Name
Abstract | ||
|---|---|---|
The use of advanced machine learning (ML) models for manufacturing could potentially reduce the pre-production testing and validation time for new processes. Once we decide that ML is indeed a suitable tool to apply in smart manufacturing processes, the challenge lies in training, validating, and testing an ML model in a pre-production environment so that engineers can be confident that the model building effort can be successfully transitioned to actual production. This paper aims at explaining the in-works of a given in-situ classifier for predicting the quality welds in ultrasonic welded battery tabs. Predicting the quality of new samples cannot attain full certainty due to characteristics of the data the model was trained on (e.g., noisy or wrongly labeled). By developing explainable methods to such connectionist learning models (also known as black boxes), we show why the classifier outputs were predicted, making these predictions better understood and trustworthy. (C) 2021 The Authors. Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.procs.2021.01.163 | PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2020) |
Keywords | DocType | Volume |
explainable AI, classifier learning systems, ultrasonic weld process monitoring | Conference | 180 |
ISSN | Citations | PageRank |
1877-0509 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudia V. Goldman | 1 | 1 | 0.35 |
| Michael Baltaxe | 2 | 1 | 0.35 |
| Debejyo Chakraborty | 3 | 1 | 0.35 |
| Jorge Arinez | 4 | 1 | 0.35 |