Predicting thrust force during drilling of composite laminates with step drills through the Gaussian process regression
PurposeHere, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting thrust force during composite laminates drilling with step drills.Design/methodology/approachUse of machine learning methods could benefit machi...
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| Published in: | Multidiscipline modeling in materials and structures Vol. 18; no. 5; pp. 845 - 855 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Bingley
Emerald Publishing Limited
12.10.2022
Emerald Group Publishing Limited |
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| ISSN: | 1573-6105, 1573-6113 |
| Online Access: | Get full text |
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| Abstract | PurposeHere, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting thrust force during composite laminates drilling with step drills.Design/methodology/approachUse of machine learning methods could benefit machining process optimizations. Accurate, stable and robust performance is one of major criteria in choosing among different models. For industrial applications, it is also important to consider model applicability, ease of implementations and cost effectiveness.FindingsThis model turns out to be simple, accurate and stable, which helps fast estimates of thrust force. Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments.Originality/valueThrough combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments. |
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| AbstractList | PurposeHere, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting thrust force during composite laminates drilling with step drills.Design/methodology/approachUse of machine learning methods could benefit machining process optimizations. Accurate, stable and robust performance is one of major criteria in choosing among different models. For industrial applications, it is also important to consider model applicability, ease of implementations and cost effectiveness.FindingsThis model turns out to be simple, accurate and stable, which helps fast estimates of thrust force. Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments.Originality/valueThrough combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments. Purpose>Here, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting thrust force during composite laminates drilling with step drills.Design/methodology/approach>Use of machine learning methods could benefit machining process optimizations. Accurate, stable and robust performance is one of major criteria in choosing among different models. For industrial applications, it is also important to consider model applicability, ease of implementations and cost effectiveness.Findings>This model turns out to be simple, accurate and stable, which helps fast estimates of thrust force. Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments.Originality/value>Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments. |
| Author | Xu, Xiaojie Zhang, Yun |
| Author_xml | – sequence: 1 givenname: Yun orcidid: 0000-0002-9464-1751 surname: Zhang fullname: Zhang, Yun email: yzhang43@ncsu.edu – sequence: 2 givenname: Xiaojie orcidid: 0000-0002-4452-1540 surname: Xu fullname: Xu, Xiaojie email: xxu6@ncsu.edu |
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| Keywords | Drilling Gaussian process regression Composite material Thrust force Machine learning |
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| Snippet | PurposeHere, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting... Purpose>Here, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting... |
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| SubjectTerms | Algorithms Carbon fibers Composite materials Cost effectiveness Design of experiments Drilling Drilling machines (tools) Drills Feed rate Fractals Gaussian process Industrial applications Laminates Machine learning Machining Neural networks Optimization Polymers Regression Taguchi methods Thrust Variables |
| Title | Predicting thrust force during drilling of composite laminates with step drills through the Gaussian process regression |
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