Machining scheme selection technique for feature group based on re-optimized bacterial foraging algorithm-Reference-Cited by-同舟云学术

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Machining scheme selection technique for feature group based on re-optimized bacterial foraging algorithm

Published:2024-06-04 Issue: Volume: Page:1-17
ISSN:2168-1015
Container-title:Journal of Industrial and Production Engineering
language:en
Short-container-title:Journal of Industrial and Production Engineering

Author:

Cheng Hao¹²,Wang Lin¹²,Wang Rui¹²,Huang Xunzhuo¹²,Zheng Zujie³,Luo Weifeng⁴

Affiliation:

1. School of Marine Engineering, Harbin Institute of Technology Weihai, Weihai, China

2. Department of Mechanical Engineering, Weihai Key Laboratory of Intelligent Operation and Maintenance, Weihai, China

3. Department of Structural Processing, Shanghai Spaceflight Precision Machinery Institute, Shanghai, China

4. Department of Structural Processing, Hunan Vanguard Group Co., Ltd, Changsha, China

Funder

National Key Research and Development Program of China

Natural Science Foundation of Shandong Province of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21681015.2024.2361044

Reference46 articles.

1. Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems

2. An intelligent approach to robust multi-response process design

3. An approach to a feature-based comparison of solid models of machined parts

4. Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process

5. Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS

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