Kaiyao Zhang, Wenlei Xiao, Xiangming Fan, Gang Zhao
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引用次数: 0
Abstract
The next generation of STEP-NC technology needs to achieve more intelligent process optimization. Currently, the calculation method of toolpath length in process optimization algorithms hinders the flexibility and adaptability of algorithm applications. Process optimization needs to generate toolpath based on dynamic process parameter combinations automatically. To address this issue, this paper deploys CAM on the cloud based on the STEP-NC edge-cloud collaboration system, enabling the automatic generation of toolpath through interaction with the process parameter optimization process. Building on this, a non-dominated sorting genetic algorithm III with CAM as a service (NSGAIII-CaaS) for process optimization is proposed. Additionally, a process optimization method for machining feature elements is introduced. Finally, the proposed method is applied to optimize process parameters for three features of a typical part from COMAC, targeting machining cost and machining time. The feasibility of the proposed method’s application in manufacturing enterprises is verified. Using the optimized process parameters for machining features, the cost is reduced by over 70%, efficiency is improved by 70%, and redundant toolpath in machining features are optimized.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.