A Graphical Approach for Freeform Surface Offsetting With GPU Acceleration for Subtractive 3D Printing

2016 ◽  
Author(s):  
Mohammad M. Hossain ◽  
Richard W. Vuduc ◽  
Chandra Nath ◽  
Thomas R. Kurfess ◽  
Thomas M. Tucker
Keyword(s):  
Free Form ◽  
Manufacturing Cost ◽  
Generation Process ◽  
Toolpath Planning ◽  
Planning Approach ◽  
Subtractive Manufacturing ◽  
Volumetric Representation ◽  
Processor Unit ◽  
Voxel Data ◽  
Graphics Processor Unit

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.

scholarly journals A Graphics Processor Unit-Accelerated Freeform Surface Offsetting Method for High-Resolution Subtractive Three-Dimensional Printing (Machining)

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Mohammad M. Hossain ◽  
Chandra Nath ◽  
Thomas M. Tucker ◽  
Richard W. Vuduc ◽  
Thomas R. Kurfess
Keyword(s):  
3D Printing ◽  
Three Dimensional ◽  
Boundary Representation ◽  
Numerical Control ◽  
Freeform Surface ◽  
Generation Process ◽  
Graphics Processor ◽  
Computer Aided ◽  
Processor Unit ◽  
Graphics Processor Unit

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.

scholarly journals ADDITIVE MANUFACTURING - APPLICATION OPPORTUNITIES FOR THE AVIATION INDUSTRY

2015 ◽  
Vol 6 (2) ◽  
pp. 63-86
Author(s):  
Dipesh Dhital ◽  
Yvonne Ziegler
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Computer Aided Design ◽  
Free Form ◽  
Aviation Industry ◽  
Layer By Layer ◽  
Printing Technology ◽  
3D Printing Technology ◽  
Subtractive Manufacturing ◽  
Aided Design

Additive Manufacturing also known as 3D Printing is a process whereby a real object of virtually any shape can be created layer by layer from a Computer Aided Design (CAD) model. As opposed to the conventional Subtractive Manufacturing that uses cutting, drilling, milling, welding etc., 3D printing is a free-form fabrication process and does not require any of these processes. The 3D printed parts are lighter, require short lead times, less material and reduce environmental footprint of the manufacturing process; and is thus beneficial to the aerospace industry that pursues improvement in aircraft efficiency, fuel saving and reduction in air pollution. Additionally, 3D printing technology allows for creating geometries that would be impossible to make using moulds and the Subtractive Manufacturing of drilling/milling. 3D printing technology also has the potential to re-localize manufacturing as it allows for the production of products at the particular location, as and when required; and eliminates the need for shipping and warehousing of final products.

Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing

2017 ◽  
pp. 334-360
Author(s):  
Ayan Chatterjee ◽  
Mahendra Rong
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Mosaic Generation ◽  
Processor Unit ◽  
Photo Mosaic ◽  
Segmentation Of Images ◽  
Graphics Processor Unit ◽  
Image Pattern ◽  
Learning Data

Today, in the age of artificial intelligence and machine learning, Data mining and Image processing are two important platforms. GA and GP are value based and program based randomized searching tools respectively and these two are very much useful in the fields' data mining and image processing for handling different issues. In this chapter, a review is made on ability of GA and GP in some applications of these two fields. Here, the selected subfields of data mining are market analysis, fraud detection, risk management, sports analysis, protein interaction, classification of data, drug discovery and feature construction. The similar in image processing are enhancement and segmentation of images, face recognition, photo mosaic generation, data embedding, image pattern classification, object detection and Graphics Processor Unit (GPU) development. The efficiencies of GA and GP in these particular applications are analyzed with corresponding parameters, comparing with other non-GA and non-GP approaches of the corresponding subfields.

scholarly journals Preflight Contingency Planning Approach for Fixed Wing UAVs with Engine Failure in the Presence of Winds

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 227 ◽  
Author(s):  
Bulent Ayhan ◽  
Chiman Kwan ◽  
Bence Budavari ◽  
Jude Larkin ◽  
David Gribben
Keyword(s):  
Geographical Area ◽  
Landing Site ◽  
Contingency Planning ◽  
Path Generation ◽  
Generation Process ◽  
Area Of Interest ◽  
Forced Landing ◽  
Planning Approach ◽  
Final Approach ◽  
Engine Failure

Preflight contingency planning that utilizes wind forecast in path planning can be highly beneficial to unmanned aerial vehicles (UAV) operators in preventing a possible mishap of the UAV. This especially becomes more important if the UAV has an engine failure resulting in the loss of all its thrust. Wind becomes a significant factor in determining reachability of the emergency landing site in a contingency like this. The preflight contingency plans can guide the UAV operators about how to glide the aircraft to the designated emergency landing site to make a safe landing. The need for a preflight or in-flight contingency plan is even more obvious in the case of a communication loss between the UAV operator and UAV since the UAV will then need to make the forced landing autonomously without the operator. In this paper, we introduce a preflight contingency planning approach that automates the forced landing path generation process for UAVs with engine failure. The contingency path generation aims true reachability to the emergency landing site by including the final approach part of the path in forecast wind conditions. In the contingency path generation, no-fly zones that could be in the area are accounted for and the contingency flight paths do not pass through them. If no plans can be found that fulfill reachability in the presence of no-fly zones, only then, as a last resort, the no-fly zone avoidance rule is relaxed. The contingency path generation utilizes hourly forecast wind data from National Oceanic and Atmospheric Administration for the geographical area of interest and time of the flight. Different from past works, we use trochoidal paths instead of Dubins curves and incorporate wind as a parameter in the contingency path design.

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