GPU accelerated voxel-based machining simulation

Abstract

The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.

Computational Numerical Control (CNC) Machines: A Systematic Review From 2015 To 2022

“…[11] Since overall optimization is largely based on problem formulation, emphasis has been placed on defining optimization criteria as crucial elements to represent performance. [12] In order to optimize manufacturing with energy efficiency is that it resorted to an immune system enabled by Big Data, programming is a critical stage to minimize the life cycle cost in manufacturing. [13] The virtual factory concept is presented as the vehicle for modeling and simulation of manufacturing where a path is proposed for its implementation that is based on technology developments and standards for the analysis of manufacturing data.…”

“…[11] improved surface roughness through the application of the automated refrigerant supply system. [12] Energy savings of around 30% and improved productivity of more than 50% have been achieved by adopting I2 S in factories.…”

Development of GPU‐based matrix‐free strategies for large‐scale elastoplasticity analysis using conjugate gradient solver

“…Though, voxel-based mesh is inherently applicable to simpler geometries with plane boundary surfaces, a recent study 29 shows sufficient accuracy for curved surfaces when using high resolution mesh. In literature, voxel-based mesh has been efficiently used to model bone failure, 30,31 topology optimization, 32 simulation of material microstructures 33,34 and manufacturing processes 35 to name a few. Since voxel-based mesh consume very less memory, along with matrix-free strategy and GPU acceleration, it is a promising alternative for large-scale elastoplasticity.…”

Image-assisted collision detection for calculation of an assembly interference matrix

“…Therefore, it is hopeful to improve the computation e ciency by dimension-reduction in viewpoint of image-space where represents the swept volume of object part by the projection area of view volume for object part along moving direction. Besides, with the performance boosting of Graphic Processing Uniting (GPU), image-based collision detection methods gain excellent performance by utilizing the powerful parallel computation capability of GPU to seed up task calculation [7]. As the two of most classic ones, RECODE [8] and CULLIDE [9] employ the depth buffer, stencil buffer, occlusion query etc.…”