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This paper demonstrates a novel, compact-sized hardware-in-the-loop system, and its verification using machine learning and artificial intelligence features in battery controls. Conventionally, a battery management system involves algorithm development for battery modeling, estimation, and control. These tasks are typically validated by running the battery tester open-loop, i.e., the tester equipment executes the pre-defined experimental protocols line by line. Additional equipment is required to make the testing closed-loop, but the integration is typically not straightforward. To improve flexibility and accessibility for battery management, this work proposes a low-cost highly reliable closed-loop charger and discharger. We first focus on the electronic circuit design for battery testing systems to maximize the applied current accuracy and precision. After functional verification, we further investigate applications for closed-loop battery management systems. In particular, we extend the proposed architecture into the learning-based control design, which is a feedback controller. We utilize reinforcement learning techniques to highlight the benefits of closed-loop controls. As an example, we compare this learning-based control strategy with a conventional battery charging control. The experimental results demonstrate that the proposed experimental design is able to handle the learning-based controller and achieve a more reliable and safer charging protocol driven by artificial intelligence.
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A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
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.
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- Award ID(s):
- 1650527
- PAR ID:
- 10137110
- Date Published:
- Journal Name:
- Journal of Manufacturing Science and Engineering
- Volume:
- 139
- Issue:
- 7
- ISSN:
- 1087-1357
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4036350
