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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >A comparison between parametric and non-parametric soft computing approaches to model the temperature of a metal cutting tool
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A comparison between parametric and non-parametric soft computing approaches to model the temperature of a metal cutting tool

机译：参数化和非参数化软计算方法对金属切削刀具温度建模的比较

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摘要

Modelling the temperature of cutting tools in industry has become a vital assignment for manufacturing process modelling. The quality and the cost of the produced products are significantly affected if the process is inaccurate. However, the process of modelling the temperature is complex because the relationship between the model variables of the temperature is nonlinear. In this article, five soft computing techniques are used to develop mathematical models for two different cutting tools. Genetic Algorithm, Particle Swarm Optimisation and Cuckoo Search are used to estimate the parameters of a common empirical model for the temperature of the cutting tools. This empirical model is also nonlinear model of the parameters. Thus, traditional modelling techniques such as least square estimation will have trouble finding the optimal set of model parameters. The challenging problem which is also tackled in this article is the development of nonlinear model of the cutting tool using Artificial Neural Networks and Multigene Symbolic Regression Genetic Programming (GP). In the case of ANNs, the model structure is hidden inside the network, whereas in the case of Multigene Symbolic Regression GP, the developed model equation shall reveal the relationship between model variables. Obtained models will be validated based on many evaluation criteria.
机译：在工业中对切削工具的温度进行建模已成为制造过程建模的重要任务。如果过程不正确，则会严重影响所生产产品的质量和成本。但是，由于温度的模型变量之间的关系是非线性的，因此对温度进行建模的过程很复杂。在本文中，使用了五种软计算技术来为两种不同的切削工具开发数学模型。遗传算法，粒子群优化和布谷鸟搜索用于估计切削刀具温度的通用经验模型的参数。该经验模型也是参数的非线性模型。因此，传统的建模技术（例如最小二乘估计）将难以找到最佳的模型参数集。本文还解决的挑战性问题是使用人工神经网络和多基因符号回归遗传规划（GP）开发刀具的非线性模型。在人工神经网络的情况下，模型结构隐藏在网络内部，而在多基因符号回归GP的情况下，开发的模型方程将揭示模型变量之间的关系。获得的模型将基于许多评估标准进行验证。

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