A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions Article Swipe
Kriz George
,
Sathish Kannan
,
Ali Raza
,
Salman Pervaiz
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ma14216717
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ma14216717
As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs within the limitations of available resources. However, finite element simulations—the most common means to analyze and understand the machining of high-performance materials under various cutting conditions and environments—require high amounts of processing power and time in order to output reliable and accurate results which can lead to delays in the initiation of manufacture. The objective of this study is to reduce the time required prior to fabrication to determine how available inputs will affect the desired outputs and machining parameters. This study proposes a hybrid predictive methodology where finite element simulation data and machine learning are combined by feeding the time series output data generated by Finite Element Modeling to an Artificial Neural Network in order to acquire reliable predictions of optimal and/or expected machining inputs (depending on the application of the proposed approach) using what we describe as a backwards training model. The trained network was then fed a test dataset from the simulations, and the results acquired show a high degree of accuracy with regards to cutting force and depth of cut, whereas the predicted/expected feed rate was wildly inaccurate. This is believed to be due to either a limited dataset or the much stronger effect that cutting speed and depth of cut have on power, cutting forces, etc., as opposed to the feed rate. It shows great promise for further research to be performed for implementation in manufacturing facilities for the generation of optimal inputs or the real-time monitoring of input conditions to ensure machining conditions do not vary beyond the norm during the machining process.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma14216717
- https://www.mdpi.com/1996-1944/14/21/6717/pdf?version=1636368694
- OA Status
- gold
- Cited By
- 1
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3213147339
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3213147339Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ma14216717Digital Object Identifier
- Title
-
A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining ConditionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-08Full publication date if available
- Authors
-
Kriz George, Sathish Kannan, Ali Raza, Salman PervaizList of authors in order
- Landing page
-
https://doi.org/10.3390/ma14216717Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1944/14/21/6717/pdf?version=1636368694Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1944/14/21/6717/pdf?version=1636368694Direct OA link when available
- Concepts
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Machining, Finite element method, Training (meteorology), Mechanical engineering, Manufacturing engineering, Computer science, Structural engineering, Engineering, Materials science, Engineering drawing, Machine learning, Physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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