Development of Robust Machine Learning Models for Tool-Wear Monitoring in Blanking Processes Under Data Scarcity Article Swipe
Johannes Hofmann
,
Ciarán-Victor Veitenheimer
,
Changshun Fei
,
Chengting Chen
,
Haoyu Wang
,
Lei Zhao
,
Peter Groche
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app151910323
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app151910323
Tool wear is a major challenge in sheet-metal forming, as it directly affects product quality and process stability. Reliable monitoring of tool-wear conditions is therefore essential, yet it remains challenging due to limited data availability and uncertainties in manufacturing conditions. To this end, this study evaluates different strategies for developing robust machine learning models under data scarcity for fluctuating manufacturing conditions: a 1D-CNN using time-series data (baseline model), a 1D-CNN with signal fusion of force and acceleration signals, and a 2D-CNN based on Gramian Angular Field (GAF) transformation. Experiments are conducted using inline data from a blanking process with varying material thicknesses and varying availability of training data. The results show that the fusion model achieved the highest improvement (up to 93.2% with the least training data) compared to the baseline model (78.3%). While the average accuracy of the 2D-CNN was comparable to that of the baseline model, its performance was more consistent, with a reduced standard deviation of 5.4% compared to 9.2%. The findings underscore the benefits of sensor fusion and structured signal representation in enhancing classification robustness.
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- https://www.mdpi.com/2076-3417/15/19/10323/pdf?version=1758628392
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- 18
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Development of Robust Machine Learning Models for Tool-Wear Monitoring in Blanking Processes Under Data ScarcityWork title
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2025Year of publication
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2025-09-23Full publication date if available
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Johannes Hofmann, Ciarán-Victor Veitenheimer, Changshun Fei, Chengting Chen, Haoyu Wang, Lei Zhao, Peter GrocheList of authors in order
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https://doi.org/10.3390/app151910323Publisher landing page
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https://www.mdpi.com/2076-3417/15/19/10323/pdf?version=1758628392Direct link to full text PDF
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goldOpen access status per OpenAlex
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0Total citation count in OpenAlex
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18Number of works referenced by this work
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| abstract_inverted_index.product | 13 |
| abstract_inverted_index.quality | 14 |
| abstract_inverted_index.reduced | 155 |
| abstract_inverted_index.remains | 28 |
| abstract_inverted_index.results | 109 |
| abstract_inverted_index.varying | 99, 103 |
| abstract_inverted_index.(78.3%). | 132 |
| abstract_inverted_index.Reliable | 18 |
| abstract_inverted_index.accuracy | 136 |
| abstract_inverted_index.achieved | 115 |
| abstract_inverted_index.baseline | 130, 146 |
| abstract_inverted_index.benefits | 167 |
| abstract_inverted_index.blanking | 96 |
| abstract_inverted_index.compared | 127, 160 |
| abstract_inverted_index.directly | 11 |
| abstract_inverted_index.findings | 164 |
| abstract_inverted_index.forming, | 8 |
| abstract_inverted_index.learning | 52 |
| abstract_inverted_index.material | 100 |
| abstract_inverted_index.scarcity | 56 |
| abstract_inverted_index.signals, | 77 |
| abstract_inverted_index.standard | 156 |
| abstract_inverted_index.training | 106, 125 |
| abstract_inverted_index.(baseline | 66 |
| abstract_inverted_index.challenge | 5 |
| abstract_inverted_index.conducted | 90 |
| abstract_inverted_index.deviation | 157 |
| abstract_inverted_index.different | 46 |
| abstract_inverted_index.enhancing | 176 |
| abstract_inverted_index.evaluates | 45 |
| abstract_inverted_index.therefore | 24 |
| abstract_inverted_index.tool-wear | 21 |
| abstract_inverted_index.comparable | 141 |
| abstract_inverted_index.conditions | 22 |
| abstract_inverted_index.developing | 49 |
| abstract_inverted_index.essential, | 25 |
| abstract_inverted_index.monitoring | 19 |
| abstract_inverted_index.stability. | 17 |
| abstract_inverted_index.strategies | 47 |
| abstract_inverted_index.structured | 172 |
| abstract_inverted_index.underscore | 165 |
| abstract_inverted_index.Experiments | 88 |
| abstract_inverted_index.challenging | 29 |
| abstract_inverted_index.conditions. | 39 |
| abstract_inverted_index.conditions: | 60 |
| abstract_inverted_index.consistent, | 152 |
| abstract_inverted_index.fluctuating | 58 |
| abstract_inverted_index.improvement | 118 |
| abstract_inverted_index.performance | 149 |
| abstract_inverted_index.robustness. | 178 |
| abstract_inverted_index.sheet-metal | 7 |
| abstract_inverted_index.thicknesses | 101 |
| abstract_inverted_index.time-series | 64 |
| abstract_inverted_index.acceleration | 76 |
| abstract_inverted_index.availability | 34, 104 |
| abstract_inverted_index.manufacturing | 38, 59 |
| abstract_inverted_index.uncertainties | 36 |
| abstract_inverted_index.classification | 177 |
| abstract_inverted_index.representation | 174 |
| abstract_inverted_index.transformation. | 87 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5103972645 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I31512782 |
| citation_normalized_percentile.value | 0.49731897 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |