| contributor author | Banyay, Gregory A. | |
| contributor author | Worrell, Clarence L. | |
| contributor author | Sidener, Scott E. | |
| contributor author | Kaizer, Joshua S. | |
| date accessioned | 2022-02-06T05:26:00Z | |
| date available | 2022-02-06T05:26:00Z | |
| date copyright | 7/27/2021 12:00:00 AM | |
| date issued | 2021 | |
| identifier issn | 2377-2158 | |
| identifier other | vvuq_006_03_031007.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4278014 | |
| description abstract | We present a framework for establishing credibility of a machine learning (ML) model used to predict a key process control variable setting to maximize product quality in a component manufacturing application. Our model coupled a purely data-based ML model with a physics-based adjustment that encoded subject matter expertise of the physical process. Establishing credibility of the resulting model provided the basis for eliminating a costly intermediate testing process that was previously used to determine the control variable setting. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Credibility Assessment of Machine Learning in a Manufacturing Process Application | |
| type | Journal Paper | |
| journal volume | 6 | |
| journal issue | 3 | |
| journal title | Journal of Verification, Validation and Uncertainty Quantification | |
| identifier doi | 10.1115/1.4051717 | |
| journal fristpage | 031007-1 | |
| journal lastpage | 031007-11 | |
| page | 11 | |
| tree | Journal of Verification, Validation and Uncertainty Quantification:;2021:;volume( 006 ):;issue: 003 | |
| contenttype | Fulltext | |