Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
-д хадгалсан:
| -д хэвлэсэн: | arXiv.org (Dec 3, 2021), p. n/a |
|---|---|
| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , , , |
| Хэвлэсэн: |
Cornell University Library, arXiv.org
|
| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full text outside of ProQuest |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2607083821 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2607083821 | ||
| 045 | 0 | |b d20211203 | |
| 100 | 1 | |a Truong, Loc | |
| 245 | 1 | |a Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 3, 2021 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance. | |
| 653 | |a Machine learning | ||
| 653 | |a Design of experiments | ||
| 653 | |a Mathematical models | ||
| 653 | |a Manufacturing | ||
| 653 | |a Classification | ||
| 653 | |a Material properties | ||
| 653 | |a Pattern matching | ||
| 653 | |a Extrusion | ||
| 653 | |a Solid phases | ||
| 653 | |a Process parameters | ||
| 700 | 1 | |a Choi, WoongJo | |
| 700 | 1 | |a Wight, Colby | |
| 700 | 1 | |a Coda, Lizzy | |
| 700 | 1 | |a Emerson, Tegan | |
| 700 | 1 | |a Kappagantula, Keerti | |
| 700 | 1 | |a Kvinge, Henry | |
| 773 | 0 | |t arXiv.org |g (Dec 3, 2021), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2607083821/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2112.01687 |