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A study of automatic CAM template extraction based on the G-TSSA-RF algorithm

Published: 01 June 2024 Publication History

Abstract

In response to the relatively inefficient template extraction situation in the CNC programming process, a method is now proposed to improve the matching of templates by using the golden sine split sparrow search algorithm to improve the RF algorithm based on the traditional use of the RF algorithm to extract CAM templates. An effective characterisation of the existing CNC process information set is achieved by establishing a model of feature-operation information. The algorithm is used to analyse the process information dataset to achieve optimisation of the number of decision trees for each process operation, resulting in a similarity matrix. The similarity matrix is substituted into the clustering algorithm formula to obtain the number of clusters. The evaluation of the clustering effect of each process operation is achieved based on the calculation of the contour coefficient formula. It is shown experimentally that the method increases the intelligence of template extraction and improves the efficiency of CNC programming.

References

[1]
P Waurzyniak, with its design-for-manufacture system, metrology supplier Renishaw reduced cycle times and improved productivity aiming for Maximum Efficiency [J]. Manufacturing Engineering, 2005.
[2]
Shengwen Zhang, Yuling Ding, Guicheng Wang CAD/CAPP/CAM integration system for frame parts of marine diesel [J]. Computer integrated manufacturing system. 2012.
[3]
M Kowalski, Method of automatic CAM programming using machining templates [J], Mechanik, 2020.
[4]
Wang, Intelligent manufacturing system of impeller for computer numerical control (CNC) programming based on KBE, Journal of Central South University, 2014.
[5]
Unification Method Study on UG Programming Template, UG Tool Library Template, CNC Machining Center Template, 2021.
[6]
Wang, H Wang, S Chen, LI Jianjun. Mold CNC Machining Recommendation Systems Based on Local Features [J]. China Mechanical Engineering. 2019.
[7]
Henderson M R, Anderson D C. Computer recognition and extraction of form features [J]. Computer in Industry, 1984, 6(4):315-325.
[8]
Cunningham J, Dixon J R.Designing with Features: the Origin of Features[C]//Proc, ASME Computers in Engineering Conf, San Francisco, USA. 1988: 237-243 [J].
[9]
Zhou, Q Wang, M Jiang, Z Zhang, Research on NC Knowledge Modeling of Combination of Process and Structure [J], New Technology & New Process, 2011.
[10]
L.Random forests [J]. Machine learning, 2001, 45 (1):5-32.
[11]
Xu, L Peng, Z Ji, S Zheng, Z Tian, S Geng. Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network [J]. 2021.
[12]
He, Y Luo, AH Li, TF Wang, YH Peng Research on Protection Optimization of Distribution Network Containing Distributed Power Generation Based on Sparrow Algorithm [J]. 2021.
[13]
Ouyang, Y Liu, D Zhu. An adaptive chaotic sparrow search optimization algorithm [J]. 2021.
[14]
Li, Y Huang, S Jin, X Hou, X Wang, Quantum spectral clustering algorithm for unsupervised learning [J], Science in China: Information Science (English), 2022.
[15]
Treshansky, RM Mcgraw, Overview of clustering algorithms [J], Enabling Technology for Simulation Science Ⅴ, 2001.
[16]
Chen, G Li, J He, Z Yang, J Wang, A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering [J], Reliability Engineering & System Safety, 2022.
[17]
Tingyu YE, Jun YE, Hui WANG, Lei WANG, A rough K-means clustering algorithm combined with artificial bee colony optimization [J], Journal of Frontiers of Computer Science and Technology, 2022.
[18]
Yang, YS Li, XX Hu, RY Pan, Optimization Study on k Value of Kmeans Algorithm [J], Systems Engineering-Theory & Practice, 2006.
[19]
Wang, F Dou, X Yu, G Liu, Scenario analysis of wind power output based on improved k-means algorithm [J], IOP Conference Series Earth and Environmental Science, 2021.

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  1. A study of automatic CAM template extraction based on the G-TSSA-RF algorithm

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    ICBAR '23: Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management
    November 2023
    1156 pages
    ISBN:9798400716478
    DOI:10.1145/3656766
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 01 June 2024

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