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首页> 外文期刊>Sensors and materials >Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools
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Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

机译：应用综合灰色系统理论与传感器技术研究切割条件对机床热误差建模的影响

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摘要

To produce a good machine tool, the thermally induced error in the machine during machining plays a crucial role and is an important issue needing to be resolved. The thermal error may account for 70% of the total error. There are three main approaches to solving the thermal error problem: preventing heat flows from hot components, designing a thermally stable structure for the machine, and compensating the thermal error using thermal error models. The first two approaches can be carried out in the primary design stage of machine tools, and they have been used in the manufacture of commercial products. The third approach, the strategy of thermal error compensation, is the most effective and popular approach. However, there are still many unsolved problems. Among these problems, the cutting conditions have a significant influence on the modeling precision of the thermal error. In this study, we develop an integral model based on the integrated grey system theory (IGST) in conjunction with a genetic-algorithm (GA)-optimized back-propagation neural network (BPNN) to investigate the influence of cutting conditions on a machine tool's thermal error. The model is chosen on account of its high ability in dealing with a small amount of training data. Results show that a single thermal error modeling formula cannot make accurate predictions for different cutting conditions. Suitable adjustment of the modeling parameters or the use of a multiple modeling scheme is needed.
机译：为了生产一款良好的机床，在加工过程中机器中的热引起的误差起着至关重要的作用，并且是需要解决的重要问题。热误差可能占总误差的70％。解决热误差问题有三种主要方法：防止热部件的热流，为机器的热稳定结构设计，并使用热误差模型补偿热误差。前两种方法可以在机床的主要设计阶段进行，它们已被用于制造商业产品。第三种方法是热误差补偿的策略，是最有效和最流行的方法。但是，仍有许多未解决的问题。在这些问题中，切割条件对热误差的建模精度具有显着影响。在这项研究中，我们与遗传算法（GA） - 优化的背传播神经网络（BPNN）结合了基于集成灰色系统理论（IGST）的积分模型，以研究切割条件对机床上的影响热误差。根据其高能力处理少量培训数据，选择该模型。结果表明，单个热误差造型配方不能为不同的切割条件做出准确的预测。需要适当调整建模参数或使用多种建模方案。

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