Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments
Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments
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DOI：
10.1007/s00170-010-3133-1
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发表时间：
2011-01
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影响因子：
--
通讯作者：
H. Sağlam
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作者：
H. Sağlam
带锯作为一种多点切割操作，是工业上切割原材料的首选方法。虽然带锯切割是一种非常古老的工艺，但与其他切割工艺相比，研究工作非常有限。合适的刀具状态在线监测系统是精密自动化机床实现更好的刀具管理的必要条件。建立了基于人工神经网络的刀具磨损监测模型，对带锯切割原材料（美国钢铁协会1020、1040和4140）过程中的刀具磨损进行了预测。基于对切削力信号的连续数据采集，神经网络具有相当快的数据处理能力，可以对某些磨损参数进行估计或分类。采用误差反向传播训练算法对基于切削力的6 × 9 × 1结构的多层前馈人工神经网络（ANN）系统进行训练，以估计带锯过程中的刀具磨损。用于网络训练和检查的数据是根据asL27计划实验的田口设计原则从实验中得到的。实验中考虑的输入因素有进给速度、切削速度、啮合长度和材料硬度。利用人工神经网络模型生成三维曲面图，研究切削条件对锯片的交互作用。分析表明，切削长度、硬度和切削速度分别对齿磨损有显著影响，进给量影响较小。在这项研究中，详细介绍了实验和人工神经网络在预测牙齿磨损方面的应用。系统结果表明，直接测得的翼面磨损量与估算值吻合较好。
The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6 × 9 × 1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned asL27. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.