation and Orientation Optimization

Producibility Index (PI) Calculation and Orientation Optimization

Producibility Index (PI) is the measure of goodness of a design’s manufacturability in a build orientation. This consolidated tool brings together the quantified outputs of the DFAM analysis, support structure parameters and accessibility for the given part geomertry, calculates the PI values of each candidate build orientation uisng weighted optimization scheme and suggests the best build orientations based on highest PI value.

Showing an example of DFAM - Producibility Index Calculation.

DFAM - Producibility Index Calculation