Chip Analysis for Tool Wear Monitoring in Machining: A Deep Learning Approach Article Swipe
Atiq Ur Rehman
,
Tahira Salwa Rabbi Nishat
,
Mobyen Uddin Ahmed
,
Shahina Begum
,
Abhishek Ranjan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3443517
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3443517
Recent strides in integrating artificial intelligence (AI) with production systems align with the trend towards highly automated manufacturing, demanding smarter machinery. This dovetails with the overarching vision of Industry 4.0, moving beyond conventional models towards employing AI for real-time modeling of production processes, enabling adaptable and learning-enabled models. This study focuses on leveraging cutting-edge deep learning techniques to monitor and classify tool wear using authentic image data from machining processes. Various deep learning algorithms, including CNN, AlexNet, EfficientNetB0, MobileNetV2, CoAtNet-0, and ResNet18, are explored for monitoring and measuring wear through images of machining chips. The collected images of machining chips are categorized as ‘Accepted’, ‘Unaccepted’, and ‘Optimal’. Due to imbalanced datasets, the study investigates two distinct strategies: upsampling and downsampling. The study also aimes to enhance sensitivity for a specific minority class to meet industrial requirements. The study showed that upsampling enhanced accuracy and almost fulfilled the stated requirements, whereas downsampling did not achieve the desired outcomes. The study evaluates and compares the effectiveness of recently introduced deep learning algorithms with other CNN-based architectures in classifying tool wear states in real-world scenarios. It sheds light on the challenges faced by the machining industry, particularly the prevalent issue of class imbalance in real-world machining data. The observed results indicate that ResNet18 and AlexNet outperform other algorithms, achieving a weighted average accuracy of 96% for both multiclass and binary classification problems when considering upsampled datasets. Consequently, the study concludes that both ResNet18 and AlexNet demonstrate adaptability to class imbalances, generalization to real-world machining scenarios, and competitive accuracy.
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- en
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https://doi.org/10.1109/access.2024.3443517Digital Object Identifier
- Title
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Chip Analysis for Tool Wear Monitoring in Machining: A Deep Learning ApproachWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
- Authors
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Atiq Ur Rehman, Tahira Salwa Rabbi Nishat, Mobyen Uddin Ahmed, Shahina Begum, Abhishek RanjanList of authors in order
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https://doi.org/10.1109/access.2024.3443517Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3443517Direct OA link when available
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Machining, Computer science, Chip, Tool wear, Engineering, Mechanical engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 142, 219 |
| abstract_inverted_index.classify | 60 |
| abstract_inverted_index.compares | 161 |
| abstract_inverted_index.distinct | 115 |
| abstract_inverted_index.enabling | 43 |
| abstract_inverted_index.enhanced | 141 |
| abstract_inverted_index.explored | 83 |
| abstract_inverted_index.indicate | 207 |
| abstract_inverted_index.learning | 55, 72, 168 |
| abstract_inverted_index.minority | 130 |
| abstract_inverted_index.modeling | 39 |
| abstract_inverted_index.observed | 205 |
| abstract_inverted_index.problems | 228 |
| abstract_inverted_index.recently | 165 |
| abstract_inverted_index.specific | 129 |
| abstract_inverted_index.weighted | 217 |
| abstract_inverted_index.CNN-based | 172 |
| abstract_inverted_index.ResNet18, | 81 |
| abstract_inverted_index.accuracy. | 254 |
| abstract_inverted_index.achieving | 215 |
| abstract_inverted_index.adaptable | 44 |
| abstract_inverted_index.authentic | 64 |
| abstract_inverted_index.automated | 16 |
| abstract_inverted_index.collected | 95 |
| abstract_inverted_index.concludes | 236 |
| abstract_inverted_index.datasets, | 110 |
| abstract_inverted_index.datasets. | 232 |
| abstract_inverted_index.demanding | 18 |
| abstract_inverted_index.dovetails | 22 |
| abstract_inverted_index.employing | 35 |
| abstract_inverted_index.evaluates | 159 |
| abstract_inverted_index.fulfilled | 145 |
| abstract_inverted_index.imbalance | 199 |
| abstract_inverted_index.including | 74 |
| abstract_inverted_index.industry, | 192 |
| abstract_inverted_index.machining | 68, 92, 98, 191, 202, 250 |
| abstract_inverted_index.measuring | 87 |
| abstract_inverted_index.outcomes. | 156 |
| abstract_inverted_index.prevalent | 195 |
| abstract_inverted_index.real-time | 38 |
| abstract_inverted_index.upsampled | 231 |
| abstract_inverted_index.CoAtNet-0, | 79 |
| abstract_inverted_index.algorithms | 169 |
| abstract_inverted_index.artificial | 4 |
| abstract_inverted_index.challenges | 187 |
| abstract_inverted_index.imbalanced | 109 |
| abstract_inverted_index.industrial | 134 |
| abstract_inverted_index.introduced | 166 |
| abstract_inverted_index.leveraging | 52 |
| abstract_inverted_index.machinery. | 20 |
| abstract_inverted_index.monitoring | 85 |
| abstract_inverted_index.multiclass | 224 |
| abstract_inverted_index.outperform | 212 |
| abstract_inverted_index.processes, | 42 |
| abstract_inverted_index.processes. | 69 |
| abstract_inverted_index.production | 8, 41 |
| abstract_inverted_index.real-world | 180, 201, 249 |
| abstract_inverted_index.scenarios, | 251 |
| abstract_inverted_index.scenarios. | 181 |
| abstract_inverted_index.techniques | 56 |
| abstract_inverted_index.upsampling | 117, 140 |
| abstract_inverted_index.algorithms, | 73, 214 |
| abstract_inverted_index.categorized | 101 |
| abstract_inverted_index.classifying | 175 |
| abstract_inverted_index.competitive | 253 |
| abstract_inverted_index.considering | 230 |
| abstract_inverted_index.demonstrate | 242 |
| abstract_inverted_index.imbalances, | 246 |
| abstract_inverted_index.integrating | 3 |
| abstract_inverted_index.overarching | 25 |
| abstract_inverted_index.sensitivity | 126 |
| abstract_inverted_index.strategies: | 116 |
| abstract_inverted_index.MobileNetV2, | 78 |
| abstract_inverted_index.adaptability | 243 |
| abstract_inverted_index.conventional | 32 |
| abstract_inverted_index.cutting-edge | 53 |
| abstract_inverted_index.downsampling | 150 |
| abstract_inverted_index.intelligence | 5 |
| abstract_inverted_index.investigates | 113 |
| abstract_inverted_index.particularly | 193 |
| abstract_inverted_index.Consequently, | 233 |
| abstract_inverted_index.architectures | 173 |
| abstract_inverted_index.downsampling. | 119 |
| abstract_inverted_index.effectiveness | 163 |
| abstract_inverted_index.requirements, | 148 |
| abstract_inverted_index.requirements. | 135 |
| abstract_inverted_index.classification | 227 |
| abstract_inverted_index.generalization | 247 |
| abstract_inverted_index.manufacturing, | 17 |
| abstract_inverted_index.EfficientNetB0, | 77 |
| abstract_inverted_index.learning-enabled | 46 |
| abstract_inverted_index.‘Optimal’. | 106 |
| abstract_inverted_index.‘Accepted’, | 103 |
| abstract_inverted_index.‘Unaccepted’, | 104 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.83314707 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |