A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Author:

Wu Dazhong1,Jennings Connor1,Terpenny Janis1,Gao Robert X.2,Kumara Soundar3

Affiliation:

1. Department of Industrial and Manufacturing Engineering, National Science Foundation Center for e-Design, Pennsylvania State University, University Park, PA 16802 e-mail:

2. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106 e-mail:

3. Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802 e-mail:

Abstract

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.

Funder

National Science Foundation

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

Reference50 articles.

1. Linking Maintenance Strategies to Performance;Int. J. Prod. Econ.,2001

2. A Survey of Preventive Maintenance Models for Stochastically Deteriorating Single-Unit Systems;Nav. Res. Logist.,1989

3. Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems,2016

4. Machine Performance Monitoring and Proactive Maintenance in Computer-Integrated Manufacturing: Review and Perspective;Int. J. Comput. Integr. Manuf.,1995

5. The Analytic Hierarchy Process Applied to Maintenance Strategy Selection;Reliab. Eng. Syst. Saf.,2000

Cited by 379 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3