A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
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DOI：
10.1115/1.4036350
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发表时间：
2017-07-01
影响因子：
4
通讯作者：
Kumara, Soundar
中科院分区：
文献类型：
--
作者：
Wu, Dazhong;Jennings, Connor;Kumara, Soundar
制造商越来越需要开发预测模型来预测制造系统或部件的机械故障和剩余使用寿命（RUL）。经典的基于模型或基于物理的预测通常需要对感兴趣的系统有深入的物理理解，以开发封闭形式的数学模型。然而，系统行为的先验知识并不总是可用的，特别是对于复杂的制造系统和过程。为了补充基于模型的预测，数据驱动方法越来越多地应用于机械预测和维护管理，将传统制造系统转变为具有人工智能的智能制造系统。虽然之前的研究已经证明了数据驱动方法的有效性，但大多数这些预测方法都是基于经典的机器学习技术，如人工神经网络（ann）和支持向量回归（SVR）。随着人工智能的飞速发展，各种机器学习算法被开发出来，并广泛应用于许多工程领域。本研究的目的是引入一种基于随机森林（RFs）的刀具磨损预测方法，并将RFs与前馈反馈传播（FFBP）人工神经网络和支持向量回归（SVR）的性能进行比较。具体来说，利用315次铣床试验收集的实验数据，比较了FFBP神经网络、SVR和rf的性能。实验结果表明，RFs比具有单隐层和SVR的FFBP神经网络产生更准确的预测。
Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.