Hybrid data-driven physics-based model fusion framework for tool wear prediction View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Houman Hanachi, Wennian Yu, Il Yong Kim, Jie Liu, Chris K. Mechefske

ABSTRACT

An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fusion framework is an extension to the regularized particle filtering technique, used to facilitate updating the state prediction with a numerical inference model, when measurement models alone are not satisfactory. The results show significant improvement in tool wear state estimation, reducing the prediction errors by almost half, compared to the prediction model and sensor-based monitoring method used independently. More... »

PAGES

1-12

References to SciGraph publications

  • 2009-12. Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2001. Improving Regularised Particle Filters in SEQUENTIAL MONTE CARLO METHODS IN PRACTICE
  • 2017-06. Tool life management of unmanned production system based on surface roughness by ANFIS in INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
  • 2012-06. Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process in JOURNAL OF INTELLIGENT MANUFACTURING
  • 2015-04. Health assessment and life prediction of cutting tools based on support vector regression in JOURNAL OF INTELLIGENT MANUFACTURING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00170-018-3157-5

    DOI

    http://dx.doi.org/10.1007/s00170-018-3157-5

    DIMENSIONS

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