Step Ring-Based Three-Dimensional Path Planning Via Graphics Processing Unit Simulation for Subtractive Three-Dimensional Printing

Abstract

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.

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

“…[17] This paper first reviews different methods for designing thermal error models, to design two thermal prediction models: ANFIS by dividing the data space into rectangular subspaces (ANFIS-Grid model) and ANFIS using the fuzzy c-means clustering method (model ANFIS-FCM. A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent test data sets [18] This article deals with the implementation of a strategic plan for maintenance. The methodology chosen was Total Productive Maintenance (TPM) and the cells chosen for its implementation were CNC lathes and CNC machining centers.…”

“…[17] ANFIS Machine accuracy at 80 ANFIC-FCM produced better result with 94% improvement in error. [18] Reduced costs increase and increase the useful life of equipment.…”

“…In [18] there was evidence of a reduction in costs and at the same time an increase in the useful life of the used equipment in which a strategic plan was used which the chosen methodology was Total Productive Maintenance (TPM), as well as focusing on eliminating losses of efficiencies; However, some limitations are the worker resistance and the time it takes to adapt in the new routine which are points against which it must be reinforced.…”

An Approach of Path Optimization Algorithm for 3D Concrete Printing Based on Graph Theory

“…Their course provides a unified methodology for surface fitting from 3D surface measurements and mapping a curve from 2D onto a 3D surface with minimal distortion. Wu et al [14] reconstructed CAD models from an original (GATech Buzz) sample with 2D image information. A CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback.…”