Author:
Wang Yangyu,Zhang Yongle,Tan Dapeng,Zhang Yongchao
Abstract
AbstractAs a starting point in equipment manufacturing, sawing plays an important role in industrial production. Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies. Due to the backwardness of intelligent technology, the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries. State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing, and a new industrial scheme of replacing turning-milling by sawing is described. The key technologies of processing-measuring integrated control, multi-body dynamic optimization, the collaborative sawing network framework, the distributed cloud sawing platform, and the self-adapting service method are analyzed; with consideration of the problems of poor processing control stableness, low single machine intelligence level, no on-line processing data service and active flutter suppression of sawing with wide-width and heavy-load working conditions. Suggested directions for further research, industry implementation, and industry-research collaboration are provided.
Funder
Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
Foundation for Distinguished Young Talents in Higher Education of Henan
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Reference119 articles.
1. Y B Wang, K J Hong, J Zou, et al. A CNN-based visual sorting system with cloud-edge computing for flexible manufacturing systems. IEEE Transactions on Industrial Informatics, 2020: 16(7): 4726-4735.
2. Y Huang, B H Lu, W H Zhao. Application of cloud computing in intelligent machine tool control system. Chinese Journal of Mechanical Engineering, 2018, 54(8): 210-216. (in Chinese)
3. D M Zhang, J Z Zhang, H W Huang, et al. Machine learning-based prediction of soil compression modulus with application of 1D settlement. Journal of Zhejiang University-Science A, 2020, 21(6): 430-444.
4. X Y Li, J Jie. Ballistic behavior of high strength steel targets during high speed impact of projectile. Journal of Iron and Steel Research, 2020, 32(5): 429-436. (in Chinese)
5. L B Zhang, H P Lv, D P Tan, et al. An adaptive quantum genetic algorithm for task sequence planning of complex assembly systems. Electronics Letters, 2018, 54(14): 870-871.
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