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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process
Advances in Manufacturing ( IF 2.568 ) Pub Date : 2021-02-22 , DOI: 10.1007/s40436-020-00339-6
Long-Hua Xu, Chuan-Zhen Huang, Jia-Hui Niu, Jun Wang, Han-Lian Liu, Xiao-Dan Wang

During the actual high-speed machining process, it is necessary to reduce the energy consumption and improve the machined surface quality. However, the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments. Herein, a novel intelligent system is proposed for prediction and optimization. A novel adaptive neuro-fuzzy inference system (NANFIS) is proposed to predict the energy consumption and surface quality. In the NANFIS model, the membership functions of the inputs are expanded into: membership superior and membership inferior. The membership functions are varied based on the machining theory. The inputs of the NANFIS model are cutting parameters, and the outputs are the machining performances. For optimization, the optimal cutting parameters are obtained using the improved particle swarm optimization (IPSO) algorithm and NANFIS models. Additionally, the IPSO algorithm as a learning algorithm is used to train the NANFIS models. The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron. The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2% and 93.4%, respectively. The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models. Based on the IPSO algorithm and NANFIS models, the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency. It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes, thereby enabling sustainable and intelligent manufacturing.



中文翻译:

在高速铣削过程中使用新的推理系统预测切削力和表面质量并优化切削参数

在实际的高速加工过程中,有必要减少能耗并改善加工表面质量。但是,在复杂的加工环境中很难获得合适的预测模型和最佳切削参数。在此,提出了一种新颖的智能系统,用于预测和优化。提出了一种新颖的自适应神经模糊推理系统(NANFIS)来预测能量消耗和表面质量。在NANFIS模型中,输入的隶属函数被扩展为:上级成员资格和下级成员资格。隶属函数根据加工原理而变化。NANFIS模型的输入是切削参数,输出是加工性能。为了优化,使用改进的粒子群优化(IPSO)算法和NANFIS模型可以获得最佳切削参数。另外,使用IPSO算法作为学习算法来训练NANFIS模型。所提出的智能系统应用于压实石墨铁的高速铣削过程。实验结果表明,采用NANFIS模型预测的能耗和表面粗糙度分别达到91.2%和93.4%。与其他智能模型相比,NANFIS模型可以更准确地预测能耗和表面粗糙度。基于IPSO算法和NANFIS模型,获得并验证了最佳切削参数,以降低切削能力和表面粗糙度并提高铣削效率。

更新日期:2021-02-22
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