CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References
id caadria2020_088
authors Kado, Keita, Furusho, Genki, Nakamura, Yusuke and Hirasawa, Gakuhito
year 2020
title rocess Path Derivation Method for Multi-Tool Processing Machines Using Deep-Learning-Based Three Dimensional Shape Recognition
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 609-618
summary When multi-axis processing machines are employed for high-mix, low-volume production, they are operated using a dedicated computer-aided design/ computer-aided manufacturing (CAD/CAM) process that derives an operating path concurrently with detailed modeling. This type of work requires dedicated software that occasionally results in complicated front-loading and data management issues. We proposed a three-dimensional (3D) shape recognition method based on deep learning that creates an operational path from 3D part geometry entered by a CAM application to derive a path for processing machinery such as a circular saw, drill, or end mill. The methodology was tested using 11 joint types and five processing patterns. The results show that the proposed method has several practical applications, as it addresses wooden object creation and may also have other applications.
keywords Three-dimensional Shape Recognition; Deep Learning; Digital Fabrication; Multi-axis Processing Machine
series CAADRIA
email kado@chiba-u.jp
full text file.pdf (4,499,895 bytes)
references Content-type: text/plain
Details Citation Select
100%; open Chai, H and Yuan, PF (2019) Find in CUMINCAD Investigations on Potentials od Robotic Band-Saw Cutting in Complex Wood Structures , Robotic Fabrication in Architecture, Art and Design 2018, pp. 256-268

100%; open Nakamura, Y, Furusho, G, Kado, K and Hirasawa, G (2018) Find in CUMINCAD Development of Automatic Path Deriving System for Circular Saw and Production of Wooden Dome with Miter Joints, New Building System with Multi-Axis Machining 2 , AIJ journal of technology and design, 24, 58, pp. 1299-1302

100%; open Nia, KR and Mori, G (2017) Find in CUMINCAD Building damage assessment using deep learning and ground-level image data , 14th Conference on Computer and Robot Vision (CRV), pp. 95-102

100%; open Nishimura, N, Yabuki, N and Fukuda, T (2019) Find in CUMINCAD Automatic Detection of Positions and Shapes of Various Objects at Construction Sites from Digital Images Using Deep Learning , Wu, P and Wang, X (eds), Innovative Production and Construction, World Scientific Pub Co Inc

100%; open Qi, CR, Su, H, Mo, K and Guibas, LJ (2017) Find in CUMINCAD PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , Conference on Computer Vision and Pattern Recognition (CVPR) 2017

100%; open Qi, CR, Yi, L, Su, H and Guibas, LJ (2017) Find in CUMINCAD PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , Conference on Neural Information Processing Systems (NIPS) 2017

100%; open Xiu, H, Vinayaraj, P, Kim, K and Nakamura, R (2018) Find in CUMINCAD 3D Semantic Segmentation for High-resolution Aerial Survey Derived Point Clouds using Deep Learning , 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '18)

last changed 2020/08/14 18:40
pick and add to favorite papersHOMELOGIN (you are user _anon_209827 from group guest) CUMINCAD Papers Powered by SciX Open Publishing Services 1.002