A cuckoo search optimisation-based Grey prediction model for thermal error compensation on CNC machine tools
A cuckoo search optimisation-based Grey prediction model for thermal error compensation on CNC machine tools
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DOI：
10.1108/gs-08-2016-0021
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发表时间：
2017-01-01
影响因子：
2.9
通讯作者：
Fletcher, Simon
中科院分区：
文献类型：
--
作者：
Abdulshahed, Ali M.;Longstaff, Andrew P.;Fletcher, Simon
目的-本文的目的是产生一种智能技术，用于计算机数控（CNC）机床的热变形所造成的机床误差建模。提出了一种新的基于鸟族寿命的元启发式算法--布谷鸟搜索（CS）算法来优化GMC（1，N）系数。然后，它被用来预测一个小型的立式铣削中心的基础上选定的sensors.Design/方法/途径的热误差-一个灰色模型与卷积积分GMC（1，N）被用来设计一个热预测模型。为了提高该模型的准确性，GMC（1，N）的生成系数进行优化，使用一种新的元启发式方法，称为CS algorithm.Findings -结果表明实验和预测的热误差之间的良好协议。因此，可以得出结论，它是可能的优化灰色模型，使用CS算法，它可以被用来预测的CNC机床的热误差。独创性/价值-尝试已首次申请CS算法校准GMC（1，N）模型。提出了基于CS的灰色模型进行了验证，并与粒子群优化（PSO）的灰色模型进行了比较。仿真和比较表明，CS算法优于PSO算法，可作为灰色模型的一种替代优化算法，用于热误差补偿。
Purpose - The purpose of this paper is to produce an intelligent technique for modelling machine tool errors caused by the thermal distortion of Computer Numerical Control (CNC) machine tools. A new metaheuristic method, the cuckoo search (CS) algorithm, based on the life of a bird family is proposed to optimize the GMC(1, N) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors.Design/methodology/approach - A Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC (1, N) are optimized using a new metaheuristic method, called the CS algorithm.Findings - The results demonstrate good agreement between the experimental and predicted thermal error. It can therefore be concluded that it is possible to optimize a Grey model using the CS algorithm, which can be used to predict the thermal error of a CNC machine tool.Originality/value - An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, N) model. The proposed CS-based Grey model has been validated and compared with particle swarm optimization (PSO) based Grey model. Simulations and comparison show that the CS algorithm outperforms PSO and can act as an alternative optmization algorithm for Grey models that can be used for thermal error compensation.