Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model
Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model
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DOI：
10.1016/j.jmsy.2016.08.006
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发表时间：
2016-10-01
影响因子：
12.1
通讯作者：
Potdar, Akshay
中科院分区：
文献类型：
--
作者：
Abdulshahed, Ali M.;Longstaff, Andrew P.;Potdar, Akshay
提出了一种新的龙门式五轴联动数控机床热误差补偿建模方法。该方法采用了一种带卷积积分的灰色神经网络模型(GNNMCI(1，N))，充分利用了灰色系统模型与人工神经网络(ANN)模型的相似性和互补性，克服了单独应用这两种模型的缺点。采用粒子群优化(PSO)算法对灰色神经网络进行优化。当数据生成成本高昂时，数据对的大小至关重要，因为获取数据所需的机器停机时间通常被认为是令人望而却步的。在这种情况下，优化用于训练的数据对的数量是校准物理模型或训练黑盒模型的首要问题。灰色累加生成运算(AGO)是灰色系统理论的基础，用来将原始数据转换成比原始数据序列具有更少随机性的单调数据序列。选择热模型的输入是一个不平凡的决定，它最终是获得与热失真充分相关的数据的能力和实施必要反馈传感器的成本之间的折衷。在这项研究中，关键位置的温度测量被可接近位置的直接变形测量补充。这种形式的数据融合简化了建模过程，提高了系统的精度，并减少了模型的总输入数量，因为否则将需要大量的热传感器来覆盖整个结构。这项工作考虑了Z轴加热试验、C轴加热试验和组合(螺旋)运动。由GNNMCI(1，N)模型计算出的补偿值被送到控制器进行实时误差补偿。测试结果表明，补偿后的热误差降低了85%。(C)2016年提交人。由爱思唯尔有限公司代表制造工程师协会出版。这是一篇基于CC by License的开放获取文章。
This paper presents a new modelling methodology for compensation of the thermal errors on a gantry type 5-axis CNC machine tool. The method uses a "Grey Neural Network Model with Convolution Integral" (GNNMCI(1, N)), which makes full use of the similarities and complementarity between Grey system models and artificial neural networks (ANNs) to overcome the disadvantage of applying either model in isolation. A Particle Swarm Optimisation (PSO) algorithm is also employed to optimise the proposed Grey neural network. The size of the data pairs is crucial when the generation of data is a costly affair, since the machine downtime necessary to acquire the data is often considered prohibitive. Under such circumstances, optimisation of the number of data pairs used for training is of prime concern for calibrating a physical model or training a black-box model. A Grey Accumulated Generating Operation (AGO), which is a basis of the Grey system theory, is used to transform the original data to a monotonic series of data, which has less randomness than the original series of data. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this study, temperature measurement at key locations was supplemented by direct distortion measurement at accessible locations. This form of data fusion simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. The Z-axis heating test, C-axis heating test, and the combined (helical) movement are considered in this work. The compensation values, calculated by the GNNMCI(1, N) model were sent to the controller for live error compensation. Test results show that a 85% reduction in thermal errors was achieved after compensation. (C) 2016 The Authors. Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers. This is an open access article under the CC BY license.