Most path planning algorithms for covering a complex 3D object ignore physical limitations or constraints on a robot's motion. Adhering to such constraints for a given path can slow down the time to cover the path because the motion may need to be adjusted. This work considers a scenario in computer numerical control (CNC) milling applications, where the robot is a cutting tool that needs to cover the surface of a complex 3D object under the following constraint: for every point on the generated path, the robot must be assigned an accessible orientation to avoid collisions between it and other parts of the object. Our proposed approach, which we call max orientation coverage, employs a two-step optimization scheme. It can improve path efficiency with respect to both the length of the path and the cost of dealing with the collision-avoiding constraints. We evaluate our approach through extensive simulation studies on four CAD benchmarks against a state-of-the-art baseline. We show that our proposed approach can improve the efficiency of the path by 29.7% on average compared with the baseline and the improvement goes up to 46.5% for certain complex objects.

This paper presents a new and more work-efficient parallel method to speed up a class of three-dimensional collision detection (CD) problems, which arise, for instance, in computer numerical control (CNC) milling. Given two objects, one enclosed by a bounding volume and the other represented by a voxel model, we wish to determine all possible orientations of the bounded object around a given point that do not cause collisions. Underlying most CD methods are 3 types of geometrical operations that are bottlenecks: decompositions, rotations, and projections. Our proposed approach, which we call the aggressive inaccessible cone angle (AICA) method, simplifies these operations and, empirically, can prune as much as 99% of the intersection tests that would otherwise be required and improve load balance. We validate our techniques by implementing a parallel version of AICA in SculptPrint, a state-of-the-art computer-aided manufacturing (CAM) application used CNC milling, for GPU platforms. Experimental results using 4 CAM benchmarks show that AICA can be over 23x faster than a baseline method that does not prune projections, and can check collisions for 4096 angle orientations in an object represented by 27 million voxels in less than 18 milliseconds on a GPU.

Coverage path planning of a known environment sees a variety of applications, including cleaning, surveillance, agriculture and 3D printing. Most approaches employ hard-coded heuristics or other applicaton-specific requirements, making them hard to extend to other problem scenarios or "configurations," such as different motion strategies or robot size. This work presents a unifying, general, and adaptive framework, called adaptive deep path (AD Path), for coverage path planning problems under a variety of configurations. It can improve path efficiency with respect to both path length and number of turns, and can flexibly accommodate different problem configuration options. We evaluate AD Path against a state-of-the-art baseline in four complex environments with different configurations. We show that our approach can produce efficient paths; our experimental results show that AD Path can reduce the path length by 21.8 % and the number turns by 38.6 % on average compared with the baseline.

Machining is one of the major manufacturing methods having very wide applications in industries. Unlike layer-by-layer additive three-dimensional (3D) printing technology, the lack of an easy and intuitive programmability in conventional toolpath planning approach in machining leads to significantly higher manufacturing cost for direct computer numerical control (CNC)-based prototyping (i.e., subtractive 3D printing). In standard computer-aided manufacturing (CAM) packages, general use of B-rep (boundary representation) and non-uniform rational basis spline (NURBS)-based representations of the computer-aided design (CAD) interfaces make core computations of tool trajectories generation process, such as surface offsetting, difficult. In this work, the problem of efficient generation of freeform surface offsets is addressed with a novel volumetric (voxel) representation. It presents an image filter-based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The compact voxel data representation and the proposed computational acceleration on GPU together are capable to process voxel offsetting at four-fold higher resolution in interactive CAM application. Additionally, in order to further accelerate the offset computation, the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The performance trade-offs between accuracy and computation time of the offset algorithms are thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is found to be robust in computation, and demonstrates a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing algorithms developed for multicores central processing units (CPU). The proposed offsetting approach has been validated for a variety of complex parts produced on different multi-axis CNC machine tools including turning, milling, and compound turning-milling.

Engineering students are often unaware of the manufacturing challenges that are introduced during the design process. Frequently, students will design parts that are either very difficult or impossible to manufacture; this often occurs because they are unaware of the intricacies and limitations of the manufacturing processes available. Design for manufacturability (DFM) education must be improved to help address this issue. This paper discusses a vision for the implementation of a rapid method for facilitating DFM education in terms of subtractive and additive manufacturing processes. The goal is to teach students about how their designs impact the ease and cost of manufacturing, in addition to giving them knowledge and confidence to move fluidly between additive and subtractive manufacturing mindsets. For subtractive manufacturing, this is accomplished through a high-performance-computing-accelerated (HPC-accelerated) and parallelized trajectory planning software package that enables students to visualize the subtractive manufacturability of the parts they design as rapidly as they get feedback when using additive manufacturing processes. A similar package is envisioned to support effective decision-making for additive manufacturing as well. The implementation of the prototype HPC-accelerated CAM package for subtractive manufacturing in a required, high enrollment, undergraduate mechanical engineering course at a large public university is described as a pilot study to test our vision. A group of forty students was selected as the experimental group from the approximately 200 total students in the course; the remaining students served as control sections. The experimental group was given training and practice with the proposed CAM package; these students were also taught how to operate CNC lathes. Both the experimental and control section students were given a number of online assessments throughout the study. These assessments were designed to measure the students’ understanding of simple concepts in both additive and subtractive manufacturability. A score comparison between the treatment and control groups reveals that the implementation of the HPC-accelerated CAM package is effective in rapidly increasing undergraduate understanding of DFM for subtractive manufacturing.

Impact of decisions in the design process is initially high and declines as the design matures.However, few computational tools are available for the early design phase, thus an opportunity exists to create such tools.New technology opens up new possibilities to create new and novel computational tools.In this work an existing application is adapted for a new novel 3D input device that is named the Leap Motion controller.The controller allows the user to interact with 3D objects on the screen by using fingers and hands.The of result of this work is a conceptual design application which enables very direct manipulation of 3D objects on the screen, which has not before been achieved for this type of application in 3D.An improved human-computer interaction can potentially improve the users understanding of the structural behavior of a model, cognitive engagement in the design task, and encourage further design exploration.Three different cases are implemented which aims to enable the user to explore different design options with emphasis on geometrical form, as this has the greatest potential to improve the structural performance.The case studies demonstrate new potential for building engineering intuition and improving design space exploration through very direct manipulation in 3D.

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring-based three-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and computer numerical control (CNC) machining via GPU simulated software. Path planning is applied through visualization of surface material removal in high-resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer-aided design (CAD) design model as stereolithography (STL) voxel, and then, local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and G-code optimization for large-scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multilevel set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring-based pass planning verses global path planning of all geometries. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high-performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

There is a growing interest in educating advanced manufacturing to a larger population, especially graduates of STEM fields, without a need for an advanced engineering background. Application of CAD/CAM software in teaching design and manufacturing skills is common, though it often relies on users' knowledge of machine language, e.g., knowing how to write G-code to work with CNC machines, in addition to fundamentals of process planning. A CAM software called SculptPrint was used to familiarize three high-school level students without any background in manufacturing with CNC machines. With simulations of automatically generated machining paths, the students were taught basic manufacturing concepts in process planning and fixturing in a semester-long timeframe. The students fabricated three axisymmetric parts when the software-generated G-code was exported to a CNC lathe. The participants created a set of tutorials and surveys that would be used to teach sophomores in mechanical engineering at Georgia Tech concepts of CNC machining and process planning and evaluate their understanding of choosing manufacturing processes, and process planning in machining specifically. Examples of questions are provided to demonstrate the pertinent machining concepts that the participants learned. The ultimate goal of this research is to help target learners to have a better understanding of choosing between additive and subtractive manufacturing for parts with specific geometries in addition to other prototyping constraints such as cost and time.

The lack of plug-and-play programmability in conventional toolpath planning approach in subtractive manufacturing, i.e., machining leads to significantly higher manufacturing cost for CNC based prototyping. In computer aided manufacturing (CAM) packages, typical B-rep or NURBS based representations of the CAD interfaces challenge core computations of tool trajectories generation process, such as, surface offsetting to be completely automated. In this work the problem of efficient generation of free-form surface offsets is addressed with a novel volumetric representation. It presents an image filter based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The scalable voxel data structure and the proposed hardware-accelerated volumetric offsetting together advance the computation and memory efficiencies well beyond the capability of past studies. Additionally, in order to further accelerate the offset computation the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The accuracy of the offset algorithms is thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is robust in computation, easy to comprehend, and achieves a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing dual socket quad-core CPU implementation.

This paper presents the hybrid dynamic tree (HDT), a novel adaptive tree-based data structure for representing high-resolution sparse volumes. Roughly speaking, HDTs combine dense volumetric grids with sparse octrees in a way that makes them both more compact and better-suited to GPUs than state-of-the-art alternatives. For our motivating applications in computer-aided design and manufacturing(CAD/CAM), we show 2× reductions in storage on realistic inputs compared to these alternatives, additionally, we show up to 16 fix speedups over multicore CPU implementations on a specific computational bottleneck known as an offset surface computation. Indeed, these combined improvements allow us to perform offsetting on a single node at resolutions well beyond that of the prior work and the capabilities of current commercial packages. And beyond CAD/CAM, HDTs may find applications in 3D geometric modeling problems for a variety of domains, including medical imaging and graphics.

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

Volume data has extensive uses in medical imaging, like, MRI (magnetic resonance imaging) scan, visual effects production, including volume rendering and fluid simulation, computer-aided design and manufacturing (CAD/CAM) in advanced prototyping, such as, 3D Printing, among others. This work presents a compact hierarchical data structure, dubbed HDT, for extreme-scale volume modeling. The name is shorthand for hybrid dynamic tree. HDT is hybrid in that it fuses two contrasting structures, namely, octree and tiled grid. Such fusion of two opposing data layouts alleviate the limitations inherent to the respective structures. We describe the HDT construction algorithm to generate volumetric representation of triangle mesh on GPUs. While HDT mirrors much of the existing works on sparse 3D data modeling, our evaluation and comparative studies with prior researches demonstrate that HDT is arguably the most storage-effective representation. The geometric topology in HDT consumes just short of two bits per voxel --- five times compact relative to the current state-of-the-art volumetric structure.

Metrology systems take coordinate information directly from the surface of a manufactured part and generate millions of (X, Y, Z) data points. The inspection process often involves fitting analytic primitives such as sphere, cone, torus, cylinder and plane to these points which represent an object with the corresponding shape. Typically, a least squares fit of the parameters of the shape to the point set is performed. The least squares fit attempts to minimize the sum of the squares of the distances between the points and the primitive. The objective function however, cannot be solved in the closed form and numerical minimization techniques are required to obtain the solution. These techniques as applied to primitive fitting entail iteratively solving large systems of linear equations generally involving arithmetic intensive operations. The current problem in-process metrology faces is the large computational time for the analysis of these millions of streaming data points. This paper presents a framework to address the bottleneck using a Graphical Processing Unit (GPU), to optimize operations and obtain significant gain in computation time.

Digital shape reconstruction is the process of creating digital models from physical parts represented by 3D point clouds.The ideal process is expected to provide a boundary representation that is likely to be identical or similar to the original design intent of the object, and requires minimal user assistance.This paper discusses alternative state-of-the-art approaches, where emphasis is put on automatic methods (i) to create complete and consistent topological structures over polygonal meshes; and (ii) extract accurate and properly aligned surface features that yield complete, trimmed CAD models with fillets and corner patches.Problems and recommended solutions will be presented through case studies using industrial parts.

Data point set registration is an important operation in coordinate metrology. Registration is the operation by which sampled point clouds are aligned with a CAD model by a 4×4 homogeneous transformation (e.g., rotation and translation). This alignment permits validation of the produced artifact’s geometry. Registration is an iterative nonlinear optimization operation assigning points on the CAD model for the sampled points. The objective is to minimize the sum of the squares of the normal distances between each point in the point cloud and the closest point in the CAD model. State-of-the-art metrology systems are now capable of generating thousands, if not millions, of data points during an inspection operation, resulting in increased computational power to fully utilize these larger data sets. The execution time for assigning the point set in registration process is directly related to the number of points processed and CAD model complexity. A brute force approach to registration, which is often used, is to compute the minimum distance between each sampled point and its normal projection on the CAD model. As the point cloud size and CAD model complexity increase, this approach becomes intractable and inefficient. This paper proposes a new approach to efficiently identify the closest point in the CAD model for a given point. This approach employs a combination of readily available computer hardware, graphical processor unit (GPU) and a formulation of the point assignment problem, using an octree data structure that is suited for execution on the GPU.

A heterogeneous object (HO) modeling system independent of any commercial CAD software packages is introduced in this paper.Through this system, CAD models can be converted into 2D slices with heterogeneous material information for the fabrication of rapid prototyping technique.In this framework, volumetric dataset (VD) is employed to represent the material variations, which offers flexible manipulability to HO representation, while geometry model is used to describe the shape of an object, which can guarantee the accuracy of final HO slices.Two schemes are used to evaluate the composition variations in this system.Ray casting is utilized to render the HO volumetric dataset with the property of transparency.

Modern computer vision and coordinate metrology systems provide an ever-increasing flow of information from the physical world we live in to the virtual world inside computer systems. Often the coordinate system of the metrology device has a different coordinate frame from that of the existing objects in the virtual world. To rectify these differences, a process called registration is often applied. This paper uses a case study to highlight the differences between various registration techniques.

Nickel and nickel-alloy microparts sized on the order of 5–1000 microns have been imaged in three dimensions using a new microscopic technique, Digital Volumetric Imaging (DVI). The gears were fabricated using Sandia National Laboratories’ LIGA technology (lithography, molding, and electroplating). The images were taken on a microscope built by Resolution Sciences Corporation by slicing the gear into one-micron thin slices, photographing each slice, and then reconstructing the image with software. The images were matched to the original CAD (computer aided design) model, allowing LIGA designers, for the first time, to see visually how much deviation from the design is induced by the manufacturing process. Calibration was done by imaging brass ball bearings and matching them to the CAD model of a sphere. A major advantage of DVI over scanning techniques is that internal defects can be imaged to very high resolution. In order to perform the metrology operations on the microcomponents, high-speed and high-precision algorithms are developed for coordinate metrology. The algorithms are based on a least-squares approach to data registration the {X,Y,Z} point clouds generated from the component surface onto a target geometry defined in a CAD model. Both primitive geometric element analyses as well as an overall comparison of the part geometry are discussed. Initial results of the micromeasurements are presented in the paper.