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1. Automated Multi-User Analysis of Virtualized Voxel-Based CAM on Shared GPUs

   Lynn, R. (June 2018, Proceedings of the 13th ASME Manufacturing Science and Engineering Conference (MSEC))

   Computer-aided manufacturing (CAM) software allows for the generation of toolpaths for computer numerical control (CNC) machine tools and enables the creation of sophisticated parts that would not otherwise be possible with conventional manual machining methods. Voxel-based CAM is a recent approach to toolpath planning that enables creation of paths for parts that would be difficult to create with traditional CAM software. However, the use of voxel-based CAM necessitates the presence of powerful hardware (specifically, graphics processing units) in order to perform the necessary computations for creating toolpaths. The concepts of virtualization and desktop-as-a-service offer a promising solution to this challenge, as they allow for many users to access computer hardware that is hosted on a single server. This work investigates the performance impact caused by multiple simultaneous users on voxel-based CAM deployed in a virtualized environment. The implementation of a Python application for multi-user simulation on the virtualized platform is described and timing results gathered from a sequence of simulations are presented and analyzed as the number of users is varied. The results from these simulations demonstrate consistent operational times for a low number of simultaneous users before a period of high performance variation due to resource sharing. 

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2. Direct Digital Subtractive Manufacturing of Functional Assemblies Using Voxel-Based Models

   144. Lynn, R. (November 2017, Journal of manufacturing science and engineering)

   Direct digital manufacturing (DDM) is the creation of a physical part directly from a computer-aided design (CAD) model with minimal process planning and is typically applied to additive manufacturing (AM) processes to fabricate complex geometry. AM is preferred for DDM because of its minimal user input requirements; as a result, users can focus on exploiting other advantages of AM, such as the creation of intricate mechanisms that require no assembly after fabrication. Such assembly free mechanisms can be created using DDM during a single build process. In contrast, subtractive manufacturing (SM) enables the creation of higher strength parts that do not suffer from the material anisotropy inherent in AM. However, process planning for SM is more difficult than it is for AM due to geometric constraints imposed by the machining process; thus, the application of SM to the fabrication of assembly free mechanisms is challenging. This research describes a voxel-based computer-aided manufacturing (CAM) system that enables direct digital subtractive manufacturing (DDSM) of an assembly free mechanism. Process planning for SM involves voxel-by-voxel removal of material in the same way that an AM process consists of layer-by-layer addition of material. The voxelized CAM system minimizes user input by automatically generating toolpaths based on an analysis of accessible material to remove for a certain clearance in the mechanism's assembled state. The DDSM process is validated and compared to AM using case studies of the manufacture of two assembly free ball-in-socket mechanisms. 

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3. Deep learning-based semantic segmentation of machinable volumes for cyber manufacturing service

   https://doi.org/10.1016/j.jmsy.2023.11.005  

   Yan, Xiaoliang; Williams, Reed; Arvanitis, Elena; Melkote, Shreyes (February 2024, Journal of Manufacturing Systems)

   Enabling the vision of on-demand cyber manufacturing-as-a-service requires a new set of cloud-based computational tools for design manufacturability feedback and process selection to connect designers with manufacturers. In our prior work, we demonstrated a generative modeling approach in voxel space to model the shape transformation capabilities of machining operations using unsupervised deep learning. Combining this with a deep metric learning model enabled quantitative assessment of the manufacturability of a query part. In this paper, we extend our prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, which output per-voxel manufacturability feedback and labels of candidate machining operations for a query 3D part. Using three types of complex parts as case studies, we show that the proposed method accurately identifies machinable and non-machinable volumes with an average intersection-over-union (IoU) of 0.968 for axisymmetric machining operations, and a class-average F1 score of 0.834 for volume segmentation by machining operation. 

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4. Real-Time Path Guiding Using Bounding Voxel Sampling

   https://doi.org/10.1145/3658203  

   Lu, Haolin; Chang, Wesley; Hedstrom, Trevor; Li, Tzu-Mao (July 2024, ACM Transactions on Graphics)

   We propose a real-time path guiding method, Voxel Path Guiding (VXPG), that significantly improves fitting efficiency under limited sampling budget. Our key idea is to use a spatial irradiance voxel data structure across all shading points to guide the location of path vertices. For each frame, we first populate the voxel data structure with irradiance and geometry information. To sample from the data structure for a shading point, we need to select a voxel with high contribution to that point. To importance sample the voxels while taking visibility into consideration, we adapt techniques from offline many-lights rendering by clustering pairs of shading points and voxels. Finally, we unbiasedly sample within the selected voxel while taking the geometry inside into consideration. Our experiments show that VXPG achieves significantly lower perceptual error compared to other real-time path guiding and virtual point light methods under equal-time comparison. Furthermore, our method does not rely on temporal information, but can be used together with other temporal reuse sampling techniques such as ReSTIR to further improve sampling efficiency. 

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5. A Novel Laser-Aided Machining and Polishing Process for Additive Manufacturing Materials with Multiple Endmill Emulating Scan Patterns

   https://doi.org/10.3390/app11209428  

   Parvez, Mohammad Masud; Patel, Sahil; Isanaka, Sriram Praneeth; Liou, Frank (October 2021, Applied Sciences)

   In additive manufacturing (AM), the surface roughness of the deposited parts remains significantly higher than the admissible range for most applications. Additionally, the surface topography of AM parts exhibits waviness profiles between tracks and layers. Therefore, post-processing is indispensable to improve surface quality. Laser-aided machining and polishing can be effective surface improvement processes that can be used due to their availability as the primary energy sources in many metal AM processes. While the initial roughness and waviness of the surface of most AM parts are very high, to achieve dimensional accuracy and minimize roughness, a high input energy density is required during machining and polishing processes although such high energy density may induce process defects and escalate the phenomenon of wavelength asperities. In this paper, we propose a systematic approach to eliminate waviness and reduce surface roughness with the combination of laser-aided machining, macro-polishing, and micro-polishing processes. While machining reduces the initial waviness, low energy density during polishing can minimize this further. The average roughness (Ra=1.11μm) achieved in this study with optimized process parameters for both machining and polishing demonstrates a greater than 97% reduction in roughness when compared to the as-built part. 

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